There are many reasons why investors and executives love SaaS. But one very powerful and often overlooked reason is the potential SaaS creates for compounding returns. This idea is captured in a metric called Net Revenue Retention (NRR).
For great SaaS companies—looking at you Snowflake—NRR can be as high as 173%. What this means is that if Snowflake didn’t add even a single new customer for the year, their revenues would still grow by a whopping 73%. Amazing!
So, if NRR is so amazing why don’t more companies tap into this power? One reason is because achieving a high NRR is incredibly hard.
I should know; I experienced this challenge firsthand. Improving NRR was one of my top priorities in my last role when I was running the FeedbackNow business unit at Forrester.
At its core, NRR includes two components: customer churn and customer expansion. To increase NRR you need to reduce churn and increase expansion.
Reducing churn can be challenging because signals that indicate a customer might leave are scattered throughout the customer journey. For example, a customer might leave because they are not getting the desired value from the product.
Or they might leave due to price. Or—as is painfully common for B2B businesses—a champion could leave, and the remaining team doesn’t have loyalty to the product.
Similarly, expanding accounts can be just as challenging. It requires being in close contact with the customer to understand both the current state of their business and where there is opportunity to introduce your solutions. This requires an inordinate amount of human time and effort.
All too often, when customer issues and opportunities are revealed during a renewal cycle it’s already too late to do something about it.
To avoid surprises during renewal cycle, many companies build a high-touch Customer Success team. The Customer Success Managers (CSMs) are charged with becoming a trusted customer advisor and concierge.
It is expected the CSMs will form a close relationship with customers and learn to anticipate their needs. The hope is this will translate to identifying churn risk and expansion opportunities.
However, finding the right ratio of CSMs to customers is challenging.
A low ratio might ensure staying close to each customer, but it is incredibly expensive to scale this approach. Whereas a high ratio makes it difficult to accomplish the desired goal of forming close customer relationships.
This challenge is exacerbated when faced with low Annual Contract Values (ACV).
So, what’s a forward-looking SaaS company to do?
One option is to build an in-house data science team focused on expansion and retention. This team can account for unique realities within the business (e.g. product usage, ideal customer profile, etc.). They can also build customized ML models for predicting churn and expansion.
While powerful, this approach involves two serious drawbacks that must be considered.
First, it requires a massive initial investment to build a data science team and allow them time to customize data models for your business.
Second, this is not a “set it and forget it” process. Rather, maintaining both a data science team and customized models takes considerable ongoing investment.
Additionally, data science alone is not enough. You still must build traditional software that can translate insights from models to your product.
Not to mention delivering the right signals to your team at the right time to generate revenue. This requires automations, workflows, and dashboards. All of which requires considerable resources.
In short, building an in-house data science team is time consuming and expensive. It also holds the very real possibility of distracting you from focusing on your core business.
Despite these drawbacks, building a data science team has been the only viable, data-driven approach available. That is, until now.
As mentioned above, my cofounder Sami and I lived through the challenge of customer churn and expansion. We understand firsthand how maddening this can be. We know this problem all too well.
Even more importantly, we are uniquely qualified to solve meet this challenge. I've helped build scalable services at Microsoft. Sami helped build ETL data pipelines and ML systems at IBM.
Together we built GlimpzIt—an AI powered Customer Experience platform. We know how to create an off-the-shelf, AI-driven solution that scales to thousands of businesses.
Today, we are leveraging our AI expertise to create predictive models that are custom curated for each and every SaaS business.
Specifically, we are training machines to understand your unique business realities and automate real time recommendations for your customer facing teams.
But that’s just the start.
Tingono is building a data-driven platform that grows and changes with your business so you can realize full potential of your customers. We are very excited about what we’re building and the power it holds to simplify achieving a higher NRR.
If you are interested in boosting your NRR, we’d love to partner with you! Please contact us today so we can start our journey together.