It might often be tempting to fuel the marketing growth engine and to accelerate sales in order to create a hyper growth company. But doing so without having found product/market fit is not smart. A pre-product/market fit company spends significant cash on acquiring customers that are not yet fully satisfied with the company’s products and services and that may churn fast. High customer acquisition costs meet low customer lifetime value and high cash burn.
In this article, I elaborate on the questions founders often have around timing. When is the right time to fuel the growth engine and to accelerate sales?
Measuring Product/Market Fit
what is the sign we should be looking for?
Marc Andreesen famously stressed: “The only thing that matters is product/market fit”. There are many (slightly) diverging definitions of product/market fit. In my view, Product/market fit has been achieved if your company’s products or services solve a significant customer problem and hence satisfy a market. If your customers additionally have a great user experience (UX) and a strong return on investment (ROI) you have created customer success.
Product/market fit and customer success are not identical. A company that solves a significant problem can scale successfully even if its customers do not have a great user experience (UX). This is especially the case if customers desperately need a solution to their problem and if there are no competitors solving the same problem with a product or service that creates a better user experience.
While you may therefore consider fueling your growth engine if you have found product/market fit I nevertheless strongly recommend you focus always and at any customer touchpoint also on creating customer success. Firstly, because other companies will definitely go after the same attractive market and, secondly, because customer success is the one decisive factor that materially impacts both unit economics and long-term profitability. If customers have a great user experience (UX) and a strong return on investment (RoI) such customers may not only be prone to buy more, which improves customer lifetime value (CLV), but will probably also spread the word and recommend the company’s products and services. These referrals will reduce the company’s customer acquisition costs (CAC).
Note however that while product/market fit is a prerequisite for entering a high growth phase, there are additional other factors that need to be considered before scaling heavily, e.g. product-channel fit, cash efficiency etc. I am going to touch upon these other factors in other articles.
How do we know whether we have found product/market fit?
There are cases in which it is apparent that a company has achieved product/market fit. The company gets tons of high-quality inbound leads. The outbound lead generation activities are correspondingly successful. Conversion rates are high. Customer support is almost redundant. And the customer success team constantly up- and cross-sells. Customers give good product reviews and refer the company’s products and services. Strong unit economics, short payback periods and an ever-increasing cash flow that can be used to further fund marketing and sales activities create a virtuous circle.
On the other hand, it is sometimes obvious that a company has not yet achieved product/market fit. The marketing and sales teams have difficulties generating and converting leads. The sales cycles are long. And if the sales team converts leads, the (early) churn rates are high. Customer support has to deal with many customer complaints and customers - rather than referring the products and services – publish bad reviews.
But product/market fit is not binary. There is no one point in time everybody would agree that now the company has found product/market fit, e.g. at a Net Promoter Score of 70, a CLV/CAC-ratio >3 or if a company generates a certain percentage or absolute number in direct traffic. Product/market fit is rather a spectrum from (i) obviously not yet found, and (ii) weak, all the way to (iii) strong or very strong. As there is often no clear answer to the question whether a company has found product/market fit, it makes sense to analyze any information that can provide answers in this regard. Information may be both quantitative and qualitative.
What quantitative measures can help us analyse whether we have found product/market fit?
When your products and services satisfy the customers’ needs and delight your customers, product/market fit should be reflected in a strong positive development of company-centric measures. Founders should therefore track all key performance indicators (KPIs) that may be relevant in this regard, e.g. conversion rate, customer acquisition costs (CAC), average revenue per account (ARPA), cross- and up-sell, churn rate, customer lifetime value (CLV), CLV/CAC-ratio, direct traffic and organic growth, retention rates and retention curves, customer engagement and early new user churn. A company that is on its way to achieve product/market fit will see significant KPI improvements.
Please see below a chart showing how retention curves may look like for products that have and have not yet found product/market fit.
What qualitative Information Can we gather?
In addition to these company-centric financial KPIs, there are also customer-centric metrics a company may track. The biggest downside with customer-centric and most of the times qualitative information is that it carries a certain probability for generating a false positive result. Therefore, a company should work with more than one customer-centric measure and the information gathered needs to be taken with a grain of salt and read in conjunction with the company-centric and more quantitative information.
Is a High net promoter score a good sign for having found product/market fit?
