+ Introduction

What is Product Market Fit?
Why is it important?
How do I separate product and market?
The PMF Journey
Assessment Exercise

+ Problem Insight

Understand your customers
In person interaction examples
Customer research tips
Personas
Empathy map
Customer journey map
Other resources

+ Value Proposition

Customer profile - pains & gains
Value map - products
Mistakes to avoid
Critical assumptions exercise
Formulate hypotheses
Exercise Sheet - Value prop canvas

+ Problem solution fit

What is an MVP?
Determine what you want to test
Marketing MVP tests
WorldCover Case Study
Product MVP tests
Other resources

+ Launch & Measure

Designing lean experiments
A/B Testing
Lean experiment examples
What metrics should I use?

+ Iterate, pivot, or persevere?

Build -measure-learn
Keep in mind
Destacame Case Study
Where to focus in pivot?
Escala Case Study
Nomanini Case Study

+ Measuring PMF

PMF path
NPS
Must-have score
Lead indicator engagement data
Engagement
Retention
How do I know when I’m at PMF?

Common Challenges

 

So how do we know when we’ve reached product-market fit and can shift to the growth phase? Product-market fit is not reached at a definitive point. Brian Balfour suggests thinking of it as a “series of tests and checkpoints that increase in difficulty, but also decisiveness.” It’s important to know where we are in this path before switching to a focus on growth.

Measuring Product-Market Fit

Leading indicator surveys

  • Net Promoter Score

  • Sean Ellis’ “Must-Have” Score

Leading engagement data

  • Acquisition, Activation, Engagement

Retention

  • Measures the % of active users over time by cohort

Trifecta

  • All three at once

product market fit path.jpg

Net Promoter Score

Net Promoter Score is a method to measure customer happiness and has been used by many companies as an indicator of growth.  NPS is calculated based on responses to a single question:

How likely is it that you would recommend our company/product/service to a friend or colleague?

The scoring for this answer is most often based on a 0 to 10 scale.

  • Scores of 9 to 10 are called Promoters, and are considered likely to exhibit value-creating behaviors

  • 0 to 6 are labeled Detractors, and they are believed to be less likely to exhibit the value-creating behaviors.

  • 7 and 8 are labeled Passives, and their behavior falls in the middle of Promoters and Detractors.

NPS is calculated by subtracting the percentage of customers who are Detractors from the percentage of customers who are Promoters.

Resources

Must-Have Score

The Product-Market Fit Survey

Sean Ellis’ of Survey.io, developed the Product-Market Fit Survey, also known as the “Must-Have” score, in collaboration with KISSmetrics.  It was designed to give you an objective metric that removes emotion from the scaling decision while also giving you other important qualitative information. The key question on the survey is:

How would you feel if you could no longer use [product]?

  1. Very disappointed
  2. Somewhat disappointed
  3. Not disappointed (it isn’t really that useful)
  4. N/A – I no longer use [product]

If you find that over 40% of your users are saying that they would be “very disappointed” without your product, there is a great chance you can build sustainable, scalable customer acquisition growth on this “must have” product.  This 40% benchmark was determined by comparing results across 100s startups. Those that were above 40% are generally able to sustainably scale the businesses; those significantly below 40% always seem to struggle.

How to Measure Product/Market-Fit with Survey.io, by VentureHacks

 

Leading Indicator Engagement Data

  • The leading indicator survey data tells you what people say they would do (i.e. be very disappointed if the product disappeared).
  • Now we need to support that with data about what they are actually doing through engagement data (typically at a small scale) that tells a story that the user is getting meaningful value out of the product.  This data must align with events or actions, not views or downloads, and the core purpose of the product.  
  • Before launch, you rely heavily on qualitative research with prospects.  This answers the “why”. With a live product, we can now start using analytics and A/B testing where we’re measuring what people actually do - the “how many”.
  • Here we are not observing individual interactions but rather looking at statistical results from a large group.

Metrics to consider

  • Retained Engagement - does a high proportion of new customers start using your product and then literally never stop?  Does your retention curve flatten to a solid % of users?

  • % of users driving revenue - Of your total users, what % are converting to provide you with revenue?  If you’re solving a worthwhile problem, people will pay.

  • Organic growth - Are you seeing growth happen organically by happy users telling everyone who will listen about it.  Is the value proposition so strong that people convert with little to no effort, and are your target customers so in need of a solution that they seek you out in droves?
 

Customer Research Tips

  • Are you tailoring to the right market segments?  Do different customer groups/segments behave differently?

  • Can customers find out about your offering? What about the time and effort required to find & use your product?

  • Are you aware of specific language, ethnic, status, age, gender, or religious nuances that may pose barriers to enrollment?

  • Are there hidden costs or stressors to gather the required documentation to sign up?  Are there ways you could incentivize first use? Is your enrollment process too time-consuming?

  • Who are our new customers? Through which channels did they come to purchase our products?

  • What's our cost to acquire new customers?

  • How can we improve the acquisition process?  

  • What are preferred transaction and servicing channels for customers?

