Lead Scoring — Bridging Marketing to Actual Sales

Problem — Sales teams find no luck with marketing leads and they keep on claiming they’re “just no good”. Contrarily, Marketing Teams say they’re putting their efforts to generate qualified leads but don’t get credit for their pipeline and revenue share.

Sounds relatable?

Solution — Building a sophisticated lead scoring system for intelligent scoring and better lead prioritization.

What is Lead Scoring?

Lead scoring is a methodology that assign points and rankings to leads to determine their sales readiness. Lead Scoring serves as a contract between your sales and marketing teams on the definition and quality of a lead. You score your leads based on their fit for your business, the content they are engaging with and their current place in the buying cycle. Lead scoring is used to automate the grouping, routing and tracking of leads.

Why Lead Scoring?

In a study done by Eloqua on 10 B2B Organizations who had implemented lead scoringsaw an increase of 30% in closure rates, revenue increased by 18% and revenue per deal increased 17%.

According to a report by RainToday.com, less than 25 out of the 100 new leads are sales ready. How does your sales reps know which 25 are ready to receive a call? How does your marketing team act upon the rest 75?

A sales rep spends 50% of his time on unproductive prospecting and 90% of marketing deliverables are not consumed by sales. Lead scoring helps you rank your qualified leads against others and enables your sales rep to focus on leads which are highly targeted and have shown an intent to buy. While your marketing team can identify at which stage of buyer’s journey a lead is and nurture them to attain a better lead score and accelerate buying decision. It’s a win-win for both.

Evolution of Lead Scoring

Common definition build over the last decade for a qualified lead is BANT. This is to qualify if a lead had Budget, Authority, Need and Timeline for a purchase. If you ask 100 sales and marketing reps about lead scoring 99 will have this same answer.

Things have changed. Buyers have evolved. They start researching and gathering knowledge way before they decide things like budget and timeline. Even if they have these decided, it is very unlikely they reveal this information to you at your very first interaction.

Explicit information like Job Title, Company Revenue might suggest the lead a good fit but implicit information like their digital body language and behavioral factors help determine their level of interest in your offerings.

Lead Scoring Models

Arithmetic Lead Scoring — All your scoring criteria (Demographic and Behavioral) adds up or subtracts down a numerical score.

For e.g. — on a 100 points scale

Title — VP or above scores 200 points

Company Revenue = $1 bilion+ scores 200 points

Filled a form on pricing page in last 5 days scores 300 points

Total Lead Score = 700 points (marked as hot lead)

Bonus Tip — A good lead scoring model should have a negative scoring and score degradation algorithm in place. For e.g. — If a visitor visits career page on your website it should be assigned a negative score. This helps your sales team to work only on the relevant and active leads and return those which need additional nurturing to marketing.

Now, let’s take an example of why this lead scoring model fails. Let’s assume a visitor visited 5 of your high value pages and downloaded 4 of your whitepapers in a single visit. This inflates your scoring value but this person might not be a right fit or the decision maker. Here comes a need of advanced lead scoring models.

Co-dynamic Lead Scoring — Lead Score is split between profile score (explicit information) and engagement score (implicit information)

An alphabet is assigned to profile score and a number is assigned to engagement score.

Next step is to carefully assign scores to the relevant profiles and all your content assets. Make sure both sales and marketing align to these scores.

This is how your profile score and Engagement Score matrices should look like:

Final Step is to calculate final lead score based on the above two matrices:

Suppressing the Lead Sending Threshold — Overtime

Before implementing lead scoring marketing use to send all leads to sales irrespective of score and engagement value, which as a result forces sales to spend their time working on non-productive leads.

I hope this article helps you with the basics of lead scoring and lead scoring models, stay tuned if you are interested to know more about how machine learning helps marketers to score leads through predictive lead scoring method.

Have you implemented lead scoring in your organization? If yes, what lead scoring model do you use? If not, how do you prioritize your leads? Let me know in the comment section.