Diagnosing the top of funnel (Part 2)
10 questions to get you on your merry way
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Last week we dove straight into 10 diagnostic steps to streamline your top of funnel. Today, I’m going review a few more of these questions in detail. As a reminder, here are the 10 questions.
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[Covered last week] What is your lead SLA? How much time does it take for the team to follow up with a lead after notification?[Covered last week] How does your team reach out? Do they call? Do they send a personalized email? Or is it an auto step? Do they connect on LinkedIn?[Covered last week] How is the team notified? Slack? Email? CRM notification?[Covered last week] Is the lead scored? What's the rating of the lead? Do you use the term "MQL" to indicate it's a sales-ready lead?What factors go into your lead score? Behavioral component? Firmographic/persona component? Can you show the breakdown?
When was the last time the lead score and its components were regressed against actual conversion data? When was the last time the score was refreshed?
If you have too much lead volume the likely culprit may be too low of a lead score as opposed to too little sales capacity
Do you have a decay component in your lead score? Lead scores have a bad habit of astronomically high scores if you do not have decay or negative component scores. Tire kickers and ebook addicts especially
What sources and mediums do you source the leads from? What's the offer and the call to action?
Segment your assets and offers into intent bands of low, medium, and high. Anything outside high might be better served moving into a nurture track
5. What factors go into your lead score? Behavioral component? Firmographic/persona component? Can you show the breakdown?
As a reminder, a lead score is essentially just a weighted algorithm to determine if a lead should be sent to the sales or growth team. For teams without many leads a lead score may not be necessary. Companies who have many leads would be wise to employ a lead score. Otherwise, the opportunity cost of spending time on not-so-great-fit prospects can negatively impact the sales team because they would have otherwise reallocated that time to better fit leads.
As a reminder, a marketing lead score is a numerical representation of the potential value of a lead to an organization. This score helps prioritize leads for sales and marketing efforts. Here are the key components that typically go into calculating a marketing lead score:
1.Demographic Information:
- Job Title: Higher scores for decision-makers or influencers.
- Company Size: Larger companies might be scored higher if they align with target customer profiles.
- Industry: Specific industries may be more aligned with the product/service offerings.
2. Firmographic Information:
- Revenue: Companies with higher revenue may have more purchasing power.
- Location: Geographic location can affect relevance and priority.
3. Behavioral Data:
- Website Visits: Frequency and depth of website visits.
- Content Downloads: Engaging with white papers, case studies, or eBooks.
- Email Engagemen*: Opening and clicking through marketing emails.
- Event Participation: Attending webinars, trade shows, or other events.
- Social Media Interaction: Likes, shares, comments, and mentions.
- Form Submissions: Filling out forms for newsletters, trials, or product information.
- Page Views: Number of pages viewed and time spent on the website.
4. Negative Scoring:
- Unsubscribes: Leads that have unsubscribed from communications.
- Low Engagement: Leads that have minimal interaction over a significant period.
- Disqualifying Factors: Any specific criteria that disqualify a lead from being a good fit.
Combining these factors, companies can create a scoring system to evaluate the potential value of each lead. The exact weighting of these components will depend on the specific business model, target audience, and marketing strategy.
6. When was the last time the lead score and its components were regressed against actual conversion data? When was the last time the score was refreshed?
When we roll out a lead score we’re not exactly sure how it will impact our pipeline. The new lead score will either increase, decrease, or hold our pipeline flat. Let’s take a real life example of a lead score I put together in the past.
Demographic:
Title seniority: manager, director, VP+
Title: include facility, facilities, project, foreman
Country (after enrichment): USA, Canada, UK, Australia
Email: business email (exclude free email providers)
Behavior:
High intent page visits: pricing page, demo page, terms and conditions, privacy policy
High point behavior: met at conference 1:1
Neutral pages (no points assigned): blog, about us, case studies
Neutral behaviors: attend webinar
Negative pages (negative points assigned): careers
Personally I like to keep track of all these behaviors. Some may consider building a custom object to track everything. Ideally we can then build out a simple regression analysis against each of these factors. The outcome(s) we regress these factors against are:
SLA Met (true/false): Lead followed up on quickly (the idea here is that good leads will be seen and immediately grabbed by the sales team)
Qualified Opportunity (true/false)
Deal progressed to Stage 3 (whatever stage you have a 50/50 win rate)
Deal won
I’m no stats expert but the goal is attempt to ground your lead score in data. Some of the attributes in my lead score may have very little to do with the positive outcomes we want. Also, I push my teams to reassess our lead scores twice a year as an exercise to try to stay up to date with the market.
7. If you have too much lead volume the likely culprit may be too low of a lead score as opposed to too little sales capacity
Let’s start with a simple question. Does lowering my lead score lead to more at-bats for my sales team? Does increasing it restrict the number of leads shared? Consider the theoretical win rate vs lead quality curve below
Lead quality increases incrementally while win rate exponentially increases in the early phases of the quality curve. But the marginal gain in win rate slows down as lead quality becomes sufficiently high.
One approach you could take is to create an A/B test with your organization. With half the team you could decrease the score and with the other half you can hold the score flat. Ceteris paribus.
What should happen is that one team should receive more leads. The ultimate question is whether this leads to more opportunities, more wins, and more bookings? The downside of this is that you might overwhelm your sellers with junk leads. Ultimately, leading to a dissatisfied sales team.
Assume that your leads today have a 20% conversion rate to MQL. Your MQLs have a 30% chance of booking a meeting and another 50% of those convert into an opportunity. Let’s call this our Primary MQL Tranche. In our CRM we use an MQL Score of 100.
MQL-to-Meeting Booked: 30%
Meeting-to-Opportunity: 50%
3% of all Leads result in a win or 15% of all MQLs result in a opportunity
Now let’s assume we lower the MQL score to create a Secondary MQL Tranche. We can do this by simply lowering our MQL threshold from 100 to 80. Historically we find that this results in a 40% increase in leads.
Team A will only work Primary MQLs while Team B will work both Primary and Secondary MQLs. Assume that the amount of time to work a Secondary MQL lead is the same as a Primary.
At some point the Team B either does not have the capacity or the energy to work that many leads. What that limit is can be worked out mathematically and backing it with anecdotal evidence from the team. Hey RevOps! Actually to face to face to your sales team!
After 30 to 60 days of running this experiment we’ll only be able to learn about the impact to MQL-to-Meeting Booked and Meeting-to-Opportunity. If your sales cycle is beyond 60 days then you’re unlikely to learn about how it affects win rate. But you’ve already learned quite a bit.
Let’s take a look at a fictitious example on Secondary MQLs:
MQL-to-Meeting Booked: 10%
Meeting-to-Opportunity: 50%
0.625% of all Leads result in a win or 12.5% of all MQLs result in a opportunity
Primary MQLs convert at 15% to a deal while Secondary convert to 5%. To generate a deal in Secondary Team B had to work 3x as much for every new opportunity from that tranche!
In Part 3 we’ll round out this series. Thanks for reading everyone.
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