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Predictive Analytics Use Cases In The Retail Industry

The volume of customer data that the retail industry currently has goes beyond all the previous figures. However, the chances of all this data translating into profitable outcomes rest on several factors. Out of all the others, the ability of a business to convert the huge amount of customer data available into actionable insights for the future is the more important. In other words, data analytics in retail plays a significant role in giving a business an edge over its competitors when it comes to planning for future sales. However, most retail businesses are not sure about the ways of making the most of the data available to them. This is what retail analytics solutions can help them with.


Retailers going with a single strategy may find turning data into useful information. Retail analytics solutions provide them the impetus to cut through the noise, get to their customers first, and make them choose their products and services over others by providing a personalised experience. This is a technological advancement retailers will never regret investing in. It gives them the ability to listen to what customers want, what is their opinion about a new product/service, and see what their competitors are doing. Having all this information at their disposal, businesses are better equipped to undergo transformation. These solutions or tools utilise the predictive analytics model to put existing data against historical records to predict trends, activities, and behaviour in the future. So what are the most popular predictive analysis use cases in the retail industry?

1.Personalisation: The initial use of predictive analysis in retail is to combine customer behaviour and demography. By doing this, retail business will be able to come up with targeted offers and schemes for different buyers. Earlier, when data analytics wasn’t as prevalent, retailers didn’t have data-backed insights to design targeted offers. Now as data analytics and online shopping have become more mainstream, it is now easier to track buyer behaviour on different platforms.

So it is easier now to monitors hoppers who collect information about different products from online stores and then goes to a local store to make a purchase. These insights along with predictive analysis allow retailers to create personalised offers for different customers based on data and their behaviour. For instance, retailers can personalise in-store experiences for customers by incentivising regular purchasing. This will help them in generating more sales across different channels.


2. Customer segmentation: The journey of a customer maps the buyer experience. The journey starts when the customer first gets in touch with the seller but it doesn’t end with the customer placing an order to buy a product. This is a long-term association that goes beyond the final purchase. This journey continues even after customers receive their products.

The customer is at the forefront of every decision that a retailer makes. So knowing how the customer has moved through different stages in their journey can give retailers a chance to take decisions that can contribute to more sales in the future. Insights backed by data can provide retail businesses access to customers’ profile and their buying history. Monitoring customer activities at different stages in their journey can allow retailers an easy way to understand customers and their buying behaviour, the best way to get in touch with them, and more. Predictive analytics can help retailers in targeting as well as segmenting customers. They can cluster their customers on the basis of their common attributes, which they will learn about after analysis.


3. Identify trends: Retailers can generate more sales if they have information about customer trends. You need a robust retail analytics solution to analyse all the data and plan and roll out promotional offers and schemes. This is possible because you get customer information from various touchpoints that they use to engage with you before making a purchase. When you have an idea about what a customer is going to do at various touchpoints, you can use relevant offers to drive sales. You can also use predictive analysis to do a lot with your product pricing. You can maximise sale by choosing the correct price point. You can run price-based offers.


Predictive analysis is a great way for retailers to use past information to determine future trends. It can help retailers to significantly increase their sales.

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