A customer-centric measure that most founders will be familiar with is the Net Promoter Score (NPS). To measure the Net Promoter Score (NPS), customers are asked a simple question: “How likely are you to recommend us to a friend or colleague?”. Customers can answer on a scale from 1 to 10, with 1 being the least likely to recommend and 10 being the most. The customers are then allocated on the basis of their respective answers into three categories: detractors (0-6), passives (7-8) and promoters (9-10). The Net Promoter Score (NPS) is calculated by subtracting the percentage of detractors from the percentage of promoters. If the Net Promoter Score (NPS) is around 0, the company has definitely a lot to do until it reaches product/market fit. A decent score is between 25 and 50. A great NPS score is 50+ and a stellar score 70+.
As you do not only want to understand if something is not yet right, but also what is still wrong, it makes sense to supplement the Net Promoter Score (NPS) survey with follow-up questions. By asking customers why they would not recommend your products or services, you get more insights as to what needs to be improved.
When should we send our NPS survey?
Choosing when and at which touchpoint to send the. Net Promoter Score (NPS) survey. essentially depends on what specific feedback you want to gather. If you want to gather information on a transactional basis to assess your customer’s opinion on a specific business transaction you need to send the survey shortly after the specific transaction has occurred. For example, if you ask a customer directly after he or she has been onboarded you may get good feedback about the process so far and especially the purchase and onboarding process, but not about whether or not the product really solves your customer’s problem, provides a great user experience (UX) and a strong return on investment (RoI). This transactional survey thus rather aims to help pinpoint what needs to be improved at a specific point in the customer journey. It may be noted that other surveys might reveal even better what you want to understand at a certain customer touchpoint, e.g. the Customer Satisfaction Score (CSAT) or the Customer Health Score (see further below).
For understanding whether or not your company has achieved product/market fit, you should rather send a relational NPS that is designed to assess the strength of the relationship between the company and its customers as well as the overall experience taking into account how well the products and services solve your customers’ problems, the user experience (UX) and the return on investment (RoI). Such a relational Net Promoter Score (NPS) survey is sent best as soon as the customer has gone through the complete customer journey and has experienced the full product and service impact.
Why shouldn't we artificially inflate our NPS?
There are many ways a company can intentionally or unintentionally game the system to engineer a higher Net Promoter Score (NPS) score. It can encourage customers to give a higher score, cherry-pick customers that are asked, strategically time the survey, compensate customers for giving higher scores etc. etc. “Please fill out the survey, but only if you give us a 9 or 10.” is just one example for how you can artificially inflate the results. Even visual cues – like in the graphic below - can have an impact on the Net Promoter Score (NPS).
Artificially inflating the Net Promoter Score (NPS) results in wrong and biased data, jeopardizes your credibility and essentially leads to a false sense of achievement in respect of product/market fit. I hence strongly recommend being honest and measuring Net Promoter Score (NPS) as objectively as possible.
What are the downsides of a net promoter score?
Many companies measure the Net Promoter Score (NPS) because (i) customers are more willing to answer quick surveys rather than long feedback calls or forms, (ii) it is simple to calculate, (iii) it produces a number that can be tracked, and (iv) measuring the Net Promoter Score (NPS) and including follow-up questions allows them to analyze why customers might be dissatisfied and to use the feedback to improve their products and services.
But the Net Promoter Score (NPS) methodology is by no means undisputed. For example, it is being criticized that
- The Net Promoter Score (NPS) is easy to game and can be artificially inflated (see above)
- Responses collected from a large, 11-point scale are extremely noisy
- The bucketing methodology that groups respondents into promoters, passives, and detractors ends up hiding user experience (UX) success: e.g. a company that has moved from all “0” ratings to having all “6” ratings has made great improvements, while the NPS doesn’t change at all
- The NPS question asks a respondent to rate the likelihood of a hypothetical future and not more reliably on past behaviors: “Do you plan to begin a diet in the next 6 weeks?”, for example, is a very different question from “Did you begin a diet in the last 6 weeks?”
- The method of calculation - subtracting the percentage of detractor respondents from the percentage of promoter respondents - also produces a metric that is difficult to interpret and hides important information as reflected in the following response sets that all produce an Net Promoter Score (NPS) of +60:
While I believe that the Net Promoter Score (NPS) methodology has its merits and can give founders an indication as to whether the company has found product/market fit, the criticism is not ungrounded. In my view, the Net Promoter Score (NPS) should therefore be used only as one means to analyze whether or not the company has found product/market fit. In addition to looking at quantitative metrics and the Net Promoter Score (NPS) survey, you should also be looking at other qualitative means to analyze the situation in this regard.
