How to Do Funnel Analysis Segementation by Customer Fit
Every week during our check-in, MadKudu Co-Founder & CRO Francis Brero & I talk about our current priorities. Our regular call also become an opportunity for Francis to download some knowledge from his time working with some of the top SaaS Sales & Marketing organizations - like applying lead scoring to funnel analysis. What started as an effort to onboard me with recordings & note-taking has turned into a series I call MadOps. A lead score is the foundation for your marketing & sales alignment. It creates accountability for both teams and is the foundation of a strong Sales SLA. A foundation is only as useful as what you build on top of it, and that's why we talk about Actionable Lead Scoring - leveraging your lead score to create a frictionless journey. Today we're going to focus on how you can leverage your lead score in funnel analysis to see where your best leads are falling off.
Funnel Analysis & Actionable Intelligence
Understanding the customer journey's inflection points and conversion rates is essential to scaling & maintaining success as a software company; however, the analysis you're doing is just as important as the data you're using to generate that analysis. The goal of funnel analysis is to look at ways to remove friction from the customer journey, to improve activation & conversion, and to make sure that the users who should engage most with your product do. Accomplishing that goal without segmenting by lead score is like turning every lead into an opportunity in sales force and then trying to improve your deal won rate. You need to start with the right metric by answering the right question: what are my best leads doing and how can I make their journey better?
Applying Lead Score to Funnel Analysis
Let's imagine you want to look at the first 15 days of user activity in your self-service product, which corresponds to your 14-day free trial and immediate conversion. Of course, you already know that 50% of conversion on freemium occurs after the trial expires, but you're looking to identify engagement drop-off before the trial even expires. After all, customers can't convert if they don't stay active. A simple cohort analysis of all users who signed up over a two-week period would show that over 60% are dropping off in the first 24 hours, a smaller chunk 5 days out, and another group at the end of trial. You might conclude that you need to rework your onboarding drip campaign's first emails in order to combat that big next-day dropoff. That would make sense, except are the people who are dropping off the prospects that matter most? Probably not.
Very good leads have a different funnel than very bad leads
One MadKudu came to this exact same conclusion, and despite various drip campaign tests, they didn't see that 60% drop-off move. Then we segmented their funnel analysis, looking at how very good, good, bad & very bad leads acted, and we found that most of that 60% drop-off was very bad leads: they had made their sign-up process so frictionless that they were getting spam sign-ups who were never going to actually use their product. As it turned out, that small dip after 5 days corresponding to the biggest area of drop-off for very good leads, who were dropping off at the end of their intense drip campaign which only lasted 5 days. In this case, not segmenting by customer fit completely masked where their focus should be, and they spent time trying to get spam signups to stay engaged with their product instead of looking at how their highest value prospects were engaging with their product.
Our recommended Setup
If you're looking to start segmenting funnel analysis by Customer Fit, our recommended MarTech stack is to feed MadKudu into product analytics solution Amplitude using Segment's customer data platform.https://soundcloud.com/madkudu/madops-funnel-analysis-by-customer-fit