13 Searchandising Strategies To Generate More Revenue For Your eCommerce Business

13 Searchandising Strategies To Generate More Revenue For Your eCommerce Business

Promoting products offline is a common and successful business practice.

But for online businesses, this is often neglected, because it can be difficult to help shoppers navigate to promotions. This guide will teach you how to effectively leverage searchandising on your webshop to increase revenue.

You’ll find out:

  • How to transform your result pages to increase your revenue,
  • How you can offer exact products in real-time, like autocomplete,
  • And which factors should impact your searchandising strategy.

Ready to start?

13 Searchandising Strategies for eCommerce

Think about how often people use the search bar on a site, be it a news site, an online store, a social platform or a message board. I bet nearly every visit.

As Econsultancy puts it: 

“Typically, up to 30% of visitors will use the site search box, and each of these users is showing a possible intent to purchase by entering product names or codes.“

This means that your search can make or break the success of your business. Your search function gives you a great opportunity to apply marketing and sales tactics to your search pages. 

Chapter 1

What is Searchandising?


In traditional brick-and-mortar stores, merchandising is basically the practice of guiding the shoppers’ attention to the offers and products they want them to notice.

This can be done in a large number of ways – hanging signs, rearranging the products on the shelf or the entire store, putting price tags with different colors on items that have a discount, and so on.

In eCommerce, there are many different ways to do this: putting large CTAs on your site, colorful offers above-the-fold on your main page, pop-up windows, and so on.

Where eCommerce merchandising becomes difficult is on the search page. Banners or pop-ups are not going to be very effective if a customer has a specific product in mind and is actively using the search bar to find it.

And this is where the power of search + merchandising, searchandising for short, comes into play.

As with every online marketing and sales process, searchandising isn’t a single step, but a whole process.

Let’s look at it now.

Chapter 2

The Process of Effective Searchandising


There are three very basic golden rules of searchandising.

  1. Understand your business’ basic priorities and goals. This will help you choose from the abundance of searchandising strategies available, many of which we describe in this article – and their potential benefits for your business.
  2. Have your KPIs ready. As with all business decisions, you should know what metrics you want to use to measure your success or failure. When using searchandising, click-through rate, conversion rate, average order value can all be important. But also, to better define what products and offers to showcase, you have to know your profit margins, inventory, and availability.
  3. Implement, watch closely, monitor, and refine. Monitor the performance of the rules you set, and fine-tune your strategy as you go. Incorporate searchandising analytics and decisions in your regular meetings and brainstormings, and define intervals for changing up things.

Does this all sound a little vague? Don’t worry.

Here are 13 very specific things you can do…

Chapter 3

Searchandising Strategies


In this section, we’ll show you some specific ways to highlight promotional offers to your customers, while maintaining their sense of control over the search process.

Keep in mind: the goal is not to bombard the customer with offers until they give up and convert. It’s to find the best offer for them by paying attention to what they’re doing, and what they’re searching for. 

Giving them what they want (even when they don’t know what that is), helps create grateful customers who are likely to return to your store for future purchases.

To do this, the first thing you want to do is…

1. Boosting Products: new arrivals, on-brand products


Simply placing certain products on the top of the results page can be successful, but it’s important you make sure it benefits your customers. 

When selecting products to move to the top of the results page, make sure they’re relevant, popular and are something your customer wants to buy.

This is a great thing to do with new models or products. 

Think about this: by searching for something specific, shoppers might miss a better, upgraded version, or they might not know a new product is available, so when you highlight a relevant, related product the benefit is clear.

If you have your own brand products, you might also want to showcase these first. 

We recommend you do this when these products are already preferred by your customers, validated by positive feedback, and you have clearly defined unique benefits that the competition doesn’t.

2. Custom collections and bundles


A great way of creating a cohesive shopping experience  while guiding your customers toward different products, is to not just place solitary products in front of them.

Creating custom collection offers can be particularly effective during holidays and special events. 

To do this, pick items that are especially popular during these periods or are frequently purchased together..

Then create Christmas bundles, Super Bowl offers or anything you want.Display these when your user includes the relevant keywords in their search query (like “christmas”). By displaying these bundles beside the product results, you retain relevance while guiding shoppers’ attention toward other products you can upsell and cross sell.

3. Boosting your highest-margin products


It makes sense to tweak your results pages in a way so that your best high-margin products are displayed first – as products at the top of the page are purchased more frequently. 

If you decide to boost products this way, make sure to monitor user behavior as this method can backfire. Keep an eye on your click and conversion rates, because altering results in this way can create a negative search experience and hurt your business in the long term.

4. Embedded Product Listings (Autocomplete on Steroids)


Suggesting keywords and phrases that your customer might be interested in, while they are still typing, is a great way to increase conversions.

Why not take it a step further, and display products that best match the query right there in the suggestions field too?

You can often do this through searchandising functionality.Like faceted search, this is a real-time solution that can greatly reduce a shopper’s path-to-purchase, which improves their overall experience.

ikea site search autocomplete feature

5. Promotional Banners Near the Search Bar


Banners may not be as popular as they once were, but if used correctly, they can still be powerful tools in increasing conversion rates.

Placing banner near the search bar and at the top of the results page effectively directs your customers’ attention to the categories, products, or discounts you want them to check out. 

Asos.com Faceted Filter function

6. Promotional Badges


Adding badges is a great way to communicate discounts to customers without interrupting their shopping experience. 

It’s one of the basic and most effective merchandising methods because you don’t have to alter the search rankings in any way.

You can use badges to announce: 

  • New product arrivals
  • Upgrades or new available products (e.g. new colors or sizes for clothes)
  • Available-in-bundle offers
  • Limited-time discounts
  • Last-chance products or sales

For a simple example, look at how Google clearly indicates which pictures are licensable when you perform an image search.

7. Optimizing and Re-ranking Product Results


When your eCommerce store has many products, most search queries yield dozens or even hundreds of equally relevant results

In order to effectively guide shoppers to the products they’re looking to buy, the next thing you should do is set up additional ranking rules to display the most popular and relevant products first.  

However, with searchandising, you can edit these ranking rules any way you want.

For example, you can choose to display relevant products that are on sale at the top of the search results page, in order to quickly clear out your stock. 

Or, you could opt to show your own brand of products first. Whatever you decide to do though, make sure that you keep relevancy and popularity as important criteria, so you don’t disrupt the shopping experience too much.

8. Personalized Result Optimization


Continuing that thought, what you should also take into account with ranking is the behavior of the given customer on your site.

If the customer has searched for men’s sneakers before, you could direct them to other men’s sportswear. 

Try to avoid the mistake that many retargeting campaigns make and don’t offer the customers the same exact thing that they previously bought.

This way, you provide a better shopping experience to the customer while increasing your cross-sell and up-sell opportunities.

9. Personalize Your Category Pages


Category pages are useful for shoppers who have some kind of idea of what they’re looking to buy, but aren’t looking for an exact product. 

By adding elements of personalization to these pages, you can quickly increase your conversion rate

As a pro move, personalize these pages based on personal preferences, behavior and their search intent – these yield the most relevant results.

10. Boost Products Based on Availability


One of the main things to consider when boosting products is their availability.

If you have a lot of a particular product in stock and want to move them quickly, boost them on the results page. 

However, if you have new arrivals and releases that are not yet in your inventory, you could also boost them so you can achieve nice pre-sales numbers. 

However, if you have new arrivals and releases that are not yet in your inventory, you could also boost them so you can achieve nice pre-sales numbers. 

This is a highly effective strategy if you are using a search that ranks products based on popularity. 

New products don’t yet have the clicks and purchases to boost them to the top of the results page, so if you know they’re going to be a hit, you can adjust the positioning to get them in front of shoppers immediately.

11. NLP Autocomplete


As we wrote in our previous article, 12 Best Machine Learning Strategies for eCommerce Businesses, natural language processing is an essential part of any site search solution. 

Here’s why:

Natural language processing and machine learning have to understand the kind of language and phrases your customers use, how often they use them, if the results are satisfactory for those phrases, and even common typos along with the correct spelling.

This way, every user can feel that the search engine is truly there to help and pays attention to the smallest details, all without being annoying.

Using the same language as your customers automatically improves any search function while providing a subconscious positive affirmation for the user about the usability and overall the customer experience of your site.

Oftentimes, you can also boost product and keyword suggestions in your Autocomplete, while taking into consideration NLP relevancy.

12. No-result Pages


Your no results pages present a huge opportunity to direct attention of the customers. 

While there might be 0 results for precisely what they’re looking for, you can use this opportunity to promote other, relevant products or things like: your current discounts and bundle offers, the most popular products (even better when personalized) or simply the products you want to move.For more advice regarding how to design no-result pages, take a moment and check out these 12 Awesome “No results found” Pages!

Sears No Search Result Found Pages

13. Updating product catalogs


Make sure your product catalogs continually stay up-to-date. Without accurate products and attributes, it’s nearly impossible to provide shoppers with a good search experience – whether or not you leverage searchandising!Look for merchant tools that allow you to easily update your product catalogs. Batch updating attributes is the easiest way to do this.

Chapter 4

Summary


Searchandising is not actually that hard to do – however, you have to know what you are doing, and why.

The key takeaways from this article if you are looking for searchandising techniques that will definitely improve your conversion rates are these:

  • Personalize: always pay attention to the history and behavior of your users, not just their location and demographics. Try to give them what they actually want, direct their attention in a way that is useful for them.
  • Use Search Rules: don’t just trust the algorithm to make the choices for you. You have your business plan, your cash flow, your inventory to consider, marketing campaigns that are set, holidays that are coming up. Based on these, make the rules yourself, and tweak the results just enough to make the most of your offerings.
  • Recommend Products: don’t be shy to suggest similar or discounted products. Put them in bundles or packages if you have to, but keep in mind that while you don’t want to be annoying, you definitely have to be a salesman. Define what and when to recommend that your customers will be grateful for.
  • Run Search Campaigns: besides your everyday sales and marketing campaigns, come up with creative ways to offer upsell or cross-sell right on your search result pages.
  • Track Data & Measure Results: once you have set the rules, start to watch them work. If they don’t, try other ones. Always base your decisions on the data that you see on your dashboards, and not gut feelings. Data-based decisions are the ones that will help you scale your business.

If you listen to the eCommerce Search Best Practices and base your decisions on the data about what your users like and what they don’t, you will increase key metrics like conversion, average order value, retention, and more.

Paige TyrrellHead of Marketing – Prefixbox

Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.

11 Features To Look For When Choosing An eCommerce Search Engine

11 Features To Look For When Choosing an eCommerce Search Engine

Site search is easily the most important function of any online store.

Apart from determining a huge part of the user experience, providing data about shoppers, it also increases your online revenue.

However, with so many providers and functionalities, it can be difficult to choose the right provider.

This guide will explain:

  • The most important capabilities in a site search engine
  • List the pros and cons of building vs. buying

Ready to check it out?

eCommerce Search Engine Features Guide

Contents

Chapter 1

The Foundations of Great E-Commerce Site Search Engine

Chapter 2

Must-Have Capabilities for Your Site Search Tool

Chapter 3

In-house search engine development or SaaS vendor solution?

Chapter 1

The Foundations of Great eCommerce Site Search Engine


Providing Relevant Results


It may seem obvious, and even an easy task, that a search engine should provide results relevant to an executed query. This is because we are accustomed to Google, which we use daily, and which is top-notch in relevancy. Google has universally set the standard of what a search function should accomplish. 

But keep in mind that Google uses an extremely complicated, ever-expanding and changing algorithm to be able to do that, backed by hundreds of developers constantly fine-tuning it – and it took them literal decades to arrive at today’s model.

Take a moment to think about what “relevant search results” actually means. For example, let’s say a shopper enters the query “cooking book” on Amazon.

The results they get are not simply based on matching keywords in the title or description of the product, because there are other important factors. Like shipping logistics and/or availability: they might get the best cooking book as a result, but how is it relevant, if the product does not ship to where they are, so they can’t actually buy it?


What if there is a typo in the query? The shopper is still going to expect relevant results, and will only get frustrated with a “no results” page and the fact that they have to repeat the search.

Then there is the question of how do you weigh the importance of product attributes – hint: if the keywords appear in a title, it should be more relevant than if it’s only included in tags, but shipping to the shopper’s location is even more important, and so on.

