Being data-driven should be standard nowadays, but many organizations still struggle with it. Every company wants to be data-driven, but putting it into practice is the tough part.
In a Harvard Business Review article from 2018, it was noted that while 99% of companies surveyed intended to be data-driven, only 20% felt they had succeeded.
Becoming data-driven requires a cultural shift. It’s not just about putting data in presentations or on websites; it’s about educating everyone in the organization about why data is important and how it will be used.
Part of this cultural shift means agreeing as an organization to be data-driven, the recognition that each individual is a data creator, not just a recipient, and ensuring that everyone is creating and acting on good data. Simply having a data team isn’t enough; it’s about integrating data into your culture.
Using Data Across the Organization
While every single function in the business plays a part in retaining and growing customers, it’s the core objective for modern CS teams. Part of that responsibility means quarterbacking the customer journey across all the other functions within the organization ensuring that customer data reaches the right departments.
CS should communicate insights gained from customer data to suggest initiatives like advocacy programs and referral pipelines that Marketing can spin up for the benefit of Sales. Over time, this can lead to a cultural shift where everyone recognizes the value of data and knows how to use it effectively across teams.
Three Ways to Think About Product Usage Data
When people think about product usage data, they’re usually thinking about dips and troughs of usage. Product usage up means good, product usage down means bad. But at a higher level, you have to think about it in three ways.
The first is understanding if the product usage of the customer is aligned with the outcomes that they want to achieve. A customer can use a product in a million different ways, but that doesn’t matter if their goals aren’t aligned with the way that they’re using the product. That’s often an oversight.
The second is a simple one. Make sure your customers are using your product the way it was designed to be used. It’s often the case that a customer starts using your product in a way that it wasn’t designed for. To an extent, that may seem great, opening up a new use case. But 9 times out of ten, that customer will churn. Make sure your customers are using your products the way that they’re intended because all products start to get wobbly at the edges.
The third thing, and arguably most important, when it comes to fighting churn and product usage is time. What you’re trying to understand is how the current state of a data point compares to a prior state and use that relationship to understand what a future state might look like. Whether you’re using a health score and see the health going up or down, or whether it’s specific usage of a feature, it’s always the current state versus the prior state and how that’s going to influence the future state.
Product Usage Data in Action
Common use cases:
Correlations between high customer retention rates and customers who’ve completed certifications. This drives adoption across their organization leading to retention as more people become familiar with the software and use it to solve more problems.
Driving expansion through intent. Expansion where a customer using one feature a lot could mean they could find value in another complementary product.
Tactical expansion around thresholds. If a customer hits 80% of a consumption threshold or 80% of a licensing threshold, an auto email can be sent to the decision maker.
Using product analytics to white space. Break out all of the products or features within your product l, and whitespace across your portfolio to find products that have high usage across a certain cohort of customers and find opportunities with similar customers.
Last point can also be applied to better understand which cohort of customers may not be adopting a certain product and may need additional enablement.
Better Collaboration Between Teams
Diminishing Data Returns
Head of Marketing
Planhat
Amberā is a global marketing executive with over 15 years of experience scaling SaaS and data-centric brands across international markets. As Head of Marketing at Planhat, she leads global brand, demand generation, and go-to-market strategy. Previously, she served as VP of Marketing at YipitData and spent over a decade at Meltwater, where she built and led teams across the Americas and Asia-Pacific. Amberā is known for her strategic depth, cross-functional leadership, and ability to craft narrative-driven programs that fuel growth and resonate across regions.
