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A leading electronics retailer increased Black Friday Revenue By 17% Through Predictive Analytics.

About Client

A leading electronics retailer in North America was looking to increase revenue during the high-sale period of Black Friday. The client chose Nabler to conduct predictive analytics and identify products and traffic requirements in order to increase revenue. Nabler’s team utilized complex data modeling procedures to derive a list of products, estimate of the traffic required, and an estimate of the lift in revenue that can be expected for every incremental product view. Utilizing these insights, the client was able to increase the Black Friday revenue by 17% as compared to the previous year. The average order value, basket size and inventory also saw an impressive rise.

$85 to $120

Average order value

1 to 3

Increase the units per transaction


Growth in inventory in the warehouse


Overall holiday season revenue increase YoY

The Need

Black Friday weekend is one of the busiest times of the year for online retailers, generating a whopping one-fourth to one-third of their annual revenue during the week. Considering the significant percentage of revenue that gets generated during the week, both brands and retailers plan exclusive budgets and promotions well ahead of time to capitalize on the action happening through the week.

A leading electronics retailer in North America approached Nabler with an objective to improve its average order value by 20% and basket size from one to three items for the Black Friday weekend. The client provided the list of products promoted during the Black Friday week of the previous year for reference and the target revenue it planned to achieve during the current year.

The responsibility of Nabler’s team was to come up with a list of products to be promoted during the current year with traffic estimates required for the products to reach the target revenue.

Black Friday Revenue

The Solution

The predictive analytics consultants at Nabler helped the client find answers to solve these business challenges through complex data modeling procedures. Our primary objective was to identify the right data set in order to maximize the revenue generation.

The approach taken by the predictive analytics team was:

  • Previous year’s Black Friday transaction data was used as the basis for the exercise, since the purchase behavior of the customers is very different between non-festive season and during Black Friday.
  • Multi-level segmentation techniques were applied to segment products based on the behavior and sales metrics.
  • Statistical tests were used to identify high value segments within the data clusters. Eliminating outliers within the high value segments, the ideal list of products was derived.
  • With details of the ratio of revenue received in the previous year, the revenue target for the current year was estimated. The team then used regression techniques to estimate the traffic required to achieve the target revenue.
  • The final deliverable contained the list of products, estimate of the traffic required, and an estimate of the lift in revenue that can be expected for every incremental product view.
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