A big part of the hiring process for us at Tingono, and in most places, is hiring for a good cultural fit.
Obviously, there’s both a knowledge and a work style component to making a hiring decision.
But sometimes it’s even more important to make sure the person meshes well with the rest of the team. Sure, the knowledge they have should be complementary.
But their presence should make the entire team better, not just fill the role they were hired for.
In the last two parts of the series, I went over the 360-degree customer view and predictions in a data-driven system.
For this 3rd part in the series, I’m going to dive into orchestration and automation.
Similar to how a hiring manager thinks about orchestrating a good team, you’ll find benefit in building a holistic data-driven system.
That means combining business knowledge and expertise with a human touch.
This is how you ensure your machine learning models will help increase recurring revenue.
Previously, I talked about how rules-based systems quickly become obsolete. Because of this, they just aren’t flexible enough to create usable predictions.
But just because rules-based systems are flawed because of human input doesn’t mean we should eschew human knowledge completely. Let’s not throw the baby out with the bathwater!
We still need a human touch to inform our decisions.
While machine learning is an amazing tool, it can’t fully replace humans. Rather, it should be used to augment and expand human capabilities.
So how should this work? How do you best incorporate the human touch?
It tends to work best when your business knowledge is used to act on the predictions generated by a data-driven system. This ensures the predictions fit with your particular way of doing business.
More specifically, this approach helps account for your organizational culture, varying roles and responsibilities, and your business goals.
Let’s say you have a predictive system that made two specific recommendations to help mitigate the churn risk of a particular customer. The system suggested you should 1) decrease the number of open support tickets and 2) increase the use of a particular feature set.
However, you know this customer well. You deeply understand the way their business works.
So, you know that increasing the use of a particular feature is not something that you can achieve in the short term because of the way their business is structured.
While feature usage will help, it’s not feasible without additional training and support.
Looking at the cost-benefit analysis, you know that this suggestion is not the best use of your company’s resources.
So instead, you and your team come up with a plan to decrease the number of support tickets, which is possible in a short amount of time.
Your next step in your longer-term planning for this customer might be to automate an offer, highlighting a training video for the specific feature not being used.
This approach would likely result in keeping the customer and increasing recurring revenue. And it could only happen because of a combination of artificial intelligence and human knowledge.
Ultimately, your customers pay you because they expect results from your product. If you can’t provide a tailored experience to them so that they can best use it and extract value from it, they won’t stay.
So let’s make our machine learning models work together with our business knowledge and create orchestrated experiences for customers that keep them engaged. This is how we produce results. This is how we boost Net Retention Revenue (NRR).
The key learning here is that making two very different parts of your predictions and automation model work together is critical to success.
Those two parts are business knowledge and business data.
At Tingono, we’re working tirelessly to build a predictions and automation model that meets you where you are. We’re making it easy for you to increase retention and expansion revenue from your current users.
Our platform integrates your data and your business knowledge to ensure the right recommendations are provided and the best next action is taken.
We believe this approach is fundamental to setting you up for success as you pursue revenue growth.
Speaking of revenue growth, in the next and final part of this series, I’ll explore the future of revenue growth in SaaS.
Perhaps as no surprise to anyone who’s been reading this series, I believe this future will be shaped by a 360° Customer View, Predictions, and of course, Orchestration & Automation.
In the meantime, if you have questions before I get that out, drop me a line!