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DATA SCIENCE USECASE IN RETAIL

Predictive model in eCommmerce

Predictive Analysis of the behavior of potential buyers on an e-commerce website

Data Science is being applied significantly in e-commerce. As data technologies gain more traction, we will see more innovative applications of AI, Machine Learning, etc., in e-commerce retail.

Coupons and promotional codes have become a key strategy for e-commerce as they help increase sales, draw traffic and customers’ attention to your products, obtain customer data, enhance brand reputation, provide maximum publicity, etc., thereby maximizing the business profits.

Our use case accurately predicts the buyers who are most likely to use coupons in their purchase journey by leveraging Machine Learning and other Data Science technologies.

Objective

Identifying buyers who are most likely to use a coupon while buying a product in an eCommerce site.

Accomplishment

We built a Prediction model leveraging Machine Learning and other Data Science technologies that predicts whether the user is going to look for the coupons or not. By this, we identify the users who come to the e-commerce website and then leave to find coupons on an affiliate website.

Technologies

Machine Learning Algorithms, Data Visualization, Data Cleaning, and Predictive Analysis. We used 6 months of historical data from an e-commerce website for the project. The data was derived from Google Analytics.

Results

Our predictive model could identify buyers most likely to use coupons with 97% accuracy.

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    To read the complete use-case with detailed information on the data sets, technologies, approach, models and procedures followed download usecase.

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    Why Choose Us as Your Data Science Partner ?

    • A dedicated team of expert data scientists with deep authority over AI, Machine learning and NLP, etc.
    • Measurable results-driven approach.
    • End-to-end services from strategy consulting to software development.
    • Complete adherence to data regulations like HIPAA, Data protection 2018, etc.

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