The landscape in SaaS has completely changed in the last 6 months. Economic headwinds have created a lot of uncertainty. And with product usage changing quickly (layoffs anyone?) identifying accurate projections for NRR is harder than ever.
We’re joined by Aseem Chandra, CEO & Co-Founder at Immersa, who spent a number of years at Oracle, Adobe and Omniture in product, sales and operations roles (and who has now launched a new company, Immersa) and Levi Kugel, who is currently running Customer Success strategy at Quantum Metric. They joined us to speak on how companies can better predict their NRR, including identifying high risk accounts, finding qualified upsells, and more, even under current market conditions.
Key Takeaways
Why Now?
Net Revenue Retention (or NRR for short) has recently become one of the definitive metrics being used to better understand the quality and health of a business. But what does that actually mean? And why is it important?
During Aseem’s time at Adobe, he noticed that as they started to really scale, the number of logos and new revenue they were bringing into the business seemed to have very little impact on the growth curve of the business (relative to retaining existing clients). And this was sort of his introduction to just how important and relevant NRR really is, because ultimately, there is no way to fill a leaky bucket.
The SaaS industry has been around for quite a while now and companies very clearly understand the value of retaining clients. But what has changed in the past couple of years was the pandemic. 2020 was a huge shock to the system. Tech started to really take off as everyone moved online. The services necessary for that transition really thrived, and that trend continued into 2021. Of course, last year, that macro environment really started turning south, and there was a lot more scrutiny on being placed on these SaaS contracts – were all of them really necessary? So over the last six months we’ve started to see layoffs and a lot more focus on cost and efficiency. The big surprise was when Salesforce said that they were unable to commit to a forecast for 2023 – that’s when a lot of people really started paying attention. Of course, this is a large change to the environment – the signal went from “How do I acquire new customers?” to “How do I make sure my existing customers are happy? And how can I retain them?” So, on account of all this volatility in the market, it’s difficult to predict what current retention rates will look like.
So why is NRR important? The math is actually really simple: if there’s a 100MM ARR business and they’re promising 40% growth, about three-quarters of that is going to come out of either growing existing customers or preventing churn in their install base – only the last bit will come from acquiring new logos. The math works better at a smaller scale – but as the numbers start to grow, dependency on NRR, retention, and growth starts to increase relative to new local acquisitions. So in a startup’s early days it’s all about getting new clients, but later it becomes: How do I retain the clients I already have? And grow those clients? Once a startup achieves product market fit, NRR should become the primary number to focus on. But starting the practice of NRR analysis earlier rather than later is not a bad thing: companies can create a lot of efficiency in the business by aligning the whole team vs. just the sales team – “Where is customer support engaging?” “Are you upselling products that are really getting traction?” “Where does product fit in?”
In the tech industry, there is something known as the rule of 40: the principle that a software company’s combined growth rate and profit margin should exceed 40%. If a company can’t prove that – there are associated valuation penalties (which have started to really deflate SaaS values over the last year or so). In light of that, a lot of high-growth companies are starting to revisit their playbooks and focus more on retention and growth within their install base.
Today, two-thirds of the companies that currently operate in the SaaS world have >5% churn. So if there’s a 100MM ARR business, they can expect to lose 5-10MM next year because they didn’t take care of the customer, or the customer didn’t see value in the solution. That loss hurts a lot more than losing a new deal, because there was already a huge sunk cost in the sales cycle. That dynamic has only become more common in the last few months because when layoffs happen, platforms are losing seats, but they are also losing usage. At some point someone might look at an offering and say “are we really getting value here when every renewal conversation turns into a downsell?”
So how can practices around analyzing NRR be better operationalized over the long run? First, signal capture is key – customer retention relies on better understanding which signals depict health and which depict risk – and then organizing a process to address those signals in order to pull ahead of the curve. If a business uncovers bad news three months before their customer will renegotiate their contract, it’s already way too late. These risk signals need to be identified and monitored by the team in charge – potentially below the Chief Customer Officer. There’s not always a clear owner here, as there’s a lot of variety in how companies approach the problem (sales or finance may own this instead). But someone needs to own this. Once these signals are ironed out, teams can use this data to address concerns well ahead of renewals (or even use them to upsell during the renewal). Given the focus on ARR and sales there has always been way more structure around sales data. If you go to a Salesforce conference you’re just as likely to run into a data analyst or a data architect as you will a salesperson. Unfortunately, customer success organizations today just don’t have that same structure in the process definitions and what actions to take when a client is signaling a red or yellow flag. Even the data sources that customer success leads are tapping into are not as clearly defined. And when this retention metric is so important to the rule of 40 – it’s clear that something has gone very wrong. There’s a huge amount of potential here to bring far greater discipline around customer journey management – analyzing and operationalizing the data – the same way that it has for sales.
