Numeric Scoring Pros and Cons

I recently read a blog post by Laura Ramos from Forrester on the subject of Lead Scoring methodologies. In the article Laura outlines the key benefits of a "Numeric Scoring Model". I agree with Laura about the need for Marketers to remove the subjectivity about the quality of a Lead. I also agree with her about some of the best practices to help Marketing and Sales bring more clarity to this topic.

1. Sales & Marketing should have quarterly meetings to review the scoring model and tune it based on results from the previous quarter.

2. Implement a scoring scheme that is easy to understand and manage. One of the methods is to use Numeric scoring. In this method a Marketer would assign points to each key Contact attribute and a weight (both self reported data and behavioral data) and compute a weighted sum of the points to arrive at  the overall Score of a Contact. This method has its pros and cons. Laura already outlined the benefits of the Numeric model that I don't want to repeat here. Here are 2 challenges with this method that I have seen:

a. Chances of sending False Positives to Sales: With a scoring model that does not support a rules engine Marketers don't have the capability to check for data completeness and authenticity for key pieces of information without which a Lead is of little value to Sales. Let's look at a sample scoring scheme (to keep this example simple I have not added any weights to each attribute):

Here is a sample Lead based on this model:

In the above example  Bob gets a score of 95, which is a very high score, and needs immediate follow up from sales. But, from the data it is fairly obvious that the phone number is bogus and we have a hotmail address. Let's take an extreme case where  Bob's  information comes into the system without an email or a phone number. In this case the Lead will still have a score of 80 points (I subtracted 5 points for email and 10 points for phone number from the original score of 95 points). In both these cases the scoring model instantly loses credibility with the sales team and we all know what happens next. 

b. How is the Lead with a score of '68' different from a Lead with a score of '77'? We all know that sales people like to spend their time selling and not learning and understanding internal processes. The last thing we want to do with a sales guy is to open a spreadsheet with 100 items and a complex formula that spits out a number. The critical thing for Marketing is to prioritize the Leads for sales and set some thresholds for follow up. For example, Leads with 80 points and more need to be followed up within 4 hours; Leads with a score between 60 and 80 points need to be followed up within 8 hours and so on. A tiered scoring method (A, B, C, D) serves the same purpose.

In my view the scoring scheme (either points based or tiered) needs to be augmented with a robust data completeness and data validation process to ensure that sales gets to work on Leads that have a high chance of conversion.  One way to augment the scoring process is to add 2 pre-processing stages:

1. Data validity check: In this step we need to check for common junk data that people enter like 'test', 'aaa', 'asdf' etc. for Name, '111-1111', '1234567890' for phone numbers, 'a@a.com' for emails etc.

2. Data Suppression: Sometimes there are legitimate Leads that are generated from Marketing campaigns that are not necessarily sales Leads. We need to suppress such Leads. For example, emails have a '.edu' extension, the Lead belongs to a partner organization or an analyst firm etc.

I would love to hear your comments.