Predictive Playbook: Sales Prioritization
Many B2B companies are interested in predictive but need help building a business case. While there are many applications for scoring, sales prioritization is one of the most common. It is easy to set up the ROI story upfront and measure the impact over the first 60 days.
Over the years we’ve had the opportunity to work with amazing companies including Tableau, Optimizely, and Zendesk. This playbook highlights how they’ve used predictive to drive more pipeline with less effort.
Inside you’ll learn how to:
Articulate the business challenge
Quantify the amount of wasted energy
Project the revenue impact of predictive scoring
Document your success story
First 3 Pages
Once you’ve built your predictive model and tested its accuracy, the next step is to determine how to apply the scores in order to unlock the most value. Below are a couple of the most common use cases and some frameworks for measuring ROI.
Use Case #1
A predictive model will identify some subset of leads that have almost zero chance of converting. By routing these leads around sales and straight to nurture, you can unlock energy for more productive endeavors.
To measure the amount of effort you’ve freed up, you can look at the percent of your reps’ tasks that were logged against low-scoring “D” leads. When you turn on predictive scoring, you should see that percentage go down. If you multiply the delta by the total cost tooperate your inside sales team, you can identify real cost savings.
It is worth nothing that many companies don’t stop working their “C” and “D” leads completely. They may have a one touch policy, or have reps focus on those leads only after they’ve exhausted their “A” and “B” leads. Any small decrease in revenue as a result of this
approach is usually tiny compared to your cost savings from decreased effort.