Often characterized by its blend of big data with predictive analytics, lead scoring has quickly emerged as a prevalent solution aimed to support demand generation and sales by identifying the right prospects to prioritize outreach. Over the past few years, investment in lead scoring solutions have increased exponentially as many companies have shifted resources to take advantage of the troves of customer data sitting in their information systems. Yet, with speed to stand-up a solution and technicality involved, these initiatives frequently fail, and more recently several clients have come to our MarketBridge team to ask us to ‘fix’ their lead scoring models. Why do some lead scoring models fail?
Is there something inherently flawed with how our customers build lead scoring models?
In every case of failed lead scoring, we’ve seen the same challenge – a broken lead scoring platform – either their data, systems, or processes. A working lead scoring model has these components done right.
1) Clearly Enforced Data Requirements
A lead scoring model is only as good as the data you put into it. Clients seeking actionable and accurate lead scores are in for a rude awakening if they employ poor data practices. Establishing clear data requirements is critical for customers who manage leads from multiple sources. Basic data requirements include:
- Which fields are required?
- Which values will be accepted?
- In what structure will the data be delivered?
This lack of basic requirements typically results in heavy manual intervention and manipulation of files before they can ever be digested by a lead scoring model, and they expose the entire lead pipeline to human error. But, of those clients who did establish basic data requirements, they were rarely enforced, which resulted in many leads running through lead scoring with incomplete or incorrect data.
Poor data requirements is the number one lead scoring challenge we have solved for our clients. And because of this, we often see good leads not making it to sales due to missing data or sometimes sales reps receive MQLS but don’t receive the contextual information to know how to follow up. All of the data points and variables connected to that lead need to transfer to the sales team.
2) Sales Visibility and Understanding
Lead Scoring models often use a large number of valuable inputs when determining the score of any given lead. The score itself is a critical indicator to help sales reps identify who to contact first, but alone it can be less valuable for companies with a large catalog of products servicing a myriad of global industries. Without additional context, sales reps don’t have confidence in the scores and frequently chose not to use them. A lot of emphasis is placed on lead scoring to provide the ‘Who’ in ‘Who do I contact next?’, however, the ‘Why’ is equally important – ‘Why does this contact have a higher lead score? Why are they being prioritized? ‘Fit’ and ‘Engagement’ information needs to flow from marketing automation and into the hands of Sales Reps to provide context to why these are qualified prospects.
3) Performance Reporting
Lastly, I have yet to meet one executive who didn’t want to measure the performance, and more importantly, the ROI of their lead scoring investments. Performance reporting is never as simple as extracting and blending data from multiple systems. The processes that govern how sales reps track and update engagement activities performed on scored leads must be considered. For example, we recently worked with a client to scale a reporting infrastructure that could map invoices back to individual SFDC Opportunities and Marketing Automation Leads. The plot twist occurred when we ran statistics on Closed Opportunities and found that many invoices were mapping to Open Opportunities instead of Closed. It turns out most sales reps were closing opportunities and not updating the CRM, which created a mismatch in reporting results. These processes for tracking activity must be carefully defined and enforced if your organization wants to clearly measure the ROI of lead scoring initiatives.