Do you have four minutes? Here is what you need to know. Organizations are redefining their lead scoring model by introducing AI into it. Contrary to traditional lead scoring which works on static filters, an AI-based model gives thousands of additional attributes related to Intent, visitor activity, technographics, firmographics, lead history, etc. AI is bringing ease to lead scoring setup and sales sequence. 

You may think it only gives more information about the leads, but it also helps in identifying and eliminating the red leads; ultimately, increasing the number of opportunity wins. AI is embedded into the existing MarTech Stack to read unstructured data as well, such as sentences as opposed legacy model which only works on field values.

AI can reduce your marketing and sales teams’ efforts to find a potential hot lead out of the haystack. The AI model is trained on historical data about opportunities that were closed won, it takes care of the qualitative aspect of the lead by evaluating all structured as well as unstructured information submitted by the lead. Hence, it reduces manual efforts exponentially. It can also give all the information about the lead such as buying signals, activity details, and firmographics to sales reps before they hop on a call with a prospect. 

AI eliminates the chance of human bias and error. The beauty of AI is taken to the next level when you just need to set the business objective and descriptive information, based on that AI can suggest a lead-scoring criterion.

However, AI is also prone to inaccuracy and false positives. In large teams owning different products, it can become complex and difficult to implement. Another aspect is the personalization of sales follow-ups is difficult as compared with that of manual outreach by sales reps. Lastly, it’s tough for businesses to measure the impact of AI-based lead scoring in terms of ROI and the success this new model brings. 

The top challenge cited by marketers in the State of Marketing report was the ‘ineffective use of tools and technologies’. And they’re feeling this pressure even at the top: 91% of CMOs say they need to continually innovate to stay competitive.

An AI-based lead scoring model requires 6-12 months of historical data about wins to do predictive lead scoring, this can be implemented with the help of an AI Partner in several weeks. When used responsibly and reasonably, AI can level up the quality and number of hot leads.

Predictive lead scoring and AI take much of the heavy lifting away from sales and marketing teams. Teams can use real insights to not only identify the strongest leads but to target them in the best way and monitor the impact of different methods. Give it a go and see those conversion rates rocket.

If you are interested in learning more about marketing analytics, here is a case study on how we helped a consumer services organization increase leads by 18%.

Feel free to reach out to us by filling out the form or writing to us at sales@dataslush.com to discuss any queries you might have.