As you can see, relevancy is a much more complicated question, especially if you have hundreds or thousands of products in your online store.

Reliability, Uptime and Speed


On the technical side, you also have to make sure that your search engine actually works: shoppers can run queries anytime, from any part of the world, and they get the results almost instantly. 

The average Google search, again, is a standard that we are used to, and most times it takes only a fraction of a second to complete. Even if you can’t compete with them, you should use a search engine and hosting that makes it possible for your shoppers to get to the result page as soon as possible.

Chapter 2

Must-Have Capabilities for Your Site Search Tool


1. Autocomplete


One of the most basic functions that shoppers expect is Autocomplete: this predicts shopper queries once a shopper focuses in the search box, or from the first keystroke. It suggests keywords, products, and categories that yield relevant results.

Predicting shopper intent  is only one aspect: what is even more useful is that it slightly guides shoppers down their path-to-purchase by suggesting searches that won’t end in zero result pages. 

There is not much more to this, except our advice: if a site search engine lacks Autocomplete, stay far away from it.

And autocomplete should also be…

2. Typo-tolerant


There aren’t many studies out there with numbers on the average amount of spelling mistakes, even fewer focusing on search engines because search engine providers don’t usually publish this kind of data. But we can take away some interesting points from a 2006 article by W. John Wilbur, Won Kim, and Natalie Xie titled Spelling correction in the pubmed search engine.

According to their analysis, there  is a “misspelling rate” of 26% for words on an academic site. It seems possible that the misspelling rate on a public site could be even higher.

Nordlie (1999) observes that two thirds of initial requests are unsuccessful in meeting their objective and an NPD survey (2000) finds that 77% of the time an initially unsuccessful search is modified and tried again on the same site.”

A typo-rate somewhere between 1 in 4 and 3 in 4 might seem extremely high, but with keyboards, fast typing, the prominence of typo-tolerant search engines and spell checking, it certainly can be possible, because we don’t really pay attention to spelling anymore.

If your site search engine can’t tolerate typos – as in, recognize them and recommend another search, or even better, show results for the correctly spelled keyword instead of returning no results – you run the risk of driving your customer away. 

3. Managing synonyms


Another important aspect: synonyms. In many cases, your shoppers are going to refer to products or categories, even certain attributes by a different name than you do. 

For example: if they are looking for a laptop, they may write “notebook” in the search field, luminosity and brightness might get confused with LED lights, someone who wants to buy an instrument panel may search for a control board.

And if your search engine can’t understand this, shoppers are going to end up on zero result pages, even if you do actually sell the product they want to buy.

Which, of course, is bad, because your products effectively remain hidden from shoppers with strong purchase intent, and you lose money on every one of them.

We recommend you choose a solution with built-in Synonym Management

You might think it would be easy to find a solution that includes this, maybe it’s just a matter of integrating a synonym database – but actually customizing a search engine this way is very tedious work and it’s not at all fast.

Which brings us to…

4. Customization


Out-of-the-box solutions are convenient, cheap, easy and quick to implement, but rarely satisfy all needs. They aren’t all flexible in terms of customization: usually you can’t expand on the existing functionality or performance.

Meanwhile, open-source search solutions can be customized as you want, but they are also resource-demanding – implementation, development, maintenance can take a lot of time and energy.

But what is a good, middle-ground solution?

The best middle ground is usually a search-as-a-service solution, where implementation is quick, there is great room for customization, and as with SaaS services in general, maintaining the system is relatively low-cost.

Before committing to any kind of site search solution, think about your unique business processes and the needs of your shoppers. 

Can the search provider meet both of these needs?

5. Personalization


Personalizing what your users see can be a great way of not only increasing your conversion rate, but also your average order value.

A few examples how you can personalize the result page based on the past behavior of your shopper:

Show them products they viewed (and didn’t buy) earlier, similar products to those, or products matching their previous searches. This way you may guide them to additional purchases, as they may see results they didn’t stumble upon before.

Overall, personalizing the result pages based on behavior can decrease site abandonment, increase engagement and conversion rates, and improve the user experience and customer journey of your webshop.

6. Dynamic filtering


Instead of providing a static results page when the shopper executes a query, you can improve user experience and increase your conversion rate by leveraging dynamic filtering.

What this means: the search engine displays relevant results and filtering options based on the initial query. The key here is that none of the filtering options will lead shoppers to a 0-results page. 

For a ‘real life’ example, let’s say you have an online book store. When a user enters “sci-fi novel”, they are presented by a number or results, and also a few checkboxes appear where they can filter by the most relevant authors like Isaac Asimov, Frank Herbert, Iain M. Banks etc. But they won’t see J. R. R. Tolikien’s name there, as he never wrote science fiction.

This also means that someone who searches for ‘romance novel’ won’t see the same filter options as those authors didn’t write romances. 

The search engine does this by analyzing the attributes of the results and applying relevant filters, which is why this is called faceted filtering

As the shopper starts checking boxes to filter results, the irrelevant filter options disappear. 

This not only guides shoppers to the product they want to buy, but it gives them a sense of control and improves their overall experience on your online store.

7. Natural Language Processing


Natural Language Processing (NLP) is a very complex topic as it encompasses both machine learning and AI, so we are going to concentrate on what it actually does when integrated into a search engine: it enables the search engine to better understand queries by, well, processing natural language.

NLP enables the search function to provide more relevant results, offer suggestions via Autocomplete, and learn from user behaviour based on the wording and language used. 

It also makes your search engine more language-independent, as NLP is not strictly tied to a specific language.

It is a resource-intensive feature to develop, and some solutions simply sell their advanced synonym management as NLP, so keep an eye on the validity of these claims. We can’t add much to the advice Shopify gives you:

When you don’t dig into the specifics [of the NLP feature], you might only be getting a demonstration of basic functionality like synonyms with a fancier label. That’s a problem if you’re expecting an automated system with sophisticated language parsing.

8. Ranking


Make sure that you have the option to customize certain ranking factors. While relevancy is the most important, if you want to be able to provide better customer experience, it might also make sense to highlight popular products on a result page for example. 

Ranking algorithms usually incorporate relevancy and popularity scores. However, search providers have dashboards to let their customers alter the boosting parameters to sell more of what they want.
These ranking algorithms should also be refreshed at least daily to ensure results remain accurate and shoppers get the best experience possible.

9. Analytics


Your on-site search engine is one of the most valuable resources of customer data. You can learn invaluable information about them including: how they search for procuts, what wording and language they use, what they’re searching for that you don’t currently sell/have in stock, if they are successful with their searches or need help, and much more.

You have to be able to view this data in an easy way. So you should be looking for a search solution, that

  • Actually can track and store this information.
  • Has the KPIs that are the most important for you (like click rates, “no results” queries, popular product, popular searches, average number of products/purchase,  average order value, etc.)
  • Has a user-friendly dashboard. 

If you have all of these, you can refine your customer journey, improve marketing messaging  and to achieve greater conversion rates!

10. Merchandising


Merchandising is a way to effectively promote the products you want to sell. These can be new products, products with a lot of inventory, or products on sale. 

You can customize the placement of these by adding banners, editing the products on the search results page, adding redirect rules, and disambiguation tiles.

11. Indexing


Indexing is the process by which the search engine creates a database with all the available data on your products and provides results based on this database.

With indexing the key is to choose a solution with a frequency that suits your business model. Many times the frequency with which the tool indexes products and attributes depends on how much you pay for the service, the tier you are in etc.

If you offer a few products and update them rarely, lower frequency would be fine – but if you add new products daily or weekly, a solution which indexes new data monthly will simply cause you harm.

Shoppers on your site expect to find products instantly – this includes new products. Most online revenue is generated by shoppers who use the search function. If your search doesn’t have access to an updated product database, it can’t direct shoppers to the products they want to buy.  

Which means you miss out on money.

Chapter 3

In-house search engine development or SaaS vendor solution?


When it comes to implementing site search in your webshop, you have three basic options to choose from:

  • You can select an out-of-the-box solution.
  • You can decide to develop your own site search engine.
  • You can opt for a SaaS solution.

We previously mentioned the problem with the first option, out-of-the-box solutions. They can be fast, easy and cheap to implement, but in the long term, they can be temporary solutions at best, as they are not at all flexible – it’s unlikely they will fit all of your needs.

Therefore,  the rest of this article will focus on in-house development and SaaS search solutions: we’ll provide you the pros and cons of both options, so you can make an informed decision yourself.

In-house Search Development with Elasticsearch or Solr


Elasticsearch and Solr are currently the most popular open-source search engines that you can work with. 

Sidenote: don’t try to develop something from the ground up, even if you have unlimited budget. The timeline of a project like this in itself will stretch out so much that you are way better off with an open-source engine as a basis.

The timeline for in-house development


Setting up an open-source search solution (1-4 weeks)

With Elastic or Solr you are getting a basic site search solution, for which the setup will take something between a week and a month. 

However, you should know that the time frame depends on your in-house capacity: the size of the dev team, their experience with search, how much time they have to focus on the project, and , the number of products and requests your online store receives. This means that if you have a small dev team (or one focused on other products) or a lot of products and shoppers, it can take longer.

Advanced Data Processing (2-8 weeks)

Your search engine must have all the data about your products – all the attributes, values should be standardized and processed, together with sentences from product descriptions, relevant keywords have to be extracted. 

This will provide the base for your engine to work, and continuously updated in the future. The time it will take to do it the frist time will depend on the expertise of your developer(s), but usually it will be around 2-8 weeks.

Configuring the Basic Features (4-8 weeks)

Synonyms, default facets, tokenizers, default stemmers and the rest of the core features get configured in this phase that will take somewhere between 1-2 months.

Configuring the Advanced Features (20 weeks)

NLP-based suggestions and autocomplete, custom filters, contextual dynamic facets and some of the more complex features: these can take up to 20 weeks to configure.

Custom Ranking (1-4 weeks)

As we have mentioned, customizing the result pages to fit your business model and marketing efforts is an important function. 

Determining and setting the basic factors for this should take 1 to 4 weeks, depending on complexity. 

(And a few days each month for updating these values.)

Custom Search Functionality (3 weeks, at least)

Depending on what kind of extra functions you and your online store require, it will take at least 3 weeks to set these up. 

Think: search assistants, voice search, personalization of the results pages etc. 

The more you want to customize, the longer it will take – this could possibly be extended by many months.

Post-Live Launch Bug Fixes and Algorithm Improvements (3-4 months)

This is the fine-tuning phase after everything is set up – which is, of course, something that will go on as long as your site is live. The first testing, bug fixing and improving phase should last at least 3-4 months after launching your search engine.

To summarize: even with a well-staffed and experienced team of in-house developers, building your custom search engine based on an open-source solution will take you at least 1 – 1.5 years and that’s for a basic version without analytics. If you can’t afford that timeline (and the budget it implies), than a SaaS solution could be more advantageous for you.

Advantages of In-House Search Engine Development

Customization 

Developing your on-site search engine with your own team means that you have complete control over the process and what to include, how to build it. This allows you to tailor everything to your business needs.

Flexibility in Terms of Making Priority Bug Fixes

If you have an in-house team, priorities can be set as you want: they can get to fixing critical issues right away and you don’t have to wait on a third party, on customer service and so on.

Results Can Be Leveraged Beyond Search

Your custom-built search engine can be useful in developing other important site functions which your developers can create based on this work.

Disadvantages of In-House Search Engine Development

Team Recruitment 

If core members leave your team, crucial knowledge can be lost with them, even if all the documentation and written know-how remains. This cannot replace their experience and the fact that they have been working together as a team, and new team members always have to be onboarded, introduced to customs and policies ang it generally could take quite a long time for them to get up to speed.

Team Management 

Your search team should be an independent one from your core dev team – otherwise multiple projects running parallel will take time and capacity from each other – consequently both basic development and building/maintaining your search engine will have less progress made in the same time.

It Takes Years to Fully Develop Great Search

No matter what you do, basic development itself will likely take 1-1.5 years – and after that, adding extra functionality, customization, bug fixes, maintenance and updates will take years until your engine is fully formed. It’s essential that your team always stays up-to-date with e-commerce search best practices and continuously implements them.

