Craig Soules, Founder & CEO of NateroOpens a new window states how the advent of SaaS and its subscription-based business model has shifted power to the customer, requiring vendors and Customer Success teams, tasked with retaining, engaging and growing accounts, to work smarter
The advent of SaaS and its subscription-based business model has shifted power to the customer, who no longer has to make a significant up-front investment in software and can more easily change vendors if they perceive advantages. This requires vendors to work smarter to respond to this challenge, and that includes the Customer Success team who is tasked with retaining, engaging and growing their accounts.
A key difference between Customer Success and Customer Support is that the former strives to be proactive in reaching out to customers, whereas the latter is a reactive response to customers who are having issues. But the traditional Customer Success approach of periodically contacting each customer is time-consuming, inefficient and doesn’t scale as the number of accounts they manage grows. Moreover, the tendency to focus on the loudest or largest accounts leaves other customers to fend for themselves just when a helping hand might make the difference between success and distress.
New Customer Success Management (CSM) solutions can monitor accounts for specific behaviors based on rules set by the SaaS vendor, and notify the Customer Success team when they detect potential issues like too many support tickets or a drop-off in usage. This is helpful but is still reactive and dependent on the SaaS vendor’s intuition in picking the right “signals†to look for. Not every customer who ultimately churns exhibits the behavior being tracked by a particular set of rules. Some customers just conclude they aren’t getting the value they sought from a solution and the next thing you know: there’s a cancellation request in someone’s inbox.
What if you could be alerted more accurately and earlier to accounts that are likely to churn? Or easily identify those customers who are most likely to convert from trial or expand their use of your product? You’d be in a better position to turn around struggling customers, as well as help those who are ready to purchase more from your company. If the Customer Success team had the ability to target the right customer at just the right moment, their productivity would improve, as would key metrics like retention, expansion revenue, customer satisfaction and Customer Lifetime Value (CLV).
Fortunately, advanced analytics that leverage the vast data SaaS vendors have about their customers make this wish a reality. In this article, we’ll explain how predictive analytics can help Customer Success teams be more effective and productive, how machine learning technology works, what to look for in a CSM solution with predictive analytics and what to expect when implementing one.
Big Data and Predictive Analytics
SaaS businesses have no lack of data about their customers – it’s in their CRM system, their billing system, their support ticketing system, and the usage data they can collect from their product. The challenge is making use of this data to identify customers who are not realizing the value they expect from a solution, as well as those that are ripe to convert from trial or buy more. Exacerbating that challenge is the volume and velocity at which this data is created, and the fact that it’s dispersed across multiple, siloed systems.
Fortunately, technology advances in big data have made it possible to merge and analyze customer data in a single platform. CSM solutions can capture detailed product usage data, and combine it with other customer data residing in a variety of systems or databases. They use this data to monitor customer behavior and identify accounts that need the attention of Customer Success managers.
Most CSM solutions require the SaaS vendor to define “rules†that describe specific customer behaviors that the vendor determines warrant outreach from the Customer Success team. For example, a SaaS vendor may create a rule in their CSM system that monitors login frequency and sends a “churn alert†to the Customer Success manager if a customer hasn’t logged in during the past two weeks. Or maybe it sends an “up-sell alert†when all of the seats that were sold to a customer are being utilized at a high level. By actively monitoring the behavior of all customers, CSM solutions can help the Customer Success team focus on those customers where they can have a meaningful impact.
While defining rules that stipulate which customers should be contacted based on their behavior is a huge improvement over indiscriminate or routine outreach, a technology called predictive analytics promises to dramatically improve the ability of CSM solutions to identify customers who are likely to churn, expand or convert from trial.
Predictive analytics is different from traditional analytics, which is also referred to as descriptive analytics. Descriptive analytics is the process of distilling large amounts of data into summary information, in order to answer questions such as “What were our average revenues by product line last quarter?†Descriptive analytics is focused on understanding “what happened†in the past, whereas predictive analytics attempts to answer the question “what might happen†in the future. For example, “what will be the impact on sales next year if we double our advertising budget?â€
Some industries are mature in their use and implementation of predictive analytics, such as detecting fraud or managing risk at financial institutions.
