How To Identify Your Ideal Customer Profile (Podcast)

Last week I had the pleasure of being invited to speak about B2B SaaS Sales on Livestorm’s podcast. In the interview I discussed how, at MadKudu, we led our research for our Ideal Customer, how we’ve kept on refining it and how it helped shape our business.

Here’s the full interview :


And here’s the transcript (a big thank you to Livestorm)

Hi Francis, first, could you tell us what is MadKudu and how you help other SaaS businesses improve their sales process and grow?

MadKudu is a predictive analytics solution. We help sales team prioritize leads. We focus solely on B2B SaaS companies, we work with companies like Segment, Mattermark and Pipedrive.

Those companies love us because they come to realize that in order to be successful their sales team need to be helpful and in order to be helpful they need context.

We provide that context on who’s talking to them and why they are talking to them. We provide all the customers data that is available on the behavioral side as well as third party data with systems like Clearbit.

We provide the triggers to sales team in order to reach out properly and maximize their efficiency.

From what I understand, you are one step ahead of traditional lead scoring where all sales interactions are based on specific lead scoring activity such as, for example, “has downloaded a PDF”.

If you think about it, lead scoring is more of a methodology to make sure that you have leads prioritized. The traditional way of doing this is: you pick certain events and certain criteria and assign point to them based on your preconception of how it is important to do one or the other.

Where predictive comes into play is figuring out what number points should be allocated to certain events, or to having certain behaviors.

The three founders of MadKudu have backgrounds in engineering and mathematics and we saw the huge opportunity to stop having preconceived ideas of what criteria were needed to consider a lead to be qualified.

We use historical data to find out what truly is important.

The predictive side is one way of doing lead scoring. It is more tailored to every business out there.

Right, but in order to get predictive, you need to have a certain amount of historical data, including “win moments” such as an upgrade, as well as “lose moments” such as churn events.

Not every company has enough data on the conversion side in order to run statistical models. So, either you have this amount of conversion events, that is top of the funnel events, or you can use “proxys”.

Basically, you can pick other events further down the funnel. Those users with less data can look at their activation rate. So, if you are a CRM it could be uploading your contacts. And this become your “win event” and you can base your model on that.

Then as you get more volume you can iterate on that “win event” and pick another criteria.

So, companies with a certain amount of data can use MadKudu but if, younger companies can also use your predictive analytics based on their activation rate, then does it mean that all companies can use MadKudu?

It’s a very relevant question to the topic today. Not every company is a good fit for MadKudu.

We define a very narrow customer profile to make sure we execute well and deliver maximum value to them.

First, if you have a low volume of data, our statistical model is maybe the way to go.

Maybe you should first make assumptions, test them and then refine your process. Up until you get to a certain point where the amount of leads requires a more complex statistical modeling.

That’s why our typical sweet spot customer have 5–30k new leads coming in every month. Which is a pretty high volume where statistical modeling starts shining.

What are the other parameters that you look at for your Ideal Customer Profile (ICP)? Do you have empirical data that helped you shape your ICP based for example on deal velocity?

Defining your ideal customer profile is the most important thing for an early stage startup. If you think about it, if you aggregate all you ideal customer profile you have your target market, that is the market you want to deliver your product for.

You have to define your product based on the market you are going for. And that’s a pretty big change lately.

200 years ago your local butcher knew exactly how you wanted your meat, a 1:1 personalized approach where the product would be defined by your needs.

Then came the industrial revolution where we became able to mass product, and it was all about how do I ship and distribute the product. That is all the marketing standards such as the 4p’s. It was all driven by “how do I ship this product”.

Today with all the data that is available, with the ability to create a product and distribute it at a very low price, we’re back at this initial stage of people wanting to build product for specific targets. It’s all about the customer. It puts back the ideal customer at the center of every single strategy.

So, you should start with early assumptions of who is your ideal customer that you want to solve a problem for. You want that to be narrow very early on.

If you take the BANT framework (Budget, Authority, Need and Timing), you want to focus on Budget and Need first. Those are the two parameters that will help you build a company.

Need is what will help you generate traffic to your website. If you have the right need you will be able to have a message that resonates and engage people. Once they are engaged you will be able to talk about budget.

When we were at Techstars, our managing director told us “call a hundred of these companies that you define as your ideal customer profile, don’t try to sell them anything, see if the need you are trying to solve is actually there”.