Manual Infrastructure Setup and Maintenance

With no third-party partner or SaaS provider, you have to purchase, set up and maintain all the infrastructure needed for both development (tools and devices the team will use) and operating the search engine. In terms of resources, this means additional costs and time.

Continuous Performance-improvement, Monitoring and Optimization

You need to continually develop new features – the needs are going to be changing based on customer requests and competition, changing habits, alterations in your business model and a lot of other factors.

Lack of Up-to-Date Technology

Search engine development is a very specialized field, and most of the times you will not have relevant, fresh technology at your disposal to use – you have to do everything yourself, which will usually take months to accomplish.

No Support

With an in-house team, you can’t rely on outside expertise: if you hit problems, you will have to figure it out yourself or find the solution on dev forums – both of which can be time-consuming and frustrating.

SaaS Vendor eCommerce Search Solution


If you don’t want to hire and maintain a full development team, and still want a high-quality,flexible solution, your best option is to use a SaaS provider.

But you are going to have to be careful about which one you choose (see the comprehensive list above for the most important factors to look at when choosing a provider)-, because search-as-a-service is a highly specialized field where every vendor, every solution has their specific strengths and specializations.

Quick integration

When using a search provider, some offer integration within weeks, which means you’re up and running in no time.

Some SaaS site search providers have integration times that take months, though. 

So if you’re looking to get started quickly, check the timeline of each individual provider, along with their integration process, to see which best fits your needs. 

Proven ROI before commitment

You not only get better search in a shorter amount of time, but also get the opportunity to commit to a service that has history: case studies, testimonials to back up its effectiveness. The core features are going to be constantly updated based on data not only from your webshop, but from all the shops and clients the vendor has, meaning best practices could be implemented with a much greater effect.

Detailed Analytics

You are going to get a dashboard and likely options to customize the analytics and reports you get, meaning you will be able to implement any useful data in your sitewide marketing efforts and business model from day one.

Dedicated Customer Support

A team of experts will be at your disposal as a part of the service, and you don’t have to pay their salaries or for the tools and devices they use. You can also be sure that they are experienced in search technology and will likely have the answers you are looking for.

Fully managed search

You and your team will not be the ones who have to make updates, develop features, manage scaling or hosting: you can reach out the the SaaS vendor and simply tell them your needs.

Freedom in Scaling Along With Your Business

SaaS solutions are traditionally scalable: as your webshop and business grows, you can simply upgrade the service to fit your growing needs, from handling a larger amount of products to including more specialized, custom functions and options in the engine.

Predictable Cost

The expenses will be easy to integrate into your cash flow, and you are going to know exactly how much you are going to pay each month for the service – and if you need custom solutions, the price for developing and integrating them will also be set.

Disadvantages of Using a SaaS Vendor


No Real Control Over Development

As just one of the clients, you will have some say in what features have to be developed, what bugs are going to be fixed – but that say will be minimal. Their development team is going to focus on what the majority of clients and the market are demanding – which might not necessarily align with your personal needs

Important note: this is not the case with all vendors. Business models of SaaS can differ – notably, at Prefixbox we are developing features based on aggregated customer priorities. But this is not an industry standard.

Important note: this is not the case with all vendors. Business models of SaaS can differ – notably, at Prefixbox we are developing features based on aggregated customer priorities. But this is not an industry standard.

Operational Features to Consider


Free Trial

Before committing to any solution, you should be able to try it out beforehand. Always look for a free trial with ecommerce search solutions, because you are about to make a long-term decision. Also, make sure that your analytics is set up correctly, so you can track any changes in  user behavior, conversion rates, exit rates and all other important KPIs after launching the free trial.

On-going Optimization Support 

Don’t count on a moment happening when you can kick back and say, “well, we are done with site search”. As one of the most important elements on the site, intimately tied to user behavior and revenue generation, continuous optimization, fine-tuning, A/B testing and bug fixing will be a part of your routine. So make sure the solution you chose has the team to support these efforts and are available to help with whatever you need (in a quick manner!).

Analytics Dashboard 

A monthly PDF in your inbox will not be enough when it comes to site search analytics. Make it a point to have access to a live dashboard where you can not only see the most important data and changes of KPIs.

Data Centers, Uptime

Uptime is a critical factor: always check the location of your provider’s data centers along with their SLA to find out their policy  on uptime and redundancy. 

The best option is to work with a provider who has  their data center in the same time zone as your business, as this will help with potential latency issues.

What is their SLA?

Service Level Agreement 

Many important factors can be found in an SLA, for example the availability they vow to provide. 99% is the standard benchmark, but keep in mind that this is not all that good a percentage – if your site search works 99% of the time, it means that it is offline 7+ hours a month.

Monthly Cost

Monthly invoicing instead of a year-long commitment gives you both security and flexibility: you can expect fixed costs while also having the freedom to opt out of the service if necessary.

Occasionally, yearly invoicing comes with a discount – so be sure to check that out.

The Decision is Yours


Choosing a site search solution should not be a fast decision. There are a lot of factors to consider to give shoppers the best possible search experience – and also support any ongoing marketing efforts and your business model, to achieve the highest possible ROI.

If you have doubts, start with the comprehensive list above, and if you still have questions, don’t hesitate to contact us here in a comment or in emails, we are always happy to help with your decision.

Do your research before making a long term commitment, and keep in mind that what you go with will fundamentally decide the customer journey and shopping experience in your store!

Paige TyrrellHead of Marketing – Prefixbox

Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.

12 Best Machine Learning Strategies for eCommerce Businesses

12 Best Machine Learning Strategies for eCommerce Businesses

In today’s world of eCommerce, it’s impossible to run a successful online shop without leveraging Machine Learning.

Check out this article to find out how to incorporate Machine Learning into your
e-Business in these easy ways:

  • For price optimization
  • For search result optimization
  • For product recommendation
  • And many more

Let’s get started!

machine learning guide for eCommerce

Contents

Today, when global ecommerce sales amount to $4.88 trillion, it’s an innovator’s game.

According to Gartner, up to 80% of customer interactions are managed by AI today. 

Introducing AI into sales, marketing, even inventory management is both a natural next step in the evolution of commerce and an absolute must for those who want to keep up and make cost-effective decisions.

And in this article, we are going to show you how you could effectively be leveraging AI in your eCommerce business.

Let’s kick off with the answer to….

How machine learning works in eCommerce


If we want to define machine learning in the simplest terms, it is the study and creation of algorithms that can learn from given sets of data and extrapolate from those to predict trends and outcomes.

This is a necessary function of any algorithm-based system that has to deal with large, frequently changing data sets – from natural language searches (where wording, typos, and new phrases are common) to facial recognition (where the system basically never meets the same input twice, but still has to determine the key features). Machine learning is used in email filtering, weather forecasts, diagnosing disease and predicting socioeconomic trends.

eCommerce is a field in dire need of machine learning. Even a small webshop can create millions of relevant data points daily that a single human, or even a team of experts could never hope to fully comprehend and utilize.

But, a machine can do it. 

It will not only monitor these data sets, changes and trends in them, but make connections. 

An AI based on machine learning will reason. Deduce, based on experience.

For example, it can help with…

1. Pricing Optimization


Price is, unsurprisingly, one of the most important factors when considering a purchase. In fact, price is one of the major drivers for at least 47% of customers according to BigCommerce.

If your prices are high – or just higher than your competitors’ – your cart abandonment rate can increase, and even more so if you don’t set shipping prices correctly. If your store offers international shipping, you also have to display prices based on the location of the customer.

There are many other factors that may influence your prices – supply and demand, which promotions are running, what time of the day or year it is and so on.

Via machine learning, these factors could be assessed in a fraction of a second and your site will display dynamic pricing: showing the customer the most up-to-date price for them based on the aforementioned factors. 


This ensures prices are instantly understood (no “how much is that in my currency”, “does that include shipping” etc.), with no surprises, and no need for you to manually change them.

2. Segmenting, Personalization and Targeting Customers


In traditional brick-and-mortar stores, segmenting was done solely by salesmen, who approached the customers. By observing and asking a few questions, they could gather more than enough information about demographics, their needs and doubts, and their overall mood based on non-verbal communication.

This made it possible to immediately address any concerns about the products, matching the words and expressions used by the customer, being able to upsell them on the spot, and reinforcing their purchase intent.

When the customer has to deal with an online experience, all of this has to be replaced, and scaled. 

Segmentation has to be based on behavior that can be measured. 

And on-site search is a great place to start.

When searching, shoppers use natural language, which gives clues about their background and native language. Based on the results they click on, an algorithm can determine what they are looking for and offer more relevant results, which encourages them to make a purchase. 

And based on what other customers in the store previously bought, in conjunction with this particular search, cross- and upsell offers could be made with great confidence. Part of the demographic information is readily available as soon as someone signs up – most times you will know their age, location and whatever data you ask for. 

You can also monitor their behavior on your site – what content do they read, what materials do the download, how often do they return and how often do they make a purchase.  

You can also keep track of the kind of emails they open and when are they most likely to make a purchase during a day, a week or year.

There is an incredible amount of data generated from all these interactions – and machine learning is what makes it possible to assess behaviors to ascertain patterns. 
These patterns help you make sense of data sets and make more effective marketing decisions. For example, you can create ultra-targeted campaigns with the most relevant messages, and increase your conversion rate by offering customers exactly what they are looking for.

3. Search Results Optimization


Providing search results based on keywords is just the very first, most basic step in on-site search. In order to provide shoppers with the best experience possible, your search should go much deeper than that. 

Given a large enough data set, you could determine which results are better for people in certain locations, how to optimize search result filters, and which products best match their needs based on their previous behaviour.

By analyzing the data and figuring out what items go together, you can also recommend similar products, and even cross-sell items that are frequently bought together by your users. 

Machine learning is able to pinpoint the trends and patterns needed to automatically determine these.

Leveraging this leads to higher click-through rates on your results pages, higher conversion rates, and a higher average order value.

It’s worth noting that filtering results in real time based on user inputs, via faceted search, paired with AI-based recommendations is currently the single most effective method of providing relevant results.

But let’s stay for a minute with recommendations…

4. Product Recommendations


If you’re curious about how effective automatic product recommendations are, look no further than Netflix. According to McKinsey, 75% of what people watch on the streaming platform is suggested to them via an algorithm that analyzes user behavior. (The same is true for 35% of purchases at Amazon.)

You might think that determining which products go well together is easy – for example, simply track what people watch after they finish Futurama, and if it’s Rick and Morty, recommend that, and that’s it.

But you should also take into account the demographics – people in non-English speaking countries might go for movies and series in their native language with more enthusiasm. The preferences of young and older audiences will differ, as will the taste of those living in East Coast metropolitan areas versus those in rural European towns.

And this doesn’t even account for personal taste of genres, styles, lengths, eras etc. 

To be able to offer ultra-targeted products to your customer, you have to work with a very large number of variables, each weighed differently.

It is virtually impossible to do this without a machine learning algorithm – even if you have just a few hundred products.

As you can see, knowing your customers is a bit like predicting the future.

So, let’s spin up the crystal ball and see what exactly it can predict…

5. Predictions About Your Customers


Machine learning can tell you a lot of things about the people who visit your site and make a purchase – even things like how likely they are to buy from you again or what they might be interested in. 

Check out what machine learning can predict.

Customer Lifetime Value Prediction

To fine-tune your communication and messages, it’s useful to know how much money  a customer is likely to spend in your store over a given time period. 

If you can estimate a lifetime value based on behavior, you can also make your marketing more cost-effective and targeted. 

You can also identify customers who are the most valuable and deserve special attention.

Predicting if a customer will make a purchase

Imagine this: you have an online store for office supplies. You have a customer who orders the same amount of ink cartridges from you about every 6 months.

Let’s say 5 months pass, and this customer logs into your site, but doesn’t order anything. Logically, this could mean that they are checking the prices in order to budget the next order, or they might be comparing your prices to your competitors’.

As a store owner, you most likely will never learn that they’ve logged in and done this.

However, the AI will notice it. 

And it can deduce that this is the right time to provide a little incentive to increase average order value – as the customer is a returning, but hesitant one, maybe it’s time to fire up a workflow that provides them with a personal discount for their next order, thanking them for their loyalty.

Predicting customer return (and purchases)

If, based on their behavior, a customer is likely to return to your store later, then an entirely different marketing message may resonate better with them.

