The future of business data and the effect on revenue retention

Data is the new oil.

We have heard this phrase being bandied around since the dawn of the information age. Some companies learned to actually leverage data. And they became juggernauts. There is a reason why Facebook and Gmail are free offerings...

Net Revenue Retention reflects how well customer data is leveraged

Simply put, when companies take the time to understand their customers well, they can more easily provide value. And this is true regardless of shifts in the market. Companies who have figured this out have been rewarded with greater expansion and minimal churn.

Unfortunately, most companies still haven't figured out how to make data a superpower. One way to quickly measure whether this might be true is to analyze a company’s Net Revenue Retention (NRR) rate. This is because NRR is a great measure of a company’s ability to effectively unlock its customer data and use it to add tangible value.

Regrettably, companies with high NRR are still the exception, not the rule. For example, last year private companies with revenue of $50M-$100M had a median retention rate of only 60%.

Of course, the median NRR was much higher for companies who IPO (114%), but that could very well be self-selection bias. Meaning, only companies with sufficiently high NRR go public lest they be punished in the public markets.

Basically, even with seemingly all the data in the world, companies are struggling to answer questions like:

• How do I attract customers that look like my best customers?
• How do I provide immediate, tangible value so prospects convert to customers?
• How do I ensure my customers not only stay with me but also increase their spend?

Why are these questions still so difficult to answer?

How data science got us here

If you asked companies 10 years ago—or even 5—they’d have mentioned the lack of systems to collect and analyze data (i.e. paucity of the data). However, over the last few years, most companies have adopted systems that collect data across the entire revenue chain (aka the customer journey).

The mental model I use to help understand the evolution of the data space consists of 3 distinct but overlapping generations.

Gen 1 included GTM Apps like Salesforce and Amplitude. These answer questions like:

• Who’s my customer with the highest MRR?
• What product features are being used the most?

These are interesting questions that provide answers that are typically useful to specific teams or functions in the company. But because they are used by teams in silos their utility for the company to drive insights across the revenue chain is incredibly limited.

Gen 2 involved CDPs and Data Warehouse like Segment and Snowflake. These solutions were built specifically to solve for the problems caused by siloed team views of Gen 1.

To do this, they consumed data from Gen 1 systems and were able to a provide a single view of the customer. They answer questions like:

• Who is my customer with the lowest MRR and more than 20+ support tickets?
• Which of my customers have a low product usage and who are renewing within next quarter?

Gen 2 is clearly a step in the right direction. They enable us to ask more complex questions and tease apart some of the inherent murkiness that exists in complex systems like business environments.

Yet, in my opinion, Gen 2 solutions are still limited. They rely entirely too much on human input, understanding, and experience. As Sami recently pointed out, our human minds were not built to compute multi-variate correlations.

The future of data science, ML and AI

This is where Gen 3 comes in. At Tingono, we believe we’re still in the early days of Gen 3. We’re building a future where organizations will have intelligence on top of consolidated data to drive automated activities. We’re answering questions like:

• Which of my customers is most likely to churn?
• What are all the factors contributing to the possible churn?
• And, much more importantly, what’s the next best action that will prevent the churn? 

With Tingono, you can boost your customer retention and customer expansion

I have personally felt this pain both at my last startup and when I was running FeedbackNow at Forrester. My team was not only swimming in data but also flying blind and operating mostly on gut!

This paradox is unfortunately too common for most SaaS businesses. And it’s even more critical in the age of PLG. We now have an overwhelming volume of signals that simply cannot be analyzed using traditional means.

As a result, we see companies with a growing set of tools who are still not equipped to bridge the gap between Data and Actions.

There are, generally speaking, three ways you can go about solving this challenge. From least to most effective:

1. Setup manual hardcoded rules that are triggered every time certain thresholds are met. This is a workable approach if you are just getting started. But these rules quickly become obsolete if the business environment changes or your company grows quickly. Then they just become more noise for the team.
2. You can build your own in-house data science team. This team would be fully dedicated to organizing data, synthesizing data, and generating actions based on historical trends. Obvious economic considerations aside, this could be a winning strategy. Though it would likely require a team completely dedicated to the task. They will also need the right mix of product management, data scientists, software engineers and business experts. But this isn’t an easy road.
3. You can use a purpose-built platform like Tingono. Tingono uses a comprehensive data-driven approach to understanding customer activation moments. Our system accounts for the uniqueness of each business. We refer to this as Micro-tailored Machine Learning (ML). Essentially, our platform provides customized ML models for every business.

This likely won’t come as a surprise to you, but I think our approach is superior. Essentially, we’re building the solution I wish I had when running my last two businesses. And I’m super excited about it.

Our approach enables you to:

• seamlessly connect with your existing enterprise data systems;
• use auto generated ML models to analyze the unique factors that drive your company’s revenue; and
• automate the best next actions to scale the activities of your GTM teams.

In short, our solution allows every company to expand revenue using their most valuable asset—their data.

If you have been searching for a solution that easily adds an intelligence layer on top of your current data systems, please reach out. We’d love to work with you!