What is predictive analytics in retail?

Michael Poyser
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Retailers are always looking for the best ways to reach their customers, and advances in retail analytics have made processing customer data much easier. With the insights it can gather, stores can learn in real-time about customer behavior, monitor purchasing decisions, and apply targeted marketing.

This has significantly changed how they reach customers, making it a non-negotiable asset for modern retailers that want to achieve a competitive edge. Today, the retail analytics market is predicted to grow significantly, from $5.84 billion in 2021 to $18.33 billion in 2028. Businesses want to capitalize on the sales and revenue generation opportunities it can offer.

How retail analytics work

Retail analytics enable retailers to better reach customers based on their personal preferences, whether they’re a large chain or a small store, so that shoppers receive a more personalized experience.

For example, if a customer always visits a store to buy vegetables, your retail analytics can link with your point of sale (POS) systems and understand this individual’s buying habits. You have an opportunity to upsell based on personal preferences, such as using a coupon at till that provides the customer with a percentage of money off their next purchase of items in the vegetable category.

The customer is more likely to return, feels like the retailer understands their needs, and benefits from the offer – they could even increase their basket size in the future.

Read the 4-step marketer's guide to retail data analytics. Discover how to  transform your data into targeted marketing. 

Predictive analytics in retail

Predictive analytics is a component of retail analytics that enables stores to predict the customer journey based on all the data signals that are available to the retailer.

It uses data modeling and machine learning to take historical information and predict future outcomes. Insight can come from big data about products or customers, and typically more data will provide a better prediction.

The biggest benefit is that predictive analytics automates how you predict human behavior. Retailers can gather information in real-time and immediately adjust decision-making to improve the customer journey.

These small details could be key to stopping a shopper from switching to a competitor, retaining their loyalty in the future, and ultimately, improving customer satisfaction. It’s also an opportunity to improve relevancy for customers and upsell items where possible.

Considering that 38% of customers lose interest because of poor marketing personalization efforts, it’s easy to see the value of predictive analytics. And predictive analytics can be used to fine-tune every part of the customer journey on a large scale too, from individual store level to regional or even multi-national stores.

3 examples of predictive analytics in retail

1. Understand product demand

Predictive analytics use historical data to forecast when a certain product is more likely to sell quicker at certain times of the year. This can be done using historical POS sales data at the store-product-day level.

With this information, retailers can adjust their stock levels so that customers aren’t disappointed by products selling out quickly. For example, during a seasonal rush for certain products. The process can be automated, so internal teams spend less time adjusting manually. With the right retail analytics software, you consolidate this information into one place so it’s easier to find, understand, and action.

2. Foresee trends

Predicting trends in shopper purchases, on both an individual and large scale, is vital to improving customer experience. However, it also lets you be proactive with your budgets, project where sales are likely to grow, and identify sales growth by product category.

With this information, retailers can issue highly targeted marketing to take advantage of trends. For example, if a certain product is in demand, you can create a personalized offer to drive more customers to your store. Or, you can ensure your stores have the right amount of product stock based on customer demand in that area.

This can even assist with online customers, you can understand whether there’s a trend for e-commerce customers being more likely to purchase a certain product.

3. Improve basket analysis

You can apply the same level of predictive analytics to individual customer baskets. Retailers can identify which products are purchased together and strategically market them together in stores. For example, if shoppers regularly buy greeting cards and flowers at the same time.

With this information, retailers can predict what customers are likely to buy and plan promotions around this. And while you can use it, you don’t have to obtain historical data on individual customers. A retail analytics platform can automatically identify and predict basket opportunities in real-time. For example, you might have more demand for greeting cards and flowers at certain times of the year.

While some predictions can be very intuitive, the benefit of predictive analytics is that it enables these relationships to be predicted at scale, so that every customer has a relevant recommendation tailored for them based on their purchasing to predict what they are most likely to be interested in buying.

Analytics is a must-have for retailers

Retail analytics, and the predictive analytics capabilities they offer, provide a significant competitive edge for retailers. They help you target customers better, upsell products, and improve customer satisfaction. But retailers shouldn’t run before they can walk.

To make the most out of your data, make sure you’re using an effective retail marketing analytics platform that helps you extract the most value from it. Then you’re better positioned to take advantage of predictive technology for years to come.

The 4-step marketer's guide to retail data analytics


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