Recommendation Engine

Right recommendation equals higher profit


A retail business survives on its customers. To keep customers interested in their brands, retailers have to constantly come up with schemes that can lure the customers, which can result in better sales for them. But, how to engage the customer so that he can be converted into a better sale at the end of the day, this is something every retailer is thinking about and putting his money on. Also, a major roadblock for the retailers is to enable a physical retail experience by linking the customer’s digital journey into their stores in a frictionless and contextualized fashion. So how can a retailer come up with recommendations that can keep the customer engagement up and running?

How retailers can recommend a better option to the customer?

In this digital age, the market is governed by data. With precise analysis of the data, a retailer can recommend the best option to customers to boost the store’s sales. Better and precise recommendations can be made to the customer with the help of AI-driven recommendation engine which allows the retailer to engage the customer both online and in-store.

With our data intelligence and analytics services, a retailer can help a customer to have multiple relevant options to choose from to complement their requirements. Using our data intelligence you can also help the customer to avail the loyalty points which helps the retailer to retain the customer and keep him engaged. With the help of technologies like machine learning and data science, Our data analytics can process massive amounts of data for retailers. It can record, cleanse, normalize and aggregate data from a variety of data sources which can result in an extensive inventory of actionable recommendations for the customers. This can be achieved by using one of the three models:

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommender systems

Here’s what our data intelligence and analytics recommends for your success

  • Anticipate demand and drive sales with individualized recommendations for the customers
  • Categorize customers on the basis of their preferences and previous purchases
  • Suggest complementary products for current purchase
  • Recognize when a consumer might need a past product again and provide more context-aware choices to them
  • Create cross-selling and up-selling opportunities, especially for items that are not very well-known or popular but have niche appeal

Talk to us about your data complexities and let data intelligence address them