Some retailers are up to speed with using analytics to track how effective their past decisions have been. But the most recent use of analytics is to forecast and predict what could happen in the future in terms of pricing strategies, assortment, customer preferences, and more.
Predictive analytics is the latest and greatest tool that top retailers are already putting to use. They dig into internal and external data to gain a comprehensive understanding of competitive behavior, customer demand, and market trends. Then, and only then, are they able to look to the future in order to give customers what they want and one-up the competition with better prices and assortment.
To help you understand why this has so quickly become a necessity, here are four key reasons retailers need to use predictive analytics to their advantage:
1. Your Competitors Are Already Using it
Let’s be clear: the top retailers in your space know what you’re doing at all times. They know how your products are priced, what’s in your assortment, and how these have changed over time. What is important to realize is that you have the power to do this as well. Sure, Amazon and other best-in-class retailers have resources that most of us couldn’t fathom to actively monitor the competition, but outsourcing analytics can bring this competitive intelligence to you more quickly and affordably than building it yourself. Regardless of the way you gain your data, retailers with predictive analytics are able to make informed pricing decisions and target promotions more effectively.
2. Show Customers Before They Can Even Ask
Steve Jobs once said, “A lot of times, people don’t know what they want until you show it to them.” The tricky thing with consumers is that there’s often a disconnect between what they say they want and what they actually buy.
Luckily, predictive analytics gives you knowledge of what consumers want based on historical trends and future projections. That way you can stock the hottest new products and price to perfection right off the bat, instead of having a trial-and-error period. There will always be failures, but tracking the volatility of categories, products, and brands will provide the insight you need to make an informed decision on whether or not to carry certain items.
On top of that, consumers have short attention spans. They might love a product for a month and then never buy it again. Don’t get stuck with a ton of inventory you’ll have to severely discount because you didn’t see the end of the road for a trendy product. Predictive analytics can steer you away from investing substantially in items that will crash and burn in no time at all.
Predictive analytics can also inform your visual and online merchandising. If you improve the navigation or suggest popular and high-margin items on your homepage or in targeted promotions, you could not only improve sales but profit margins as well. When a shopper comes to you to find something specific, they need to be able to find it quickly and painlessly. If your eCommerce taxonomy is too complicated or if product information isn’t in-depth enough, then visitors (and sales) could be gone in a quick click over to a competitor’s site. Whether a shopper arrives on your site or in your store to browse or with a list in hand, you need to have what they’re looking for, not to mention appropriate high-margin add-ons, and make all products simple to find.
3. Predictive Analytics Subtracts Natural Bias
As a seasoned professional in the retail industry, you have seen a lot and gained plenty of wisdom. But with the fast-paced nature of the digital marketplace, there could be trends lurking that you haven’t noticed just yet. Making decisions based on intuition might have worked fine in the past, but the reality of retail today is that it’s moving too quickly for any one individual to keep up.
This is especially true when it comes to pricing. Just because you’ve always sold a commodity item with that 99 cents tacked on the end, that doesn’t mean it’s always going to be the best price. Many companies are investing in machine learning algorithms to power dynamic pricing and find the best price for their products, instead of allowing those in charge of pricing to decide based on their intuition. This fully automated pricing approach can incorporate multiple pricing variables and adapt to market changes in real time to find the right price. And that’s just one application of predictive analytics—the possibilities are endless. The bottom line is that without predictive analytics, bias can seep into decisions. Using data instead is the best way to create a clear and profitable plan for the coming quarters.
4. Minimize Risks with Data-Driven Decisions
Predictive analytics cuts down on the chances you’ll make a bad decision. After all, you know how a category or brand has performed over time, and you have a good understanding of the potential results you could see going forward. That’s much better than operating on a hunch.
Winning in retail requires taking calculated risks. Even Amazon has introduced a number of failed products and categories. But when you really understand your customer base (which Amazon surely does with its endless amounts of data) and drill down into what they like, then risks are much more likely to lead to rewards. Amazon has a “fail fast” mentality, which allows the company to experiment intelligently. They scrap anything that isn’t well received and double down on the successes. If you want to figure out the way forward without having to throw tons of time, money, and effort into a black hole, predictive analytics is the only way to go.
Another common example is the beer and diapers myth that has historically been attributed to Walmart (although that isn’t exactly true). It posits that Walmart moved their beer aisle closer to the diaper aisle because fathers shopping for diapers on Friday evenings were likely to have beer in their carts as well. Moving the aisles closer together was a way to boost beer sales as shoppers picked up necessities. While this is mostly just a myth, it does show the power of predictive analytics in merchandising. If retailers are aware of items that shoppers generally buy together, there is less risk in making these types of merchandising decisions. Moving the right items to the right parts of the store based on data can lead to a bump in sales for those supplementary products.
Predictive analytics sits at the intersection of data and action. It delivers the information retailers need in order to boost sales, margins, and customer satisfaction. It does take considerable time and effort to turn that data into strategy. But it’s a worthwhile endeavor because Amazon is no longer the only one with the key to the data kingdom. Any retailer can gain access to the data they need to make the best business decisions. After that, the next step is to use those insights to improve pricing, assortment, promotions, and beyond. Take a page out of Amazon’s book and use data to inform your strategy. If predictive analytics can turn a fledgling bookseller into one of the largest retailers in the world, there’s no telling the impact it could have on your business.
About the Author
Rich Siefert is a Wiser (formerly Quad Analyix) advisor and omnichannel retail executive who has introduced competitive pricing technology to IR Top 100 retailers. He supports the company’s go-to-market efforts, advises on the product, and brings his extensive leadership in driving innovation in eCommerce, mobile, and retail channels.