{"id":2865,"date":"2020-07-19T13:06:00","date_gmt":"2020-07-19T13:06:00","guid":{"rendered":"https:\/\/www.prefixbox.com\/blog\/?p=2865"},"modified":"2024-08-27T08:50:20","modified_gmt":"2024-08-27T08:50:20","slug":"shopper-searches","status":"publish","type":"post","link":"https:\/\/www.prefixbox.com\/blog\/shopper-searches\/","title":{"rendered":"Can Shopper Searches Help You Write Sales Copy?"},"content":{"rendered":"\n

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Can Shopper Searches Help You Write Sales Copy?<\/h1>\n\n\n\n
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If you work in the eCommerce industry, you should be familiar with the value of reviewing user searches when it comes to adapting your store\u2019s UI. <\/p>\n\n\n\n

Find out what shoppers are searching for and you can put the most profitable in-demand products front and center, save people time, drive more conversions, and generally improve store performance for a negligible time and design investment.<\/p>\n\n\n\n

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Let’s go<\/p>\n\n\n\n

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And of course there\u2019s clear value in determining your product range, too. <\/p>\n\n\n\n

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If your prospective customers are heatedly clamoring for an item that you don\u2019t have in stock (or don\u2019t stock at all), then you can take action to meet those needs.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n

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But what else can you achieve by looking at how shoppers are searching for things? Can you, for instance, use that information to improve your product copywriting? <\/p>\n\n\n\n

That\u2019s what we\u2019re going to investigate in this piece. Let\u2019s review the kinds of things you can glean from searches, and consider to what extent you can use them to boost your writing.<\/p>\n\n\n\n

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The Complexity of a Search String<\/h2>\n\n\n\n
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What\u2019s in a search string? As it happens, quite a lot. Search engines have long struggled to parse searcher intent for a reason: it\u2019s very complicated and much of the information is unavailable (and thus can only be guessed).<\/p>\n\n\n\n

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Just think about the factors that can affect how a search string is composed:<\/strong><\/strong><\/p>\n\n\n\n

  • The brand awareness of the searcher<\/li>
  • The vocabulary of the searcher<\/li>
  • The presence of autocomplete or autosuggest options<\/li>
  • The existence of products with similar presentations<\/li>
  • The context of the search<\/li>
  • The device being used<\/li>
  • The input method<\/li><\/ul>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n

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    And that\u2019s just a basic list \u2014 I\u2019m sure I could drum up some more. Regardless, there\u2019s clearly a lot to unpack. <\/p>\n\n\n\n

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    If you approach your search analytics with a keyword-obsessed mindset, you\u2019ll end up missing a lot of nuanced details that can give you meaningful pointers.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n

    Let\u2019s look at an example search to see how we can interpret it. <\/p>\n\n\n\n

    Suppose that you look into your store\u2019s search records and find that someone searched for \u201cbuy book store\u201d. <\/p>\n\n\n\n

    What could that mean? Well, let\u2019s break it down. Buy has imminent purchase intent, so that part is entirely clear.<\/p>\n\n\n\n

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    The next part is more ambiguous, though, because there are several possible readings:<\/strong><\/strong><\/p>\n\n\n\n