Prioritize Leads with AI-Based Predictive Lead Scoring
How do marketers define success? Traditionally, a marketer’s success is defined by how many leads they have generated. But, is this still the case? While having high lead numbers in your reports can still be considered a plus, 68% of marketers would rather have high-quality leads.
A qualified lead is a prospect created by the marketing department and reviewed by the sales team. After initial contact from marketing, sales continue to investigate their interest and capability to purchase. If sales add them to their queue, the lead is deemed qualified.
However, qualifying leads can be a cumbersome process. Lead scoring is not a ‘set it and forget it’ process—you need to properly score your leads’ actions and regularly update the scoring model. From regularly configuring reports and checking your lead engagement, there is no easy way to find out which leads are more likely to qualify. Without the proper tools, marketers spend too much time monitoring leads, and less time creating the content that drives lead engagement.
Until today.
For the uninitiated, we launched the SugarPredict AI engine, initially for Sugar Sell, to deliver accurate business-critical predictions for leads and opportunities, even with limited or incomplete CRM data.
Today, we bring the power of artificial intelligence to our marketing automation solution, by launching SugarPredict for Sugar Market to accelerate lead qualification and conversion with AI-based predictive lead scoring.
SugarPredict for Market uses your contacts’ engagement activities and previously qualified leads to uncover how likely your leads will convert to marketing qualified leads (MQLs).
In Market’s Leads and Contacts List View, we introduced a new Interest Prediction column. Prediction scores are color-coded, and range from Very High (most likely to convert to an MQL) to Very Low (least likely to convert).
Marketers can also access Interest Predictions in the record view to see which factors contribute to their leads and contacts score.
Additionally, the Interest Prediction score is available in Custom Reports and can save you a significant amount of time when building your mailing lists for nurture campaigns by enabling you to target leads based on their level of engagement.
Predictive Scoring vs Point-Based Scoring
Point-based lead scoring allows marketers to assign values based on behavior and demographics to determine when they should send leads to sales. However, point-based scoring profiles can be subjective, and maintaining them is time-consuming, leading to ineffective lead management. Additionally, traditional lead scoring methods apply points based on your leads’ quantity of engagement, not quality.
Market users will be happy to learn that predictive scoring requires no setup or maintenance—SugarPredict constantly looks at your leads’ behavior to uncover engagement quality and provides accurate insights into their likelihood to convert to MQLs.
Closing Thoughts
Using Market and SugarPredict’s AI-based predictions, marketers can now let the marketing automation platform do the work, and leverage artificial intelligence to:
- Qualify leads faster
- Build and maintain optimized mailing lists
- Automate complex scoring models
These are just a few of the benefits for marketers included with the release of SugarPredict for Market. We’re only getting started with predictive scoring—more AI-powered features for Market are on the way!
If you would like to learn more about SugarPredict for Market, make sure to watch our special edition of The Scoop with Clare Dorian, Rich Green, and Zac Sprackett, and learn how SugarPredict lets the platform predict the future.
