We recently blogged about our “Quick and Easy Guide to Managing Your Leads.” One of the key factors in lead management is lead scoring. We proposed three general categories of lead scoring: demographic, behavior, and account based. Each of these is useful in understanding where a prospect is in her buying journey. This blog post focuses on a methodology you can use for demographic scoring.
A high demographic score tells you the lead is close to your ideal buyer persona.
It is based on information like job title, industry, company size, and annual revenue. It is critical because it tells you how interested you are in the potential prospect.
A good way to score your leads’ demographic information is on a scale from zero to 100. Zero implies you know nothing about who your lead is, or if they fit one of your buyer personas. We recommend setting the maximum score to 100, which indicates that the lead is an ideal fit. You may be asking, how do I know if the lead is an ideal fit?
To answer that question, you should divide your demographic data into four categories: Critical, Important, Influencing, and Bad Fit. You should look at all the possible demographic information you are capturing (i.e. Job Title, Decision-Making Role, Industry, Company Size, Company Revenue, and Geographic Location) and determine which potential values fall under each of the four categories.
Critical (10-15 Points) – Critical attributes are those that are exact fits with your buyer personas. For each critical attribute, you should increment the lead’s demographic score by 10-15 points. An example of how we use this at ORM is Chief Marketing Officer, Decision Maker, SaaS Technology Product, 1000 Employee Company, $500M in Revenue, and Headquartered in Texas. – Meet Marketing Mike. His demographic score is 72 (6 Critical Attributes x 12 Points).
Important (5-9 Points) – Important attributes are those that are not quite as close to center on your buyer personas. For each important attribute, you should increment the lead’s demographic score by five to nine points. Examples of important lead attributes are Demand Gen Marketing Manager, Part of Decision Committee, Technology Company, 250 Employee Company, $40M in Revenue, and Headquartered in the US. A prospect with these attributes has a demographic score of 42 (6 Important Attributes x 7 Points).
Influencing (1-4 Points) – Influencing attributes are as you might expect, aligned with those people who are still further from your ideal buyer. For each of these attributes you should increment the lead’s demographic score by one to four points. To continue with the ORM example, influencing demographic information is Marketing Specialist, Potential User, Manufacturing Company, 40 Employee Company, $15M in Revenue, and Headquartered in Quebec. This prospect has a demographic score of 12 (6 Influencing Attributes x 2 Points).
Bad Fit (-25 Points) – It is important to decrease behavior scores in the event a person is a poor fit for your company. When these attributes are present we suggest reducing the lead’s demographic score by 25 points. Sticking with the same example we have: Student, Not Making Buying Decisions, Works for University, and is based in a Non-English speaking country. This lead is a bad fit and has a demographic score of 0. We do not recommend using negative values.
Demographic scoring is based on having relevant information on your leads. If your lead database does not contain these attributes, you need to start augmenting it. There are several a third-party services that will integrate directly with your Marketing Automation Platform.
You are now ready to start scoring your leads’ demographic information. At ORM we specialize in marketing analytics to include lead demographic scoring and revenue attribution. If you have questions or have some ideas to discuss, please let us know at email@example.com.
For more information on lead scoring, our white paper on creating an Effective Lead Scoring Model details how to use demographic, behavior, social, and account scores to obtain a full picture of your leads.
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With over a decade of experience with advanced technologies, Pete leads both the Sales and Marketing teams at ORM Technologies. Previously, as the VP of Engineering, he led the technical development of ORM's advanced analytics and optimization solutions.
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