Algorithms can predict, on the long term, whether this is the case and can initiate longer workflows with messages that target loyalty and reinforce branding.

Customer churn prediction

Retaining existing customers is one of the most important tasks if you want to keep your marketing budget from bleeding. Acquiring new customers is much most cost-intensive.

A machine learning algorithm can determine which shoppers are the most likely to abandon your site – based on things behaviour such as: returning to your store less frequently, smaller purchases, etc.

When the AI realizes this is the case, it can initiate workflows targeted at retention, giving shoppers incentives to stay.

Client size prediction

Based on average order value and purchase frequency, along with other information like number of employees, and company type, an algorithm can estimate the size of the client for you.

This gives you great insight into deciphering which potential customers to pay special attention to. You can make more personalized offers and give them deals that are more cost-efficient in the long term.

As you can see, machine learning really tells your future by predicting what your customers will or won’t do.

Another forward-looking application of machine learning that deserves its own subsection is…

6. Site Search Autocomplete


A truly useful autocomplete has to learn, instead of scouring through different product attributes and description.

It has to understand the natural language of the users instead of the often technical and machine-like phrases of database entries.

Ecommerce search best practices dictate that online stores use an artificial intelligence-based autocomplete as it streamlines the shopping journey and provides shoppers with a sense of comfort as this is what they’ve come to expect. 

So, natural language processing and machine learning have to understand the kind of language and phrases your customers use, how often they use them, if the results are satisfactory for those phrases, and even common typos along with the correct spelling.

This way, every user can feel that the search engine is truly there to help and pays attention to the smallest details, all without being annoying.

Speaking of details…

7. A/B tests using AI


A/B testing is a powerful tool in online marketing, but it can also be tricky.

Let’s say you want to run an A/B test for a product page.

Firstly: what do you change? The display of pricing? The location of your CTAs? The background color?

You see, if you change more than one thing, you can’t really be sure what causes the next positive or negative change. 

But, if you change only one thing, the change may be so small it can’t even be registered.

And what KPIs should you track? 

Yes, conversion and purchase rate are the most obvious numbers to track. 

But time spent on page, number of clicks, and return rate are also important. 

Machine learning and AI makes this testing process easier:

  • Based on historical data, it decides which elements should be tested and automatically creates variants.
  • It can dynamically change page elements based on test results. For example, displaying pages differently for different demographics or locations.

It can find the optimal versions much faster, because it can take all the variables into account and find the connections between even small changes.

8. Chatbots for Automated Customer Support


With customer support, there rarely is an optimal choice. If you want to solve all issues with manpower, your support team will be huge and expensive – and not at all efficient, as many times they will be dealing with things that could be solved by redirecting the customer to an FAQ page.

On the other hand, you can’t completely automate support, because a lot of issues will require human assistance, and your customers will quickly get annoyed if they can’t get it.

On the other hand, you can’t completely automate support, because a lot of issues will require human assistance, and your customers will quickly get annoyed if they can’t get it.

The solution to this problem is often implementing a chatbot based on machine learning. 

These chatbots are able to maintain a conversation with the customer. Not only by using previously defined answers, but also via AI – they’re able to learn about natural language from every conversation.

Of course the chatbot will require time to learn, to get to know the products and services as well as the customers and their way of communication. 

It will never pass a Turing test, but it could, in time, identify opportunities for upsell, create customized coupons, and open tickets for humans in customer support to handle.

Keep in mind, however…

A professional custom chatbot can cost anywhere between $30,000 and $300,000, depending on the desired functionality.

Speaking about money well spent…

9. Inventory Management


You know how a smart fridge reminds you you’re out of milk and puts it on your grocery list for you?

ML does that for eCommerce, times a billion.

Well, a trillion actually, as around 7% of the US GDP (more than $1.1 trillion) is tied up in cash in inventory along with accounts receivable and accounts payable.

Inventory management and logistics is nothing to sneeze at in terms of complexity. 

If you run a successful online store, you have to monitor your stocks, reorder items, predict trends in demand, coordinate contractors, deal with manufacturers, suppliers, mailing services, and manage your revenues accordingly.

This is exactly the kind of job machine learning was made for. 

By monitoring all your inventory, and even predicting future trends in supply, demand and even cash flow, you can be sure that with AI, you won’t be the airline that overbooks flights or flies empty.

You will be the airline that plans their next year schedule with absolute confidence.

10. Omnichannel Marketing Boosting with ML


Let’s get the obvious out of the way: omnichannel marketing brings you higher retention and conversion rates and boosts your revenue. But only if you use the available channels wisely.

Of course, you could just hire a marketing team, where there are dedicated people for social, email marketing, content creation, and tell everyone to give it 100%.

But is there a better way to do it?

Actually, yes.

Analyzing the data your customers create when interacting with your activities on those channels is majorly important. 

Based on customer behavior – ads that perform well, content that’s frequently read, email open rates – machine learning algorithms can analyze your messages and display them in a way that ensures every customer gets the perfect one.

Online real estate became more important than television and radio partly because of how results can be tracked and campaigns can be optimized. 

ML is the natural next step in this evolution.

11. Image Processing and Recognition


Image recognition can be a great tool for an online store with thousands of products in their inventory. 

Ideally, it enables a customer to just upload a photo they snapped of a given product at home or at the store. The system then processes it on the store’s servers, and instantly displays a response with availability, current price, shipping info so the shopper knows where to buy it.

More often than not, this leads to a purchase as it’s the most convenient shopping method. 

Beauty.com, for example, was able to increase sales by 15% after implementing their visual search feature.

Another possible use of image processing is to give ultra-targeted recommendations. For example, processing a photo of the customer and then applying clothes available in store, so they can see how different styles would look on them.

12. Fraud Protection


Fraud is inseparable from commerce as a whole, and eCommerce is especially vulnerable. 

Online shopping offers huge opportunities for those who want to take advantage of automated systems, which is why it is important to have algorithms in place that can detect fraudulent activity.

Integrating a CAPTCHA is not enough. You also have to monitor behavior and look at how certain people use your site. 

Integrating a CAPTCHA is not enough. You also have to monitor behavior and look at how certain people use your site. 

With machine learning, you can identify repetitive patterns that don’t match with human behaviour; such as: filling in forms too quickly, opening dozens of pages for split seconds, entering multiple different sets of information rapidly at checkout, etc.

Conclusion


Introducing artificial intelligence in eCommerce processes is not something futuristic or even a highly complex task. There are applications and services readily available for most functions that have been developed by people over the past decade.

It’s not always cheap, but it’s worth the cost.

  • It will help you understand your customers and audience better
  • Increase your sales and average order value
  • Cut unnecessary work  
  • Give you deep insight that no human ever could

You don’t have to hand over everything at once to the machine of course. Start small. Implement a smart site search solution, start using recommendations based on machine learning – and get to the rest when you are ready.

But get moving, because the analogue methods are phasing out fast.

Balazs VekonyOnline Marketing Manager – Prefixbox

Balazs is an Online Marketing Manager at Prefixbox, a leading eCommerce site search solution. He’s a Budapest based marketing enthusiast, who’s interested in new technologies and solutions and believes in the power of search.

Algolia vs ElasticSearch: How To Choose The Best Site Search For Your Business [+2 Alternatives]

Algolia vs ElasticSearch: How To Choose The Best Site Search For Your Business

If you run an online business or a webshop, providing a seamless search experience for your customers is probably one of your main concerns.

However, choosing the right provider can be tricky as there are a lot of factors to take into account.

To help you make the right decision, we put this article together to:

  • Provide an overview of some of the most popular search solutions
  • Show different pricing models
  • Explain the pros and cons of different solutions

So, let’s get started!

Algolia vs Easticsearch site search comparison guide

Contents

Chapter 1

How do site search solutions work?

Chapter 2

Algolia vs Elasticsearch: what are the differences?

Chapter 3

What are some alternatives to Algolia and Elasticsearch?

Chapter 4

Site Search Solution Pricing Comparison

Chapter 5

Pros and Cons of the Site Search Solutions

Chapter 1

How do site search solutions work?


In essence, site search, as a whole, is the functionality that enables users of a website to search certain keywords and/or criteria and get relevant, ordered results from the site.

The functionality itself can greatly vary – on some sites, you will only find a search bar where you can write keywords, on other sites faceted search will guide you step by step, in real time, based on all relevant attributes.

The basic idea is the same as on Google, but the underlying mechanics can be vastly different. The way  individual site search engines interpret individual keywords and phrases, whether they can understand typos, which product attributes are weighed as more or less important, the speed, the functionality of facets and filters – all depend on how the site search solution.

Something important to note…

Most searches do not take place in real time. In general, a search engine will index the searchable content, create the possible results pages in advance, and display these when the search is performed, hence the great speed. 

However this also means that this index has to be updated regularly so that it takes changes into account and provides truly relevant results.

With other search solutions, such as with faceted search, the process is done in real time, based mainly on all the different attributes provided for the different results. This is of course a more resource-heavy, but also much more user-friendly solution than regularly updating the search index.

Now that we’ve gone over the basics or site search, let’s dive into a few of the site search solutions.

Shall we begin?

Chapter 2

Algolia vs Elasticsearch: what are the differences?


Algolia is mainly praised for its speed, easy implementation and excellent support. Typo handling and search analytics are some of the key features it offers.

In the case of Elasticsearch, the developer community values its powerful API, that it’s open-source and flexible.

What is Algolia?

Algolia is an on-site search engine that includes analytics. It enriches the search experience with Autocomplete,  filters, query suggestions and infinite scrolling.

For developers, it offers back-end API clients, front-end widgets, several integrations, a customizable relevance algorithm and extensive documentation. It is usually described as having a developer-friendly API and an extensive toolset for development.

Algolia is a more full-service search provider, but can still be built up further.

What is Elasticsearch?

Elasticsearch, by its own definition, is a “distributed, RESTful search and analytics engine”. The open-source solution offers various APIs and great search speed, and is widely favored by developers. It also has built-in analytics and is a fully managed Search-as-a-Service solution.

Elasticsearch is a basic search application, which developers can build a deep search on top of.

Hosting Services to Consider when Using Elasticsearch


AWS


Elastisearch is offered as a part of the Amazon Web Services as a fully managed search service, which means deployment is usually easy and fast, and security is provided by AWS. 

This makes it easier to develop and troubleshoot your own Elasticsearch based application in their environment and scale it as you need. 

Another strength of this offer is its cost-effectiveness: as with many SaaS solutions, you only have to pay for the resources that you actually use.

Elastic Cloud


You can also opt in for the Elastic Cloud managed service, which is compatible with not only Amazon Web Services, but also Google Cloud and Microsoft Azure.

The main advantages of this solution is its flexibility, as it is easy to configure and deploy (given you have experts for the job), it includes a lot of Elastic features like machine learning, Canvas, APM, workplace search, and it provides unified analytics. 

You also get backups, constant monitoring and ticket or SLA-based support as part of the managed service.

Chapter 3

What are some alternatives to Algolia and Elasticsearch?


In this section we are going to show you two options that you might opt for if Algolia or Elasticsearch is not suitable for you

  • Azure Search: a search solution by Microsoft
  • Prefixbox: a SaaS option with constant optimization and feature development with a focus on client needs.

What is Azure Search?

Azure Cognitive Search is a solution by Microsoft, and by its own definition, “the only cloud search service with built-in AI capabilities”. The aim of the service is to make search at scale easier and faster with AI.

It is based on the same natural language stack as Bing and Office, and includes the key features of auto-complete, geospatial search, filtering, and faceting capabilities, and also OCR, key phrase extraction, and named entity recognition.

What is Prefixbox?

Prefixbox is an  eCommerce search provider with products enriched with AI capabilities. The solution is composed of different modules, which make  it flexible and a suitable solution for medium to large eCommerce enterprises.

The modules are: Autocomplete, Related Searches, Semantic Search Engine, and Merchandising. These encompass features such as typo tolerance, advanced ranking,, thorough search analytics, and of course faceted navigation for enriched user experience.

Chapter 4

Site Search Solution Pricing Comparison


Every search solution has a different pricing model.

Here, we’re breaking it down based on what is available on each provider’s website.

