Traditionally, retailers have been heavily reliant on sales data, specifically transactional sales data, to drive many of their decisions on future pricing, cross-selling, and availability. They even use that data to make decisions about who to buy from and what collections to feature next season. But are they missing out on an ever-greater data point? By limiting their data collection and review to what did or didn’t sell, instead of trying to understand what customers were really looking for, retailers are missing an opportunity to get deeper into a customer’s psyche.
The online shopping experience, by contrast, provides a natural opportunity for these retailers to expand their perspectives. By taking advantage of the data surrounding online shoppers and their search behaviors, not just their buying patterns, retailers can obtain a more effective view into unmet demand.
Take, for example, a chain of brick-and-mortar shoe retailers. They likely have great insight into what shoes are selling best by region, how quickly certain sizes sell, and all kinds of tidbits of granular details about user behavior – after the sale. On the surface, that is a major improvement from the days of manual stock checking and quarterly forecasting. This data may help them reorder similar shoes, plan for sales, and even help spot trends in quicker fashion.
But what about all the missed sales whose data never gets collected? All the potential customers who left the physical store empty-handed because they were looking for the hard-to-find size, the non-traditional color, the “emerging” designer that the chain had never heard of? All of these unsuccessful interactions actually contain a treasure trove of data. For retailers willing to mine their search data, especially their data of unsuccessful searches, they just might uncover a new market or unmet need.
Two search behaviors are logical starting points for retailers looking to get more out of their onsite search data:
- Top unsuccessful searches. Understanding why a search is unsuccessful may provide new insight into trends or emerging demand for certain sizes, colors, manufacturers, and brands. This could lead to more effective size options, suggestions to designers of new colors and combinations, the potential inclusion of new designers in their inventory, and even better collaborating with the offline stores to feature regional or emerging trends – all driving additional sales.
- Top purchase after unsuccessful search. Understanding what users purchased after a failed product search can help not only understand how a customer sees comparable brands, but can be used for better targeting and visual merchandising, both offline and onsite. Understanding these alternative brands and then retargeting with a visual of the product or featuring the alternative product in a primary banner are two great ways to introduce the brand to a customer who may not be aware of the second option.
It’s not just retailers that can benefit from analyzing their non-sales-related data to identify unmet demand. Netflix, the online TV, movie, and video game subscription service, had been in the business of distributing content online or via disc in the mail. With its uncanny ability to suggest content based on your past viewing behavior – and its speedy delivery – it was able to create a new model for distributing media. However, it was still relying on the traditional vendors to produce the media.
No longer. The same dataset that helps offer compelling recommendations also indicated there was another unmet need from Netflix’s audience. A need that could not be met by its current distribution-based model. Adding more existing content would not improve these failed searches. The need: more original programming. Specifically, these failed searches and related user activities indicated to Netflix that the demand for new episodes of certain niche series was so high that Netflix has decided to make a major move by beginning to commission original programming to extend these series.
The company is so confident in its data from customers searching for shows that are no longer in production that it is actually going to change part of its business model and develop original content to meet this need. While not all businesses may have the data, confidence, or capital needed to create new products or business lines to support an unmet search, all retailers should consider how effectively they are mining their onsite search behavior to improve their product mix and customer satisfaction.