Predictive Analytics: How Brick and Mortar Stores Can Compete in this Digital Age

Even with 3D technology or advanced modeling that shows what a piece of clothing will look like on you, an online experience can’t compare to a brick-and-mortar one.

In a store, shoppers have the ability to touch and feel products and perhaps even try them on. And although virtual assistants are better than ever, there’s nothing like friendly, competent customer service to make a purchase feel more like an investment.

Online retailers shine through product selection, convenience and knowing their customers by monitoring their browsing habits. Physical retailers of all sizes can help level the playing field by using predictive analytics to get to know their customers better—how and when they shop, their needs and their product preferences. Successful retailers have a “data-first” mindset that helps them to better understand customers and changing products in return to their needs.

But you’re probably thinking, “How can smaller retailers afford big data resources when the large players have whole departments devoted to crunching customer demographic numbers?”

Big data and predictive analytics are affordable for retailers of all sizes. Facebook and Google have targeted advertising platforms to help with segmented marketing strategies. Big Data-as-a-Service is a $2 billion global market and is expected to hit $7 billion by 2020. And for those interested in boosting their online presence, several startups analyze social media statistics and help retailers uncover potential customers.

Check out these six ways that brick-and-mortar retailers can use predictive analytics to their advantage.

  1. Improve customer service. Your store is well-stocked and your sales staff are friendly, knowledgeable and approachable. But how well do you really know who your best customers are? The answers could be revealing. Predictive analytics can help you better understand your ideal customers, their likes and dislikes and their typical behaviors. This information also can be used to help you set attractive displays and position products for maximum customer traffic.
  2. Motivate customers to remain brand loyal. The same information also can be used to determine when a customer is in danger of straying and help figure out the appropriate enticement. Is it free delivery? A 10% off coupon? An exclusive, after-hours shopping experience? Once you know who your best customers are, you can determine what keeps them coming back to you.
  3. Predict optimal pricing for products. You may be surprised to discover that the best time to drop the price on seasonal products isn’t at season’s end but when the initial demand starts to lessen. In this arena, predictive analytics can be used to study historical price trends, demand, competitor pricing and more to optimize revenue and profits. It even can be used to determine the timing of promotions, what products will be on sale and where to focus marketing efforts.
  4. Determine desired retail locations. Once you know your customers, it’s not a leap to figure out where they live in proximity to your store. Even if you have a single location, is it in the best place to attract the ideal clientele?
  5. Improve inventory management. Imagine using previous sales data, seasonal information and product demand to order precisely the right merchandise to reduce overstock and out-of-stock items. Walmart doesn’t have to imagine it. With the My Productivity app, managers use their smartphones “to set the wheels in motion to restock specific items, access real-time sales data and trends, and even answer customer questions.”
  6. Help improve merchandise mix. By examining an online searches, patterns emerge that can help retailers see trends to identify gaps in merchandising and potential future products.

Every retailer has a cash register, merchandise displays and staff. They should also be using predictive analytics to better understand their customers and to improve profits and margins through higher sales, higher merchandise turns and fewer issues with overstock and out-of-stock items. 

Image of a store shelf.

Successful retailers have a “data-first” mindset that helps them to better understand customers and changing products in return to their needs.