How to Create a Data-Driven ICP in 5 Steps: Ideal Customer Profiles for B2B
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How to Create a Data-Driven ICP in 5 Steps: Ideal Customer Profiles for B2B

Five simple steps to create an ideal customer profile that your entire company can agree on
Adam Rutkowski
Updated on
March 12, 2024
Published on
January 23, 2023
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The problem with how ICPs are created today

Try this: Ask stakeholders at your company to define your Ideal Customer Profile aka ICP.

Every answer will probably be different.

Each person – from the Head of Marketing to the CEO to Head of Sales – will give you an answer that reflects the data that matters to them. These may seem like small differences but they actually have a big impact on your business.

Often the answers will also include qualitative attributes that aren’t actionable or measurable. Think of “data-driven” or “tech-savvy.” These are common descriptions that come from the ICP research done by the product marketing team, but can’t be converted into useful targeting criteria.

Or the description might include attributes that seem to be targetable but are not. Describing your ICP’s industry as “B2B SaaS” is a good example of this. You’d assume B2B SaaS to be a commonly-used classification, but it’s not.  The below is a persona. Not an ICP.

So, first and foremost, you need to realize that ICP builds upon definite and quantifiable metrics. Check our free B2B templates, and you’ll get the idea.

How do I create an ICP?

The following five steps will help you build an ICP tailored to your company:

1. Spend time together understanding the objective of building your Ideal Customer Profile

2. Gather inputs from Sales, Marketing, Product – and CS.

3. Identify input data that could potentially skew your results.4

. Enrich with additional data points.

5. Get buy-in from the entire company

Most companies start defining their ideal customer profile with a bit of intuition—i.e., a founder has a prediction about their perfect customer, or there’s some early customer feedback. Then, the ICP is solidified through additional market feedback, user research, and analysis of GTM data.

Over time, teams will start to make tweaks to the ICP definition based on the data that’s important to them and the systems they’re operating in. For example, marketing will optimize their ICP definition to find more leads inside Linkedin, while sales will think about who makes a good “sales-qualified opportunity” in Zoominfo.

Compare Zoominfo's efficiency for sourcing data on high-quality B2B leads with Apollo’s B2B data for an educated choice.

When the process isn’t systematic or data-informed, the result is that key teams start speaking different dialects. Eventually, conversion starts to fall because sales, marketing, and product are all moving in different directions. Marketing will bring in leads that don’t convert, while sales may convert opportunities that aren’t served in the best way by the product.

The bottom line is that a data-driven view of your ICP that everyone can buy into is critical. But how can you get there? Here are the steps I, as Primer's Head of Sales, recommend:

Step 1: Spend time together understanding the objective of building your Ideal Customer Profile

Identify each team’s use case for an ICP. For example, marketing wants an ICP definition to help with targeting criteria while the product team wants it in order to create a product roadmap. Figure out how to create a central ICP definition that can extend into each of those use cases.

Step 2: Gather inputs from Sales, Marketing, Product – and CS.

Often, customer success (CS) teams are ignored in the data gathering process. But getting data from them on your most successful customers will likely lead you to building a better ICP - one concentrated on companies that renew. Focusing solely on marketing or sales can lead to leads that don’t close, or closed deals that don’t succeed.

Get additional insights on how to build and maintain productive Sales and Marketing alignment.

By following the entire funnel as you identify your ideal customer profile, you’ll gather valuable data points on what matters to creating a successful customer from the top of your funnel all the way through it.

Step 3: Identify input data that could potentially skew your results.

Weigh all your input data based on identified gaps in the funnel, and then discount it accordingly. For example, you could be seeing a drop off between lead-to-opportunity, which would lead you to identify that some 30% of the leads you’re getting aren’t a great fit for your business. You’d then have to reduce the weightage of that data in the model you’re building.

Step 4: Enrich with additional data points.

Next, enrich your primary dataset with as many additional data points as possible from an enrichment dataset like Apollo, Zoominfo, or Clearbit. We have a list of recommended filters/attributes at the bottom of this post. At Primer, we maximize the amount of information we can collect by using a combination of several databases. We partner with the best data providers in the industry so you can have a wealth of data to target your ICP right down to their personal email addresses.

Source: sayprimer.com

Once you have an enriched dataset, you’ll analyze it to find the points of correlation. Then you’ll decide how tightly you want to define your ideal customer profile. For example, when we conducted this ICP analysis at Primer, we found that there was an 80% correlation between sales-qualified opportunity and an employee count of 11-2K (i.e. 80% of our opportunities came from companies with 11 to 2,000 employees.) We could’ve refined that further to a 90% correlation, and narrowed our ICP profile to companies with 50 to 2,000 employees but we decided to keep it broad.

Step 5: Get buy-in from the entire company

Take your analysis to all stakeholders at the company (Marketing, CS, Sales, Product) and ensure that there is wide alignment on your newly-defined ICP. Ask them if they want to include any additional data sets and make sure that they’ll be able to use the definition in their own work.

Examples of filters/attributes

Company-level attributes:

  • Location
  • Employee count
  • Industry
  • Revenue
  • Funding
  • Website traffic
  • Technology stack

Person-level attributes:

  • Title
  • Department
  • Seniority

Need help with this process? Well of course we have to shamelessly plug Primer. Our ICP analysis and lookalike feature is in Beta.

Why Primer?

Need help with better ICP research and testing? Sure thing! Read our playbook on how to set ICP-relevant targeting options with company and people-level data to make your paid social ads more impactful.

Check out the live demo to learn exactly how Primer works, or book a call with our team, and we can help you through the ICP definition process.

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