There are few metrics in SaaS more important to profitability and enterprise value than customer lifetime value, or Net Revenue Retention (NRR) as a more near-term proxy. Yet, if you survey most Revenue leaders out there (both Sales + Customer Success), few would claim to have a precise understanding of their revenue line – and that’s only been exacerbated by the macroeconomic headwinds of the last year. As outlined below, many leaders find themselves (and their teams) overwhelmed with initiatives, where others have great intentions. With the revenue line under a lot of duress, is there a way to better empower leaders and their teams?
We think so, and it’s called RevenueSignalsTM. When we see a signal out in the real world, it’s generally quick and indicative of what will happen. It doesn’t require substantial interpretation. In the SaaS world, customer analytics are rarely this. Account managers often have to seek out a signal and dig through layers of contributing variables, only to be faced with a customer health score that is ~60% accurate. Just slightly better than a coin flip!
At prescienceAI, we have built a synthAI platform that integrates data from your enterprise systems, and with the help of advanced machine learning, finds patterns amongst your customer journeys. For example, what account manager behaviors are consistent with accounts that renew and grow? What product features are critical for a certain segment of your customer base as it relates to retention? How timely does your implementation need to be before you start adding risk to account retention?
Each of these represents a signal, but it may not even be the most important signal as it relates to a target outcome. Our RevenueSignals™ platform leans heavily on machine learning, feature engineering algorithms, and even sentiment to amplify critical signals and reduce noise. The result? Our customers confidently take action with up to 90% accuracy 9-12 months before a key event, such as a renewal or upsell. And our platform can support any number of revenue events across a customer lifecycle, from prospect qualification through to customer renewal, cross-sell, or upsell.
So what goes into making a prediction on what is likely to impact revenue?
- Product usage data! Not just logins, but feature-level granularity. Is there a combination of features that unlocks amazing customer lifetime value? That might be nice to know.
- Account manager behaviors, which is everything from emails to meeting frequency to QBRs.
- Customer behaviors, in the same vein, tell a powerful story. Responsiveness to outreach, participation in events, number of contacts, levels of engaged contacts, etc.
- Implementation details, such as project and phase duration, and other relevant data points coming out of a CRM or project management system.
- Financial data is a must to understand which customers grow, which stay flat but still renew, which decrease in ARR, and which leave altogether. Most of this can be accessed through invoice history, but pricing and other contractual details are also an important factor.
- Sentiment! Natural language processing of customer emails, recorded conversations, and any other sources can tell a powerful story, though it’s only one component to a larger picture.
Taken together, this can represent hundreds of disparate raw and derivative data points, which are competed against one another to determine which combination provides the most accurate prediction of a target revenue outcome. This is the kind of thing that machine learning was made to solve, versus team members spending a half day coming up with a list of 12 data points that matter most and need to be monitored. We consistently see a 30 percentage point delta between DIY or even customer success platform predictions, and the impact of using machine learning. Precision is absolutely possible! And when you operate with that level of confidence, everything gets more efficient – QBRs, initiatives you choose to devote time to, forecasting, and maybe most importantly, prioritization.
RevenueSignals are part of the ongoing evolution of SaaS companies and how they best serve the needs of their customers, stay efficient, and create amazing customer lifetime value. Today’s SaaS model makes it far too easy for customers to switch to another provider. When lower barriers to entry are combined with low switching costs, it means that proactively managing customer lifetime value has become absolutely Mission Critical to the success of SaaS companies. Consider the following questions, and how much better your business would be if you could answer them confidently:
- Which of my customers are most likely to be ready for an Upsell?
- Which of my accounts are at greatest risk for Churn?
- If we are going to focus on Upsells this year, can I get a stack ranking of accounts that are most likely to need additional products (Cross-Sell)?
- A customer account I manage is showing a retention propensity of only 35%. Why? What can be done to increase this over the next 6 months.
- How do I measure what works, what doesn’t, and learn how to improve CLTV?
- Is product adoption going up or down in my accounts?
- Is Sentiment increasing or decreasing? Relative to other accounts, is this positive or negative?
- How can I forecast with high confidence ?
- As the VP of Customer Success, can I get a list of all customers below a 50% retention propensity threshold?
- I am a CSM with 120 accounts and I can reasonably only spend time on 3 accounts per day in addition to all my other duties. How do I know where I have the best chance of moving the customer health needle?
- How can we identify initiatives across multiple segments to address system issues that occurred in the on-boarding process we had 2 years ago?
- As a CSM Team Leader, How can I better balance accounts across my team so that opportunity for portfolio improvement is balanced across my team members?
And the list goes on. Whether you are interested in this overall paradigm shift and our RevenueSignalsTM Platform or simply want to talk about how you can get started identifying and predicting Revenue Signals in a spreadsheet, we can help. We are passionate about how Customer Success technology is changing our industry’s ability to serve customers across the SaaS landscape and would be happy to discuss how we might be able to help you get started.