Authored By Mark Roberge and Summarized by Mohammad Kazemi
Thought challenge
Look at the diagram below. The upper right is optimal. It represents exceptional revenue growth and exceptional customer retention. However, if you had to choose between path A or B, which would you prefer?
I posed this question at the 2019 Annual Saastr Conference in Silicon Valley. The audience almost unanimously chose Option A.
I called B.S. I agree with the choice. I disagree that, as entrepreneurs, we follow it.
Why? As entrepreneurs, the first metric we mention when describing our business is revenue. As investors, the first question we ask is about revenue growth. We don’t prioritize customer retention. We are obsessed, almost out of the gate, with revenue growth. And it’s killing our businesses.
Startup failure is unnecessarily high due to a premature obsession with top-line revenue growth.
Scaling Mindset
I’m not saying grow slower. I’m saying grow healthier. In all fairness, we have improved as entrepreneurs over the last decade. Thanks to the outstanding thought leadership by Eric Reiss and Steve Blank on lean startup methods and agile development, we no longer lock ourselves in a room for a year to build a product and then cross our fingers, hoping it will sell. Instead, we navigate from idea to solution by co-creating with customers, developing MVPs, and navigating test/learn/iterate cycles as we pursue product-market-fit.
However, it is at that moment that we lose our way. Once we hit that supposed product-market-fit, We are scaling haphazardly rather than scientifically. Great businesses with noble missions fail because of inadequate answers to the following two critical questions:
1. When to scale?
2. How fast?
We as entrepreneurs have much to gain from a more scientific, data-driven approach to these two questions. I sincerely believe a more rigorous process will unlock a higher success rate of Series A-funded startups.
After peering inside the go-to-market machinery of hundreds of startups, I found the following five issues as the most common diagnoses for missed revenue targets in Series A funded businesses:
1. Premature focus on top-line revenue generation instead of consistent customer value creation
2. Inadequate, non-data-driven definition of product-market-fit
3. Misunderstanding of go-to-market capabilities needed before hiring salespeople
4. Front-loading sales hires at the beginning of the year rather than pacing throughout the year
5. Confusing temporarycompetitive advantage with sustainablecompetitive advantage
Reflecting on these common issues, I have been using the following framework to guide entrepreneurs and their new ventures through a more calculated approach to scale.
Scaling Framework
The framework has three sequential stages:
1. Product-Market Fit, defined as generating customer success consistently
2. Go-to-Market Fit, defined as generating customer success consistently and scalably
3. Growth and Moat, which provides a scientific approach to the pace and defensibility of scale
The framework includes quantifiable milestones defining when staging achievement occurs.
And illustrates how vital go-to-market decisions, such as price, hiring profiles, demand generation channels, and sales process, evolve as progress is achieved.
Product-Market-Fit
Let’s be on the same page:
- We use “product-market-fit” to make critical decisions, such as when to scale. However, we lack a scientific, data-driven definition of the term.
- Customer retention is the best statistical representation of product-market fit. However, customer retention is a lagging indicator.
- Assuming long term customer retention is the best statistical representation of product-market-fit then:
- Organizing our customers into acquisition cohorts and measuring their progress toward the customer retention early indicator enables early identification of product-market-fit.
We use “product-market-fit” to make critical decisions such as when to scale, but we lack a scientific, data-driven definition of the term.
“What is product-market-fit?”
how can we take a more data-driven, scientific approach to product-market-fit?
Companies use long-term customer retention as an indicator of product-market fit. I agree customer retention is the best statistical representation of product-market-fit. However, customer retention is a Laggingindicator. It often takes quarters or even a year for companies to understand the actual retention rate of customers that we acquire today. We do not have years or even quarters. Time and money, especially in an early-stage setting, are not on our side. We need to test, learn, and iterate in much faster cycles.
For this reason, “best-in-class” startups use an leading indicator of customer retention to quantify product-market fit. Some entrepreneurs in Silicon Valley refer to the leading indicator as the “ah-hah’’ moment.
If the leading indicator is objective rather than subjective and correlates with long-term retention, we have defined a data-driven, time-sensitive approach to understanding product-market fit.
Leading indicator(s) of customer retention
Unfortunately, there is no single leading indicator of customer retention definition universally applicable to all company contexts. However, the following definition framework is universally optimal.
[Customer Success Leading Indicator] is “True” if P% of customers achieve E event(s) within T time
Documented examples of leading indicators from modern-day unicorns, organized in this format, are below.
- Slack: 70% of customers send 2,000+ team messages in the first 30 days
- Dropbox: 85% of customers upload one file in 1 folder on one device within 1 hour
- HubSpot: 80% of customers use five features out of the 25 features in the platform within 60 days
We have deduced the question of product-market fit to the values of P, E, and T. Below are best practices for defining these variables for our business.
P is the percentage of customers that achieve the leading indicator. If P is surpassed, we have product-market-fit. But what is an acceptable P? Evaluating the extremes, 5% seems way too low. If we acquire customers and only 5% achieve our leading indicator of retention, that will be a terrible foundation for a business. At the same time, 95% seems way too high. The primary reason for this analysis is determining when to scale. Waiting until 95% of customers achieve the leading indicator seems too cautious, exposing us to the risk of waiting too long and missing the market opportunity or losing unnecessary ground to a competitor. A final consideration is the market’s perception of annual solid customer retention, which we previously mentioned is 90%. I often see P set at between 60% and 80% with these considerations.
