Product usage data deserves to be at the top of the pile for GTM teams. It should rise above intent data, dark social, and any other source that has the promise of magic.
Driving revenue isn’t magic. It’s science.
Product usage data is the foundation of being product-led. Product-led companies optimize for customer value, creating short time to value, which is a durable indicator of long-term success. They quickly find product-market fit and see that hockey stick growth everyone wants over the long term.
So why don’t most companies use product data to drive GTM today?
Because the data isn’t accessible to the people who are going after accounts.
That needs to change.
A data-access project without executive buy-in is unlikely to lead to business users getting the actionable insights they need. The goals of a data workflow need to be aligned with business goals, so C-level leadership needs to be involved.
Chances are, your company leadership is already curious about getting insights out of product data. Nine out of ten SaaS C-level executives who I talk to want to be product driven, even if their business models aren’t set up for what people think of as product-led growth.
Still, buying into data-access projects feels to most business leaders like buying a used car. They know enough to know they don’t know enough, which leaves them feeling like they’ll be scammed.
I have insight here because I live in a rare camp: I’m a deeply technical person who’s also a business leader.
As someone who knows exactly what it takes to operationalize data and exactly what the business benefits are, I would love to wave a magic wand to make every business leader internalize these three things:
Given that magic isn’t the answer, let’s look at each of these in detail.
Product usage data will change how products are sold.
To grow your revenue, you need to deeply understand your ideal customer profile. This is no surprise to anyone.
The biggest indicator of your ideal customer profile is your own product usage data … because this data is your actual ideal customers showing you what they love about your product. It’s the voice of your customer, unfiltered.
If you want to acquire more customers, what could guide you better than your current best customers? Same idea for making your customers more engaged or keeping your at-risk customers from churning.
Product usage data should drive daily workflows for sales, marketing, sales dev, and customer success teams.
In a product-led world:
There is solid evidence to believe that being product led or product based sets up a business to find a strong product-market fit quickly and to drive revenue growth on a long time horizon.
When I talk to a CEO who isn’t sure about investing in product usage data, I ask them to investigate the ROI of their current data warehouse, digging into the cost of the warehouse itself and the army of analysts who are the only people with access to it, and the value of the dashboards that come out of it.
At that point, they usually get ready to invest in data access, quickly.
Here’s a hard truth: we’ve become data hoarders. We collect mountains of information, and people still can’t access the insights they need from that data. This should tell us there’s a problem.
The good news: All the technology to get that data in front of business people in a way that it can be easily consumed and activated already exists. The data engineering work involved is actually simple.
Any data engineering team should need no more than one quarter’s worth of work to put data in front of GTM teams on a daily basis, in a way that is easily consumable.
Tools are on the market today that can operationalize product usage data for GTM teams to use in the tools where they already work, like Salesforce and Marketo. These products are focused on turning data into insights, and insights into actions that drive revenue.
Shameless plug: Falkon is one of these tools! We need two weeks to get teams up and running with actionable insights based on unified product, marketing, and sales data.
The idea here isn’t to replace analysts with machines. It’s to free up analyst time to focus on bespoke investigations while machine learning tackles the insights that are ripe for repetition and automation.
Regardless of whether you want to build your own solution or use one that’s purpose-built to operationalize data for GTM teams, try this exercise: Imagine you had access to all your product data and create an inspirational one pager of all the things you would do with it. Think about the impact that would have on your business and your personal career.
If that inspires you, put some more energy into the idea of presenting your CEO with the case for data access.
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