My Guest
Craig Zingerline is a serial entrepreneur currently working as the CEO of Growth Minded, a company that helps startups and other technology companies scale their growth and retention strategies.
Prior to Growth Minded, he founded Velocity Growth and scaled to 7-figures of ARR, was Chief Product Officer at Sandboxx, and the Head of Growth at Upside Travel.
He has been a co-founder of 6 companies (4 startups & 2 agencies) and has advised or consulted for close to 200 others. He is an active startup coach who is well versed in all aspects of strategy – including product, marketing, growth, and retention, among others.
Listen in Your Browser
Evaluating Customer Retention in Recurring Revenue Businesses: With Craig Zingerline – In The Trenches
Listen on Other Platforms:
Apple | Spotify | PocketCasts | RadioPublic
Episode Summary with Timestamps
- 00:00 Introduction and Career Journey
- 07:23 Customer Retention Metrics Overview
- 12:01 Cohort-Based Retention Tracking
- 15:05 LTV to CAC: Importance and Limitations
- 22:20 Pricing Strategies and Retention Rates
- 28:01 Predictive Metrics for Future Retention
- 43:01 Understanding Customer Usage and Retention Metrics
- 44:37 The Role of Customer Success in Software Companies
- 49:14 Voluntary vs. Involuntary Churn: A Critical Analysis
- 53:04 Evaluating Revenue Quality
- 59:51 Transactional Revenue Models vs. Traditional Models
- 01:04:22 Assessing Revenue Quality Beyond the Basics
- 01:08:21 Lessons Learned: Embracing Uncertainty in Business and Life
Download a Written Transcript of Our Conversation
Access a text-based version of our discussion to highlight, copy or take notes by clicking on either of the buttons below
Thanks to our Sponsors
This episode is brought to you by Boulay, the industry standard for Quality of Earnings reports, tax, and small business audit services. Over the past 20 years, Boulay has worked directly with hundreds of search funds from capital raise to exit, currently assisting over 150 funds in the search phase, another 125 in the operating phase. They work with Searchers across the entirety of the ETA journey: They perform financial due diligence and create QofE reports that your investors can rely on, they provide a full suite of tax services both for your search fund and for the acquired company, they perform the annual audits required by most debt and equity investors, and also perform outsourced accounting services, acting as a fractional bookkeeper and controller for those companies whose needs might not necessitate full-time in-house resources.
This episode is brought to you by Oberle Risk Strategies, the leading insurance brokerage and insurance diligence provider for the search fund community. The company is led by August Felker (himself a 2-time successful searcher), and has been trusted by search investors, lenders, searchers and CEOs for over a decade now. Their due diligence offering (which is 100% free of charge) will assess the pros and cons of your target company’s insurance program, including any potential coverage gaps, the pro-forma insurance pricing, and the program structure changes needed for closing. At or shortly after closing, they then execute on all of those findings on your behalf. Oberle has serviced over 900 customers across a decade of operation, including countless searchers and CEOs within the ETA community.
Discover more from
Subscribe to get the latest posts sent to your email.
great information. Craig covered all the basics. To add on, it’s more important to look at both recency and frequency together when assessing customer retention. In addition, most analyst and founders are still evaluating retention at very entry level by looking at average retention rate. Craig pointed out a good that you have to run cohort based analysis. As company grows fast, the next group of customers you acquire will likely be very different from the first cohort you acquired. That non-stationary factor will have great impact on retention rate and CLV from cohort to cohort. Last, we are more familiar with trailing metrics. In today’s world with abundant data and easy to record data, we can build accurate forecast models to predicate retention and estimate RLV (residual life-time value).