I recommend the lower end of the spectrum if the company I recommend the following considerations when defining our leading indicator:
1. Objective: The event should be factual and binary. It either happened, or it didn’t. There is no subjectivity or room for interpretation. “Processed the first transaction” is objective. “Customer sees value” is not.
2. Instrument-able:We need to automate the measurement of the event. It will be necessary to instrument the leading indicator measurement before scale. “Logging in at least once per day” is instrumental. “Mentions of the product in executive meetings” are not instrumental.
3. Aligned with customer success and value creation:Intuitively, creating customer value and success will lead to customer retention. Not doing so will lead to churn. Therefore, leading indicator events that represent customer value and success are recommended. “10% reduction in processing time” represents customer value. “Signed the contract” does not.
4. Correlated to the company’s unique value proposition:The go-to-market team will be focused on driving leading indicator events in the new customer base. The Marketing will focus on driving awareness with segments where leading indicator achievement is easiest. Sales will convince prospects that the leading indicator events are most important. The customer success team will focus on onboarding efforts on leading indicator event achievement. Suppose those events are aligned with our unique value proposition. In that case, we will amass a customer base that is very sticky to our strategic positioning and very difficult for our competitors to disrupt.
5. Event combinations are OK, but keep it simple: As the company expands its product, multiple combinations of events may represent leading customer retention indicators. These combinations can be “AND” or “OR” definitions. For example, remember Slack’s leading indicator of “2,000 team messages”. Two thousand team messages exchanged between 100 people are likely far more adopted and valuable to the customer than 2,000 team messages between 2 people. Therefore, Slack may evolve its leading indicator to be “2,000 team messages AND 20+ users involved”.
T is the time by which the leading indicator event is achieved. T should be as short as possible to maximize the pace of learning. However, it needs to be realistic. T often depends on how complicated it is to adapt our product and how long it takes to see the value.
Customer acquisition cohorts
Once the customer retention leading indicator is defined, we should assemble a cohort chart illustrating the percentage of newly acquired customers that achieve the leading indicator over time. This approach maximizes the speed by which we can evaluate progress toward product-market-fit. Below is an example of a company measuring its leading indicator by monthly customer cohorts.
We can bring this chart to life using a fictitious company, TeleMed. TeleMed sells software to doctors enabling them to meet with patients over video rather than in-person. Well-designed customer retention leading indicator could be:
[Customer Success Leading Indicator] is “True” if 70% of customers conduct a video conference with a patient within two months.
Therefore, the chart tells us that the company acquired 24 new customers in January. After one month, 3% of those 24 customers had conducted a video conference with a patient. After two months, 27% of those 24 customers showed a video conference with a patient. After three months, 33% of those 24 customers conducted a video conference with a patient. According to TeleMed’s definition of the customer success leading indicator, they had not achieved product-market-fit in the early part of this year. However, the company executed many adjustments, likely changes to the product, target customer, sales process, and onboarding approach, and the situation has greatly improved. In October, they acquired 55 new customers. After one month, only 6% of those 55 customers conducted a video conference with a patient. However, after two months, 70% showed a video conference with a patient! The execution paid off. This company has achieved product-market-fit. We do not need to wait for long-term retention to surface. This company is ready to proceed to the go-to-market stage.
Customer acquisition cohort guidelines
1. In order to align all levels of the organization around product-market-fit pursuit, we recommend this chart be the first slide in the board deck, ahead of the P&L and top-line revenue performance.
2. The cohorts can be organized by daily, weekly, monthly, or quarterly time periods. Selecting the appropriate time metric is similar to defining the “T” factor in the customer retention leading indicator discussed earlier. A company like Dropbox should probably use daily customer acquisition cohorts and evaluate the cohorts’ progress toward the leading indicator on a daily basis. Workday should probably use quarterly customer acquisition cohorts and evaluate the cohorts’ progress toward the leading indicator on a quarterly basis.
3. The “Customers Acquired” column is not cumulative numbers. These figures represent new customers acquired in that month.
4. It is possible that product usage within a cohort declines over time. Customers could dedicate their energy early on to using the product, find that it is not useful, and stop using it. Companies need to instrument the cohort analysis to capture this behavior shift if it occurs.
5. The time (T) of achieving the leading indicator is less important than continued improvement within the cohort over time. In the example above, we could argue transitioning to the go-to-market-fit stage in November even though the pure definition of product-market-fit had not been achieved yet. None of the prior cohorts have achieved 70% within two months. However, the prior cohorts showed continued improvement month-over-month with the expectation that they would reach 70% and continue to rise. Furthermore, looking down the columns, recent cohorts at their two-month and three-month anniversary were substantially healthier than past cohorts at the same tenure.
The above cohort analysis does not work for early-stage ventures selling 6-digit deals or higher to large enterprises. These ventures can surpass $1 million in revenue with less than ten customers and acquire only 1 or 2 new customers every quarter. Therefore, an alternative approach to evaluating the pursuit of product-market-fit is necessary.
In these situations, companies assemble a customer health card with a half dozen or so criteria.
1. Status on the technical setup and integration of the product
2. Number of users that are activated and active
3. Breadth of product usage
4. Quantifiable value realization
5. Executive sign off on reference-ability
The board literally reviews the “green,” “yellow,” “red” summary status for each company, as well as the statuses of each of these criteria, especially for new customers and laggard deployments.
It will continue in other parts in the future.
