Importance of E-commerce site search

Site search serves as the way through which consumers navigate vast product catalogs, seeking the perfect item to meet their needs on Ecommerce sites. A great search experience can be the difference between a satisfied customer swiftly finding what they seek and a frustrated user abandoning their shopping journey.

With Jon MacDonald’s famous words from The Good:

“If you’ve been treating on-site search as a necessary function, rather than as one of the most powerful revenue-generating tools in your arsenal, I have three words for you: Please stop that.“

In a previous article, we delved into the essential features necessary for an E-commerce search engine to provide your shoppers with the best experience possible. From intuitive autocomplete and typo correction to advanced filtering options and personalized recommendations, these features are paramount in meeting the evolving expectations of today’s online consumers. You can find this comprehensive guide here.

Why would e-tailers choose third-party site search tools?

Many online stores now opt for search solutions developed by third-party providers rather than investing in developing their own. The main advantages of this approach are the following:

Less effort, more expertise

Specialized search companies are typically more knowledgeable about the latest technologies and can leverage their expertise to offer enhanced search functionality for their partners’ stores. They typically offer pre-built solutions with latest tech or easy-to-implement APIs that can be integrated seamlessly with existing platforms, minimizing development time and effort.

Outsourcing site search to a third-party provider this way allows online retailers to focus on their core competencies, such as product development, marketing, and customer service, rather than diverting resources to non-core activities like search engine development.

Advanced technologies

Third-party providers continuously innovate and integrate advanced features and technologies into their site search solutions, such as

• AI-powered search algorithms
• Natural Language Processing
• Personalization, and
• Voice search capabilities.

These features enhance the user experience and drive better conversion rates. Vector search, for example, is the latest technology in search that goes beyond looking for matches in keywords, and understands context. 

Machine learning-based solutions can offer users a whole another level of personalization, be it by recommendations or smart search results (e.g. returning women’s shorts if the user had been browsing women’s clothing previously).

Developing and maintaining such advanced capabilities in-house requires significant investment in research and development.

Merchandizing

As Shopify found, many e-tailers look for a third-party search solution because their own setup does not offer advanced merchandizing features. Having the ability to re-rank search results and promote the products with the highest margin is an essential capability for retailers.

Most third-party providers bring a wealth of specialized expertise, advanced technology, and industry insights to the table, making them valuable partners for retailers looking to elevate their merchandizing strategies and drive revenue growth. 

In 2024, amongst the most known E-commerce search providers are Algolia, Klevu, DooFinder, Constructor and Luigi’s Box. 

While third-party providers undoubtedly offer compelling advantages, it’s crucial for retailers to conduct a comprehensive evaluation before integrating these solutions into their E-commerce platforms in order to make sure their criteria will be met.

Challenges with third-party E-commerce Site Search Tools According to Public User Reviews

Without further ado, let’s delve into the intricacies that retailers encounter while navigating the quest for the perfect search solution.

User Interface / User Experience (UI/UX)

While third-party search solutions offer a range of features, they may not fully align with the specific needs or branding of every online retailer. Customization options might be limited, which could impact the seamless integration of the search function with the overall website design and user experience. For example, retailers may be unable to fine-tune search rankings or adjust filtering options to reflect specific product attributes or customer preferences.

It’s also common that some customization options are available, but require developer effort from the retailer’s side. This goes against the idea that search providers should take weight off retailers’ shoulders, so they can focus their efforts into other areas. 

So, if you don’t have a large development team with capacity to work on your search customization, be sure to avoid choosing a third party solution that comes with these possible limitations.

Several users of the biggest Finnish site search provider, for example, consider the limited customization options for the search results layout, and the time needed for styling their store to match the search pages with the rest, as a disadvantage.

Usability

It’s imperative for third-party search providers to prioritize user-centric design principles, streamline on-boarding processes, and offer intuitive interfaces that empower retailers to efficiently configure and manage their search functionalities.

Retailers often face a learning curve when integrating and configuring third-party search solutions, compounded by the lack of comprehensive documentation or user-friendly interfaces.

Complicating matters further, certain settings may only be adjustable with the assistance of the provider, leading to potential delays and frustrations in fine-tuning the search experience.