Algolia pricing


Algolia offers three main options. They serve different volumes of searches, defined as units, where 1 unit equals 1,000 search requests.Your paragraph goes here.

  • A Free plan that will serve up to 10,000 search requests.
  • A Standard, $1.00/unit/month plan (with 20% off if you pay annually), including analytics.
  • A Premium $1.50/unit/month plan (with 20% off if you pay annually) with some advanced features including custom rules, merchandising and customization.

On their site you can also create your own custom package where you can set search volumes and the needed features for yourself.

Elasticsearch pricing


There are four main plans you can choose from with the Managed Elastic Cloud service:

  • Standard, $16/month, with the core security features and basic functionality.
  • Gold, $19/month, custom plugins and support during business hours.
  • Platinum, $22/month, with advanced security features, workplace search, machine learning, full 24/7/365 support.
  • Enterprise, with custom pricing, which includes endpoint protection, detection, response and event collection.

All these plans are available as a free trial, and you can learn more about the detailed functionality included in each one if you visit their site.

Azure Search pricing


Azure has many plans for smaller and larger businesses too, with a Free plan also available, with limited features of course. The prices are given as an hourly rate and per unit in US dollars.

  • Free: you get a 50 MB storage, 3 indexes per service and not much more – this plan is basically a demo version so you can test the service and decide which paid plan is ideal for you.
  • Basic: with 2 GB storage and  maximum indexes per service, it costs $0.101/hour
  • STANDARD S1: includes 25 GB storage (max. 300 GB per service) and 50 indexes per service for $0.336/hour. All standard plans have a scale out limit of 36 units per service.
  • STANDARD S2: Includes 100 GB storage (max. 1 TB per service) and 200 maximum indexes per service for $1.344/hour.
  • STANDARD S3: Includes 200 GB storage and 200 or 1000 indexes per service per partition in high density mode (max. 2 TB per service) for $2.688/hour.
  • STORAGE OPTIMISED L1: 1 TB (max. 12 TB per service) and 10 indexes per service for $3.839/hour.
  • STORAGE OPTIMISED L2: 2 TB (max. 12 TB per service) and 10 indexes per service. The pricing for this plan is custom.

As for image extraction, the basic and all standard plans are priced as follows:

  • 0-1M images – $1
  • 1M-5M images – $0.80
  • 5M+ images – $0.65

Prefixbox pricing


Prefixbox offers flexible pricing as they have no less than 6 plans you can choose from.

The first two plans are aimed at SMBs and include search analytics, on-boarding and technical support and front-end integration:

  • SMB S1, with 30-150k searches: €500/month
  • SMB S2, with 150-300k searches: €840/month

The three Prefixbox plans aimed at larger stores include, in addition to the features of the SMB packages, A/B testing, 24/7 technical support and API integration.

  • Corporate C1, with 300-450k searches: €1,260/month.
  • Corporate C2, with 450-650k searches: €1,680/month.
  • Corporate C3, with 650k-1M searches: €2,320/month.

The last plan is a custom enterprise offer, which includes an enterprise-level SLA, a dedicated account manager at Prefixbox, an expert team responsible for integrating the search solution, commercial TOS and priority custom feature requests for the client stores. 

The pricing for this  is custom, you can ask for a quote on the Pricing page.

If you’re not sure how much you should be spending on a site search solution, you can use this calculator to see how much revenue an optimized search will generate.

Or you can request a demo and talk to an expert about your search needs.

Chapter 5

Summary: Pros and Cons of the Site Search Solutions


Since we covered so much information, check out this quick list of pros and cons of the four site search solutions to get a simple overview of the core features and functionality.

Pros of Algolia

  • It’s fast and easy to implement.
  • It has excellent support.
  • The underlying technology is modern and easy to handle (for experts).
  • It can handle typos, which is one of the most important search engine functions.
  • It also provides detailed analytics.

Cons of Algolia

  • According to some developers, the documentation could be problematic and thus hard to work with because sometimes it is incomplete.
  • Algolia is one of the more expensive solutions, so it is usually not the best choice for small businesses. 
  • Scaling can be difficult.
  • Customizing the ranking with Algolia is not perfect, as it is based on prioritizing factors instead of weighing factors individually.

Pros of  Elasticsearch

  • It is generally considered easy-to-use.
  • It is well-scalable – easily managing Terabytes of data (given the technical conditions are in order.
  • It can handle complex keyword searches very efficiently.
  • Analytics is usually listed as one of the greatest pros as it provides a thorough overview of user behavior and search data for its clients.
  • With APIs based on REST, an HTTP interface and schema-free JSON documents, its relatively easy and fast to build different applications based on it for a variety of use-cases (including, in addition to on-site search, processing genetic data, managing and analyzing spatial information, modeling data behavior etc.).

Cons of Elasticsearch

  • In reviews, developers list a number of cons for working with Elasticsearch, such as occasional lack of documentation, debugging issues, and a steep learning curve.
  • Because of the pricing, it is not an ideal choice for small businesses.
  • Support is not always proactive and some developers noted that community support is more efficient than the one the company provides.
  • Switching between different versions and  configuring it can be tricky even for experienced developers. 

Pros of Azure Search

  • It can be seamlessly integrated with Azure Cloud, making it an ideal choice for clients who already use the Microsoft cloud service.
  • It provides a fully-managed service for loading, indexing and query content.
  • Cognitive search capabilities enrich not only the search experience, but also make it easier for the client to get in-depth analytics and data.

Cons of Azure Search

  • As a primarily enterprise-level product, it requires a larger budget.
  • It works well primarily with Microsoft products, can be tricky to integrate with other ecosystems.
  • Implementation requires experienced team members on the client side as for non-technical administrators this is a nearly impossible task.

Pros of Prefixbox

  • AI powered search provides a seamless search experience.
  • The faceted real-time search makes it easier for customers to find the products they are looking for on the site and improves their overall shopping experience.
  • Prefixbox is constantly releasing new features catered to eCommerce retailers.
  • Contains a full suite of products focused on increasing eCommerce revenue and conversion rates. 

Cons of Prefixbox

  • It’s not an ideal solution for SMBs where there are less than 30 000 searches performed on a website per month.
  • Does not include content search functionality.

A final few notes before you make your choice…


Pricing can often be complicated, so make sure that you correctly calculate prices based on your needs. 

Never make a final decision before consulting with the technical people on your team. Developers can encounter or even foresee many issues that others can completely miss.

Always look at the availability and proactiveness of support: site search solutions can be a bit complicated, and it can make a fundamental difference if you are able to get expert help when you need it.

And lastly: before making any final decision, always look up reviews from real clients who actually use these products: get first-hand information on their experience instead of reading general product reviews!

And as always, if you have any further questions, feel free to comment or contact us: we will be happy to help you with additional info!

Paige TyrrellHead of Marketing – Prefixbox

Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.

12 Tips to Optimize Your Search Result Filters

12 Tips to Optimize Your Search Result Filters

Search result filters are generally placed on the left side of the Search Engine Results Page (SERP) for eCommerce sites and help users refine their initial query.

By helping users find what they’re looking for, filters can increase your site’s revenue.

It seems pretty basic, but they should be intuitive and easy to navigate in order to help users aptly refine search results.

How to Optimize Your eCommerce Search Result Filters?

If your eCommerce shop carries a large number of similar products, filters help people find specific results. If they can’t quickly find what they’re looking for, they will most likely take their business elsewhere.

Check out the tips below to find out how to make the most of your search result filters.

1. Show filters that are relevant to the search query


Providing search result filters is a great way to help users easily find what they’re looking for, but be sure to show filters that are relevant for each search (this is often referred to as faceted search).

relevant search result filters

If someone searches for “laptop”, it would be helpful to provide filters for screen size, brand, price, and processing power, while if someone searches for “purse”, it would be helpful to provide filters for colour, material, and style.

Showing unrelated filters for a search is actually worse than not providing any refinements (because they visitors’ time and take up valuable space), so be sure the filters you display add value!

2. Place your filters appropriately


Place your refinement options in a location that’s easily visible, so people can find and use them.

appropriately placed product category filters

Usually, filtering sections are placed on the left side of the page or at the top, but test the placement so you can find what works for your site.

3. Make them intuitive


Name your search result filters with easily understandable terms that make sense for the products you sell.

search result filters with understandable terms

Employ selection styles that are easy to use – drop down menus, check boxes, or range selection bars are all good ideas. If users are familiar with the means of selection, they will be more likely to use the refinements.

4. Show the number of results next to each filter


Showing the number of results for each filter provides insight into how many products visitors can find for a specific refinement.

number of results for each product category filter

This helps streamline the path-to-purchase by indicating how many filters they should use to get to a manageable number of results.

5. Don’t show too many filtering options


Don’t provide too many filtering options as this may overwhelm or discourage people from using them.

Don't show too many product filters

In order to find your site’s sweet spot, test different numbers of filters and see how they perform. Collect data about the most frequently used refinements on your site and use it to optimize your filter menu.

6. Don’t suggest filters with no results


It’s a good idea to offer refinements that change based on search queries, so you provide relevant options.

It’s great to let users filter results by colour, but if colour isn’t applicable for the search term or there are 0-result pages for a product with that filter, don’t provide it as an option. 

If a user comes to your site to find a black shirt, searches “shirt” and sees a filter for black shirts – they assume you offer that product in black.

If you don’t offer it, don’t act like you do.

By making your filters for search terms accurate, relevant, and applicable you build trust with your users, which means they’ll revisit your site in the future and will probably use your refinements!

7. Make some search result filters visual


It’s often easier for people to use visual refinements.

If you have products that come in different colours, show an image of the colour instead of writing it in text.

show image instead of text in product filters where you can

This helps users visualize the colour and can clarify names if you have offer products in very specific colours like “taupe”.

If you carry different brands in your store, try showing an image of their logo instead of/in addition to writing the name.

8. Include an “on sale” filter


Everyone loves a sale – some people love it so much they only buy things on sale, so having a refinement for items on sale can help users quickly find the discounted items they’re looking for.

use "on sale" filters on eCommerce category pages

9. Have a ratings and reviews filter


Reviews play a big role in the decision making process for people on eCommerce sites, so people appreciate a refinement that lets them sort results based on high ratings.

use customer reviews and ratings as product filter options on eCommerce category pages

You can offer filtering options based on the number of reviews or star rankings, or provide filters based on unstructured data (like product features) that can be derived from product reviews.

10. Provide an option to set a price range


Cost is an important factor for most people, so it makes sense to offer a price refinement.

Price should be shown as a list of ranges, since people don’t often have an exact price in mind.

Provide a price slider feature on eCommerce category pages

This range can be written in text or you can provide a sliding bar, which lets users manually select the price range and offers more flexibility than a list of fixed price ranges.

11. Allow users to easily change filters


If a user selects a refinement and later wants to change it, make sure it’s easy for them to re-select and immediately see the results change.

This is much more efficient and user friendly than having them navigate back to the original SERP to change the refinements.

Providing boxes that can be checked and unchecked is a simple way to do this. You can also provide a button that will clear the selections and let users re-start. 

12. Save previously used filters


This is a great way to offer a personalized experience to users who visit your webshop.

Store customer information in a cookie so when they return to your site, their previously selected search result filters will already appear.

Ok, this sounds pretty creepy, but here’s an example, so you can see it in context.

If someone sets their clothing size in a filter, have that saved, so next time they visit your site their previous selection will already be filled in and the product results will be personalized. This is especially useful if you have a retail site or if customers frequently abandon your site before completing their purchase order.

If that’s too aggressive for your store, provide an option in the refinement box that lets customers select an option to save their previously used filters.

Final Thoughts


Search result filters are an essential tool in enabling shoppers to find the products they’re looking to buy.

In order to provide shoppers with the best experience possible, be sure:

  • Leverage faceted search to ensure all displayed filters are relevant to the query
  • They’re intuitive and conveniently located
  • Display the number of results beside each filter
  • Never show filters that lead to 0 results!
Balazs VekonyOnline Marketing Manager – Prefixbox

Balazs is an Online Marketing Manager at Prefixbox, a leading eCommerce site search solution. He’s a Budapest based marketing enthusiast, who’s interested in new technologies and solutions and believes in the power of search.

eCommerce Optimization: 7 Simple Ways To Improve Search Data Infrastructure

eCommerce Optimization: 7 Simple Ways To Improve Search Data Infrastructure

The back-end of your search function is equally as important as the front-end.

eCommerce search data can give you insight into which of your products are popular, which search queries yield no results, and give you overall insight into how to best stock your store.

