The seeds for Tingono were planted roughly five years ago when Sami and I were building our first AI startup, GlimpzIt.
It was a great adventure, and we were having fun. We were a small tight knit team solving an interesting problem. Our revenues were growing at a healthy clip, and we were lucky to have amazing customers like NBC Universal, Johnson & Johnson, and VMware.
However, like most startups, we had our share of challenges. For instance, we struggled with customer retention in our midsize customer cohort.
The only way we could address this challenge at the time was through superhuman efforts and high-touch customer success. We did this because we knew we needed to do some things that don’t scale, even if they weren’t viable long-term solutions.
Additionally, our Customer Acquisition Cost (CAC) made it abundantly clear it would be much more efficient to generate the same dollar amount by expanding existing customer accounts rather than chasing new ones. In other words, we had a classic leaky bucket problem.
And to make matters worse, every time we “topped our bucket” it was even more expensive! Being engineers, we desperately wanted to tackle this problem. But alas, solving this problem was not at the heart of Glimpzit and would have been a distraction for us at the time.
Fast forward a few years.
After Forrester acquired GlimpzIt, I was responsible for a business unit at Forrester and was laser focused on Net Revenue Retention (NRR) as a key business metric. Because NRR measures the combined impact of customer retention and customer expansion, it was a perfect way to measure the health of our business. In other words: same challenge, different situation.
However, this time around I was determined to find a solution that could truly scale customer retention. I set up meeting after meeting with vendors in this space. After countless demos, it became abundantly clear that none of the available solutions could account for my unique business needs.
The only way we could get a technical solution was to build an internal data science team. While this worked, it was time consuming and expensive.
After Sami and I left Forrester, we began exploring new market opportunities. But it didn’t take long to circle back to the hairy problem of simplifying customer retention and expansion.
We began by speaking with dozens of Revenue and Customer Success Leaders to understand the problem from multiple perspectives. We then took a deep dive into the current state of AI/ML. This effort made a couple of things crystal clear.
First, our experience was in no way unique. In fact, every subscription company struggles with the post-sales challenges of customer retention and expansion. Unfortunately, until now there hasn’t been a simple, data-driven way to identify leading indicators of churn and expansion.
Second, Machine Learning has advanced in the last few years to a point where it can predict churn and expansion because it accounts for the uniqueness within each and every business. Critically, it can also suggest and even take actions to meet a goal. In other words, it’s now possible to automate actions based on AI-driven insights.
All of this brings us to where we are today. Tingono is the result of being sufficiently agitated by the problems I mentioned above. We are incredibly fired up by the challenge to make it truly easy to retain and expand revenue. And we couldn’t be more excited to share with you what we’ve been building.
In the meantime, please share news about Tingono with others you know who will benefit from solving customer retention and expansion.