5 Use Cases of Data Analytics in Retail Industry

The retail industry has undergone many changes in recent years. The retailers are required to reduce the noise and reach their target audience by providing a unique and personalized approach. With the technology-driven approach, retailers are constantly listening to their customer needs, their reaction towards a new product and the competitors approach. This is where data analytics in retail industry comes into play!

     

With the help of information gained from big data analytics, retailers are preparing for the next wave of transformation. The big data is fuelling the retailers to formulate a strategy that helps them monitor their customer behaviour and offer services that meet their requirements.

   

Use cases of data analytics

   

Retailers can harness big data to uncover hidden trends that reveal new opportunities. And make better business decisions. Let’s take a look at the below use cases to better understand the value of data analytics in the retail industry.

   

1.      Personalize in-store experience

 

Data Analytics offer a new way to understand and analyse the behaviour of customers. Many retailers arm their sales executives with point-of-sale devices to make personalized recommendations and connect with their customers in a better way.

     

Understanding customer preferences will help retailers to highlight merchandise efforts that prefer their taste and budget. When highly personalized customer experience is provided to millions of individual customers – retailers gain a competitive advantage.

   

2.      Increase conversion rates

 

Retail companies need to target the right customers to increase conversion rates. To target the right customer, they need a 360-degree view of customer’s information. Today, customers interact more than they transact on social media or any other platform. Retailers should turn the interest of customers into wealth.

     

For example – several customers liked a travel channel on YouTube and frequently shopped at travel portals. The retailer can use this information to target their ads by placing special promotions on travel or related channels.

   

This gives the retailer a higher conversion rate and lower customer acquisition cost.

   

3.      Analyse customer journey

 

Today, customers are more connected with companies than ever before. Using social media platforms, they can access any kind of information about the company. Taking advantage of these trends, retail companies need to understand each customer’s journey across these channels.

 
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The analytics results from structured and unstructured data can reveal new patterns that aren’t convincible with traditional models. With customer journey analytics, retailers gain visibility into every behaviour of the customer – which in turn valuable insights to make informed decisions.

     

There are different stages of customer journey analytics – acquisition, activation and adoption. These three fundamental stages help retailers design an end-to-end journey experience for their customers.

   

4.      Inventory Management

 

Retailers often strive to maintain the balance between demand and supply. Organizations can unleash the potential of big data to minimize the risk of stock leftover and optimize inventory. Our models help retailers comprehensively plan their inventory based on the demands of their customers.

       

Leverage advanced analytics techniques like – supply-demand forecasting, supply chain optimization, inventory management etc for overall inventory optimization.

   

5.      Fraud Detection

 

Retailers can avoid bad user experience by identifying fraudulent behaviour. They can even flag it by predicting a fraud event at a very early stage. We often hear about incidents like – fraud in delivery, fraud in return, credit risk, abuse of rights and so on.

     

These incidents harm the retailer’s reputation significantly. With predictive analytics, retailers can detect fraud behaviour and safeguard their reputation.

   

Conclusion

 

Hope the above use cases of data analytics have you better understand of the benefits of data analytics in the retail industry. No doubt, data analytics can help retailers explore massive sets of structured and unstructured data to uncover customer insights, know about hidden patterns and other useful business information.

   

It is estimated that 60% of companies are gaining a competitive advantage for their organizations using data analytics.

   

If you want to use retail analytics and win more sales, get in touch with us!