Infrastructure is also important – minimizing the downtime of your search function is critically important to your company’s success since search users convert the best on websites.

Frequently review search suggestion quality


It’s important to review your eCommerce search data to see if your search relevance is improving.

Frequently checking your search analytics will help you find problems when they arise, so you can fix them before they escalate.

Your site search solution should be continually improving – most solutions do not self-improve, so you will have to manually make changes by tracking user behaviour on your site.

However, there are some solutions that do improve automatically, such as Prefixbox.

These solutions eliminate the time consuming effort of monitoring and continually updating your search platform and thus are generally a great investment.

In any case, you should check for improvements in categories like click-through rate, revenue, and average clicked position, which are important metrics to track.

Make note of the most frequently searched terms


Stay up-to-date with the terms people are searching for on your website.

These popular searches give insight into which of your products are performing well, the seasonality of your products, and which new items your customers are looking for.

Note this: These popular searches give insight into which of your products are performing well, the seasonality of your products, and which new items your customers are looking for.

This eCommerce search data is especially helpful when you are re-stocking.

Use popular searches for SEO and PPC campaigns


Once you make note of the popular searches on your site, be sure to make the most of them!

The language your website visitors use on your search function is the same they use for online search, so your database of previously searched terms is a PPC gold mine.

You can use these keywords as a base for your SEO efforts along with your PPC campaigns.

By having insight into the direct phrasing your customers use, you can fine-tune campaigns to reach your exact audience.

This insight will help your campaigns perform better and boost the SEO of your website, enabling more people to find your products.

Find search queries that have poor results


Search queries that have poor results can provide valuable insight. These searches are terms with a click-through rate of 0.

This can happen when people search for products you don’t carry or when customers search for terms you don’t use to describe your products and end up on the 0-results page.

  • If visitors frequently search for products you don’t carry, use this insight when considering what products to stock for the following year.
  • If you decide you don’t want to stock these products, you could provide information about them or recommend similar products, so you still provide value to customers searching for those items.
  • If your customers use different wording to find products on your site, consider switching to their terminology or creating synonym rules so both terms will direct the customer to the product. 

After you make these changes, be sure to keep monitoring your search data in your site to ensure the changes you made were beneficial. If not, continue testing options until you can perfect your product names and stock.

Check search data for keywords that become popular


Keep an eye out for search terms that become more popular, so you can keep up with demand. Popular search terms often change – you might see regular seasonal changes or spontaneous cultural changes.

Terms that become more popular signal which products your customers want to buy. If you see a sudden increase in a search term, stock more of that product, so you can keep up with (the anticipated) demand.

Keep an eye on your top listed products


Having data on the click-through rate of your first 5 listed products per search term is important, since these products should be the most relevant to the search and therefore most clicked.

If you consistently see a high click-through rate on these products, great!

If not, try re-ranking your products to place the most popular ones first; this should increase the conversion rate on your site.

Gather mobile website data too


It’s important to track data from your mobile site, so you can see which features are the most important and frequently used.

Site traffic patterns provide insight into how campaigns are performing and about user behaviour on your site, so you can see where to improve.

Be sure to track things like which browsers are used, how long visitors stay on your site, how they navigate, and any abandonment.

Have multiple data centres


Search plays a big role in directing customers to the products they want to buy, so it’s important your search solution doesn’t suffer from downtime.

Data centres sometimes fail and go offline, so in order to avoid down time, host all the critical parts of your web store in multiple data centres.

Consider using a SaaS search solution


SaaS, software as a service, solutions can save you time and money.

These solutions often work at a higher capacity and come at a lower price than on premise solutions.

Plus, they’re usually hosted in multiple data centres, which minimizes down time.

If you’d like to start collecting data on your eCommerce site, you can set up Google Analytics.

If your eCommerce search data collection and infrastructure are properly set-up, make sure your faceted filtersSERP, no result pages and mobile version of your site search are also optimized.

Balazs VekonyOnline Marketing Manager – Prefixbox

Balazs is an Online Marketing Manager at Prefixbox, a leading eCommerce site search solution. He’s a Budapest based marketing enthusiast, who’s interested in new technologies and solutions and believes in the power of search.

11 Must Have Conversion Boosting Search Features for Ecommerce Sites

11 Must Have Conversion Boosting Search Features for Ecommerce Sites

Site search is a powerful tool that can boost your online revenue and conversion rates while providing your shoppers with a better experience, but a lot of online stores still aren’t fully leveraging it.

Check out these 11 must have conversion boosting search features to find out how you can optimize your shop.

Site Search Conversion Boosting Features Aricle intro

Does Your Online Store Satisfy 85% Of Visitors?


People research what they want to buy before making a purchase. In fact, 87% of shoppers begin their product searches using online channels. Shoppers most often research products by executing a search from the search box to get directly to the product they’re thinking about buying. They’ll read a bit of information about it and check the same product on your competition’s site to compare prices.

If they can’t find the product on your site, it’s a sure bet they will be taking their business elsewhere.

Site search functions that are complete and clear keep visitors on your site instead of driving them to the competition.

Site search also maximizes revenue.

Did you know?

  • Site Search users convert to sales 4-6 times more often than non-search visitors.
  • Revenue from search function users makes up about 50% of a web shop’s total revenue.
  • A simple text-based search bar reduces site abandonment from 65% to 40%.
  • Semantic searches, which identify customer intent, reduce abandonment further from 40% to 2% and lead to an average of 16.2 page views per session.

Site search is one of the most powerful tools you can use to increase your sales, but most e-commerce sites are terrible at using it. An overview from a few years ago revealed how many businesses are failing site search basic training. Here are a few numbers:

  • 16% of ecommerce sites do not support searching by product name or model number.
  • 70% require users to use store jargon instead of applying any kind of synonym matching.
  • 60% of ecommerce sites do not use faceted search despite it being the foundation for filtering results properly.

If you’re making any of these mistakes, it’s likely that search is hurting your business.

How Much Does Bad Search Cost Your Business?


E-commerce search engines can boost conversion rates by an average of 15.8%. Optimized search drives an online revenue increase of 47%. What does that mean for your business? Let us think about just two numbers:

  • Your total revenue from sales on your website last month.
  • 1.5

Now, multiply your total revenue by 1.5. The answer is how much revenue your site could have generated last month if you had already been using an optimized search function.

ecommerce lost revenue calculation

We’ve compiled a list of the hottest 25 eCommerce site search best practices to help you reap the full benefits of search. Imagine putting all these into practice. Your user experience and conversions rates will skyrocket!

Consider These Must Have Site Search Features to Improve Your User Experience


By now, you should be convinced of the importance of a great site search function.

Your search directly guides shoppers to the products they want to buy – if your search can’t do that while meeting their expectations, you’re losing out on business.

Here are the search features you should include to boost your conversions and sales.

Site Search Box


A search box is one of the most basic site search features you can have on your website. Site search functionality is crucial to a positive user search experience.

Let us imagine a simple example: You sell island-themed shoes and clothes on your website. A visitor comes to your site hoping to buy a men’s extra-large Hawaiian shirt. After browsing your catalogue for a few pages, he is not closer to finding his size or design. Your visitor has two options:

  • Continue browsing slowly, while feeling more and more frustrated.
  • Use the search function to find exactly what he is looking for.

Which choice do you want him to make?

You want him to search, find the product, and make a purchase all while delivering a great experience.

However, just having site search isn’t enough; all site search isn’t the same.

There are some features you can use to make sure your visitors have a great user experience and easily find the products they want to buy.

Here are some of the features your site search box needs to include:

Typo-Tolerant Autocomplete


Your Autocomplete has to be able to decipher typos. These occur in nearly 30% of searches and even more frequently on mobile. If your Autocomplete can’t, it’s going to lead shoppers to “no results found” pages – even if you sell the product they want to buy.

Ranking


Ranking algorithms apply to product and keyword suggestions in the Autocomplete, as well as the products on the Search Engine Results Page.

These algorithms take into consideration both the relevancy of the results along with the popularity to provide the best results to shoppers. In some cases, search providers have a portal where you can edit these values to “boost” certain products you want to show.

Personalization


Your business can use machine learning to personalize your visitors’ experiences. For example, personalization could capture a user’s previous search history to show more relevant results to them on their current visit. As your site search function remains in place, your users will receive better results.

6% of retail visits that involved AI suggestions led to a 4.5 times greater cart rate and a 500% increase in per visit spend.

Keyword, Product, and Category Suggestions


Your Autocomplete shouldn’t only suggests keywords. But instead, should recommend keywords, products, and categories.

This type of variation means that every shopper can have a great experience.

ecommerce search autocomplete extended with product and category suggestions

People who know the exact product they want, can quickly navigate to the product page. Shoppers who have a vague idea can browse through whole categories. And keyword suggestions can guide shoppers who have a semi-defined idea of the product they want.

Dynamic Filtering for Different Searches


Filters can’t be universally applied to searches. There is not much point in displaying shoe size filters to someone searching for bananas. That’s where Dynamic Filtering comes in.

This means that different (relevant) filters are applied to different searches. It also means that whenever someone starts using the filters, they update so that no combination will lead shoppers to a 0-results page.

A study done by Screen Pages showed that 20% of shoppers who use the search function further refine their search by using filters. This is why it’s important to have a full search-suite, not just a search box.

This is a subset of Faceted Search.

Faceted search, or guided navigation as it’s sometimes referred to, is a search method that utilizes the metadata attributed to a product in a store, providing visitors an opportunity to filter and refine their search queries when looking for specific products.

If you leverage faceted search, you’ll improve your shoppers’ experience by directing them quickly, and easily, to the products they want to buy.

Breadcrumb Trails


Breadcrumb trails keep people from getting lost on your site. They come in a few different versions:

  • History – People are very used to a history breadcrumb trail because it works the same as the back button on their browser. A simple trail shows the previous page, the one before that, and so on back to the beginning of their journey through your website.
  • Category – Trails broken down by category start at the broadest section of your website and then narrow down through different filters. For example, a clothing website could have Home –> Clothes –> Men –> Shirts as a trail through its pages.

SERP Product Result Visualization


Search Engine Result Pages have become standardized with the rise in online shopping. This means that shoppers tend to navigate these SERPs in a certain way and if yours doesn’t follow the best practices, you could confuse shoppers and cause them to abandon your store.

One of the most important things to remember is that all of the product thumbnails on your SERP should be similar – same size, same style, and the product visualized the same way.

standardized ecommerce search result page

Here are other things you should consider when designing your SERP:

Number of Products Per Page


SERP product visualization is customizable – you can determine the number of product results per page and also provide options to let your shoppers change the layout. Google shows ten per page for a reason: people don’t look any farther. We recommend you follow their example.

Keeping results numbers low goes back to faceted search and dynamic filtering. Helping customers find what they are looking for on the first page, in the first slot, is the goal of good search.

Keep in mind: you should display fewer results on mobile because of smaller screens.

Quick View and Add to Cart


Even better than just showing products to shoppers is helping them actually make a purchase.

add-to-cart and quick-view features on category pages

The add-to-cart feature lets shoppers immediately add items to their cart to purchase later. This is a big win for your business because it shortens the sales journey.

Dynamic Product Ranking


Google has been attempting to master search intent for years.

The big idea is your website must show visitors what they want to find. So, when they search, you want to display the best results to your users.

Dynamic product ranking filters your products according to their popularity for the search results. This ensures the customer sees the best search results every time.

Out of Stock Products


There are a few different ways to handle this situation:

  1. Remove them from the results
  2. Show the products anyway with a restock date
  3. Display related products

Zero Result Pages


Landing on a “no results found” page is one of the worst experiences a shopper can have. This tells them that you don’t have the product they want to buy – which means they’ll take their business elsewhere.

You will never completely eliminate them, but you should aim to reduce the frequency as much as possible. Since they’ll always occur, follow these best practices when designing your “no results found” pages.

Related Searches


Including Related Searches on your SERP helps your shoppers easily navigate to other keywords/products or refine their initial query. This feature suggests relevant alternative, or complementary keywords and products to shoppers.

