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.
Average order value
Increase the units per transaction
Growth in inventory in the warehouse
Overall holiday season revenue increase YoY
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.
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: