Four Ways Retail Predictive Analytics Boost Store Performance

Research shows that, more than ever, shoppers want their favorite brands to immediately fulfill or even predict their needs and desires. Purchasing an item is no longer an isolated event, but part of a seamless, integrated experience that blurs the line between online and offline shopping. While changes in retail technology trends have had noticeable effects on brick and mortar stores across the United States, with many chains experiencing massive closures, integrating modern digital technology with the appeal of in-person shopping can have many benefits for companies.

Today’s benefits of omnichannel retailing include being able to gather previously untapped information regarding shoppers’ behavior and interactions with brands. The potential uses of this data are plentiful, but one of the most impactful in today’s retail world is the application of predictive analytics to shopping behavior. By understanding how past shopper actions can be used to determine future actions, analytics can anticipate and fulfill the needs of customers for more successful marketing than ever, both online and in-store.

The following benefits of retail predictive analytics can help shape the future of retail companies and find success for stores in ways that were previously impossible.

1. Improved Targeted Promotions

Targeted promotions are commonly used by companies across industries in order to add a layer of personalization and improve customer relations. However, poorly planned targeted promotions can have the exact opposite effect on a customer base. Research from Access Development found that 57% of the customers they surveyed said receiving offers for products after submitting negative reviews of related products to be a top reason for ending their relationship with a brand.

Properly targeting your promotions means that in-depth knowledge of individual customers will greatly inform the offers that they receive. This includes being aware of past shopping behavior and predicting customers’ future needs, such as supplemental products to what they have previously purchased and well-timed refills based on behavior patterns, like ink cartridges for printers. This can also improve engagement both online and in stores, as well as form a stronger relationship between brand and customer.

2. Predictive Search

Modern websites help customers find what they are looking for in as little time as possible through strong search functions, which are designed to pull up accurate results that reduce the time spent looking for an answer. However, the use of predictive analytics takes things one step further by using personalization to predict what customers will search for ahead of time. This includes both autofilling searches as users begin to type out their query and populating landing pages with products and services that visitors may have come to look for before they can even begin their search. The Amazon analytics system is one of the great examples seen today, as the system keep users coming back to the site and becoming interested in products they may have not normally seen.

Strong retail predictive analytics systems properly understand user behavior and make accurate, helpful predictions that encourage return sales completion without offending or interrupting customers. However, it is very important that these predictions are accurate, as providing inaccurate or unwelcome results can unnecessarily complicate the search process or potentially offend customers, such as the now-infamous Target pregnancy ads. When done correctly, website visitors will be less likely to explore their options with competitors and more likely to return to your site for future needs.

3. Optimized Inventory Management

The use of personalized shopping isn’t just about making customer interactions easier. It’s also about making sure your stores are properly stocked and prepared for their clients. This will not only decrease customer frustration caused by out-of-stock items, but will also save money by decreasing the chances of understocking or rush shipping. As discussed by Harvard Business Review, predicting demand is far more effective in cutting costs and determining needed inventory than basing inventory on aggregated total sales, as using analytics will create hyperlocal forecasts that distribute inventory geographically.

According to Accenture research, only a third of retailers currently offer omnichannel basics for their audience, such as store fulfillment and a visible inventory accessible across multiple channels. Consider how cutting costs through predictive analytics and more can impact your company and in what ways you can efficiently meet customer needs for the most effective uses of omnichannel retailing, which connects online shopping with brick-and-mortar stores.

4. Continued Customer Relationships

Customers want to feel like they are known by companies and seen as individuals, including before, during and after a purchase. Rosetta Consulting research indicates that highly engaged customers will complete purchases with a brand 90% more often than those who are seen as not being engaged. In addition, they were shown to spend 60% more per transaction. By using predictive analytics, omnichannel retailing can show customers that they are aware of their needs. Purchases made in-store are reflected online and online shopping history can be provided to brick-and-mortar employees so that they can identify individual customers and quickly answer their questions, creating a continual and consistent relationship between brand and customer.

All three of the previously discussed aspects of retail predictive analytics are focused on continuously improving customer engagements. Predictive shopping will grow in importance in the coming years for retailers as audiences begin to expect intuitive online shopping and companies who can understand their needs and interests. When used correctly, these features can show that your brand cares about customers and is on the leading edge of modern shopping technology.

Equip Your Brand with Predictive Analytics

While each retail brand will need to determine the role that predictive analytics plays in their strategy, its effects on the speed and accuracy of each store’s reactions to customer needs can be a great help in this era of shifting industry trends. Building your front- and back-end systems on a platform that can collect customer data and build actionable insights will help you better understand your customer base and take effective action as soon as possible.