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?
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