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QuickPivot Launches Behavior-Based Scoring Model for Marketers
QuickPivot's Scoring Model enables eCommerce and B2C marketers to apply deep customer data and logic to segmentation and targeting.
Posted May 10, 2017
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QuickPivot, providers of a cloud-based, cross-channel marketing platform, has introduced Scoring Model, a lead scoring feature that enables agile, real-time customer segmentation and targeting. The Scoring Model uses logic models that factor in value- and risk-based scores, real-time insights from complex queries or batched queries, and additive logic.

"For years marketing platforms forced users to build lead score models using a cookie-cutter approach, using limited data," said Paul Mandeville, chief product officer at QuickPivot, in a statement. "That approach has diminishing returns when applied to vastly different businesses challenges, especially when you cross over from B2B to B2C."

With Scoring Model, marketers can now create and label any type of score. Marketers can write any combination of queries to define scenarios that are meaningful to their businesses and score them. The user interface of QuickPivot's Scoring Model assembles those queries in the Scoring Model table and lets marketers simply run the model.

Scoring Model also lets marketers assemble any number of simple and complex queries, run them in real time, optimize and adjust total points based query results, and then apply the new model method to their planned campaigns. Queries can also be saved, reused, and combined to create new scoring schemes.

Five scoring methods are available in Scoring Model: Additive, Min, Max, First Matching, and Last Matching. Additive models enable agile segmentation and targeting in real time by increasing or decreasing assigned point values based on the outcomes of each query.

"Buyer behavior is not a simple equation," Mandeville continued. "A one-time website visitor who spends 15 minutes on your pricing page is probably more valuable than a window-shopper who glances quickly at seven pages. If your model allocates more points for more visits, the window shopper wins. Recognizing these lead scoring models' limitations, we built a system that gives users the flexibility to score the nuances of behavior over time, get more accurate lead results, and drive more sales."


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