Can't we assess Product/Market fit analysing actual referrals?
The criticism that the Net Promoter Score (NPS) question asks a respondent to rate the likelihood of a hypothetical future can be countered by asking customers about their past behavior in terms of referrals, i.e. whether they have recommended your products or services to a friend or colleague in the last 6 weeks. You may additionally ask your new customers whether they have purchased your products or services based on a referral from a friend or colleague. And you should track over time the development of the respective answers.
What can the customer health score (CHS) tell us about product/market fit?
The Customer Health Score (CHS) is a value that indicates the long-term prospect for a customer to churn or, in contrast, to become a high-value customer prone to up- and cross-sell. Companies gather multiple dimensions of customer data, usually with a strong focus on usage and engagement, in order to establish the Customer Health Score (CHS). Data for customer health is often visualized by using a traffic-light model, in which green stands for a healthy customer, yellow for a customer that has not yet experienced full customer success, and red for a customer that may churn and needs immediate attention. Sometimes, the Customer Health Score (CHS) is also reflected in a number on a scale between 1 and 100.
The main advantage of a Customer Health Score (CHS), especially over the Net Promoter Score (NPS), is that it can be refreshed in real-time across all customers, not just a select few who answer an NPS survey. This may be one of the reasons why calculating the Customer Health Score (CHS) has become default for SaaS and platform businesses that measure customer health in order to improve and speed up communication, prioritization and forecasting in the customer success team.
The Customer Health Score (CHS) can also be used as one additional indicator for product/market fit. For instance, if the majority of your customers is on red or yellow, you have probably not yet found product/market fit. If your customer base develops into a customer base of mainly green and yellow customers, you might be on track.
should we measure the customer Satisfaction score (CSAT)?
Customer Satisfaction Score (CSAT) measures customer satisfaction by asking a simple question, such as "How satisfied were you with your experience?" There's a corresponding survey scale, which is very often 1 – 3, 1 – 5, 1 – 7 or 1 – 10 (see the Hubspot example below).
This type of survey allows a customer to respond to a variety of questions at various touch points along the customer journey, e.g. purchase, onboarding, activation, product, customer service and the overall experience.
A big strength of the Customer Satisfaction Score (CSAT) lies in the facts that it can be applied using a smaller scale than the NPS 11-point scale and enables the company to remedy any dissatisfactory experience at a specific customer touchpoint. At the same time, the Customer Satisfaction Score (CSAT) comes also with some weaknesses. There is not only some ambiguity as to what a good or bad score is but also severe subjectivity in the word “satisfaction”. Further, it is also questionable whether a company should actually strive for only satisfying their customers. If customer success is determined by a great user experience (UX) and a strong return on investment (RoI), a company may actually strive for more than just satisfying its customers.
The Customer Satisfaction Score (CSAT) is therefore commonly used mainly for measuring short-term satisfaction at certain customer touchpoints. Other surveys may correlate better to long-term satisfaction. Especially in the context of analyzing product/market fit a satisfied customer base may therefore only be a weaker – and only an additional - indicator for product/market fit.
What can the emerging product stickiness analysis tell us?
As the Net Promoter Score (NPS) critics argue that a 11-point scale was extremely noisy, you could analyze product stickiness by asking your customers how disappointed they would be if they could no longer use your products or services. Would they be “very disappointed”, “somewhat disappointed”, or “not at all disappointed”? There seems to be a rule of thumb that you have found product/market fit if more than 40% of your respondents would be "very disappointed".
At the same time, analysing product stickiness might not only enable you to assess product/market fit but also to identify what has to be improved in order to get there and how your ideal target customers look like. For instance, if you supplemented your survey by asking for qualitative feedback you could learn what features are missing. And this information together with the information that a customer is a "somewhat disappointed" or a "not all disappointed customer" could help you better define your target customer segment. E.g. if you can identify a pattern in the customers who would be "not at all disappointed", you may disregard this type of customer segment at all, because chances are high you will never satisfy these customers in a way that they would respond "very disappointed". Instead of working on these potentially low-value customers you could better spend your resources on trying to delight the customers that are "somewhat disappointed" and tell you what you need to improve.
