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The Cost and Hassle of Predictive Lead-Scoring Drift

by Yaron Zakai-Or  |  
December 15, 2014

Companies and their data scientist consultants work feverishly setting up lead scoring—gathering, entering, and analyzing data to create detailed customer and prospect analytics.

Then comes drift, the fundamental shortcoming in predictive lead or opportunity scoring in marketing.

Things change. Data points become outdated. Lead scores are no longer accurate. Inevitably, the data scientists must re-engage so the data can be examined anew and the scoring updated.

The problem is that hiring data scientists for periodic re-tuning is both expensive and time-consuming. In the US, these mathematical geniuses earn upwards of $300K annually. Translate that into an hourly rate, and factor in profitability for the data scientist's employer, and it gets costly very quickly.

Most midsize to large companies—particularly those in competitive markets—should re-examine prospect and customer scoring quarterly. But what most do is delay. And delay again. And they drift.

The Need to Stay Up to Date

Companies lose business when their scoring is not up to date—and they know it. No one wants to send out their sales team to meet with prospects using a GPS that doesn't auto-correct when there's a detour. Still, there's that big expense associated with being properly steered.

The solution is finding a way to stay on track without breaking the bank.

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Yaron Zakai-Or is CEO and co-founder of SalesPredict, a company that helps sales teams find the best customers using predictive analytics.

LinkedIn: Yaron Zakai-Or

Twitter: @yzakai

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