The use of predictive analytics in sales and marketing platforms has been growing rapidly. Organizations are adopting a variety of solutions that incorporate predictive analytics to increase sales productivity and opportunity close rates by predicting which leads are most likely to buy. Other solutions help optimize deal size by predicting how much a particular customer would be willing to pay.
What is Machine Learning?
Predictive analytics describes a broad set of statistical and data mining techniques that attempt to predict the future by analyzing data from the past.
One of the most common techniques used in predictive analytics is linear regression, which works by trying to understand the relationship between variables. Consider the advertising example, where you want to determine the impact of increased advertising spend on sales. You start with an assumption (i.e. more advertising creates more sales) and analyze the effect that changing one variable has on the other. You then use this analysis to predict the sales level that would be achieved for a given amount of advertising spent.
Linear regression starts with an assumption and tests the relationship between cause and effect to develop a model that can predict the change in one variable based on the change in other variables. But what if you don’t know what causes something to happen? For example, what are the causes or factors behind customers churning or buying more?
That’s where machine learning comes in.
Machine learning is a form of predictive analytics, but it turns the process upside down. Rather than start with an assumption of cause and effect, machine learning starts with an outcome and lets a computer uncover the causes that are driving this particular outcome. It evaluates hundreds or thousands of possible factors, including complex interactions between those factors, to determine the best signals for a given outcome.
In the case of Customer Success, machine learning models can be created to predict which customers are likely to churn, which are likely to upgrade or buy more and which are likely to convert from a free product or trial to a paid account. The models can be built using the customer data collected by a CSM solution, including product usage, CRM, billing, support, etc. The machine learning models can even show a probability that a particular outcome (e.g. churn) will occur for each account.
How Predictive Analytics Helps Customer Success Teams
Machine learning unlocks the value hidden in the data captured by a CSM solution and identifies the factors that drive various outcomes. Using machine learning technology to predict customer behavior has tremendous benefit for the Customer Success team vs. depending solely on rule-based alerts.
Rule-based alerts rely on the SaaS vendor’s intuition as to what customer behaviors signal the likelihood of churn, expansion, conversion, etc. Many vendors struggle to define these behaviors, but even if they can come up with a handful of rules, that’s only a handful of factors that are being evaluated to predict an outcome. Machine learning evaluates hundreds to thousands of factors and identifies which ones are the best predictors for each type of outcome. Whereas rules are based on intuition, machine learning is real data science.
CSM solutions that incorporate machine learning technology are continuously capturing data, and they update their predictive models based on this new data. That means that the models get more accurate over time, with no effort on the part of the SaaS vendor. Rules never improve in accuracy unless you change them, and changing a rule is no guarantee of increased accuracy. Remember, it’s typically based on intuition so a new rule could be less accurate than the old one.
The dynamic nature of a machine learning model also means that it can automatically adapt to changes in overall customer behavior. Customers may act differently over time as product offerings change, a client base evolves or the macroeconomic landscape shifts. Rules are static and assume that the future will be like the past. As time goes by, rules may become even less accurate than when first applied.
Having a more accurate predictor of customer behavior can dramatically improve the productivity and effectiveness of the Customer Success team. More accurate predictions mean there are less “false alarmsâ€, which wastes time that could be better spent on customers who actually need attention. And not receiving alerts for situations that warrant them can result in lost business (e.g. no up-sell alert) or lost customers (e.g. no churn alert). The net result of employing predictive analytics is that Customer Success managers spend more time with customers who need it, and where they can make an impact on growing the business.
Conclusion
Predictive analytics is a powerful technology whose use in sales and marketing platforms is growing rapidly. Advanced CSM solutions incorporating machine learning let you operationalize the technology by building it into business processes where it can provide significant benefits.
By enabling the Customer Success team to proactively focus on accounts that are likely to churn, expand or convert, CSM solutions with predictive analytics can significantly improve the effectiveness and productivity of the team. Customer Success managers can prioritize their efforts on the accounts that most need their attention, and in so doing they are equipped to maximize customer retention, engagement and lifetime revenue.