Related Searches can appear as keyword suggestions above/below the SERP or as product suggestions below the SERP or on 0-result pages.

Deep Query Understanding


Understanding customer intent is difficult, but possible. The better a search solution can understand shopper intent, the better results (and user experience it can provide).

Search engines often leverage Natural Language Processing and AI to get better over time.

Some search providers also implement Synonym Management to further improve the quality of their results.

Frequent Data Refresh


Your website could change quickly when people are shopping. Stock might run low, a fad might suddenly cause one of your products to skyrocket in sales, or a global pandemic could lead to a big spike in demand for your personalized face masks.

If you don’t have frequent data refresh, your catalogue could easily become outdated and confuse shoppers.

Frequent data refreshing is also important if you leverage dynamic ranking (which you should) – this ensures product and keyword suggestions stay relevant.

Detailed Site Search Analytics


Google Analytics doesn’t provide enough insight when it comes to eCommerce site search – you need a solution with dedicated analytics.

This powerful tool lets you plan more product lines, identify weak products, see the language your customers use, and reduce 0-result search rate (among many other things!) and keep an eye on your customers’ shopping behaviour.

In general, site search analytics can be broken down to these three areas:

  • Search Engine Analytics tell you how many searches visitors conducted, what the results were, how many visitors clicked on a result, what rank the result was, and many more data points. Check out this guide to find more information on Search Engine Analytics.
  • Autocomplete Analytics reveal how users interact with the Autocomplete function.  Reports include data about suggestions usage, usage at position one, search box abandonment, conditional usage, and more. You can find more detailed information here.
  • Related Search Analytics provide data about popular products, popular searches, 0-result searches, engagement, cart actions, and search term details can help you tweak your search engine results pages to show your customers more of what you want them to see. More information is available here.

The data in these reports can help you decide which products to stock more of, how to name products, and information to include in marketing campaigns. This are just some of the ways you can use Site Search Analytics to boost your business!

Key Points


Let us recap a few key points to keep them fresh in your mind:

What is on-site search?

On-site is more than a simple query box. It encompasses Autocomplete, Related Searches, and Semantic Search Engines. It’s the best way you can enable shoppers to make a purchase on your site. And for you, it is a window into the mind of your customers. You can use it to understand their shopping desires and you can use its data to plan new product strategies and refine your business model.

How can search functionality be improved?

If you don’t have a site search function – get one! If you already have one, check that it has the features in this guide. And always keep an eye on your site’s analytics to find out the specific improvements you can make.

What features should an e-commerce website have?

The basic features your search should include are: typo-tolerant Autocomplete with keyword, product, and category suggestions, dynamic filtering, a cohesive SERP, Related Searches, effective 0-results pages, and detailed search-specific analytics.

Now that you’ve seen how much site search can impact your revenue, we hope you’re ready to optimize yours. If you implement these 11 conversion posting features, we’re sure you’ll see an increase in your conversion rate and revenue in no time.

Paige TyrrellHead of Marketing – Prefixbox

Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.

What Ecommerce Brands Often Get Wrong about Site Search

What eCommerce Brands Often Get Wrong about Site Search

In my life so far, I must have visited thousands of ecommerce sites, ranging massively in quality. And while the worst offenders have found countless different ways in which to take up far too much of my time and raise my stress levels, few things infuriate me more than a site with weak on-site search.

Imagine finding yourself at a loss trying to locate a particular product in a grocery store and flagging down a store assistant, only for them to stare at you blankly and sullenly refuse to provide you with any help. You’re trying to buy from them — why won’t they help you?!

So whether you’re a customer looking to codify why exactly some stores annoy you so much, or you’re a store owner looking for some direction on how to improve, this piece can help. Let’s go ahead and run through the main things that ecommerce brands get wrong about site search.

They Don’t Present It Clearly


The whole point of a search system is to help you find things that you might otherwise have missed (or taken a prohibitive amount of time to happen upon). As such, having the search system itself be tricky to find is a colossal design failure, yet some ecommerce stores do this — some intentionally (not considering search important) and some unknowingly.

There are two major ways in which stores fail to present their search functions clearly:

  • The search box is not in a prominent areas. In almost all cases, a search bar should be shown above the fold and helped to stand out through contrast and spacing. Adding a small link to a dedicated search page in an otherwise-uninteresting corner of the screen will ensure that very few people ever discover its existence.
  • They don’t make reference to them. At a minimum, a site with internal search should mention it in a help section of some kind, and there should be occasional prompts for the user to return to the search function if uncertain about where to go. If there’s no reference to a search option, people will assume it doesn’t exist.

The failure to prominently display a search bar is particularly frustrating from sites that actually have robust search functionality, because it ensures a near-total waste of effort.

They don’t tag products WELL


A search facility is only as useful as the product database into which it provides a window, and incompetence or apathy on the part of eCommerce retailers can lead to product tagging that is bland at best and utterly incomprehensible at worst. Good product tags are:

  • Worth including. The quantity of product tags isn’t inherently important, and loading a product description with fifty distinct tags is only going to cause confusion. Most of them will invariably be insignificant, and thus serve to distract from the tags users care about.
  • Implemented consistently. If you’ve ever used a marketplace such as Ebay, you’ll know how tough it can be to narrow down a search when sellers use different tags to communicate the same things. Cases for the iPhone 6 might be tagged as “Compatible with iPhone 6” by one seller, “iPhone 6 Compatible” by another, and just “iPhone” by a third — how do you get a full list of all relevant cases?
  • In line with user expectations. The terminology you use for tagging also matters, because you’ll want to mirror the phrasing used by the searchers. Tagging a phone for “NFC” might be accurate, but if searchers are looking for “contactless”, you should adjust your phrasing to suit.

An eCommerce store with a solid search system can entirely undermine it by failing to achieve any degree of consistency or sense with its product tagging, and it’s relatively common because product tagging is an arduous and boring task that can’t meaningfully be sped up — but it has to be done for search to be maximally useful.

They stick with default systems


In many cases, eCommerce brands never even think to make any changes from whatever default systems are present on their stores. Whether because it’s most cost-effective or because of a lack of technical expertise, the average store today is the product of a user-friendly storefront creation tool, and goes largely untouched from a technical standpoint (beyond some basic setup work and template tweaking).

And while using DIY software is likely to ensure a basic search facility, it’s going to be a big disappointment relative to a smarter solution by doing the following:

  • Providing no product filtering. The larger a product range is, the more important a filtering system becomes. Relying on tags, a good filter system allows you to incrementally narrow down a search instead of repeatedly searching, checking the results, and revising the search from the beginning.
  • Missing valuable data. Any search feature that doesn’t properly segment sequences of search strings is wasting valuable data, because knowing how users step-by-step alter their searches gives you tremendous insight into how they think — insight that you can use to improve your UX and drive more conversions.
  • Overlooking rhetorical potential. Through autocomplete terms, related products, and recommended items, a high-quality search window goes a long way to provide the user with value, saving them time and helping them expand their order.
  • Giving generic results. Personalization is increasingly key, as we expect the search engines we use to have some awareness of context when interpreting our searches. Implemented well, it raises the value of a search function for any given user over time, something very important for customer retention.

Any ecommerce brand that wants to maximize its profits (so every eCommerce brand) should explore using integrated search services to provide stronger functionality.

Conclusion


To briefly recap, ecommerce brands get three major things wrong about site search:

  • They don’t give it the prominence it deserves.
  • They don’t tag their products to be search-optimized.
  • They don’t make any effort to customize it.

Where this is the result of indifference, it’s hard to know what to say to the offending companies other than to ask them about how they use ecommerce sites in an effort to have them realize why search is so important. Where it’s a matter of ignorance, though, it’s really a matter of making them aware of how incredibly transformative an exceptional search tool can be.

Patrick FosterWriter and eCommerce Expert – Ecommerce Tips

On a regular basis, Patrick blogs about the latest developments from the ecommerce world. For updates on new activity when it happens, head to the ET Twitter @myecommercetips.

How to Increase Revenue with B2B Search

How to Increase Revenue with B2B Search

While eCommerce search function is important for every webstore, it plays a different role on B2B shops than it does on B2C. In order to fully optimize your site for conversions and revenue, it’s important to understand the function of search on your site and how you can leverage its full potential.

Differences between B2B and B2C search


When outlining the differences between B2B and B2C search, it’s important to start by thinking about each site’s customer profile.

People shopping on B2C websites are shopping for themselves or for friends. They’ve either arrived to your site with an idea of what they’re looking for or are happy to just browse around.

The browser
the "browser" type of online store visitors
The searcher
the "searcher" type of online store visitors

If they’re using your search function, they’re looking for something particular although it could be something vague like ‘black shoes’. These shoppers are often open to seeing different/related product suggestions and will frequently order something they didn’t initially intend to purchase.

On B2B sites, people aren’t necessarily browsing as much as they are placing an order. They’re often re-ordering something they’ve already purchased for their company or want to stock in their shop. They know the brand, product SKU number, and exact items to buy.

They aren’t usually open to browsing around and will almost never buy something they didn’t initially intend to. They order about 4X as many products as B2C shoppers.

In this way, due to the high number of different products and shopper’s specific needs, search on a B2B site is incredibly important as it plays an integral part in quickly (and accurately) directing shoppers to the product(s) they’re looking to buy.

That’s why 95% of B2B shoppers use the search function, while only 20% of B2C shoppers doThe intent to purchase for users who use the search function is 3-4x higher than users who just browse. This explains the huge difference between B2B and B2C sites.

B2B product catalogues are also significantly larger than B2C product catalogues. For example, an average product catalog for a large B2C site contains around 10,000X products, while a B2B product catalog contains 100,000X products.

With such a wide range of products, it’s virtually impossible for a shopper to navigate to products on a B2B site without the use of search and oftentimes, category navigation is not a viable solution for product discovery.

Search on a B2B site is a key driver in providing shoppers with a great and efficient experience.

With the wide volume of available products and the predictability of shopper orders, search gets shoppers to where they want to go. The importance of B2B search is clearly visible in the 95% use rate. If your B2B search isn’t optimized, you could be missing out on a lot of revenue.

So let’s dive into some best practices on how to properly optimize your search.

B2B Search Best Practices


Now that you see how important search is on a B2B site, let’s dive into some of the best practices. By optimizing your search accordingly, you can effectively increase your conversion rate and online revenue.

Focus on accuracy


The B2B shopper is on a mission to complete an order. They have their shopping list ready and want to get in and out as fast as possible; so accurate results from the beginning are critical to providing a great experience.

Your Autocomplete makes the first impression and can save a shopper a lot of time, so it’s important it’s optimized for speed and accuracy.

In order to meet customer expectations, you need a strong Autocomplete with a deep understanding of natural language that can accurately decipher typos.

  • Your Autocomplete needs to follow the general guidelines: Be prominently featured at the top of the page and be wide enough to support your typical search queries.
  • It should recommend popular keywords and products as soon as someone focuses in and it should never suggest products or keywords that you don’t carry or are out of stock.

To further improve accuracy, your Autocomplete needs to be able to understand and process all the ways your shoppers search. Some people will search by brand name, some by product specifications, and others by product ID number – your Autocomplete needs to be able to understand all of this and provide meaningful results.

You need a Search Engine that can accurately display search results based on accuracy and popularity. In order to stay up to date, it should update these rankings at least daily, with more frequent catalog updates.

In order to get amazing accuracy, it’s important to leverage the latest search technologies. As they become more widely adopted, it helps your store stay relevant and provide a better user experience than your competitors.

Leverage ML and NLP


Machine Learning and Natural Language Processing, while buzzwords, are essential in adding value to your search results. When leveraged correctly, these technologies will increase the number of accurate search results, which enable more shoppers to find the products they’re looking for.

Natural Language Processing technology enables search engines to deeply understanding search queries and user intent, which ultimately provides shoppers with more, and more relevant search results.

A great way to bolster your NLP search is by adding relevant synonyms to keywords and products or to leverage a synonym database, which does this automatically.

A search leveraging Machine Learning continually optimizes search results based on executed queries and aggregated user behavior.

With this, search results are updated frequently (or in real time) based on how people are interacting with them (e.g.: search result clicks, add to basket and order actions). For example, products that are often purchased in after a certain executed query will become more closely related and will show up higher on the Search Engine Results Page when the query is executed in the future.

