How a 1950s Neonatal Assessment Tool Can Improve Your Customer Lifetime Value

This guest post by Thad Tremaine originally appeared on LinkedIn Pulse.

- - - -

In 1952, Virginia Apgar invented a method for quickly assessing the health of a newborn.

Virginia’s “APGAR” score, still in use at hospitals around the world, allows physicians to evaluate a newborn baby on five simple criteria, each on a scale of 0 - 2. The individual scores are summed, creating an overall score that ranges from 0 to 10.

A low score necessitates immediate intervention to further assess what’s wrong, improving the odds of the child’s long-term welfare.

If a newborn has a low APGAR score, he may simply need a little extra oxygen for a few minutes until he gets used to breathing on his own. Another newborn could have more life-threatening complications, and need to be whisked to the ICU.

To draw parallels with SaaS, a new user without the technical aptitude to fully leverage your platform may need some remedial Excel training, or a customer without the structured data required to integrate with your system may need to be introduced to a third-party partner.

In SaaS Customer Success, as in the business of delivering babies, a system for determining early on when someone needs intensive care can make the difference between success and failure. The ability to assess the customer’s likelihood of success, based on both quantitative and qualitative measures, will allow your Customer Success team to intervene and differentiate their approach to ensure the highest rate of success possible.

To make sure those customers that have the highest likelihood of churning end up being long-term happy customers, consider building your own onboarding APGAR score.

Here’s how to put APGAR to work for you.

Define the Key Attributes of a Successful Customer

With input from your onboarding or implementation team, hypothesize which customer attributes are most indicative of long-term success. Most SaaS platforms require some type of customer data, which makes having high quality data an important characteristic.

Consider other factors such as the percent of her week the primary user says she will dedicate to the implementation project (indicative of engagement level), as well as the user’s technical aptitude.

Depending on the product / service you provide, there could be dozens of attributes that can predict whether or not a customer will need some extra attention early, so start by tracking as many attributes as you can.

Build Your Discovery Process

Once you have at least five attributes defined, develop the questions your implementation managers or Customer Success Managers (whoever is the first point of contact) will ask to assess each attribute on a 0-2 scale.

To determine a customer’s technical aptitude, you might ask how the customer uses Excel in his day-to-day, or ask questions pertaining to his familiarity with APIs.

Once you have your questions, determine which types of answers warrant a 0, 1, or 2. For the technical aptitude questions, if your primary contact asks “What’s Excel?” he’s probably a 0. If he says, “I can create a macro with one hand tied behind my back!” he’s likely a 2.

A user who says she can only dedicate about an hour per week to learning your system might be scored a 0 for your engagement criteria, while a user who claims she will spend 50% of her week dedicated to the implementation will be a 2.

As your implementation team is running through the questions during the initial onboarding call, record the scores in your CRM or Customer Success platform, and then sum the individual components to get your aggregate score.

Prove Your Hypotheses With Statistical Relevance

Continue to capture data for every new customer for several months until you have enough to determine the validity of your hypotheses (i.e. customers with a lower APGAR score typically end up as detractors, or end up churning, and customers with a high APGAR score typically end up being promoters and have a higher LTV).

Once you have validated that your overall score is indicative of future results, you can hone in on the specific attributes that are MOST predictive of a customer’s future success (or failure).

You may find that 90% of customers you rated a 0 for data ended up being detractors and/or churned. You may find that those customers who were rated a 2 for their time commitment to the implementation of your platform have an 80%+ likelihood of being promoters.

Leverage Your Knowledge for Customer Success

Once you have proven your predictive model for Customer Success, you can now use your APGAR score in powerful ways, including:

  1. Determining your customer health score early on, and identifying which customers may require a differentiated onboarding path based on their risk profile. Now that you can quantify risk for your new customers, building a differentiated implementation playbook for those customers may just be the ticket to ensure their long-term success. These customers should immediately hit a manager’s radar so she can keep an eye on their progress through the key implementation milestones and time-to-value indicators. Get creative with your approach to these customers, and consider actions such as an early customer visit, keeping the sales rep involved throughout the implementation process, or even a phone call from your CEO to let the new customer know they are highly valued.
  2. Determining which prospects won’t be a good fit for your solution based on the profile that’s needed for success. If you really want to raise your CLV, it’s critical to put your APGAR learnings to work before you onboard new customers by moving your implementation team’s line of questioning upstream to the prospecting phase. If you find a threshold below which an acceptable LTV / NPS / retention rate is statistically likely to materialize, you have a strong argument for improving the way your sales team looks for your next customers.

Your ability to understand the customer attributes that have the highest correlation to your customers’ long term success can pay huge dividends.

While it will take time to develop your process and capture enough data to guide your strategy, it’s an effort worth undertaking. The time investment will pay you back in happier customers, happier implementation managers, a higher CLV, and more promoters.

And to that I say, "Thanks Virginia!"

About the Author

Thad Tremaine is a Customer Success and services professional with a proven record of transforming service operations into a competitive advantage and expanding top-tier client relationships.

View his Linkedin profile or follow Thad on Twitter

Data-driven Customer Success teams run on Natero. LEARN MORE
customer-success-logo-reel