Attributes Tell the Whole Story!
Color, size, style, vendor…retailers are overwhelmed with data.
It is no secret that retailers are drowning in data. All aspects of the retail business are generating, and ideally capturing, data including digital properties like mobile apps and e-commerce sites, RFID tags in the supply chain, social media likes and mentions, and customer experience data inside the brick and mortar retail store.
Some of this data is extremely valuable, like digital data used to get to the bottom of why so many shopping carts are left abandoned on your e-commerce site. Some of this data might not be valuable; Facebook likes may not be an indicator of success or failure. However, one area of data collection that is universally valuable are the attributes that describe a product at the lowest level or the SKU, the individual brick-and-mortar retail store, and the customer.
Attributes matter to a retailer because they tell the whole story around a retailer’s product assortment, retail store footprint, and customer base. Take a t-shirt as a fictitious and simple example: our fictitious t-shirt has 5 sizes (S, M, L, XL, XXL), 5 colors (blue, red, white, black, orange), and 3 styles (Summer, Winter, and Special Edition). If a buyer is charged with buying new products for their assortment using a limited budget (open-to-buy), how many of our fictitious t-shirts should the buyer buy, with which combinations of attributes to ensure maximum profitability? The answer can be derived by combining the attribute data of the t-shirt with historical sales data, sales forecast data, open-to-buy data, marketing campaign data, and current market trends data which will help guide the buyer to the right buying decision.
What do you Need for Attribute Analysis?
Get to the Detail– Aggregations are not relevant with attribute analysis, a retailer cannot add up sales for all size small products across the assortment to identify any type of sales trend, because size small is different for every type of product (shirts, shoes, tables, vacuum cleaners, etc).
Analysis not Hierarchies – Attributes describe the lowest level of the hierarchy rather than clean hierarchical drill paths that make sense. Focus on the analysis not the hierarchy.
Combine Data Sources – Product, store, or customer attributes by themselves do not provide value. Attributes need to be combined with sales, forecast, open-to-buy, marketing, market trends data, and syndicated data to be valuable. This type of data will live in disparate systems.
Uncover Relationships – Products, stores, and customers could have hundreds of attributes, having a visual analytics platform that shows the associations in the data across attributes is vitally important to determine which attributes are most important.
Thankfully the Qlik Visual Analytics Platform is nearly purpose-built for retail analysis of all types, including attribute analysis. I invite you to find out for yourself, by taking the Qlik Digital Consumer Analytics solution demo for a test drive. The solution demo showcases the linkages between consumer demographics and buying patterns by day of the week for individual products. I think it will also demonstrate there are a world of possibilities out there for using Qlik with your attribute data to drive better decisions.