That started generating traffic, people got interested, then we were able to look at the data at how the message resonated with smaller categories than what we had defined with the ICP.

Then we closed our first clients and we refined our definition of the ICP more and more to the point where it was super precise.

We started aiming B2B SaaS that had raised an A round in the past 6 months, that had an Alexa rank lower than a 100 and integrations on their website such as Mixpanel, KISSmetrics or Segment.

So, when we reached out to them it was really relevant and often on point. We had a huge reply rate.

So everything started from those hundred calls, then you refined your ICP, until you reached this level of precision. What specific data points did you focus on?

At that time, we were focusing on improving our trial conversion rate and selling to B2B SaaS appeared to be extremely important. Also, you had to use a technology that we could connect (e.g Segment, Mixpanel, or KISSmetrics.).

Behavioral data and declarative data must be tied together. They bring different kind of information.

I recommend you watch the Ted talk from Hans Rosling called The best stats you’ve ever seen. The main point is that, in this world, all the data is available. The big issue is that we drive our decisions on preconceptions.

We have this customer, very similar to Clearbit, that monitors companies’ growth. They had a definition of their ICP being mostly VCs. The sales team was to trained to deal with those profiles, they knew the playbook to convert them.

What we found in the data is that they had a huge amount of conversion in the recruiting space. They did not understand it and the sales team was constantly rejecting those leads. We realized that those HR companies were interested in spotting companies that were not growing in order to find sources of engineers for their own clients.

There was a great use case and they had not trained the sales team to sell to those companies.

Then, this is where behavioral data come into play. You want to make sure people get a successful experience. Those are events you monitor through behavioral data. For this company, we were able to determine which persona were getting the most successful experience.

So, it’s really important to combine the demographic and the behavioral together.

And how do you integrate the sales feedback to complete that empirical approach and close the loop?

Usually, marketing teams have a budget, they find leads, qualify them, marked as MQLs and send them to the sales team. Then on the other side, on the sales standpoint, they have SALs (Sales Accepted Leads). They take the marketing leads, they see if they are qualified enough and they accept it or not.

So, it’s super important to have this interface between sales and marketing and for any MQL there should be only two options: either it’s accepted or it’s rejected.

Being able to monitor those rejected is where you are going to gather a great amount of feedback. Feedback that can actually correct historical patterns that could be misleading.

It’s also important to have regular meetings with the sales team and go over the list of those rejected leads and say why they were rejected. That’s where you can optimize your MQLs.

photo credit: Francis Brero

30-day trial? 14-day? Freemium? Here’s why it probably doesn’t matter

2/29/2016 update – We’ve had a number of requests to expand on this post and provide examples of behavior-based conversion incentives. We decided to write a 3-part series on this topic. You can read the first one here

Whenever I launch a new SaaS product I obsess about sales and onboarding details.

Should I offer a free trial? How long?
Or should I have a free version with no trial (freemium)?

The blogs and books have opinions but most are based on limited data or anecdotes from one SaaS marketing team. Here at MadKudu we try to answer these questions based on data – how our customers are selling.

This week Erik, Sam and I tried to learn how trial length affects SaaS conversions and revenue acceleration.

Here’s what we learned.

Data from 9 representative SaaS companies

We selected 9 companies with different models and relatively clean data. We then identified every trial user who converted to a paying customer and grouped them by of days it takes to convert.1


Here is a simple example you can copy to illustrate the process.

Screen Shot 2016-02-12 at 4.36.16 PM

2 days after customers sign up for this fictitious SaaS company 142 (87+55) converted, 142/305 = 47% of the total who will eventually convert.


After looking at the number of daily conversions we graphed their accumulation relative to starting a trial and came up with the following.

Screen Shot 2016-02-12 at 4.47.23 PM

How to read this graph

This graph shows the rate at which a SaaS company converts trial customers to sales. For example, here is how evaluate the results for Company C:

Screen Shot 2016-02-12 at 4.57.56 PM

Company C offers a 30-day free trial – not surprisingly, most customers who decide to pay do so at the end of their free trial. But customers also convert before and after the 30th day of the trial. This graph illustrates the rate at which that happens.2

Sales acceleration

You accelerate sales (and get a high-five from Tomasz Tunguz) by pushing this curve up and to the left without sacrificing top-line revenue.

Screen Shot 2016-02-12 at 5.00.04 PM

SaaS companies accelerate sales to hit profitability faster. VCs like Redpoint’s Tunguz look for companies who can get customers to start paying sooner.


