Customer Success Management solutions have the capacity to accelerate the productivity and efficiency of CSM teams.
In this excerpt, we focus on a key value of a CSM solution: identifying risk and opportunity across the customer base. We explain how CSM solutions predict which customers are at risk of churn, as well as which are likely to convert or upgrade.
The primary goal of a CSM solution is to improve the productivity and effectiveness of the CSM team by alerting them to customers who need attention (as well as informing why, and suggesting what to do about it).
Most solutions generate alerts for accounts at risk of churning, but some can also predict which customers are likely to upgrade (up/cross-sell) or likely to convert from trial to a paid account.
There are two methods used to trigger proactive alerts 1) rule-based systems, and 2) predictive analytics.
The majority of CSM solutions use a rule-based system to generate customer alerts. This is a simple way to get started that relies on the SaaS vendor’s intuition as to what behavior to look for. For example, a rule may evaluate if a customer hasn’t logged in during the past 30 days, which then triggers a churn alert for the corresponding Customer Success Manager.
CSM solutions that rely solely on rule-based alerting have some significant limitations.
Many CSMs don’t know what behavior to look for to generate an alert, so creating rules is, at best, a guess. Others might create a handful of alerts, but a small set of rules means you’re looking at a small set of signals — not a highly accurate way to predict customer behavior.
Rules are also static, which means they don’t improve in accuracy unless you change them — which itself isn’t a guarantee they’ll become more accurate. They also don’t adjust to changes in customer behavior, such as when a product evolves and offers new functionality.
To address the shortcomings of rule-based systems, some CSM solution vendors suggest using external analytics programs to look at historical data and determine what signals to look for, so the rules can be fine-tuned. This requires either data science expertise on staff or paying for professional services from the CSM solution provider.
Advanced Customer Success platforms are incorporating predictive analytics, including machine learning, to determine the most relevant indicators of customer churn, expansion, or even conversion.
Compared to rule-based systems, advanced CSM solutions that incorporate predictive analytics are true data-driven platforms, and offer greater accuracy vs. those that solely rely on intuition or guesses.
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