Here's a deep dive into why most companies lack proper ICP definitions, how the situation is changing for the better, and what are new challenges that come with it.
Practical Limitations
Historically, the development of a comprehensive ICP definition was often seen as an exercise in futility. Many companies actually had a good understanding of their ideal customers, but the data required to transform it into actionable acquisition strategies were not available.
It’s like designing an aerodynamically super-efficient car but only having basic Lego pieces to build it. Without the means to execute, even the best plans remain on paper.
From generic NACE codes to custom company and people data
Traditional targeting methods relied heavily mostly on broad categorization based on size, location, and NACE codes, which (often inaccurately) categorize businesses with labels such as "Computer Software," "Information Technology & Services," or "Manufacturing."
These categories, while useful to some extent, often proved to be too generic for effective targeting. It’s like starting a journey with a rudimentary map that shows you only the major landmarks like mountains or rivers but miss the details such as roads, borders and cities.
Embrace new standard - AI-powered, custom, data enrichment
The landscape has shifted dramatically with the AI, Large Language Models and the ecosystem of autonomous agents. Companies can now have access to a new kind of data that could be collected only manually (and thus not in a scalable way).
It allows companies to enrich their accounts almost without any limits which is definitely great but also challenging to do right. Today's ICP definitions can incorporate any information that can be found without direct contact, such as:
- Insights about companies’ products and services
- Definition of their customers, users or partners,
- Business model including pricing details
- Customers, partners’ & candidates’ journeys
- Financial health in case of publicly traded companies
- And more…
together with familiar information such as recent funding rounds, technology stacks, the complexity rises exponentially.
This transition from generic to specific data marks a significant leap forward, allows you to build competitive advantages in acquisition like never before but also brings a whole new set of challenges. One of them is…
The main challenge: Going out the box without going nuts
While the possibilities are vast, they also demand more from people who run these operations. The ability to think outside the box, evaluate the feasibility, ensuring the data consistency is now critical. This requires:
- Strong Creativity: With almost unlimited ways to define an ICP, businesses must leverage their creativity and industry knowledge.
- Strategic Judgement: Identifying the most relevant data points and indicators requires sound judgment and strategic insight.
Conclusion
AI revolutionized a lot of operations and industries. Targeting is no exception. But to use AI properly in targeting, you need to adapt your mindset, skills and processes. In upcoming articles, we will describe the essential components of what a robust but usable ICP definition should have and outline the process for creating one.
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