The challenge

The Challenge

VSSL approached Keydabra and Constructiv Works with the desire to learn which of their website visitors had some propensity to buy and how they could target those prospects with relevant products and offers in order to effectively boost sales.

Keydabra’s tracker code was first used to capture user interaction data from the VSSL website. This data was then processed through Keydabra’s proprietary algorithm and 25 other machine learning algorithms to compute a predictive buyer score (probability of conversion) and engagement score (customer experience score) for each website visitor.

Ideal customer

Finding the Ideal Customer

Keydabra used the “engagement score” metric to segment the population of website visitors into high, medium, and low conversion prospects to determine which visitors had the highest potential to make a purchase. Keydabra also categorized existing customers into groups ranging from “loyal” to “at-risk” to enable effective marketing campaigns that would target the right individuals.

For a period of two months, Keydabra monitored two key metrics to find the ideal customer: percent conversion and savings with variable offers that were utilized for conversion. These factors allowed VSSL to target ideal customers and therefore increase their conversions and ROMI.


Recommendations for Continued Improvement

The Keydabra platform was also able to configure a variable function, based on a visitor’s Engagement Score, to target different prospect segments with a relevant offer for their predicted product of purchase. These variable and personalized offers were configured to display to customers on their second visit to the site. The result? Prospects received the most relevant ads and VSSL increased the efficiency of their marketing spend.

Beyond identifying products that would resonate with specific website visitors, Keydabra also recommended further targeted campaigns, automated loyalty program enrollment, and clustering to provide better segmentation of visitors. This segmentation would serve to group the most engaged and responsive visitor populations with various attributes so VSSL could perform targeted marketing to the website visitors most likely to convert.

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