Accuracy and Relevance Problems

While returning the most relevant results for a search query is a basic functionality of site search engines, achieving this consistently can be challenging.

Third-party site search solutions often come with advanced search algorithms and functionalities with features like autocomplete, typo correction, and faceted search, which help users find products more easily and quickly. However, some of them may struggle with accuracy and relevance due to limitations in their algorithms or indexing methods.

Without advanced technologies like Natural Language Processing (NLP) and vector search, these solutions may rely solely on keyword matching, leading to inaccurate or irrelevant results, especially for complex or ambiguous queries.

E.g. some of the biggest providers’ solutions still return inaccurate results sometimes, based on customer reviews.

To make sure your search always returns relevant results, choose a third-party provider that stays on top of technological advancements and leverages the latest search technology.

Issues with Integration and Technical Requirements

Ensuring seamless integration of third-party search with the existing systems and platforms of the retailer is crucial for maintaining operational efficiency and providing a consistent user experience.

Sometimes however, compatibility issues, API documentation clarity, and ongoing maintenance requirements may pose challenges for the retailer’s development team. 

Some of the users of a globally known enterprise search provider, for example, find that the complexity of their features, and the need for developers in the integration phase takes away from the user experience (“It’s hard to know what you can/can’t do. No applied examples of how to use features, makes it hard to test and implement.”)

Similarly, a Finland-based provider’s reviewers mention that the non-seamless integration of the search solution may make it feel like the landing pages come from a connected system and stand out from the rest. 

These kinds of issues, partnered with limited technical documentation and lack of compatibility with the retailer’s existing platforms can be quite a let-down in the retailer’s user experience, which is why these are factors to be aware of before making a decision about a search provider.

On the positive side, many providers offer comprehensive support and resources to facilitate seamless integration processes.

Insufficient Speed

Delays in indexing frequency and slow updates to product databases can negatively impact the speed and accuracy of search results. This can result in customer frustration for and lost sales opportunities for retailers. Improving indexing frequency and optimizing search result speed should be a priority for third-party search providers to enhance overall performance.

Weak Analytics Functionality

Access to user-friendly dashboards and robust search analytics are essential for retailers to gain valuable insights into customer behavior and preferences. Monitoring user search behavior, including autocomplete queries, can offer valuable insights into undiscovered content or tasks on your website.

However, some third-party search providers may fall short in providing predictive analytics and actionable insights, leaving retailers without the tools necessary to make informed decisions and drive business growth.

Some of the retailers using a known American search provider’s solution, e.g., mention having to work with a less than user-friendly analytics dashboard, but also give credit to their helpful support team, and their readiness to get the best out of every situation as a mean of compensation.

Customer Support

Effective and responsive consumer support is critical for retailers relying on third-party search providers to address issues and optimize performance. However, it’s not uncommon for some providers to struggle in delivering timely assistance, which can result in frustration and dissatisfaction among retailers.

While providing extensive technical documentation is undoubtedly valuable, it alone is not a sufficient form of support. Retailers often require personalized guidance, troubleshooting assistance, and proactive communication to resolve issues efficiently and ensure optimal performance of the search solution.

Wrap up

All in all, choosing a third-party E-commerce site search tool allows retailers to leverage specialized expertise, advanced features, scalability, and cost-effectiveness to enhance the search experience for their customers and drive business growth.

However, it’s worth noting that challenges may arise depending on the chosen provider, such as usability and customization issues, limited search analytics, and integration and support concerns. Given the framework of your team and the objectives of your company, it’s crucial to assess providers with the potential issues discussed above. This evaluation will enable you to identify the search solution that aligns most effectively with your needs and company goals.

About Shopify and Shopify Search

Merchants looking to expand their businesses and set up their own online store often turn to E-commerce platforms like Shopify, WooCommerce, Salesforce and others. Shopify has emerged as one of the most popular e-commerce platforms due to its highly customizable solutions, catering to stores of all sizes and categories.

As we like to highlight every once in a while, the power of search cannot be overstated when it comes to online stores. Just consider the fact that search users spend almost 3 times more money shopping online, compared to non-searchers. 

