Building an effective multi-model AI lead generation solution

Following extensive testing of the dimensions used to build the AI solution, we embarked upon a further phase of work lasting 1 month during which the machine learning models were retrained to include revenue as a feature, resulting in highly accurate predictive AI models. This resulted in a 91% predictive accuracy rate for the ‘likely to buy’ AI model which is used to generate a probability score of how likely a customer is to buy a product. As well as a 95% predictive accuracy rate for the ‘customer fit’ AI model which is used to predict whether a registered user is a good fit for a particular product.

This enabled the clients’ marketing team to be able to successfully and accurately know the grading of leads sent to sales. In turn giving them confidence that 4x more of the marketing qualified leads (MQLs) provided turned into sales qualified leads (SQLs) with an increased potential of going on to become paying customers.

For their counterparts in sales, significant efficiencies have been gained so that time isn’t wasted contacting low-quality leads. Allowing them to confidently focus their outreach efforts on leads with a good customer fit and high likelihood-to-buy, which currently convert to an active sales opportunity in an average time of just 2 weeks.