Machine Learning algorithms are an effective way to mine through a complex amount of data to add coherence to search patterns.

These are incredibly effective ways of reducing the 0-result rate on your site. Landing on a 0-results page is one of the worst experiences a shopper can have as you’re telling them you don’t carry the product they want to purchase. They, therefore, will take their business elsewhere and you miss out on an extra sale.

By leveraging NLP and ML, you’re able to significantly reduce your 0-result rate and increase your online revenue (by increasing the average order value for the B2B shopper), all while providing your customers with a better shopping experience.

Improve product findability


While strong merchandising features and a dependence on filters come to mind when thinking about optimizing a B2C webshop, they also play an important role in B2B webshops due to the immense size of product catalogues. Improving product findability allows shoppers to quickly make a purchase and thus increases your online revenue.

Filters are an essential way shoppers sort through products on the search results page to find what they’re looking to buy.

You should leverage dynamic filtering, filters that change based on product results shown, to allow shoppers to narrow down a wide range of results. For example, someone shopping for laptops may want to filter by brand, size, processing power; while someone shopping for printer ink would need to filter results based on printer type.

Since shoppers aren’t able to effectively navigate your store manually, it’s important that you have the ability to promote products on sale.

If you’re looking for a search provider, make sure you select one that allows you to set rules to boost specific products, keywords, or even categories (depending on your business, you may also want to be able to do this based on different geographic locations).

By providing intuitive filters and leveraging merchandising, shoppers are better able to find the products they’re looking to buy and will be more likely to make a purchase; increasing your bottom line.

Leverage data


While all of the previously mentioned tactics are important to use in your B2B shop, the most important thing you can do is utilize data.

Monitoring the performance of your search with detailed analytics, is one of the most essential things you can do to optimize it.

When looking to implement an analytics tool – you need something that tracks search metrics more in-depth than Google Analytics. Oftentimes, site search providers will offer these tools for free, so do a basic Google search and evaluate offers.

Once you’re using analytics, make sure you track the following things:

  • Search terms that lead to 0-results, so you can optimize your naming conventions to quickly reduce 0-result rates. Alternatively you can add synonyms in your search engine to fix popular searches in one step, that way you don’t need to rename all the products in play.
  • Popular products/searches will give you insight into what your customers often buy. You can filter these by seasonality, so you can optimize your product stock.
  • Your search usage and find out exactly how much revenue comes from search users. By checking this regularly, you can instantly see if/when there’s a problem with your search and quickly repair it.
  • By checking your search rate/device, you can see where most of your shoppers come from an optimize your site accordingly.

The benefits of frequently checking your search analytics isn’t just better for improving your bottom line.

This insight also benefits other departments. For example, marketing can get insight into customer language by checking the words they use when searching for products. This can then be used in newsletters or in PPC campaigns to make marketing more effective.

Analytics is the basis for a strong search and shouldn’t be something you overlook.

Get Personal


When optimizing your B2B search, think about adding some elements of personalization. Frequently, B2B shoppers return to the same site to place the same order monthly or quarterly. In the B2B industry, the rate of return customers is high, so you should consider personalizing your search by recommending previously searched/purchased products in the Autocomplete or saving previous orders.

You could also customize product recommendations on 0-results pages. You could do this by suggesting other previously purchased products or highlight related (or complementary) products/keywords, so they can navigate to a product page with just a click.

This makes the shopping journey even easier and faster for your customers and provides them with a seamless shopping experience that boosts your online revenue.

Summary


Search on a B2B website is critically important as more than 90% of shoppers interact with it at some point during their shopping journey.

In order to provide your customers with the best experience possible, you need to focus on a few key areas. Most importantly, your search should be accurate and typo-tolerant, you should leverage the latest technologies, implement dynamic filtering and personalization, and monitor results and adjust accordingly based on detailed analytics.

While it can seem overwhelming to optimize the nuances of search, site search providers are able to help and are often happy to suggest some best practices or ways to improve your existing store.

Paige TyrrellHead of Marketing – Prefixbox

Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.

12 Awesome “No results found” Pages (Plus, UX Design Best Practices and Tips)

12 Awesome “No results found” Pages
(Plus, UX Design Best Practices and Tips)

No matter how much optimization you do, it’s inevitable that some searches on your online store will lead to no-results found pages. Planning for these inevitable dead-ends should be an integral part of your UX design from the start. Search is the most important tool in guiding shoppers to products, but even if you follow all of the most important eCommerce search best practices, “no results found” pages still happen.

12 "No results found" Page Best Practices

Contents

Chapter 1

No results, no control

Chapter 2

“No results found” page design guidelines

Chapter 3

The best “no results found” page examples

Chapter 4

“No results found” page design best practices

Having a great “no results found” page design is an enormous opportunity for you to build trust with your customers, but you can only do that if you design these pages consciously.

Even though these pages are critical to a online store, according to Baymard, “68% of e-commerce sites have a “No Results Page” implementation that is essentially a dead-end for users, offering no more than a generic set of search tips.” Which also means that customizing your page in a user-friendly way is an opportunity to do get ahead of your competitors. And it only takes minimal effort.

Why are these pages so important?

It’s because these pages keep shoppers on your site even though they can’t immediately find what they’re looking for. If you have a great “no results found” page, you can effectively re-route the shoppers and help them make another purchase. Whereas, with a bad “no results found” page, the shopper will most likely leave your site and go to your competition instead.  

This is a big deal. You can avoid this by thinking of the following:

Chapter 1

No results, no control


It’s important to note that while you should aim to guide shoppers with a search bar, this also gives them a sense of confidence and control over their shopping experience. This sense of control is important in creating a good user experience and building brand loyalty with your customers.

Landing on a “No Results Found” page that doesn’t provide alternate products or information on what to do next, can easily make shoppers feel like they’ve lost control over their shopping experience.

Why does this happen?

As Nielsen states, shoppers don’t always realize that they arrive at a “no results found” page, simply because conducting searches on the web is such an integral part of our daily lives – we are wired to expect certain results within a certain structure. That is: some information on the top about the search that was just performed, which could usually be skipped over, and the relevant results below.

In case of a “no results found” page, information about what happened is usually included in the spot that people often skip over, so they don’t realize they search didn’t return any relevant results. Shoppers are often surprised when they see other webpages or products where they expect the results to be. This often confuses shoppers and leads to a negative experience on your website.

This is why providing tips and alternative products, in a way that the user actually reads them, is very important.

In this article we are going to look at some awesome examples of “no result found” pages.

But first, we have to talk about the basic guidelines of how these pages should be designed.

Chapter 2

“No results found” page design guidelines


Now that you see why “no results” pages are so important, let’s check out the best practices for designing pages that help reduce your site-abandonment rate.

Clearly explain what happened


Clear up any potential confusion at the first opportunity to do so.

And be sure to do it in a way in which the shopper actually sees your message – otherwise it’s pointless.

To figure this out, do a heat map analysis of your search results pages, and put the information about the failed search in the place shoppers instinctively look first.

You don’t have to over-complicate this: just tell them the search term they entered yielded no results, but they shouldn’t just stop searching. It often happens that the product they’re looking for is in-stock, it just didn’t turn up due to a typo or a difference in naming.

Always take the blame


Always start these messages with an apology.

Even if you just include something like “our bad”, “sorry”, “apologies for this” in the text, it eases the frustration the shopper might feel, and will help keep them on the site and take the alternative routes you provide.

Speaking of which…

Provide alternatives


Never leave the user with just a notice and apology: always engage them, urge them to try again, or to take further action on the site.

Keep in mind, your site is likely not the only one they are browsing: if their search ends in no results found in your online store, chances are, they’ll leave and go to one of your competitors.

Check their spelling


Actively provide tips if the shopper misspells a word.

Instead of simply informing them that they misspelled a word, you should provide a clickable version of the right spelling that will immediately direct them to the results page (yes, like Google and other search engines).

Suggest similar results


You can do this in a number of ways:

  • Suggest similar keywords and products based on their initial search term with Related Searches
  • Make personalized product and keyword suggestions based on their previous shopping behavior on your site
  • If you can’t track their behavior, at least offer products that are popular among your other customers.

And if this is not enough…

Engage


Even if they can’t reach the product they’re looking for, provide some alternatives for staying in touch.

Include your contact information (email, phone number etc.), or valuable content, links to your social channels, or a newsletter sign-up form that asks for their email address.

The goal is to not let go of their hand, in hopes that later they will return and make a purchase.

And now, let’s check out how some of the best websites out there make use of these best practices in their search results page design…

Chapter 3

The best “no results found” page examples


We covered all the best practices, so are you ready to see them in action?
Get some inspiration for your “no results found” pages from the following examples!

Nordstrom


The two basic things that Nordstrom does right here are:

  • They provide clickable, relevant alternatives based on the keyword provided.
  • They provide an alternative way of finding the desired product via direct contact.
    Note: they don’t only suggest contacting themselves, but also recommend a relevant specialist to contact if you’re not sure what you’re looking for (and provide you a direct path for doing so).

Best Buy


What they do well is that:

  • They offer a way to circle back to your history on their site: view and track previous orders
  • They provide an option for easily contacting their customer service in case you need more help.

Walgreens


Walgreens excels at helping shoppers retry or refine their search, by giving clear tips and instructions:

  • Directing you to a log of your history on their site
  • Informing shoppers that not all of their products might be on the site
  • Providing shoppers with a chance to contact customer service

They also provide a feedback field, which could collect valuable user information they could leverage to further improve.

Build.com


The search results page at Build.com, even with no results, is informative and offers several ways to proceed, including:

  • Multiple suggestions on how to fix the search query
  • Logging in to check your order history
  • Suggesting alternative, popular products

Sears


At Sears, they not only explain what happened, but they also suggest other, relevant products based on your previous activity.

Costco


Take note how Costco designed their “no results found” page:

  • They apologize
  • They provide an easy way for shoppers to execute another search and even include an extra field for doing so where the results should be (where shoppers instinctively look first).
  • They provide suggestions on how to re-execute the query, as in: check your spelling, try more general or different keywords.

Although, they miss an opportunity by not offering alternative results based on popularity or user behavior.

Ikea


The main reason their “no results found: page is user friendly is because they immediately show the search query, which is a great way to enable shoppers to identify and fix typos.

This comes along with an apology, to take away some of the frustration. And to alleviate the rest, they also offer alternative, personalized product suggestions.

GAP


GAP is great because they actively identify typos.

And just like Google, they immediately show results for the keyword you might have misspelled.

eBay


eBay has a unique, highly effective tactic on their “no results found” pages – they enable shoppers to save their search and set alerts for when the desired product becomes available.

This is the best idea for a zero results page I can think of.

Wayfair


Here, you can see a great example of how search result design can be user-friendly: shoppers are not only given information on what went wrong with the search, but the search engine actually takes that search query and uses the individual keywords to provide other relevant result suggestions.

Debenhams


Again, the focus here is on immediately providing help: by placing a search bar in a shopper’s line of sight increases the chance they’ll stay on your site and make a purchase.

Disney Store


Finally, a unique example.

This “no results page” design isn’t very helpful, but it’s still user-friendly.

Disney simply uses some humor to ease possible tension for hitting a dead end and urges shoppers to continue.

You should consider using cute or funny images or language as part of your “no results page” design as it can make your online store more relatable and memorable. By creating an enjoyable shopping experience for your customers, you’re more likely to generate brand loyalty.

Chapter 4

“No results found” page design best practices


A shopper who uses the search function on your online store has a strong purchase intent. According to the Demac Media’s Q3 2016 Benchmark Report, users who use the site search are 216% more likely to convert than those who don’t.

No matter how much you optimize your site search, “no results” pages are inevitable. So it’s essential you spend some time optimizing these to help shoppers continue on their buying journey.

You’ll provide your customers with a better user experience and see an increase in your conversion rate and a decrease in your site abandonment rate.

When designing these pages, the main things to keep in mind are:

  • Be helpful
  • Always provide a way for the shopper to immediately continue shopping
  • Don’t leave shoppers staring at dead ends without offering help

Do you have a “no results found” page that you think is even better than the examples here? Send it to us or include it in a comment, and we might just add it to this list!

Paige TyrrellHead of Marketing – Prefixbox

Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.