Given that 60% of E-commerce internal search engines offer low-quality search results, and using an advanced search solution can lead to a ~20% boost in sales, it’s no surprise that store owners (including those using Shopify) constantly seek ways to take their search functionalities up a notch.

Many of them give Shopify’s own Search & Discovery app a chance, considering how it already has some additional search features compared to many themes’ default engines. 

Before delving into why this app might be a good but somewhat limited solution for your search problems, here’s a fun fact you might not have heard:

E-commerce search queries can be categorized into 8 (!) very different types.

Shoppers might search for:

• exact product names and specifications
• features alone
• describe the problem they need a solution for, or
• use slang and abbreviation for the product they have in mind, among others. 

In an ideal world, your search engine could return relevant results for every one of these search query types. However, 72% of retailer sites still can’t.

Limitations to Shopify Search and Discovery app

After thorough analysis of the reviews left by Shopify Search & Discovery users on several platforms, let’s examine the most commonly experienced challenges and pain points Shopify platform users have to face: 

1. Filtering Issues

Most store owners that reviewed Shopify Search complain of the filtering function in some way. In general, filters on Shopify aren’t very sophisticated or “smart”, even with the app’s added functionalities. 

For instance, filtering is not available (or doesn’t work as intended) on every site with Shopify Search & Discovery. For example, filtering is not available for product collections (like men’s clothing) containing more than 5,000 items, and search result pages with more than 100,000 items. This is unfortunate as stores with large product catalog need filters the most in order to facilitate a great online shopping experience.

If a product fits the filter selected by a shopper, it will be shown in the results, even if it’s out of stock and can’t be ordered. There is an “Availability” filter however, which your customer can tick to only see products currently in stock, but this can be quite frustrating and illogical for many.

On the topic of availability, many retailers agree that filter values should only be displayed if they correspond to items that are in stock. For example, if there’s no red women’s t-shirt in stock, the filter value ‘red’ should not be available under women’s t-shirts. However, Shopify’s search does not filter these options out, which means shoppers may still end up with zero results when navigating via filters.

Some users also find it a disadvantage that they can’t set their own minimum and maximum prices on the price filter.

Shopify’s filtering options are also quite rigid, as there’s minimal space to customize them in order to fit a product collection. This could be problematic for stores that sell products with unique features that should be filterable values. Moreover, Shopify Search & Discovery only allows you to display 1000 filter values maximum, which can be a pain point for retailers with a wide product catalog. 

2. Typos and Synonym Management

Most traditional search engines rely on keyword matching alone, which is not the latest, state-of-the-art method of search (vector search is). Outdated engines aren’t always able to recognize typos, conjugated versions of the same word, or synonyms that are used to describe the same product.

Unfortunately, Shopify Search & Discovery is not much better in this regard, as it mainly looks for a full-text match between the product titles, descriptions, and the search query.

On the positive side, however, it partially recognizes typos and misspellings. It’s able to decipher a query if it only contains 1 misplaced letter, or 2 exchanged ones. Typo-tolerance is only applied for product titles, product types and product variants. Additionally, the first 4 letters of the search term need to be entered correctly for it to work. 

There’s the opportunity to manually add and manage synonyms as well, which could lead to more accurate results with a bit more effort and time invested. However, there is a limit of 20 synonyms per synonym group, and a maximum of 1000 synonyms for the entire store.

The Shopify Search & Discovery team suggests having product tags that contain any words a shopper might type in the search bar when looking for a specific product, as a loophole in the matter of synonyms.

3. Limited Personalization

According to BigCommerce, personalization can boost conversion rates up to 50%.

According to reviewers, Shopify Search & Discovery does not automatically utilize previous user behavior, thus can’t offer your site’s visitor an advanced personalized shopping experience.

In practice this means that if a user searches for “tiles,” the search engine won’t consider that they were exploring bathroom tiles and bathroom decoration just two days ago. It most likely won’t prioritize similar results based on their recent activity; instead, it will simply display everything containing the term “tiles.”

Check one of our previous posts on the importance of personalized (AI-driven) product recommendations, and best practices to maximize their impact.

4. Weak Multi-language Support

Many Shopify stores are available in more than one language, which means they need a search solution that can support multiple languages.

Shopify Search & Discovery is lacking on this front, as it is not entirely language-agnostic. For example, predictive search is only available in English, and not all filter types can be translated either, as of now.

5. Poor Customer Support

The quality of customer satisfaction plays a key role in a customer deciding whether to keep on using a product or service. 

Unfortunately, the users of Shopify Search & Discovery often experience issues with Shopify’s support team, ranging from being hard to reach at times to working with pre-written material and documentation alone.  

6. Limited Analytics, No Predictive Analytics

Insights into your customers’ search habits, purchasing patterns and user behavior in general can serve as invaluable assets in your future decisions. 

Shopify’s Search & Discovery app is equipped with some insightful metrics, but based on users’ experience, they’re not quite thorough when it comes to analytics.

You can check your:

• click and purchase rate,
• your customers’ top searches,
• top no-result searches,
• most searches that result in no click on products.

These can be helpful, but will prove to be insufficient when it comes to providing you with predictions for the future.

Predictive analytics can assist in identifying customer needs, recognizing shopping patterns based on purchase history, and forecasting future customer behavior.

If you’d like to know what other metrics you should be monitoring in order to have deeper insights into different areas of your business, and the performance of your online store, check out our article about E-commerce KPIs.

App Store Alternatives to Shopify Search and Discovery

There are many other apps available in the Shopify App Store, developed by companies specializing in search and discovery, that can help you take your store’s search capabilities to the next level. Most of them offer a free trial, and different packages for different search needs.1

Implementing an alternative third-party search solution with enhanced features can alleviate stress for you and your team, and most probably lead to an increase in revenue. To help you with discovering them, we’ve gathered a few of these available third-party search apps below.

1. Fast Simon

Fast Simon is available for Shopify and Shopify Plus stores, and offers advanced search functionality for large E-commerce retailers. The platform leverages shopper behavior, store inventory, data and visual signals to enhance the online shopping experience. 

They offer 6 different search packages, with the features changing from package to package, so you can choose the best fitting one based on your needs and budget. 

2. Searchanise

Offering Site Search, Filtering, Merchandising, Upsell and Cross-sell, and Analytics features, Searchanise advertises itself as a solution that improves customer experience across all stages of the shopping journey. 

Their Search&Filter app is available on a wide array of platforms, such as Shopify, Magento, BigCommerce or WooCommerce. The packages start at $0 (best fit for small businesses) and include custom plans, depending on your product catalog and overall usage. 

3. Prefixbox AI Search

Prefixbox is an AI-powered, data-based search and discovery solution for E-commerce retailers. Prefixbox’s Rich Autocomplete, Hybrid Search Engine, dynamic filtering, Merchandizing and automated synonym mining functions are made to improve your conversion rate and your customers’ experience on your site. 

The solution is fully managed, meaning that their customer support team will optimize your settings to maximize revenue. Prefixbox Search Analytics are the most detailed on the market, and offer unmatched insights.

Prefixbox uses vector search, the latest search technology that sees behind keywords, and learns from user behavior to return the most relevant results.

The Prefixbox Hybrid Search package on Shopify comes with custom plans, each offering a 30-day free trial. 

4. Boost AI

Boost AI advertises itself as a smart search app leveraging AI to ensure swift and relevant search results. Users like it because it’s customizable, offers insightful analytics, and good customer support. 

The app integrates with Shopify easily, and prioritizes increasing your conversion rate via relevancy. Their four product suites are: AI-powered Search, Product Recommendation, Merchandising, and Advanced Analytics. 

Boost AI is fit for Shopify stores of various sizes, as there are various packages available, starting from basic plans for smaller stores, to custom subscription offers for bigger ones. 

5. Findify

Having been working with Shopify since 2015, Findify is a Certified Shopify Plus App. It offers high-level personalization and customization options, utilizes NLP and lets your customers search for content besides your products. 

Merchants have the option to rely on Findify’s personalization algorithm for automated assistance, or they can customize the customer experience according to their preferences.

It can be integrated with various platforms, including Shopify and BigCommerce. 

