Using Only Half Your Data Is Not a Waste

It's not uncommon for people to say half their data is completely wasted.

I talked to Scott Stouffer, CEO and Founder of scaleMatters about this because, in all honesty, I'm sick of hearing this rhetoric spewed by anti-data theorists.

Bad data is not the same as unused data

Here's my personal standpoint: As a mechanical engineer, bad data gives me a tic.

Bad data is poorly collected, badly collated, and irrelevant data. However, not all good data is "felt" in your end-game. Your strategy might only see 50% of your good, relevant B2B data in a SaaS sales model.

Why do companies think they're using only half their B2B data?

In simple terms, this perception happens because B2B companies, especially SaaS, have to go through several go-to-market (GTM) iterations to get something usable.

It's a tough market, competition is fierce. You have to experiment to find your zone of genius, as Brianna Wiest calls it. That experimentation implies some waste.

The best companies in the end are the least wasteful in their GTM strategy — not the ones who waste nothing. The best are the ones who iterate intentionally.

Scott Stouffer calls this:
Conscious iteration.

I asked Scott about this solution to the perception of data waste in B2B. If you're reading this, I don't need to persuade you that data is not a waste of time. But you will find Scott's 2-step plan to conscious iteration undeniably useful. So let's dive into that...

2-steps to valuable b2b intent data with conscious iteration

Step one is easy to say, time-consuming to do. That's why few SaaS sales model strategists s will suggest this for your first 3 months as a company. But it's worth the time.

 1. You have to model everything.

Scott gave a great example on the Selling SaaS podcast:

"Let's say a $20 Million company wants to be worth $26 Million. You have some retention loss so need to book $8 Million of new business this year... Do you need 30% more sales? No, it's not that simple.

"You need a detailed play-by-play process because so much happens before sales get their hands on the pipeline."

Let's continue with the example: We're going to predict that 20% of our pipeline will come from paid search, to do that, we have to spend $30,000 per month on paid, which means CPC will equal X, leading to Y number of leads. Of those, 32% will take 1st intro discovery meetings, Z% will convert...

"Once you have that SaaS sales model planned out, set up your tech stack against those numbers you've predicted," says Scott.

When this scientifically planned experiment is complete you can use it, plus the data gathered, to inform your next iteration.

According to Scott, what's critical when gathering B2B data is being able to measure to a level of precision that most people don't even think about.

Think you can do that? Move on to step 2.

2. Instil disciple in process compliance.

I know this isn't usually well-received but you have to have some discipline on the sales team. If your salespeople are not following certain procedures in the CRM, much more than half your data will be a waste...

For me, this is a symptom of 1 of 2 things:

• A tech stack that's too complex for sales reps to follow, or...

• A faltering team leadership to get them on the same page.

We don't want to spend our entire lives updating admin in the CRM, you say.

Cool. Lucky for you, Scott and I also talked about how to make CRMs easier for sales teams:

"Use sophisticated tools AND hire someone skilled enough to administer those tools in a RevOps role. RevOps relieves the burden from the sales team," says Scott.

Simple enough, right? So why aren't more companies nailing this already?

In my experience, too many RevOps people gave come from data, and were never exposed to sales. Plus, they're not accompanied by someone who's been in a sales role. However, the IT involved is often too complex for sales and marketing to make the most of the tool...

A role that Scott believes is key to solving this issue is a GTM analyst. That means a data analyst that started in sales or marketing, as opposed to operations.

The proof is in the pudding

We know this SaaS sales model and data strategy works. Scott made it happen. 

Just by exporting, analyzing, and perfectly optimizing data, he was able to reduce CAC by 75% for a company he later sold. Even better? He shortened its sales cycle by 45%.

"This is only possible with modelling and building up a large enough, clean database," he says.

Got your teeth into that insight? Listen to our conversation or check out more episodes from Selling SaaS.