The retail industry by nature is centered around the constant application of big data, descriptive analytics, and predictive analytics to ensure customers return and spend money. Think about it, which other markets are as volatile and season driven than retail? Rather than fly blind, predictive analytics provide a strategic method for retailers to plan inventory, upcoming sales, and customer outreach ensuring their risk is kept to a minimum. Analytics can also anticipate customer behavior and draw a clearer picture of what products will be in-demand and how to best communicate with a specific customer base.
While data is critical in the analyzation of customer behavior, picking and choosing from an enormous array of data is not only tedious, but inefficient — which is why choosing a data science team to dig into your analytics is the most efficient way to increase sales.
While keeping customers is important, the flip side of retention is attrition — knowing when a customer has (or is going to) leave you. The tricky thing about retail is that it can be extremely difficult to know if a regular shopper plans on taking their business elsewhere or is becoming a less frequent buyer. For example, at a bank there are tangible accounts that customers can open or close to signify their business. However, a retail customer can easily phase out their patronship somewhat quietly.
It’s important to note that all retailers are different– a car dealership is different from an apparel brand is different than a grocery store, because of what’s known as the purchase cycle. Clearly the timeline for customers buying a car and a new sweater is different, but knowing how to understand the purchasing intricacies of a specific client base is crucial. Analytic modeling can help to illustrate the habits of your average customer and what deviations from that norm will look like.
Price/Promotional Sensitivity Modeling
One of the largest problems that brick and mortar retailers are experiencing is the negative effects of mass discounting — thus giving rise to the need for need for price sensitivity modeling. When stores flood the market with frequent sales, customers become trained to expect new discounts more frequently than they probably should. The initial perception of recurrent sales from a business perspective is that it will bring more customers into the store, but that’s not always true — it disregards those who would have spent full price to begin with.
Promotional sensitivity modeling is a tool that can be used in conjunction with price sensitivity to gain insight and control over the frequency of promotional advertising. For example, say a popular apparel retailer is consistently sending out 40% off coupons — but rather than attracting new customers, they’re noticing a decrease in overall profits. When data scientists delve into promotional sensitivity modeling, they’re able to break down a variety of customer segmentation factors to better pinpoint the most effective way to draw customers in, without compromising profitability.