Wrap Up

In conclusion, optimizing the search functionality on your E-commerce site is crucial, since the vast majority of shoppers rely on search bars. While Shopify’s Search & Discovery app may provide a convenient starting point, it does come with limitations such as filtering issues, a lack of robust support, and insufficient analytics.

Several third-party alternatives in the Shopify App Store, however, offer advanced features for basic search engines to overcome these challenges. Exploring these allows E-commerce businesses to tailor their search functionalities based on specific needs, leading to improved user satisfaction and potential sales and revenue growth.

Prefixbox AI Search is one of these alternative apps, addressing common pain points such as filtering issues, recognizing typos and synonyms, lack of personalization and limited language support.

With brands popping up faster and further than the eye can see, E-commerce platforms need solid digital infrastructure they can rely on to deliver quality shopping experiences to their audience.  

These days, consumer expectations for online shopping are high, and they’re changing all the time. Accuracy and speed are top priorities for modern e-shoppers, and vector search is the powerful tool that E-commerce platforms are using to provide them.  

Vector search is a type of search technology that utilizes machine learning (ML) to provide faster, more accurate, and more personalized search results to online consumers, thus driving sales and encouraging repeat business for E-commerce brands.  

We’re going to unpack how vector search works, what its role in the current E-commerce world is, and various other solutions for enhancing online shopping experiences for online customers across the globe. 

Understanding Vector Search: A new era of E-commerce

Before we jump into vector search’s impact on E-commerce, let’s give it some broader context. Vector search is essentially a form of search technology aimed at providing online users with more accurate and relevant search results.  

The technology used in vector search indexing relates to Machine Learning. ML is an adaptive technology that continuously learns and evolves as it receives data input in order to become more precise and compatible with the searcher’s goal.  

It can be applied to a variety of different websites or platforms that feature search engines, but E-commerce websites are among those that benefit most from this powerful form of technology.  

Vector search can also be referred to as similarity search. This is because the technology seeks to understand the inherent relationships and overlapping qualities shared between multiple different datasets.  

The result is a search mechanism that identifies and produces search results that align with individual consumer preferences more profoundly than other purely keyword-based search tools offer.  

Vector search’s prevalence in the current E-commerce landscape

Vector search is prevalent today because E-commerce shoppers rely heavily on search tools to find and purchase the items they seek. In fact, 69% of online shoppers make a beeline for the search bar as soon as they access an E-commerce platform.  

Furthermore, consumers place a high level of importance on the quality of search results provided at an online store. If the results do not immediately surface or are too distant from what they are looking for, it may drive them to exit the page in search of a better site to browse.  

As such an integral component of the online shopping experience, search functionality must be outstanding, and vector search technology helps accomplish this.  

How does vector search technology influence the E-commerce industry?

The use of vector search technology impacts the E-commerce experience in a number of ways. This is partially due to the fact that the search function is so vitally important to the consumer sales journey, and a satisfying (or dissatisfying) search result can mean the difference between a successful and unsuccessful purchase decision.  

However, this unique form of technology also has influence due to its more technical faculties, such as faster loading speeds and correcting common errors.  

Bearing all this in mind, let’s take a closer look at six fundamental ways in which vector search positively impacts the E-commerce industry as well as the troves of people who sustain it.  

1. Enhanced personalization capacities

One of the biggest and most significant ways in which vector search impacts the E-commerce experience is by creating the potential for more personalized consumer experiences. We know from a plethora of studies that consumers value personalization highly, especially when it comes to online shopping, so using technology like vector search immediately boosts the chances of a satisfying purchase.  

Vector search enhances personalization by analyzing customer preferences and using their purchase history as a guideline for gauging which search results to offer on each individual consumer’s device.  

This style of personalization extends beyond just making similarly-related product suggestions – it factors in what each consumer is most likely to want to find on the website and offers it to them in an organic and convenient way. This drastically increases the likelihood of a sale and helps drive repeat business.

2. Managing typos and synonyms

A lot of older, more traditional search engine models struggle to identify what consumers are searching for when the words used include misspellings and typos. Vector search can overcome this problem.  