Product stickiness can therefore be a very good additional product/market fit indicator and help you improve your product and customer segmentation.
Should we measure the customer effort score (CES)?
The Customer Effort Score (CES) measures how much effort a customer had to put into the interactions with a company. The ultimate goal underlying this scale is to offer customers a more effortless experience. The customers are asked “how much effort did it take to deal with us?”, with participants typically being asked to mark on a scale of 1-5, or “the company made it easy for me to handle my issue”, with the ratings varying from strongly disagree to strongly agree.
Just like the Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT), this survey has its merits and can help reviewing customer satisfaction and especially user experience (UX). But it is rather unqualified for measuring product/market fit. It is possible to have both effortless interactions and products and services that do not solve the customers’ pains.
Why shouldn't we fuel the growth engine before having found product/market fit?
In the period before product/market fit, the unit economics that are determined by the ratio between customer lifetime value (CLV) and customer acquisition costs (CAC) are usually unfavorable. As the conversion rate from lead to customer is usually low, CAC is generally correspondingly high. As customers do not see that the company’s products and services solve their pain points, their willingness to pay is rather low, a fact that leads to a low average revenue per account (ARPA). Customer service costs that go into and reduce Gross Margin are supposedly high. The same applies to the churn rate. Customers churn rather fast. As a result, a company that has not yet achieved product/market fit normally shows high CAC and low CLV.
If the company now fuels the growth engine by spending significant capital on Sales and Marketing activities, it essentially uses expensive venture capital to acquire low-value customers that churn fast. A pre-product/market fit company may also be constrained by long payback periods so that the cash that has been invested to acquire customers is trapped and cannot be reinvested quickly. A fact that may require the company to raise even more venture capital in order to fuel unprofitable growth.
While it might be tempting to fuel the marketing growth engine and to accelerate sales early on, doing so without having found product/market fit is dump and will fend off next financing round investors.
Product/market fit is not binary. Likewise, there is no one specific point in time that can be precisely determined and proves a company has found product/market fit. Hence, there is also no specific point in time where everybody agrees that now the time has come to start fueling the growth engine. But you may gather and analyze both quantitative, company-centric and qualitative, customer-centric information in order to answer the question whether you can start scaling heavily.
As to quantitative information, you can look at relevant key performance indicators (KPIs) like conversion rate, customer acquisition costs (CAC), average revenue per account (ARPA), cross- and up-sell, churn rate, customer lifetime value (CLV), CLV/CAC-ratio, direct traffic and organic growth, retention rates and retention curves, customer engagement and early new user churn.
In terms of qualitative information, you should try to get a holistic view by using several methods. The Net Promoter Score may be supplemented by analyzing actual referrals, product stickiness, the Customer Health Score (CHS) and the Customer Satisfaction Score (CSAT).
Eventually, all metrics and methods can only give you an indication as to whether you have found product/market fit. You will also have to use your intuition in order to determine whether investing heavily in growth makes sense.
If you have not yet found product/market-fit you should do whatever is required to get to product/market-fit. Certainly, you need to be agile and will have to constantly adjust your product while flying.
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Sources and Further Readings
- Marc Andreesen: Product/Market Fit
- Brian Balfour:
- Ha Nguyen: Net Promoter Score: The Best and Most Humbling Way to Improve Yourself and your Business
- Jay Kang: NPS Questions to Gauge Customer Satisfaction (+ Examples)
- Margaret Kelsey: How to make NPS work for you
- Susan Levermann: How to use the right Touchpoints to improve your NPS
- Evan Klein: 10 Examples of How You May Be Artificially Inflating Your Net Promoter Score®
- Brian Weinstein: Moving beyond the Net Promoter Score
- Kyle Poyar: Can’t we do better than NPS?
- Jared M. Spool: Net Promoter Score Considered Harmful (and What UX Professionals Can Do About It)
- Dan Steinman: How to Score Customer Health
- Alex Birkett: What is Customer Satisfaction Score (CSAT)?
- Rahul Vohra: How Superhuman Build an Engine to Find Product/Market Fit
- David Skok: SaaS Metrics 2.0 – Detailed Definitions
- David Skok: SaaS Metrics 2.0 – A Guide to Measuring and Improving what Matters
- Dr. Patrick Flesner, Dr. Richard Meyer-Forsting: How to Calculate Unit Economics for Platform Businesses