Planning an assortment of products for a store is one of the hardest things to get right in retail. Merchants need to make decisions around which items to maintain or remove, what products to add, and what the overall mix of products should be in order to meet local customer preferences and demand. For most retailers, this is a combination of art and science.
The science part is typically focused on an analysis of historical merchandise sales, margin, and inventory productivity data for a store. But this kind of product-focused, rear-view approach misses the most important questions: what are customers looking for? How are their preferences and expectations changing? This is traditionally where the art of assortment planning comes in, which often means a combination of merchant intuition and qualitative research such as consumer surveys.
In its 2014 Benchmark Report on the state of Retail Assortment Planning, Retail Systems Research makes a number of recommendations around how retailers must adapt their brick and mortar assortment planning processes to the realities of an omni-channel world. Their first recommendation is for merchants to focus on the relationship between digital domain browsing and physical domain purchasing. Research continues to back up the point that a significant percentage of in-store purchases are preceded and influenced by digital research; in fact, Deloitte Digital puts that figure at roughly 50% and projects that it will continue to increase.
While shoppers may visit a variety of digital properties to get information about a retailer’s products, much of their research will take place on the retailer’s web site, thereby generating invaluable first party data related to shopper preferences and needs. But how can retailers transform this data from the site into actionable store merchandising insight? The answer lies in much of the data that is being produced by a retailer’s web analytics package.The key is to redefine what "conversion" means in the context of the website. Instead of thinking about conversion in terms of making a purchase on the site, the goal is to identify an event that conveys intent to purchase in-store. One obvious candidate for most retail websites would be a click on a store locator. In the web analytics package, store locator clicks should be defined as micro-conversions and all preceding activities tracked and analyzed; for example, site search terms, product hierarchy drill-downs and product views. This is invaluable insight for the merchant, as the data from these browsing activities can be tied directly to a store through the store locator. This data can then be extracted and summarized into a report by product category/store location; merchants can analyze which products and/or product groupings are over-indexing in terms of views for specific stores, as well as glean insights into what product characteristics are relevant to shoppers in a particular physical location through an analysis of search terms that led to the store locator click. These insights can be incredibly useful in terms of informing the assortment strategy for a particular store or store cluster.
However, even if there is no specific event directly indicating intent to purchase, data from the web analytics tool can still help inform store assortment decisions. For example, site search activity and product views not resulting in a store locator click or an online purchase could potentially be tied to IP addresses. Using Merkle’s Connected DataSource, the IP addresses associated with these activities can in many cases be matched to specific zip codes, which can then be mapped to stores based on how store trade areas have been defined. While this is not 100% accurate due to the fact that IP address is not always a reliable indication of the actual location of a site visitor, directionally this will still provide great insight for the merchant into shopper preferences and expectations in specific store trade areas.
In order to begin taking advantage of this data to inform brick-and-mortar assortments, retailers will first need to work with partners like Merkle to assess the current state of data collection on the site. Are store locator searches being tracked as micro-conversions, and is store locator data being captured properly? Are search terms and product hierarchy drill-downs being captured in an optimal manner? In addition to site data collection, are store trade areas well-defined so that site behavior tied to an IP address can be seamlessly tied to a store? Once the right level of data collection is in place, retailers can focus on extracting this data and getting it into a format that is consumable for merchants in the assortment planning process.
Ultimately this data will not tell the merchant exactly which products to buy for a particular store and in what quantities-the merchant’s skill and intuition is still critical. Furthermore, merchants will need to be careful of making overly broad conclusions from the site data due to the possibility of sample bias (although, as the percentage of in-store purchases influenced by digital continues to increase, this becomes less of an issue). But by taking advantage of the site behavior data that is being generated, the art of adapting the store assortment to evolving customer preferences and expectations becomes much more data-driven. In reality, the biggest impediment to merchants’ use of this data for store assortments is the traditional silo mentality that exists in many retail organizations, where site data is often only considered to be relevant for e-commerce and digital marketing. But breaking down these data silos is quickly becoming a defining characteristic of retail leaders vs. laggards.