When people are searching for a product in a hurry, they may use the wrong word or an inaccurately spelled one to find it, and normally, this disrupts the data sorting process.  

Statistic-wise, roughly 20-30% of all searches contain misspellings. But with vector search technology, the engine is able to handle more nuanced queries, thus allowing for faster and more accurate results regardless of how the words themselves have been phrased.  

This is related to the fact that, while other search engine systems rely on exact words to recognize products, vector search relies on their inherent features.

3. Uncovering hidden data patterns

Have you ever been recommended a product you weren’t actively looking for, but once you saw it, you knew instantly that you wanted to buy it? If so, you may have been dealing with a vector search engine.  

Vector search engines don’t just focus on offering you information on products you are looking for, they also continuously seek out underlying patterns of consumer behavior to determine whether you would like to be recommended certain products.  

This function can lead to surprising and delightful experiences for shoppers who feel that the algorithm “intuitively” understands their needs – perhaps even before they themselves do. As an E-commerce platform, this kind of experience is worth its weight in gold.  

4. More precise visual search for online shoppers

Visual search is a relatively new approach to search engine input (similar to voice search tools) that is rapidly gaining traction in the E-commerce world. So far, it’s an approach that only works with vector search technology.  

With visual search, consumers can opt to use reference images of products instead of using words or their voices to find them. For instance, if a consumer snapped a picture of a t-shirt they really liked at the mall, they could upload or share that picture with an E-commerce platform with vector search tools. It would then present them with a range of options that relate to it as much as possible.  

While visual search may not be as common as regular word-based searches, it’s a unique feature of product searching that can provide a lot of value to the consumers who need it.  

5. Optimized search speed

Another important and highly valuable way that vector search influences E-commerce experiences is through its seamless and lightning-fast search loading speeds.  

Because vector search is founded on mathematical representations, it is able to find the fastest and most efficient way to draw up accurate results. This helps keep consumer’s attention fixed on the site and encourages them to continue their sales journey.  

Fast loading speeds are one of the most pivotal and crucial elements of a successful E-commerce website or any website for that matter. If consumers don’t see results in under 2 seconds, they‘re likely to grow frustrated or bored and take their business elsewhere. But vector search aims to prevent that from happening at all costs. 

6. Enhances shopper’s overall experience

Ultimately, the value that vector search brings to the consumer experience is multi-faceted. It takes into account speed, efficiency, accuracy, and personalization in order to provide each individual consumer with a map to conversion that they can follow in an easy and natural-feeling way.  

By bringing consumers closer to the products they want in as short a time span as possible, vector search technology elevates the consumer experience while simultaneously boosting sales and conversion rates.  

Furthermore, vector search is redefining the way consumers engage with E-commerce platforms.  

With additional features such as visual search, enhanced personalization, and the ability to anticipate hidden patterns of buyer behavior, vector search is set up to be a hallmark of effective E-commerce business going forward.  

Seeking solutions for enhanced E-commerce experiences

Vector search is one of several technological solutions that E-commerce platforms can use to improve their digital infrastructure and make consumer’s shopping experiences better. Software services like Elastic make vector search engine integration simple and offer a range of tech solutions that can improve the efficiency, manageability, and usability of E-commerce platforms, such as: 

• Threat hunting – Advanced analytics allows E-commerce brands to hunt down, identify, and kill digital threats in order to safeguard both customer and business experiences online.  
   
• Generative AI – ElasticSearch offers a range of Large Language Models (LLMs) to produce powerful new applications for online businesses through semantic search tools, transformer model flexibility, and vector database management.  
   
• Log monitoring – Elastic’s scalable, centralized log monitoring tools allow E-commerce businesses (and other types of businesses) to deploy and manage logs at a petabyte scale.  

These solutions consider observability, security, and fruitful engagement with consumers. By focusing on the integration of cutting-edge E-commerce technology and software tools that both protect and enhance the usability of websites, brands can experience higher ROI, better search engine rankings online, and a much deeper, more lasting relationship with consumers.  

Conclusion

Vector search is a cutting-edge form of search technology powered by ML that enables consumers to find the products they are looking for with renewed accuracy and compatibility.  

