Utilizing Predictive Analytics in Retail to Increase Sales

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. There are few other markets as volatile and season driven as retail.

Utilizing Predictive Analytics in Retail to Increase Sales

Edited April 23, 2020; published November 30, 2017


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. There are few other markets as volatile and season driven as retail.

However, in the time of COVID-19, predictive analytics can seem futile. With customers slashing their discretionary spending and stores stocking whatever the public demands, how does predictive analytics fit in to the current landscape? Rather than fly blind and assume that the landscape will never return to normal, onboarding predictive analytics now provides a strategic method for retailers to plan future inventory, upcoming sales, and customer outreach ensuring their risk is kept to a minimum.

 

Customer Retention

While keeping customers is important, the flip side of retention is attrition -- knowing when a customer has (or is going to) leave you. Historically, 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, draw down, or close to signify their engagement with the business.

It’s important to note that all retailers are different -- a car dealership is different from a grocery store due to different purchase cycles. Since the timeline for customers buying a car and produce is different, knowing how to understand the purchasing intricacies of your specific client base is crucial. Analytic modeling can illustrate the habits of your customer segments and what deviations from that norm will look like to enable action to retain them.

 

Price/Promotional Sensitivity Modeling

Beside the obvious problem of reduced customer discretionary spending, one of the largest problems that retailers are experiencing is the negative effects of mass discounting, thus giving rise to the need for price sensitivity modeling. When retailers flood the market with frequent sales, customers become trained to expect new discounts. The initial perception of recurrent sales from a business perspective is that it will bring more customers to the store or website to transact, but that’s not always true,  it can disregard those who would have purchased at 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 shopping behavior across a variety of customer segmentation factors to better pinpoint the most effective way to draw customers in without compromising profitability.

 

Customer Segmentation and Share of Wallet

Customer segmentation refers to the act of bucketing customers into multidimensional segments such as demographics and location to better understand the level of engagement among different groups of customers. Which ones are more likely to buy when sent email promotions versus those who historically prefer in-store deals? How much are specific customer segments spending and when? Questions like these can be answered by better understanding the nuances within each unique customer base.

Share of wallet builds off customer segmentation by calculating customer spending habits and how they relate to category retail spending as a whole. A company with a high share of wallet has a stronger relationship with its customers -- so becoming familiar with where a business stands in terms of share of wallet can help to illuminate weaknesses in the customer relationship at the product or market level.

There will always be significant challenges facing the retail industry, but especially now with COVID-19. Gearing up with predictive analytics now can ready a variety of tools to address many customer and product diagnostics in the future.  Planning and preparation today provides a future leg up on the competition. Within the COVID-19 framework or not, retailer understanding of customer segments, habits, and future behavior is paramount in maximizing customer relationships.