Stop being a Data-Dobby
For a lot of our regular Revenue Letter readers, this email might look a bit different. As we’ve announced in the last couple of weeks, we have officially moved to Substack. Welcome! While you’re here, make sure to check out my entire library of previous letters.
If you had to choose, what would you rather have?
Correct data that no one trusts?
Or wrong data that everyone trusts?
If your brain hurts, then you are with most people out there.
Why do I know? I asked my Linkedin-Crowd this question recently:
The question sounds unusually philosophical to me.
But the background is pretty straightforward.
Building and selling Growblocks - which is at its heart a data product - got me to talk to many, many, many teams.
Without exception, all of them are ashamed of their data.
It's wild.
I even recently had a new customer that told me during the sales process how terrible he thinks his SFDC instance is.
And then we did the implementation in 4 days without any issues, because the data was literally flawless.
Something I, nor the team, have ever seen.
But still… “Toni, my data is really bad”.
Instead of having this Dobby-attitude towards your data.
Let's go through a few steps to get to a decent baseline so you can be a free elf.
Step 1: Go on a data diet
First, try and limit what you’re looking at.
I think instinctively, a lot of people are doing this already.
The idea here is that instead of basically looking at every piece of data you can, limit yourself to 5 steps in your funnel.
An example:
MQLs
SQLs
Meetings Held
Won Deals
Renewals & Upsells
Like a Twitter-validated checkmark (before you could just buy them), treat every one of these steps as certified.
And I guarantee you, someone in your org can vouch for at least one of these numbers.
Next, agree on the processing metrics connecting these volume metrics.
What we usually see, volume metrics are good for target setting, but processing metrics are good for steering and monitoring.
So you do need to get these right.
Processing metrics
ACVs
CVRs
Sales Cycle Time
Lead to Opp time
Etc.
While you need to agree on how to calculate each of them. Once you agree, the result is “verified” because they are directly derived from “verified” volume metrics.
So no fucking around with this.
And lastly, decide on your key dimensions.
A dimension is:
Inbound, outbound, partners
EMEA, US, APAC
SMB, Mid-MArket, Enterprise
Product A, Product B, etc.
The reason why you need those is that in 99% of cases, anyone working with processing and volume metrics will ask about splitting them.
Many of you have step 1 already taken care of.
But what makes this hard to maintain is that you are missing Step 2.
Step 2: Put pressure on the System
Now that we have these data points, the next step is to start assigning owners to each metric.
This already happens with bonuses and commissions.
CMOs will be comped on MQLs.
Inside sales will be comped on outbound opps.
And so on…
The trick is to assign owners to metrics that aren’t comped on them.
For example, you wouldn’t pay anyone based on a conversion rate.
But in this case, you should think of whom to make responsible for the CVR from MQL to SAL.
And don’t just give the VP Marketing everything in Marketing, and VP Sales everything in Sales.
Break it down. Make it granular. Connect them to their actual reality.
And then, connect allllll of these numbers to revenue.
This is how you really make it real to them.
The way you connect all of this to revenue is by making these metrics part of a plan … some people even say a “budget”.
Instead of just budgeting for headcounts and a revenue target.
Create a “budget” for the funnel as well.
How many MQLs? What does the CVR need to look like? How many Opps from outbound?
Set those targets or expectations and show everyone how they are leading to the revenue target you are shooting for.
Final Step: Monitoring the data
After the last 2 steps, you should see people caring about the data.
You’ll have people making sure it’s clean and proper, so it doesn’t fail the pressure test.
You’ll also start seeing people looking at those numbers all the time.
And when people start obsessing over numbers, you’ll start hearing more questions.
“Why did this one not count?”
“Did we drop a deal here?”
“Why would that have happened?”
What’s actually happening here is that you’ve created a system of self-validation.
Now it’s not just the RevOps person in the corner worried about if the data is right, or if AEs put in their deals to the CRM correctly.
You’ve actually established a system where leaders across the GTM understand their vital metrics while being accountable for them.
And most importantly, trust the data they are being fed.
Here’s your sock.
Now stop being the Data-Dobby in your company.