With the ability to personalize searches, uncover hidden patterns, manage common typing errors, and optimize search speed, vector search technology makes online shopping more satisfying and successful than ever before.  

Alongside other E-commerce solutions such as log monitoring, threat hunting, and generative AI, vector search has become a key player in the evolution of the modern E-commerce industry.

With the E-commerce industry growing at an extraordinary rate globally, businesses are faced with the challenge of getting ahead of their competition and providing the best user experience possible in order to grow their business.

As numbers speak louder than words, the following statistics demonstrate the influence of AI on the E-commerce sphere: 

• A report by McKinsey & Co. shows that adopting AI can generate 20% extra revenue for your business
• Amazon’s AI-powered recommendation engine drives 35% of their annual sales
  

As NexGenAI Solutions phrased in a recent article:

“Harnessing the power of Artificial Intelligence (AI) is no longer a luxury – it’s an imperative”

Let’s go over a few of the hot topics in the field, and see how you can leverage these trends to your E-commerce business’s advantage. 

Emerging Trends for AI in E-commerce in 2024

1. Conversational Commerce

Conversational AI serves as the basis for chat commerce, as we discussed in a previous article.

Shortly put, conversational shopping aims to recreate the in-store experience online by enabling shoppers to chat with an AI powered shop assistant to get answers to any questions they have (about the store or products).

Conversational AI tools are capable of processing unstructured speech or text inputs and can further enhance their abilities through additional training and human feedback.

Using an AI-powered chatbot can reduce in-house costs, improve the user experience, and open a new online sales channel.

According to Markets&Markets’ recent study, the Conversational AI market is expected to increase from USD 10.7 billion in 2023 to USD 29.8 billion by 2028.

Read more at: 

• Conversational Commerce Use Cases
• Conversational Commerce Platforms

2. AI-Powered Customer Support

In addition to conversational commerce, Artificial Intelligence unlocks the door to seamless, lightning-fast customer support.

AI-powered chatbots are changing the rules of customer interactions by bringing efficiency and personalization to the forefront. They ensure 24/7 availability, break language barriers, reduce the need for human agents and enable higher degree of customer loyalty at the end.

3. Predictive Analytics

Predictive analytics algorithms are used to analyze various data derived from transactions and customer behavior.

Through the utilization of big data and statistical analysis, predictive analytics can assist in identifying customer needs, recognizing shopping patterns based on purchase history, and forecasting future customer behavior.

With the use of these analytics, you can efficiently generate personalized messages and offers, along with product recommendations that cater to the customer’s interests and preferences.

Providing suggestions tailored to the customer’s needs and preferences is one of the easiest ways to make them feel understood and valued. Customers are more likely to return to a store that prioritizes their satisfaction.

4. Personalized Recommendations

The concept of personalized product recommendations is not exactly new for E-commerce companies. Technological developments (including Artificial Intelligence) however, significantly contribute to making these recommendations more sophisticated and effective.

AI- and ML-based algorithms are the best choice when it comes to analyzing data related to customer interactions, as they can provide deeper insights into their purchasing patterns and behavior overall. Thanks to this, your recommendations can become even more personalized.

All in all, predictive analytics play an important role in customer satisfaction and retention as well.

5. Visual Search and Image Recognition

Computer vision, which is a subset of AI, gives machines the ability to “see” and interpret the world. It serves as a basis for visual search, where instead of a text-based query, the user searches with an image. 

How is it helpful in the E-commerce sphere?

The shopper can just take a photo of the product they’d like to buy, and upload it to the search engine, instead of writing lengthy queries, then browsing and filtering to find the best fit. 

Target and IKEA are great examples of visual search.

To easily get started, you can make use of ready-made visual search engines like Google Lens or Pinterest Lens. The other method of implementing this technology is to choose a site-search solution that supports visual search.

6. Vector Search

Vector search is the latest development in E-commerce search technology. It is based on a mathematical representation of language that transcends traditional search methods that rely on keywords alone. 

Vector Search dives deep into the very nature of products, understanding their features and nuances. This means more relevant search results, better product recommendations, and an altogether more satisfying shopping journey.

