The days when marketers could rely on vanity metrics to justify their ROI has long gone. Instead, studies repeatedly demonstrate that marketers are focusing on the direct financial benefit derived from their activity.
This is especially the case in B2B marketing where sales and marketing professionals are being drawn ever closer together. The tight alignment of these functions ensures the best possible revenue outcome for the business.
To this end, marketers need an objective way of ranking of one sales lead against another. This approach, helps to prioritise leads according to revenue potential and buyer readiness, and ensures that the best leads are followed up on immediately.
When done correctly it helps marketing and sales professionals understand where each prospect is in the buying process.
And by jointly establishing an objective definition of a quality lead, sales and marketing can exchange better feedback on the quality of leads being passed to sales.
These processes of lead scoring, and the best approaches to take are outlined in an ebook from Oracle Marketing Cloud called “Lead Scoring: A guide for Modern marketers.”
According to the author’s of the eBook, “Lead scoring is more than just a means for ranking leads. It is a contract between sales and marketing—a mutually agreed-upon process for defining lead quality, sales follow-up and cross-departmental collaboration. “
They argue that by collaboratively developing a lead scoring model, your marketing and sales teams can arrive at a common definition of what constitutes a hot lead.
“It also enables your organisation to develop a lead-ranking system that priorities quality interactions or activities that demonstrate high prospect interest,” they write.
The guide offers an easy three part lead scoring model for marketers to follow;
It starts by identifying the two of the most commonly used scoring dimensions which are Prospect Identity (who the prospect is, signified by explicit data that determines fit such as, title, industry and company revenues) and Prospect Engagement (how interested the prospect is, indicated by implicit data that determines level of engagement, such as frequent visits to website and responsiveness to promotions.
To define the prospect identity part of the lead score you must:
- Determine four to five explicit data categories to define a sales-ready stage of qualification.
- Define how important these categories are in relationship to one another by assigning a percentage ranking. All percentages should total 100 per cent.
- Assign a tiered set of corresponding criteria values within each category.
- Assign a letter from A-D, with A being the best fit, to indicate how much a lead meets the ranked identity criteria.
To determine the engagement score, follow these steps:
- Determine the implicit data categories to define a sales-ready stage of qualification.
- Define how important these categories are in relationship to one another.
- Assign values by weighing actions based on recency.
- Assign a value from 1-4, with 1 being the most engaged, to indicate how much a lead meets the ranked engagement criteria.
Combining Fit and Engagement
The next step involves creating a graph that maps out the overall rating of a lead based on the combination of profile fit and engagement level.
Profile Fit has a range of A-D and Engagement has a range of 1-4, with A1 being the most qualified and D4 being the least qualified. This will help you visualise the marketing qualified leads.
You then need to map the score to an action. The authors use an example showing once scores have been calculated and a rating assigned, marketers can determine the correct follow-up action. This could include sending the lead to a CRM system for priority follow-up or entering it into a long-term nurturing program.
They also recommend splitting the scores into two dimensions; A-D is the explicit dimension where the profile fit of customer is ranked 1-4, and the implicit dimension where the level of engagement is ranked A-D.
By taking this approach marketing and sales teams will have more insight into the score’s meaning, as well as the approach to be taken with follow-up.
About the author
Athina Mallis is the editor of the Digital Intelligence Unit of which Oracle is a corporate member. Members provide their insights and expertise for the benefit of our senior executive audience. Membership fees apply.