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 isn’t a task that’s just completed once—you need to properly score your leads’ actions and regularly update the scoring model.
Even then, lead scoring and qualifying can still be a precarious matter that often causes rifts and misalignment between sales and marketing teams, which is no good for business or your office culture.
Aside from regular reporting and checking up on lead engagement, there is no easy way to find out which leads are more likely to qualify than others. Marketers often spend too much time monitoring leads, and less time creating the content that drives lead engagement.
SugarCRM initially launched the SugarPredict AI engine for Sugar Sell, which allowed sales teams to receive accurate business-critical predictions for leads and opportunities, even with limited or incomplete CRM data.
Now, that same AI technology is available for Sugar’s marketing automation solution, Sugar Market.
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, there is now 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.
SugarCRM is taking their motto “let the platform do the work” seriously with the introduction of this technology. The bottom line is, manually scoring leads is a lot of busy work. With AI-based predictive lead scoring, we have way more time to focus on customers and other more important matters.
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.
Using Market and SugarPredict’s AI-based predictions, marketers can now let the marketing automation platform do the work, and leverage artificial intelligence to:
These are just a few of the benefits for marketers included with the release of SugarPredict for Market. And Sugar is 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 get in touch via the form below.