Retail Analytics – Transactions, Customers, Stores, and Products for a retail consulting company in the UK

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


About the Client

The client is an omnichannel ecommerce platform company based in the 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. The client 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 to provide the most relevant results 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 performance by 62% and reduced the data processing time. It also reduced IT costs by 400% and helped the client to handle large volumes of data smoothly.

The Impact Aptus Data Labs Made

    The AI/ML-based predictive engine enabled the client to strategize their sales and conversions by running targeted campaigns to promote products among the different audience or customer segments. It also helped the client to understand customer expectations, personal preferences, and retail trends better. The solution provided the client with advanced business intelligence and valuable real-time insights.


The Business and 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

Azure, Azure ML Studio, R , Python, Azure ML Server

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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