Enabled a leading retail consulting company to analyze historical data to increase performance, sales, and conversion ratio

Maximizing Customer Value for Customer Segmentation and Lifetime Value Prediction

Case Study: Maximizing Customer Value: Customer Segmentation and Lifetime Value Prediction

About the Client

The client is an omnichannel ecommerce platform company based in UK. The client provides B2B and B2C commerce solutions to businesses to increase business performance and make informed decisions.

The Business Challenge

The client wanted to understand customer behavior in the continually changing market to stay one step ahead of its customers. Additionally, the client also wanted to analyze the past click-through behavior, shopping history, product preferences, and customer behavior to bring a positive difference to the business.

What Aptus Data Labs Did

We built an advanced AI/ML-based predictive engine to allow a continuous analysis of customer data, with Machine Learning capabilities. Hence, it is to provide the most relevant results on customer segmentation and recommendations to users. Below are the steps implemented as a part of this solution:

The Impact Aptus Data Labs Made

The new analytics platform boosted the business performance by 62% and reduced the data processing time. Moreover, it also reduced IT costs by 400% within a limited time and helped the client to handle large volumes of data smoothly.

The Business Approach

This enabled the client to strategize their sales and conversions by running targeted campaigns to promote products among the different audience or customer segmentations. Therefore, it also helped the client to understand customer expectations and retail trends better. Additionally, the solution provided the client with advanced business intelligence and valuable real-time insights.

The Technology Approach

Azure Machine Learning Studio makes it easy to connect the data to the machine-learning algorithms. Figure 1 below shows one of the models that we built.

Tools Used

The Outcome

The AI/ML-based predictive engine performed well with a consistent average accuracy of 85-90% that helped the client draw major hidden insights/patterns and take necessary measures to add more value to the business.

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