Best Examples of Predictive Analytics in Retail

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Written By: eyos Marketing | Last Updated: January 2023

The retail industry is driven by tech-savvy customers who can easily access several buying channels in a few seconds. Customers can now compare deals and pricing to ensure they get the best option. 

Therefore, it’s essential to monitor and analyse various data points across your business to understand existing and potential customers better. To stay ahead of the competition, predictive analytics in retail is essential.

While an omnichannel business is crucial for boosting sales and improving customer experience, predictive analytics takes things a step further.

This article will provide some predictive analytics examples to showcase its various uses in the retail industry.

What is Predictive Analytics?

Predictive analytics is a set of techniques leveraging data to help discover hidden patterns. Thanks to these patterns, very useful insights can be retrieved and used to make predictions. 

It is a form of advanced analytics that attempts to predict what might happen in the future based on historical information combined with statistical modelling, data mining techniques and machine learning.

Recently, more and more organisations have been turning to predictive analytics to take care of business problems and discover new insights from their customers’ preferences. In recent years, predictive analytics has become vital to significant business decisions. 

According to reports, the global market for predictive analytics has been on the rise since 2016, expected to reach $28 billion by 2026. 

How Does Predictive Analytics in Retail Work?

Predictive Analytics in retail works in different ways. Most notably through product recommendations.

An example of predictive analytics is when an online store suggests a customer adds some specific product to their shopping cart before checking out. The recommended product is chosen by the system based on historical customer behaviour. 

For example, if, in the past, many people have purchased a pen with a notebook, then it makes sense to suggest the pen to a customer attempting to checkout an order that includes just a notebook. Predictive analytics does this for you on a very large scale. With predictive analytics, retailers can enjoy the fruit of deep insights they would have otherwise never learned.

Customers find product recommendations valuable, and Amazon utilises this technique perfectly. Another example of predictive analytics at work is when Netflix suggests a show similar to other shows you’ve watched. Some retailers do not know this, but you can glean a lot of knowledge from the customer data you already have. 

More use cases of predictive analytics in the retail industry include inventory management, effective marketing campaign, and operational efficiency.

Cross-Selling and Upselling

Upselling involves trying to get a customer to buy a more expensive version of a product. Cross-selling involves trying to get customers to buy additional products that complement what they have purchased or are about to purchase.

Predictive analytics analyses your customers’ journeys and purchasing profiles and can help you cross-sell and upsell effectively. 

For example, Amazon’s predictive analytics system recommends purchasing a shoe rack or an electric kettle after noticing that you have bought many household items in recent weeks but haven’t bought a shoe rack or a kettle yet. The system knows that you may have just moved into a new apartment and need other household items to complement the recently bought ones. 

Predictive analytics will ensure you offer the right product to the right customer at the right moment. 

Personalisation for Customers

A personalised shopping experience will drive customer loyalty. Understanding your customer’s behaviour and combining that with consumer demographics is part of predictive analytics. Retailers can use this to provide personalised and targeted deals to specific shoppers. 

Many fashion brands do this effectively. A good example of predictive analytics for a personalised shopping experience is when a fashion store learns what you like, your preferred range of colours, your size, and your favourite materials. This information will shape your shopping experience. 

The recommended products may even come at a reduced price, making them more appealing. Beyond driving customer loyalty with a personalised experience, you can also build customer loyalty with digital receipts.

Improved Targeting for Marketing Campaigns

Today, users are even more attracted to targeted ads. Retailers are best positioned to collect personal data like search history, preferences, spending habits, and more. 

With easy access to this wealth of data, it’s easier to analyse customers on a more detailed level. Rather than launch a costly campaign with limited reach, predictive analytics in the retail industry helps to tailor the marketing process.

Retailers can influence the content, when, and how it is displayed to improve ROI and create a positive customer experience. Research also shows that analytics-supported targeted marketing can improve conversions by as much as 66%.

In-Store Recommendations Based on Customer Data

Another way you can use predictive analytics to enhance customers’ shopping experience is by providing your employees with devices to access relevant customer data. Your sales associates can easily find the customers that use the number on a loyalty card to check their shopping habits and previous purchases. 

Employees can then provide suggestions based on customer information across various channels to improve the shopping experience in-store. 

Analytics from the Customer Journey

Customers evolve quickly, and due to this, data, combined with real-time analytics, is priceless. As a retailer, you have to evolve with changing customer behaviour. 

You must continually analyse your customers’ journeys and make any necessary changes to give them an excellent experience continually. 

To enjoy predictive analytics to the fullest, you need as much data as you can get your hands on. As a retailer, it is crucial to track and use both online and offline data. You can use offline conversion tracking to get a complete picture of your business.

Offline conversion tracking can help businesses improve the customer journey. By using offline conversion tracking, marketers can now get a much better understanding of what brought customers to their business and how customers have interacted with the company before making a purchase. 

Get Advice from Eyos Retail

At eyos, we provide retail software solutions that enable brands to identify in-store customers through digital receipts, connecting 100% of in-store transactions to any platform they use in real-time.

Connect in-store transactions to a platform with digital receipts. And that means huge amounts of valuable data at your fingertips – ready and waiting to be used in your next marketing campaign. 

If you’d like to learn more about digital receipts and how they work, get in touch with our team. 

Additional Reading

Using Digital Receipts to Improve Retail Store Management

What is Omnichannel Retailing?

5 Omnichannel Marketing Tools to Improve Engagement