We studied relationships between trial lengths, conversion rates and models – the results surprised us.3

Screen Shot 2016-02-12 at 5.19.57 PM

Observation 1: It takes about 40 days to get 80% of SaaS conversions

It takes most SaaS customers a little more than a month to test out and purchase a product. This general rule seems to hold true regardless of trial length or whether a product has a free version.

This is … well … surprising.

For instance, look what happens when we isolated a SaaS company Freemium (G), 14-day trial (E), and a 30-day trial (C).

Screen Shot 2016-02-12 at 5.29.57 PM

Not surprisingly, freemium conversions are faster since customers can quickly choose to purchase the premium versions. And, of course, a 14-day trial accelerates faster than a 30-day trial.

But these curves converge when 80% of customers convert, around 40 days after a trial starts.

Observation 2: Half of SaaS conversions happen AFTER the trial ends.

A free trial creates artificial purchasing urgency. But there isn’t anything magical about the last day of a trial – some customers continue to convert at their own rate based on incentives or their perceptions of value.

Every single company we studied had customers who converted more than 100 days after signing up – most had customers who converted 6 months after signing up.

Observation 3: The “S” curve is the $ curve

Ok – this is cool. When we first noticed these results we assumed it was an error. It isn’t. Check out how similar the curves are between company B and C:

Screen Shot 2016-02-12 at 5.48.12 PM


Identical curve … different businesses.

Why? Both companies have 30-day trial SaaS products and similar pricing. But that’s where the similarities end.

They sell completely different solutions to different types of customers. One seems like it would have a much faster adoption rate than the other.

After a little investigating we have a hypothesis: both companies rely primarily on the final day of the trial to drive conversions.

In other words, both companies may be missing opportunities to accelerate sales by:

  • creating additional incentives to convert sooner, and
  • continuing to drive conversion after the trial ends.

That’s why we call the “S” curve the $ curve – we see opportunities for using predictive analytics to grow revenue by developing unique conversion incentives for different customers.

All of your customers are exactly the same – so use the same conversion incentive for everyone

See how silly that sounds? Obviously we suggest you do the very opposite.

The trial period isn’t magic – whether it is 14 days, 30 days, 177.6 days, or π days. Your customers will convert at different rates based on who they are and what they do.

Which … drum roll please … takes us to our conclusions …

My advice for accelerating your SaaS sales

Running A/B tests looking for the 1-size-fits all trial period might be a waste of time – there probably isn’t a magic number for all of your customers. Instead, try to optimize your conversion rates based on qualification and behavior.

Pursue post-trial sales

If you simply toss all post-trial customers into your “nurture” campaign you are almost definitely missing opportunities. Predict those most likely to buy based on qualification and behavior – target and pursue them aggressively for at least 90 days after your trial ends.

Or ask us to do it for you.

Create during-trial conversion incentives

The lesson from Observation 1 above is that your customers will convert at different rates. Identify behavior predictors and create incentives to convert them as fast as possible. This is especially true if your conversion curve fits the “S” pattern above.

Yes, we can also do this for you.

Optimize conversions based on value creation – not time

The end of a free trial only serves one purpose – creating purchasing urgency. The best time to create this urgency is soon after the customer generates value from this service. This isn’t as hard as it sounds – and I can prove it to you: try MadKudu for free – we won’t ask you to pay until we start creating real value for you.

Wasn’t that simple?

Epilogue: YOUR business is special

You’re probably wondering … what’s up with Company A?

Screen Shot 2016-02-12 at 5.57.44 PM

Why does it appear to do a lousy job at sales acceleration? Did Company A hire a bunch of monkeys to answer support emails?

monkey (Mixed-Breed between Chimpanzee and Bonobo) playing with a laptop (20 years old) in front of a white background

Nope. No monkeys.

Company A has an awesome product and knows their customers well. Their product is complex and has a slower adoption rate because a customer needs to integrate it into their infrastructure over time – that’s why they have a slow-and-steady adoption rate powered by converting and up-selling freemium users.

What works for them probably won’t work for you.

Every SaaS business is different and special, especially yours. Predictive analytics isn’t magic or a robotic solution – just the math we use to amplify what is already special about you.

Want us to write more posts like this?

We love doing posts like this but it also takes us a HUGE amount of time to look through and interpret the data. If you want us to write more like these please share it on Twitter and vote for it on Growth Hackers.

photo credit: Thalo Porter