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Chuck Schaeffer Lead Score Models

 
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Example Lead Scoring Models

From the prior lead score best practices article, here are two sample lead score models.

The first lead scoring model below shows example explicit and implicit criteria and score values.

Explicit Score (Demographics)
  Implicit Score (Behaviors)
Company Size: 0 to $10M (< 100 staff)
-5 points
  Search by company name
10 points
Company Size: $10M-$100M (100-1K staff)
5 points
  Website visits: 10+ minutes
5 points
Company Size: $100M+ (1000+ staff)
15 points
  Website visits: 20+ minutes
5 points
Contact Title: C-level executive
15 points
  Website visits: 30+ minutes
5 points
Contact Title: Project Manager
10 points
  Website visits: Each unique visit
2 points
Contact Title: External Consultant
0 points
  Each unique visitor (same company)
10 points
Contact Title: Executive Assistant
-5 points
  Website visit: 15+ minutes on careers
-50 points
Department: Sales or IT
15 points
  Website search query on ‘price’
10 points
Department: Finance or Support
5 points
  Website visit to pricing page
5 points
Department: Marketing
0 points
  Form or landing page conversion
10 points
Department: Purchasing
-5 points
  Sign up for news letter
5 points
Industry: Retail
25 points
  Sign up for test drive
25 points
Industry: Not Retail
0 points
  Read nurture campaign email
2 points
Location: Outside North America
-50 points
  Click link on nurture campaign email
4 points

Sales Ready Lead =

Explicit score of ______ ; or
Implicit score of ______ ; or
Composite score of ______ .

The above sample lead scoring items are typical, but for illustrative purposes only. In this example a sales ready lead may be identified using the Explicit score, Implicit score or mostly like a composite score (combining both values).

The second lead scoring model below expands upon the first to show how lead scoring can mature using additional subject criteria and over multiple phases.

BANT
  Buyer Profile
Budget: Project budget is disclosed
20 points
  Buyer Goals: Clear
20 points
Project is formally budgeted
10 points
  Buyer Goals: Unclear
0 points
Budget: Project is not budgeted
-5 points
  Buyer Objectives: Revenue/Growth
15 points
Authority: We have access to power
15 points
  Buyer Objectives: Margins/Cost Savings
5 points
Authority: We have no access to power
-25 points
  Buyer Objectives: Mix of Revenue & Costs
10 points
Authority: Power unidentified
0 points
  Decision maker persona: Quite Confidence
15 points
Need: Growth or Trouble
40 points
  Decision maker persona: Novice
0 points
Need: Over Confident or Even Keel
-30 points
  Decision maker persona: Cowboy
-10 points
Timeline: Schedule of dates/compelling event
10 points
  Decision maker experience: Veteran
10 points
Timeline: No schedule or event/artificial date
-10 points
  Decision maker experience: Practiced
5 points
 
  Decision maker experience: None
0 points

The explicit and implicit criteria are generally captured entirely through digital lead tracking using a marketing automation system. A lead threshold score based on these two categories triggers an outbound sales call by a telesales/inside sales person in order to further qualify leads largely based on the BANT questions.

The BANT criteria can be obtained digitally using progressive profiling or similar marketing software features, or may be achieved from telesales conversations. The information acquired will update the BANT scores, which combined with the prior explicit and implicit scoring may reach a new lead score threshold value which transfers the lead to sales for additional qualification (pursuant to the Buyer Profile items).

The buyer profile criteria is generally obtained from a sales person discovery call, although some of the information can be obtained from inside sales/telesales or even digitally using social media channels. These criteria may be used in both lead scoring and opportunity scoring and will clearly help prioritize the best sale opportunities. Compare these criteria in win/loss exercises for review and adjustments.

Understanding each of these lead score categories and how they collectively calculate a composite score gives sales and marketing multiple vantage points to determine the best criteria for identifying a sales-ready lead, and fine tuning the lead scoring model.


 

 

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Understanding multiple lead score categories and how they collectively calculate a composite score gives sales and marketing several vantage points to determine the best criteria for identifying a sales-ready lead.

 


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