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eCommerce Search: Autocomplete vs. Auto-Compete

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eCommerce Search: Autocomplete vs. Auto-Compete

Avoid accelerating general queries into specific products, and focus on providing the middle ground between need and solution.

· AI Zone ·
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Prediction is a difficult thing, and folks in eCommerce can certainly relate. For example, take autocomplete dropdowns in an eCommerce search, an operation created to predict shopper intent and ultimately facilitate conversion based on a general query.

Sure, you can consider the odds to be in your favor when you suggest popular, trending, or well-reviewed products in response to general queries, however, this practice makes a dangerous assumption. That is, shoppers prefer to jump from the need to solve rather than examine their options. Based on our experience, we have an alternative in mind and strongly recommend the following. Avoid accelerating general queries into specific products, and focus on providing the middle ground between need and solution.

To better explain our thinking, let’s begin by evaluating an autocomplete practice that we’ve seen time and again on small, medium, and even household brand names. We’ll call it auto-compete, or the practice of recommending specific products from general queries via autocomplete. As an example, let’s assume we’re searching for one of the most ambiguous apparel goods available, a pair of jeans. With auto-compete, an example experience may look like this:

  1. Shopper types in a general query, such as “jeans”
  2. Autocomplete drop down features a list of popular categories and suggests two or three specific pairs of jeans
  3. Shopper clicks on one the specific pairs and is taken the product page
  4. After a quick review, the shopper decides this particular pair isn’t correct for them, and upon returning to the previous page, they must begin their entire search process from the beginning

Based on this example experience, what are a few issues that come to mind? We have two primary concerns, with the first being a dependency on assumptions. By using this method, the shopper is rapidly escorted from a general query to a specific product via the autocomplete drop down’s suggestion without allowing time to browse alternatives. To put it another way, it’s like a waiter making the assumption you wish to order a lemonade based your request for a drink. In the waiter’s defense, lemonade is a particularly popular item, however, does that mean you wish to order it? Considering the disparity and volume of alternatives, it’s unlikely.

The second primary concern that comes to mind is that this method inhibits shopper momentum. For example, what happens after the shopper clicks on a featured product from the autocomplete drop-down? Ideally, the shopper will make a purchase, or be re-directed to similar items that meet their goals. Unfortunately, this practice only works if the product converts since returning to the previous page to seek alternatives is not a viable option. After all, what can they return to after navigating through the auto complete drop down?

Despite these concerns, we want to be clear, autocomplete is certainly an excellent feature when it comes to search. However, we believe it should be used with a specific intent. That is, use autocomplete to facilitate the shopping process by suggesting popular, trending queries rather than specific products. In contrast to auto-compete, this practice facilitates momentum, and instead of focusing on what’s popular, it caters to the individual. Returning to our jean example, here’s how it looks like in practice:

  1. Shopper types in a general query, such as “jeans”
  2. Autocomplete drop down features a list of options that feature popular, trending, or well-reviewed categories of jeans rather than specific products
  3. The shopper selects a featured category of jeans and chooses a pair to examine in greater detail
  4. Deciding this pair isn’t correct for them the shopper returns to the previous page and continues navigating among their preferred category of jeans

So, what noticeable benefits does this method bring? First, a general query suggests the shopper has an idea of what they want, but they lack the specifics. As a result, they are really looking for suggestions to inform their thinking, rather than direct recommendations that may not apply to their particular needs. Second, momentum is maintained since they may return to the category specific page whenever they chose as they continue to shop.

We feel autocomplete is best suited to serve as the middle ground between a need and a solution by featuring popular, trending, or well-reviewed queries or product categories instead of specific products. In contrast, auto-compete inhibits momentum as it attempts to skip the middle ground by making the risky assumption that popular is greater than personal.

So, what do you think? Find out more about Infrrd Retail Solutions.

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Topics:
ecommerce ,artificial intelligence ,retail ,autocomplete ,auto-compete

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