Cross-Selling Revenue with Reliable Recommendation Solution

We implemented ML-enabled recommendation engine to help our client scale the business across multiple product segments.

Cross-Selling Revenue with Reliable Recommendation Solution

Discover how we excel in generating cross-selling revenue through our reliable recommendation solution. Our case study showcases the power of data-driven recommendations in enhancing sales strategy and driving revenue growth.

About the Industry

With the rising trend of social commerce and influential marketing, the retail sector has seen unprecedented growth at the industrial level. Creating brand awareness, improving the churn rate, and achieving the optimized level of customer satisfaction has become even simple with the implementation of intelligently wired analytics solutions.

The coming era will witness the applications of disruptive technologies like Augmented Reality, Recommendation Engines, and Data-enabled Solutions at the grass-root level. Omnichannel retail strategies will set the new business dimensions for the newcomers and the established players. Immersive Shopping will become the new normal. The future holds good for the retails.

About the Client

Our client is one of the most reputed US-based retail giants. With a maintained legacy of almost 60 years, our client has successfully established a chain of hypermarkets in the US, Argentina, South Africa, and Canada.

With annual revenue of US$100 Billion and an employee strength of 10000, the organization has ventured into the areas of household products, furniture, jewelry, and healthcare.

The Business Challenges

Following were the pain areas of our client: –

  • The organization was looking to upgrade the existing recommendation system for better business benefits.
  • Making accurate recommendations for 20000+ products across multiple stores and online platforms was a crucial concern for the enterprise.
  • The recommendation engine of our client failed to provide insightful suggestions on the contextual information. As a result, the organization had to face challenges in customizing and recommending the right products to its customers.
  • Lack of efficiency of the existing solution model delayed the enterprise progress, thus, hampering the business profitability of our client.

The Business Solution

  • We deep dived into the business framework model of our client, did an exhaustive study of their diversified product portfolio, and planned out the methodology for successful business outcomes.
  • Our Machine Learning experts created the data fragments based on the information gathered from multiple sources such as demographic study, locations, and weather details.
  • The data team deployed ML-enabled recommendation algorithms on different segments of the clustered data. The collaborative filtering technique helped collect information in the form of interactions that usually takes place among the customers.
  • We finally leveraged the cross-sell/ up-sell opportunities based on the results of the recommendation generated from the machine learning model.

Key Results

  • Our client saw a rise in cross-sell from 10% to 15%, owing to the phenomenal improvement in the product recommendations.
  • The latest recommendation solution was easily approachable. The users can have access to the new system from the website, mobile devices, or the kiosks.
  • We narrowed the selection criteria for our client’s customers. To arrive at an accurate business decision, now they need not browse through many products.
  • With improved user experience and reduced cancellation rates, the client successfully saved billions of amount a year.

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