By Sheila Berkley, Vice President of Business Development, Shiloh Technologies
Since 1992 we have developed Shiloh Software to provide our customer with a powerful set of tools for working with retail and related data. In addition to being a demand signal repository (DSR) for multi retailer data, Shiloh also offers advanced tools for working with the very complex data available in Retail Link. Some of the most powerful functionality is around the analysis of store traits. This is what we call Store Genetics.
Store Genetics is the term we use to describe the collection of attributes or traits that identify each retail store and the consumers that shop at that store. A store can have many traits but of course, not all of them will apply to every product or category.
Traits can be store attributes such as city, state, store type, and store size. Information about the modular such as size description, height restrictions, and fixture specifics can be used as traits. Data that your company has about the stores should be amassed and used as traits including performance metrics such as POS dollars and units, as well as third party and test market data. And of course there are thousands of traits available in Retail Link. Many sources of data exist that can be used in your quest for understanding the genetics of the retail store. Even the phone number of a store can identify the market and sub-market of a store.
Our customers take these attributes from many sources and load them into the Shiloh database along with the POS data and other performance metrics. Then using Shiloh's analytic tools it is simple to identify the traits that the top performing store have in common as well as what is shared among the worst performing stores. It is seldom a single trait that is the determining factor in a store.
Consider then the concept of a Super Trait, this is a group of traits that define a larger description of a store and its group of consumers. Maybe your best stores have a super trait for "Hispanic AND close to competition AND high disposable income AND in an urban area". In Shiloh it is easy to group traits into Super Traits and then use it to find stores that should perform like these best stores. If these stores do not currently have your item, this creates a fact based story to the buyer for increasing distribution to these stores. In addition, many retailers like Wal-Mart have dozens of single traits that could identify a store as "Hispanic" like bilingual signage, Mexican ancestry, and Cuban ancestry. Super Traits make the management of these disparate pieces of data even easier.
An important factor in looking at traits and item/store performance is in relation to how the chain performs as a whole. This is accomplished by first excluding stores that are anomalies; otherwise the traits that correlate with strong business patterns could be misleading. Depending on your business, in Wal-Mart data this could mean: excluding Alaska, Hawaii and Puerto Rico stores; or using size or volume parameters to exclude "stores" that are not brick and mortar stores; or only using comp stores. This smaller group of stores creates a baseline store group for comparison purposes. Shiloh provides a Trait Matrix analysis that compares the item(s) performance within each selected trait (or super trait) to the performance of the stores in the baseline store group. Giving a clear picture of where the item(s) over or under perform.
A brief case study:
One of our Shiloh customers had been working with the same buyer for 8 years. The modular process had become predictable and standard clusters of income and store type dominated the modular decision for the stores. Using the census data and Wal-Mart traits that were loaded in Shiloh, they performed trait analysis. The analysis identified two new clusters of stores. Both affluent, both with above average housing starts, but one was in urban locations while the other was in resort locations. The urban group purchased higher end items for their primary residence. While the resort group bought lower end items for their investment or time share properties. This analysis broke the cycle of allowing the past to dictate the future direction of the category. A better product assortment reflected customer needs in the stores.
In summary:
Unlocking Store Genetics with trait analysis is a proactive task:
Find traits by using Retail Link in a new way
Engage others in your organization in identifying and capturing traits
Load Traits into Shiloh or other tools to make them more efficient to work with
Use traits when you talk to you buyer and members of your own company
Make trait analysis and building company traits second nature
Sheila Berkley is Vice President of Business Development with Shiloh Technologies. Shiloh Technologies provides retail suppliers with powerful data warehousing and analytical software, The company's application, Shiloh, automates loading and analysis of retail & related data and simplifies complex tasks such as store trait analysis, Contact Berkley at SheilaB@ShilohTech.com.