So back to NRR: there are different ways to look at metrics today to get a better understanding of what your NRR will look like. One is to look at product usage data and compare that against what you expect the customer should be doing as they’re progressing. For example, compare that to their forecasted use, or plan use, or contracted use in Salesforce, and figure out if they’re tracking to that or if they’re under that number. Or if they’re over from a pacing standpoint, that itself creates a signal for an upsell opportunity. There are also more complex ways of looking at this including feature usage (establishing certain majority curves). Clari, for example, noted that if their customers sign-up and use them for forecasting for the first few months, then that’s a good sign of adoption a year later. The features that customers are using will be different at each company, and different at each stage of maturity.
Unfortunately, there has been a huge proliferation of metrics being measured today – and it can be hard to tell where to focus. Initially, SaaS go-to-market was pretty simple – new initiatives were spearheaded by marketing to launch a product, at which point the baton is passed to sales, and then onto customer success – but today the reality is very different. There are a variety of go-to-market strategies that span everything from marketing-led to sales-led to partner-led, and now product-led, plus there are many mixes of multiple strategies within the same organization depending on what they’re trying to optimize for. So, the SaaS landscape has definitely become far more complex. So, now more than ever, these processes need to be more data-driven and more automated, to better uncover the best possible signals – and act against them. Over the last few years, product data and usage data have risen to prominence, for example. So when you look at NRR, ARR, etc. – thinking about how to optimize that against product usage data starts to play a very large role in reflecting what users are really trying to accomplish. The product usage data, and other useful indicators, have to be the focus – and NRR is merely an aftereffect of a successful strategy there.
Also unfortunately, there can be real value in many kinds of data today that are often locked inside the engineering org or other hard-to-reach places (who is logging in, how often, what are they doing?). This can be very different from the transactional data that sales and CS teams tend to engage with, and merging it is difficult without shared IDs.
How Immersa Helps
Immersa has built out a platform to enable business users to collect data ranging from product usage, GTM, and the CRM, and then run that data against predefined models using industry best practices. This allows for the combination of those data sets (e.g. Mixpanel and Hubspot data without a shared key or shared ID) to come up with probabilistic outcomes. In addition, Immersa has predefined some great metrics on the GTM side around different stages of the customer’s journey (e.g. intent, activation, engagement, adoption, consumption), and these can be custom-tailored to ensure that they’re specific metrics that you a customer might care about. Finally, the automation piece comes in. Thresholds can be established: At what point should an action be triggered? And those actions can be triggered in Hubspot or any number of different destination SaaS applications…e.g. Salesforce, Intercom, etc. Immersa sits in the middle to bring the data into one place, and quickly provide a better understanding of activity inside of key accounts. Some examples have been customers having Hubspot create a new deal, or adding information to an existing account from the product side, to bring into a deal (so that the sales rep can see what the customer is actually doing without logging into product analytics tools like Mixpanel that weren’t designed for sales teams). So Immersa centralizes the data, applies intelligence, and triggers actions in destination apps. One live use case today has been measuring the time it takes a retailer to deliver online pickup items to a car and alerting business users if that time exceeds a certain threshold.
When defining metrics and figuring out what’s important to measure, it’s crucial to think about where there is direct risk in the customer journey (this could be consumption usage for usage-based models, support ticket volume, etc.) – but the metrics need to say “hey, this is my team, and this is what we’re doing for the company.” Indirect risk, e.g. user adoption, value realization, or things that can’t be controlled at all, e.g. a recession, a company going out of business, the health of an industry, etc. are less important. The other piece that’s so important is understanding the funnel of value: are the teams using your software driving success within their organizations? And is that reflected in the culture? Ensuring that there is executive involvement on the customer side is so important, as is keeping in constant communication with all levels of the business. Champions can really help you drive that – sometimes if a company is unsatisfied, they may not churn because they know you’re a great company and that you’re going to get there – but something along the way was missed. Relationships matter, as does having these discussions early.
Read more about this topic here: https://www.immersa.ai/2023/01/how-to-predict-nrr-in-2023/
Watch the webinar here: https://share.getcloudapp.com/OAu2Xve8
