There’s a long history of reliance on modeling to provide revenue guidance in finance — it essentially predicts corporate performance based on myriad factors. In the sales and marketing world, the parallel concept is lead scoring. However, experience shows that current lead scoring efforts leave much to be desired, as their ability to predict the sales-readiness of a lead is, well, pretty unpredictable.
Seventy percent of sales professionals say that only half of their initial prospects end up being a good fit. In other words, if a lead scoring model was a map directing us from San Diego to Boston, we’d end up somewhere in Louisiana if we followed it.
Effective lead scoring modeling is a prerequisite for effective account-based marketing (ABM); without it, you’re just picking accounts by gut feeling. It’s also an essential process to align marketing and sales for revenue operations (RevOps). Unfortunately, today’s process of developing lead scoring models is plagued by three fatal flaws:
1. Confirmation Bias
A human-built scoring model is subject to the biased beliefs of its developers. The people building the model select the attributes and engagement actions of a lead to score, and the relative weight of these attributes and actions.
It's critical that intent indicators are measured for impact to conversion. However, while many intent indicators lead to conversion on paper, they may have very little correlation in practice.
2. Mistaken Identity
Studies show that 70% of CRM data goes bad every year. Worse, it's limited to the information a company had the foresight to collect and is biased toward the data it felt was relevant at the time of system implementation.
As a result of this disparate data disorganization, what sales and marketing teams think is their ideal customer isn’t the case. This “mistaken identity” problem persists due to poor data internally both within CRM systems and a lack of third-party data.
3. Stale-State Lead Scoring
Traditional lead scoring is a static model; you create the model and it generates a score. Evaluating the efficacy of the model and then optimizing it is an exercise left to the end-user. We’ve seen a dramatic shift in customer behavior over the past year, which underscores the need for agile sales and marketing efforts.
Lead scoring is not a one-and-done activity. To keep up with the constantly changing business environment, organizations must reevaluate and update their lead scoring models continuously. This is an impossible task to do manually, as the time, energy and focus needed for this devotion far exceeds bandwidth and resources.
Building Better Lead Scoring Models
Without good data and powerful methods of analyzing that data, lead scoring is a “black art,” involving much trial-and-error. Leveraging machine learning for AI-driven model selection takes the black art out of developing effective scoring models.
AI can analyze historical customer and lead conversion data to build a scoring model that is objectively and quantitatively based on historical results. Additionally, it's key to leverage data enrichment to supplement internal data with external information to increase the quality and scope of customer information. The AI-driven analysis can then include external customer data to provide more traction and a wider variety of attributes to consider than is typically collected in a company's CRM or marketing automation system.
AI can continuously improve the lead scoring model based on actual performance. This provides an objective, measurable and agreed-upon qualification standard for passing leads from marketing to sales, as well as the means to prioritize them and ensure the right sales and marketing resources are focused on opportunities that hold the greatest promise.
SugarCRM's Vice President of Product Marketing David Campbell leads a critical area of the Sugar team. With over 30 years of experience in software and technology and substantive achievements in all facets of product marketing and management, David has successfully conceived, developed, launched and campaigned multiple, industry-leading products and services in a variety of markets.