Thanks to Natural Language Processing, vector search enables shoppers to search more generally, while prioritizing relevancy, which means even if they cannot exactly explain what product they need, they can search for the concept and receive matching results

Vector search can be combined with traditional keyword search to improve search result relevancy leading to increased conversion rate and revenue.

Read more at:

• Vector Search – the Future of E-commerce
• Unveiling How Vector Search Works

7. Live Commerce

Live commerce (also known as Livestream shopping or live video shopping) is not yet a mainstream concept in the West.

However, recent studies show that in China, 77% of people have already purchased something via livestream shopping. It is an emerging trend in several Asian countries. 

Live commerce is when retailers sell products via videos broadcasted in real time. The main idea is that people can see the products from all angles and in the context before making a purchasing decision.

There are different ways to implement this new avenue of sales, but the two most common are omnichannel live streaming to promote and sell products, and live Q&A sessions to initiate conversation between the viewers and the representative(s) of a business. Either way, there is always a link or another action item in the videos that enables potential customers to reach the store in an instant. 

Social media is the most common platform for live video shopping, but some businesses like streaming on their websites as well.

8. Fraud Detection with AI

AI is emerging as a powerful asset in the fight against financial fraud as it can process huge amounts of data and recognize patterns in order to find anomalies and unusual activity.

ML- and AI-powered tools can uncover cases of fraudulent transactions involving several accounts, different locations, devices, and channels.

Natural Language Processing (NLP) plays a key role in AI fraud detection efficacy, by making sense of written communication, such as emails or text messages, related to a suspicious transaction.

This technology can be used for multiple other functions, including: identifying fraud both online and offline, filtering out fake reviews on websites, and blocking bot traffic.

9. Automated Content Generation

Using AI to generate content can save retail businesses a lot of time and effort.

It can be used to augment the product catalog by automatically generating high-quality product descriptions. It can help with automatic translation as well, to adapt content for different regions and languages, ensuring a consistent brand message and catering to a global audience. 1

Generative AI, like ChatGPT can also be a helping hand in creating content for bundle offers and cross-sell recommendations, directing customers to relevant items. 

Anticipating Future Developments in E-commerce AI: 2 predictions

+1 Augmented Reality / Virtual Reality

As customers look for more interactivity with a product before making a purchase, AR and Virtual Reality Shopping are most likely to emerge in the upcoming years. 

A VR/AR online shopping experience, for one, lets the customer take a thorough look at the product from all angles, even simulating the feeling of using the item (visual try-on). Journey, for example, a quite well-known shoe store in Northern America, allows their customers to try on most of their shoes virtually.

Foreseeably, this elevated level of convenience and comfort is on the list of future demands from shoppers. 

Some stores have created entire virtual stores that mimic their existing physical ones.

Ralph Lauren allows their customers to take a virtual look into their stores in several locations, while Charlotte Tilbury has an entire browsable virtual store. 

Virtual or Augmented Reality can help shoppers get educated about products, before making a purchase decision. 

+2 Innovative Omnichannel Payments

Merging all kinds of payment methods into one omnichannel strategy, and blurring the lines between them is another avenue of development in AI. In practice, this means that the customer can handle any transaction related to their shopping journey in whatever form they prefer (browse online, pay in-store with cash, get a coupon via email or text message, return an item through a pickup point, see their refund on their bank account the next day). 

Payment integrations like Amazon’s palm scanning, and biometric technology (payment by physical features, fingerprint), as well as social media payments, are examples of innovative ways to utilize AI in stores’ omnichannel payment strategy. As Michael Johnson, the CIO of SVP Worldwide phrased in his post,

“The Future of E-commerce is Here and It’s AI-powered“

Wrap Up

The latest developments in Artificial Intelligence can equip your business with the necessities to enhance the customer experience in your online store, helping to make you a leading retailer. 

To maximize the potential of AI, it’s crucial to have a solid understanding of the technology by staying informed to be up-to-date on the latest developments, then take the initiative and invest in AI tools to realize their benefits as soon as possible. 

Make sure your efforts align with your objectives and leverage Artificial Intelligence in the most fitting manner for your E-commerce business.