Extracting insights from search data in your eCommerce store
Posted on December 22, 2016 by
We recently shared a detailed post on the need for Search in eCommerce where we refer to the Search function as speaking to a sales person in a brick & mortar store. So lets continue with the same analogy here.
As a retail store owner, your in-store sales person (search), is interacting with visitors and getting to know their interest in products, colors, fabrics, specifications etc., every minute detail of interest is explicitly shared by your visitors during the search process. This is further fine tuned by the selection of filters to drill down and find that product that will convert them to a customer. This treasure trove of data (search queries & filters) should be used by your marketing and planning teams to improve the topline and bottom line of your business.
With access to search data and resulting visitor engagement details for these queries – impressions delivered, what was clicked, position of clicks, time spent, visitor segments etc., your team has data to understand what your customers seek.
For example, if the top search query in New York City, is for “Kohler Faucets” and in San Diego is for a “Moen”, for the same data period across segments, it would be wise to promote products that match that query or specific products within that query in your SEO and SEM campaigns, geofenced to New York city and San Diego. This process of creating focussed marketing campaigns to target each customer segment is bound to deliver higher ROI for your digital marketing budgets. The only caveat is that you need “enough” data to make sense out of it.
Search data can also be used to decide what banners within the website, to guide visitors to a landing page with relevant results. Our advise on home page banner strategy is to dedicate 2/5 banners to Search data (historical data) and 3/5 banners to external trend data. Hence if your top site wide searches are “Faucets” and ” Moen”, create a landing page dedicated to these two keywords, that include products which received the most engagement for these keyword searches and maybe throw in an offer or incentive, to accelerate engagement, conversion and revenue generation.
Bottom Line Impact
Analyzing search queries gives you access to customer demand data. This can be used to better plan stock to ensure supply meets demand. For example, if your customers are seeking “Ruby rings” and only 5% of your stock in rings contain the Ruby stone and over 50% is Diamond rings, there is a good chance, you are going to be subject to low inventory turns. This is not good for your cash flow and will drive the “Discounting” act if you need to move the stock. Stocking products that are in demand could increase the probability that customers are less inclined to discounts as they need it NOW.
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