“[There is an assumption that] more is better… they don’t think about any precision with regards to who they’re selling to and why. They think sales have to be feature, benefit or product-led. There’s very little customer understanding or deep customer understanding.”
—Moeed Amin, Founder, and CEO at Proverbial Door and expert on the neuroscience of trust and decision-making.
I heard this quote from the Sales Hacker podcast I listened to a couple of weeks ago. The episode guest, Moeed Amin, was discussing common mistakes in B2B sales. He says one of these mistakes is not thinking about who you’re selling to and why. This happens because “there’s very little deep customer understanding”
You may say, “well, we got an Ideal Customer Profile, so we understand who we’re selling to and why. We know what industry they’re in, we know on average how many employees they have, and more.”
But unfortunately, that doesn’t count as a “deep customer understanding”.
I found support for this in a LinkedIn post highlighting that most of us define our ideal customer profile with externally available data: industry, employees, revenue, etc. While this does support targeting and qualifying leads, it doesn’t really help us actually convert leads. And ultimately grow.
For that, you have to actually resonate with your ICP.
And how do you do that?
By researching your customers.
By developing a deep customer understanding.
If you follow me or any of my colleagues on LinkedIn, you’ll have seen us talking about the ResearchXL (RXL) playbook and the intricacies of the role of each of its research methods.
RXL is very extensive. It takes us about 6 weeks to complete. But what if you want to start gathering insights and deep customer understanding before that?
So, where do you start today? Well, we’ve designed a framework for B2B businesses specifically looking to build “deep customer understanding”.
B2B Ideal Customer Profile RXL
You can start gathering insight into your ICP using existing data.
There are two streams of data included in this ICP framework.
- Analysis of existing data
- Analysis of data gathered via research methods more like the RXL model.
And the good news is, your existing data already exists.
CRM Data Analysis
Analysis of historical prospect and customer data lets you identify the common attributes of the most (and least) valuable accounts.
You analyze the data from CRM and sales tools to identify the key attributes which correlate to value. For example ACV or LTV. The specific data you analyze will depend on the data already available to your business.
We also recommend you use the ‘Spiced’ Framework. It involves gathering data on prospects':
- Compelling Event
- Decision Criteria
This data provides an additional layer of insights to inform the refinement and development of your ICPs.
In the picture example above, we asked the customers what made them seek out a solution from our client:
- 63,5% of customers were looking for an automation solution.
- 20% of customers mentioned that they need a better approval process.
- 18,5% included the fact that companies were expanding and growing rapidly played an important role in decision-making.
- 11.5% of customers seemed to be using a competitor which they were not satisfied with.
As you can see, with this kind of you can quantify the ‘pains’ your customers are experiencing and use it to develop your ICP. Here's the Spiced Data Coding Template.
Here we asked them to tell us about how they and their team decided on new software tools?
- 38,5% said integration with other platforms.
- 33.5% mentioned pricing as the most important factor.
- 18,5% the ability to have multi-entity functionality and work globally.
Customers were also looking for easy invoice processing/approval (16%) along with automation of processes (15%) as much as possible.
Key Takeaways From ‘Spiced’ Framework
With the ‘Spiced’ framework, we discovered that the top customer concerns include:
- Multi-entity support
This kind of data lets us address these concerns within our messaging and content.
On the other hand, we also discovered that the top motivators include:
- Automating manual processes
- Improving approval process
- Rapid growth
- Frustration with Bill.com
So now we have factors we can use to motivate customers as well.
Sales Team Interviews
Another excellent place to start gathering your ICP data is sales team interviews. Ideally, you should be getting this info direct from customers, but still, this is a good starting point.
The Sales team interviews are designed to talk to internal teams—who interact with the customers every day—to uncover their motivations, FUDs, and frictions.
We can uncover some of this tribal knowledge learned from sales and implement the learnings to inform the ICP strategy.
Depending on the size of the business/teams, we run interviews with up to 5 representatives. This could be scaled up for larger businesses or to include representatives from different business functions as needed.
Keep in mind that a survey might be more appropriate for gathering insights on a larger scale.
Example questions include:
- How would you describe your ideal customer/account?
- What attributes make for an ideal account?
- What are their key objectives?
- What is the infrastructure or operating environment of ideal customers/accounts?
- What are the key buying triggers that drive these accounts to take action?
- What are the main reasons accounts don’t buy from you?
- What constitutes an account we absolutely can’t sell to? Why?
Here's the Sales Team Interview Coding Template you can use in your own research.
Wynter is an app that lets you conduct messaging tests on your current website copy so you can understand where the current messaging strategy is falling short.
Recruitment is important here to ensure you’re gathering feedback from the right people who fit your ICP. Sample questions include:
- What resonates? What doesn’t?
- Does the copy address FUDs?
- Are there any clarity issues?
- Does the messaging communicate the problem the tool/service can solve?
Conducting messaging testing as part of the initial ICP research also provides you the opportunity to benchmark the data against future iterations.
Do you even resonate?
Why did I bring up Wynter? Because testing messaging was the reason we developed ResearchXL.
Peep Laja from Wynter came to us and said there are all these businesses running messaging tests with no real idea about their ICP, their motivations, fears, uncertainties, doubts, what actually matters to them, and more.
How does all this relate to personalization?
So you might be wondering ‘How does all this relates to personalization?’
Well, it’s just like with any experimentation.
You can guess what might work.
Or you can run research to inform hypotheses for validation.
ICP research helps you create well-informed hypos about what to test on your website based on what actually matters to your ICP and the different segments within it.
“100 cold calls a day, this is going to sound controversial but it’s total horse shit. It really doesn’t work. When we looked at the numbers… we saw something ridiculous like less than 1% conversion rate on average. It’s just a hugely inefficient way to connect with your buyers and your buying community.” — Moeed Amin
Another quote from the same podcast nicely summarizes the issue with a “spray and pray” sales methodology. This is relevant to website optimization as much as it is to phone sales.
Only by truly understanding your customers and potential customers, you can begin personalization and stop trying to be all things to all people.
Even if you have a deep customer understanding, the real growth opportunity comes when you identify the intricacies and nuances between segments.
Test to Validate, Test to Learn
With one of our clients, messaging tests and user testing helped us discover two behavioral segments of their customers:
- Some users are interested in product lists
- Others need more support and are happier to be guided by an expert
So, we need to design a better landing page experience for the users coming to the site and 'traffic cop' them to the appropriate experiences as needed (product-centric vs. advisor-centric experience).
But before the design, we developed a hypothesis. We believed that by surfacing an element for users to in effect, 'choose their own adventure' and choose what information they're looking for, we'll see engagement metrics increase, and CVR metrics increase as a result.
This was also a learning opportunity.
Regardless of the result, this test would help us validate these behavioral segments and understand the size of each segment.
We would also use this data to inform further optimization efforts—which segment should we prioritize? What are the characteristics of users who selected one or the other, e.g. device, channel, etc?
ICP Research Benefits
For starters, adequate ICP research gets you out of the weeds. You’ll finally understand their motivations, FUDs, and frustrations. Not their age or income.
You will also see the bigger picture. Not only how, but why they buy.
Your stakeholders and managers will feel more engaged. Because you will be able to communicate where experiment ideas came from.
In the end, you will mitigate the risks of “random” and guessed test ideas.
Data is always stronger than guesses.
PS- Here's the link for the Miro Board I did for workshop that includes tasks and sample questions for CRM, sales interviews, website polls, and customer surveys.