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

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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.

  • Real-time Basket Abandonment Prediction: Given an active session, the model would predict whether the associated cart/basket would be abandoned or not. The model also provided the customer details and the items in the cart so that the client could undertake some immediate actions to prevent any abandonment.
  • Lead Scoring: Built a program to predict the likelihood of a new customer/old customer (who has not converted yet) getting converted or not based on the lead score and classifies leads into Hot/Warm/Cold/Initial Categories based on some business rules.
  • Customer Segmentation using RFM and ML-Based Models: Extracted all the necessary customer information such as demographic as well as transactional characteristics from the client’s systems to build a conventional model RFM (Recency, Frequency, and Monetary), and an unsupervised machine learning model to segregate customers into different groups/clusters.
  • Product Demand Forecasting: Considered the demand history of all the products (~4000 SKUs) and made predictions using several time-series models to handle trend/seasonality as well as any randomness. These predictions helped in inventory optimization as well as monitoring the overall costs in the demand-supply chain.
  • Product Association Mining: Product association algorithm, APRIORI helped to uncover all relationships between items from large transactional datasets. It made the client understand how the purchase of one product affected the purchase of another product based on support, confidence, and lift.
  • Customer Life Time: Built a program to predict the lifetime value of a customer that calculated the future value of a customer over an entire lifetime to identify the potential of an unsatisfactory customer transforming to a satisfied customer in the future.

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.
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  • Collected the data from several data sources of customer and used the Azure ML studio to do further processing.
  • Did an extensive data preparation and fed selected attributes into the model.
  • Developed a set of models and selected the best model by evaluating several metrics for each developed use case
  • Implemented an API-based web service for client interaction that could integrate with MS Excel and any third-party client.
  • Identified the data to use, retrain, and refine the data pipeline to maintain the consistency of the model’s performance.

Workflow/Architecture/Screenshot

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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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