4 ways to validate customer demand for your product or service
“They love it, they really love it! We’re going to be rich!”
”40% of my prospects told me they’d be GUTTED if I was to take the product away!”
”Yeah, but are they the ones paying for it?”
……..
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As a Get Stuff Done person on a team, it’s always been my role to work out how to translate the theories into practical steps, specifically from a marketing and growth perspective.
In one particular business, we had a nice number of customers signing up to the platform but our focus seemed to change constantly. And, after a while, I questioned what “good” looked like.
We’d get opinions, we’d get ‘hot takes’, we’d get well-intentioned ideas… but for a while, nothing seemed to work. The metrics or things we were told to focus on never translated into growth.
Then, I came across the work of Brian Balfour and product market fit (PMF).
Whilst there’s a lot of info out there on the concept of product-market fit Balfour’s work on using meaningful signals to validate customer demand helped us to understand if we were going in the right direction. More importantly, it helped us to understand if we were focusing on the right things.
In the article, he suggests when looking to hit a good traction rate and reach PMF, your business needs to pass a series of signals made up of quantitative and qualitative indicators.
It’s accepted that these four signals should give you direction and allow you to quickly assess if something needs fixing.
I recently spoke about my experience with them whilst presenting at a recent startup accelerator workshop session. It was obvious that talking about their application rather than the theory behind them resonated with organisations looking to launch their new product.
So, here’s a breakdown of how I have applied the four signals of customer demand.
Was it easy? Hell no!
Did I feel like pulling my hair out? Of course.
Did I sigh every time an expert told us how THEY’D do it? You betcha.
Four product-market signals to validate market demand:
The leading indicator survey
Leading engagement data
The flat retention curve
The trifecta
Using the leading indicator survey
This is the one I’m certain you’ll be aware of.
“How would you feel if you could no longer use [product]?”
It’s the question you ask your customers and, if 40% or more say they’d be “very disappointed” if they could no longer use your offering… in theory, you have reached product-market fit.
I’ll state the obvious: you can’t build a business around this.
But, it’s a good idea to start surveying your customers if you’re not already. Don’t worry, I’ve written at length about the importance of talking to customers and applying what you’ve learned.
Using it to validate market demand
Think about who you are asking.
For months we’d hear from people that they’d be gutted if we were to take away our product from them. We’d send the survey out, collate the results, and give ourselves a pat on the back.
Yet, revenue was stalling. The reason? We were asking the fans of our service who weren’t necessarily the person paying to use the product. I know it’s back to basics stuff, but if you haven’t done so already, map out the key people involved in using and paying for your product. Think user, manager, and cheque payer. Then think who should get the survey. Clue: “user”.If you operate a business that has B2B and B2B2C sides - sometimes known as a two-sided market - ask them both. The results will help you to shape what level of supply and demand is needed to grow your business.
This is particularly useful if you operate an HR, learning or recruitment offering where you have two stakeholder groups and you don’t know where to start.
2. Using leading engagement data
This was a really good step for us to implement. That’s because it gave us focus on looking at engagement data around actions that reflected if our users were getting value from your product.
New sign-ups and logging back in on day 3 is not a sign of meaningful engagement.
Back when I was a newbie to all of this subscription and service marketing, I was content with getting new sign-ups and people logging back in on day three. I thought this signalled engaged users.
But that bubble had to be burst. This wasn’t reflective of true engagement.
I did a little testing. Users were logging in, looked at a feature, realised that the experience was gross - they had no idea what they were meant to do - and logged out very quickly.
A few days later, probably prompted by a boss, they’d log back in. “Maybe it was a dream” they asked themselves - they logged in and realised… “yep, I still have no idea what I am doing here”.
Try this when using leading engagement data to validate demand for your offering:
Break down what your ideal users need to do to achieve value
Consider whether this [action] is a task or a completion of a workflow (e.g. does it reflect someone completing a core action to extract value?)
Record these events and look at your repeat users. Are your ideal customers repeating these actions?
On the note of ‘achieving value’ - I appreciate this is quite fluffy, even for me. So, for example here are some examples of meaningful actions I have recommended and used:
Fill in the survey, attend offline meeting, add in notes, and then be given a new survey to complete.
Complete 3 online courses, create a playlist and share with 3 colleagues
Upload data, data cleanse, send report, download insights report, and fix recommendations.
3. Using the flat retention curve
We all know that when you’re in the phase of working towards product market fit, it’s retention that needs to be our focus. Generally speaking, at this point we are looking for a flattening retention curve that indicates users are finding your offering of value and returning.
But come on, we know that the shiny pull of leads gets in the way. Or, we think that recording churn alone is what we need to do without really digging into the numbers.
When looking at the retention piece for your customer demand, it’s worth extrapolating the number presented. Here’s two ways I’ve dug into the retention piece with a view to seeing who loves the product and who’s more of a meh user.
Considerations when looking at the flat retention curve
Break your retention rate down by cohort.
Talk to someone friendly in the product team and ask them to delve into the data and see how different user groups are engaging with your product. I have done this by looking at our ICP segments, and by date signed up and activated. Trust me, it’s likely there is a subgroup of users that needs a little more love and attention.Consider the aha moments for your product journey.
After you’ve got the data for your segments (however you want to splice it up) look at their experience map. Are you directing them to the part of what you offer as soon as you can? Or are you making them jump through hoops?It’s okay to have a different onboarding sequence for different segment journeys as you grow. (Here are some ideas on how to approach your product onboarding).
4. The trifecta - the power of three
Brian Balfour, in his article, finishes with the concept of the trifecta. He states that organisations with product-market fit will have a trifecta: “three high line indicators that show business is going well”.
His example is based on Snapchat. For him, he shares their trifecta: stats around non-trivial top line growth (200k downloads), retention (50% of downloads are active daily), and meaningful usage. This last one looks at users and how they weren’t just coming back daily, rather, they were taking meaningful action. For these users, this meant sending 10 pictures a day.
But that’s Snapchat, right? How about real-life businesses that lack the resources they do?
If you’re a product-led business, consider what your power of three may look like by digging into metrics such as: distribution, adoption, and expansion. The idea is these high-level stats could indicate meaningful usage - so they’d be a good place to start.
A quick note on this. This stage only really made sense with a larger business I worked with. We had a turnover of £3million a year and the data - more importantly - to back it up.
If you’re smaller or at an earlier stage of business growth, I’d suggest looking at Matt Lerner’s work around north star metrics. Specifically, the drivers and rate-limiting steps that help support this growth.