  • Do customers read or respond to various communications?

  • Which customers use multiple products/features?

  • How well do you understand the context in which customers use your products and services?

  • What incentives do you have in place to increase use? How may the context be changing?

  • Are there key pain points customers face that you may not be aware of?

  • What incentives you have in place to increase uptake?

  • Who has the most influence on decision-making behaviors of your customers?

  • Do you know why this could be happening


Make your pirate metrics actionable, by Amplitude

The Ultimate Guide to Customer Acquisition (Kissmetrics)

Retention Curve

Retention is the metric most closely related to PMF, and can be measured in a retention rate and tracked with a Retention Curve.  This is a useful way to visualize retention where the Y axis is the % of users returning and X axis is time since first use.  

  • We need to plot the % active over time for each cohort, or group of customers that share one common characteristic, such as signed up in the same month

  • You can expect to see an “initial drop off” as customers who first try it don’t come back  

  • If the curve becomes flat at a certain value, that’s the % of customers that eventually retain from that cohort

  • The stronger the product-market-fit, the lower the initial drop and the higher the % of customers who try the product and use it in the long run

  • What/who is the retained market, and how big is it?  We need to identify characteristics of those that retained vs those that left.

 
retention curve.jpg

How do I compare across cohorts?

First we plot the raw data for cohorts in a matrix with the cohorts as columns and time as rows

 
cohort1.jpg
 

Next we calculate the retention rate for each cell, which is number of active users (for that cohort and timeframe) / the cohort’s initial number of customers

 

Finally we plot the data points on a line graph as in the example above. If PMF is improving, the cohort retention curves should move up over time.

user retention graph.jpg
 

What if I’m experiencing a retention problem?

  • What are the biggest technical barriers to ongoing use? Does the customer understand the product value ?  Where can you respond to customers changing needs through better, more consistent dialogue?
  • Trust and Loyalty - Are you giving them sufficient choice, respect, and control drivers?
  • How actively do customers use our products?
  • What are inactivity or dormancy rates?
  • Are you getting significant referral business?
  • Which customers use a service once – then never use it again?
  • Which customer/ customer groups are terminating? Why are they terminating?

Mastering Retention, by Amplitude

 

How do I know if I’ve reached PMF?

“You can always feel when product/market fit isn’t happening. The customers aren’t quite getting value out of the product, word of mouth isn’t spreading, usage isn’t growing that fast, press reviews are kind of ‘blah’, the sales cycle takes too long, and lots of deals never close. And you can always feel product/market fit when it’s happening. The customers are buying the product just as fast as you can make it — or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account.” - Marc Andreessen

Product-Market Fit, by Marty Cagan (SVPG)

  • For a B2B product, I consider the product to have demonstrated Product Market Fit once you have achieved a threshold number (I advocate at least 6) of live, referenceable customers for a given vertical market.

  • For platform products, the idea is similar but we’re looking for a threshold number of applications to have been successfully built and deployed using your API or platform services.

  • For consumer products, especially those that are free services, we’re looking for an indicator of serious engagement.  I like Sean Ellis’ 40% benchmark but we often need to craft the test for Product Market Fit based on the unique situation

 

Common Product Market Fit Challenges

  • Not separating the problem from solution, or jumping into the solution too quickly before you really understand the customer and their pain points

  • Starting with a technology and focusing on how you’re going to sell it.  Start with the customer experience and work backwards to the technology.

  • Being too responsive to a few MVP customers.  The first people who use the product are likely to be non-representative of the larger market, so you need to know what the problem is you’re trying to solve, who you’re designing a solution for, and what your vision is.  Blindly following customer feedback just because it’s coming in can be risky

  • Fooling yourself into believing you’ve reached PMF by only focusing on the user groups that are responding well to early versions of the product.

  • Declaring victory too quickly, or thinking revenue traction is PMF.  It’s tempting to declare that PMF is done and spend a lot of money trying to scale when the product is not yet ready

Follow through the next chapter


+ Introduction

What is Product Market Fit?
Why is it important?
How do I separate product and market?
The PMF Journey
Assessment Exercise

+ Problem Insight

Understand your customers
In person interaction examples
Customer research tips
Personas
Empathy map
Customer journey map
Other resources

+ Value Proposition

Customer profile - pains & gains
Value map - products
Mistakes to avoid
Critical assumptions exercise
Formulate hypotheses
Exercise Sheet - Value prop canvas

+ Problem solution fit

What is an MVP?
Determine what you want to test
Marketing MVP tests
WorldCover Case Study
Product MVP tests
Other resources

+ Launch & Measure

Designing lean experiments
A/B Testing
Lean experiment examples
What metrics should I use?

+ Iterate, pivot, or persevere?

Build -measure-learn
Keep in mind
Destacame Case Study
Where to focus in pivot?
Escala Case Study
Nomanini Case Study

+ Measuring PMF

PMF path
NPS
Must-have score
Lead indicator engagement data
Engagement
Retention
How do I know when I’m at PMF?

Common Challenges