Browse & Search Analysis – Win Back Shoppers

About the Company

An American chain of omnichannel retailers, widely recognized by customers for their quality fashion at affordable prices at approximately 640 locations in 43 states, as well as to customers in more than 100 international destinations through leading e-commerce sites.

Challenge

The client wants to enhance the customer experience of its website by carrying out various experiments. The product team struggled to understand the difference in customer interaction over online shopping platforms when they search v/s when they browse.

‘Search & Browse’ on an eCommerce website serves an important purpose as it helps the retailer to understand the customer’s intent. Some questions like what does the customer like to do on the site: search for something specific or browse through your catalog?

The key challenges we found were as follows:

• Building relevant hypotheses.
• Identifying the right KPIs to test these hypotheses.
• Identify and understand how the combination of Search & Browse has an effect on the behavior of a customer.

Solutions

• Factspan came up with the MECE hypothesis (Mutually Exclusive, Collectively Exhaustive) set to test the effect of the ‘Search’ option.
• Descriptive analysis for each hypothesis for all possible data cuts.
• Factspan provided inquisitive & actionable insights from the analysis of multiple hypotheses.

Hypothesis

• Customers tend to spend more time on the website when they are searching for the product.
• When searching for things, consumers are more likely to buy than when simply browsing
• When a customer uses a search instead of browsing, they are more likely to view more recommended products.
• When compared to browse, the average order value (AOV) for orders placed through search is typically higher.
• The AOV of recommended products tends to be higher with search discovery as compared to non-recommended products.
• Customers usually perform positive interactions while using search mechanisms as compared to browsing.
• AOV for orders with more than X search tends to be higher as compared to AOV for X browse orders.
• Order Conversion usually reduces with search as compared to browsing.

Impact

• Search customers tend to buy 3% more as compared to browsers and it was observed that a survey needs to be done to define the intent of customers using Search mechanisms and browse mechanisms.
• Factspan highlighted dynamic advertising when the user starts searching for products on desktop devices as the conversion rate is 5.5% more than on other devices.
• Search interaction post browser interaction led to higher overall conversion as compared to browse interaction followed by search interaction.