Everyone wants more leads, but the more we are able to generate, the harder it becomes to identify which of them are actually worth the time and effort spent in order to try to convert them. Lead scoring models let you automatically rank your leads in order of the perceived value each lead represents to your company. Resources for marketing and sales can then be distributed by the priority determined by the model.
Lead scoring allows sales and marketing resources to concentrate on qualified leads only, increasing productivity and returns. A good model will generate an accurate ranking and allow resources to be put to good use generating high-ranking leads while less time and money are wasted on low-ranking leads.
Essential internal data includes data from lead-capturing forms, also known as landing pages. These pages prompt visitors to fill out contact information and additional data of your choice in exchange for a piece of content. Landing page fields that help assess and qualify a lead include company, role, objectives, website, etc. Companies have discretion here to include whatever data they find useful.
Finally, a good model should also use lead intelligence and behavioral data based on visitors’ interactions with your website.
Internal data only includes information submitted to lead-capturing forms and past interactions. Therefore, developers should use news reports and social media to create a more comprehensive view of a lead.
Buyer intent data can also help understand a lead’s buying intent. This data refers to anything that indicates where a lead is in a buying cycle and whether they are preparing to purchase from you or a competitor.
The main external data sources include leads’ website data, off-site activity, social media data, shared credit data, and content consumption.
As with many predictive AI models, a significant challenge for predictive lead scoring is insufficient data. A good model requires a large amount of information but this can only be achieved after many leads are generated using lead-capturing pages. In other words, young companies will not have sufficient data to create a good model for some time.
Another challenge is deciding which are the right fields and attributes to focus on during scoring and how to score them.
In 2019, Gartner found that there’s growing interest around predictive functionality from marketing technology and that predictive lead scoring is the most used function.
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