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Let's face it: creating a predictive lead scoring model can feel like trying to solve a Rubik's Cube blindfolded. You know all those colorful sides need to line up, but which moves will get you there? Well, here's a little secret – some of the most powerful pieces in your CRM puzzle might be hiding in plain sight.
Today, we'll explore six CRM fields that you might be tempted to ignore but shouldn't when creating your predictive lead scoring model. These often-underestimated data points can provide valuable insights into your leads' behavior and intentions, significantly enhancing the accuracy of your predictions.
The number of pages a lead views on your website can be a strong indicator of their interest level. A higher average page view count often correlates with increased engagement and a greater likelihood of conversion. By including this metric in your predictive model, you can gain insights into which leads are actively exploring your offerings and may be closer to making a purchase decision.
Tracking whether a lead opens or clicks on your marketing emails provides valuable information about their level of engagement with your brand. Leads who consistently interact with your email content are typically more interested in your products or services. This field can help your model identify leads who are actively paying attention to your marketing efforts and may be more receptive to sales outreach.
We all know that a form submission is a clear indicator of interest, however are you counting number of form submissions across your website, online content and offline presence? Counting form submissions per contact can be a clear indicator of commitment and is an important number to track at the contact or lead level.
The last page a lead viewed before leaving your website can offer insights into their interests and where they are in the buyer's journey. For example, if their last page view was a pricing page, it might indicate they're closer to making a purchase decision. Including this field in your model can help prioritize leads based on their most recent interactions with your site.
The first page a lead lands on can reveal how they discovered your business and what initially piqued their interest. This information can help your model understand the lead's original intent and tailor predictions based on different entry points to your website. For instance, leads who first land on a specific product page might have different conversion patterns compared to those who enter through a blog post.
Tracking a lead's conversion history provides a comprehensive view of their journey with your brand. By including fields for their first, last, and most recent conversions, your model can identify patterns in behavior that lead to successful outcomes. This data helps in understanding how leads progress through your sales funnel and can be used to predict future behavior more accurately.
First Conversion: This indicates when a lead first showed significant interest in your offerings.
Last Conversion: The most recent conversion can show if the lead is still actively engaged.
Recent Conversions: A pattern of recent conversions may indicate a lead is getting closer to a purchase decision.
Now, before we jump in, lets keep in mind: Every business is as unique as a fingerprint. Your perfect lead score model might will and should be different from the next company. The fields we explored above aren't a one-size-fits-all solution
Remember, the key to a successful predictive lead scoring model is continuous refinement. Regularly review the performance of your model and be prepared to adjust the weights of these fields or incorporate new data points as your understanding of your leads' behavior evolves.
By leveraging these crucial CRM fields, you'll be well on your way to creating a more precise and actionable predictive lead scoring model, ultimately driving more efficient sales processes and higher conversion rates for your business.
