Nine ways to use site search data for merchandising

The terms that customers type into your site search box represent a wealth of valuable data that can be used to learn about your users’ behaviour. They are essentially telling the retailer what they want in their own words. 

This data can be used in a number of ways: to improve the site search functionality, to optimise results pages for common searches, and to improve merchandising. 

Here, I look at 10 ways to improve merchandising with smart use of site search data, with thanks to some examples from SLI Systems. 

Use ribbon overlays to draw attention to specific products

Using data from site search, you can highlight certain products in search results pages using ribbon overlays: 

Thus, items which are reduced, with limited stock, or new in can be highlighted simply. Be careful not to overdo it though. Too many overlays will dilute the impact. 

Create landing pages for groups of products

You can create dedicated landing pages to promote a set of products, triggered by certain search terms.

For example, if you search for ‘TVs’ on John Lewis, then you get this page, which gathers together the retailer’s range of TVs, along with buyer’s guides, special offers and a clear reinforcement of the five year guarantee, a key sales-driver. 

From looking at site search data, retailers can identify product categories which deserve this kind of merchandising. 

Make it easy for users to find products with synonyms

This is something that site search, and ‘no results found’ searches can tell you. Perhaps there is a common misspelling, or users are searching for a brand you don’t stock. 

If so, rather than showing no results at all, serve up results that are related to the search term. 

In this example, users searching for ‘Esky’ (a brand of cooler boxes) are shown similar products from different brands: 

Make the search results page nice and visual

The search results page should enable customers to quickly scan and identify whether products are relevant or not. 

There is also the option to promote certain products with larger images or through ‘quick look’. 

Show results in colour

Perhaps you have products in multiple colours. If a customer searches in this way, show it in that colour. 

This provides the extra information to help the customer understand what the item would truly look like, which is helpful in reinforcing the decision to make a purchase. 

The ability to do this will depend on whether images of the product in all colors are available in the retailer’s data feed. If so, this is a useful tactic. 

Use analytics to predict future trends

The search terms and products that customers are entering into site search boxes call tell ecommerce merchandisers a lot about products that they want but you don’t currently sell, or products that should be promoted more prominently. 

Keep an eye on this data and be prepared to act in response to these trends. 

Give site search data to your buyers/writers/salespeople

It’s fairly easy in any analytics package to set up automated reports of top searches. These are often really useful for buyers, who need to know when demand is bumping up and down, but also need to be able to judge potential demand for products that aren’t already stocked.

Use auto-complete for merchandising

Auto-complete is very useful to help customers find the search term they want, and to avoid issues like misspellings, but it also offers opportunities for merchandising. 

Here, as the site search sees that I’m looking for a wine gift basket, it starts to recommend products, complete with price, image and a snippet of text. 

Site search data can be used to identify which products are most likely to appeal to searchers. 

Tune site search results to promote particular products

Search should be about providing relevancy, but this can be fine-tuned to allow merchandisers to push certain products to the top of the results page. 

This may be seasonal products, high-margin or high inventory items. 

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Nine ways to use site search data for merchandising

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