Finding ICP Accounts is a Process

Photo by Mel Poole on Unsplash

If you have a comment or are interested in sponsoring, hit reply.

Photo by Mel Poole on Unsplash

The challenge: you want accounts that match the criteria for your ideal customer.

The reality: start by making a list of companies and then find the ones that match the qualities of your ideal customers.

Also reality: spend a lot of time thinking about what data points are discoverable and which data points potentially describe a solid prospect.

It’s hard to define the combination of factors that make an arbitrary company a good prospect. Just because a prospect matches some aspects of existing customers doesn’t guarantee they are an ideal prospect for your company.

Data is often available but unstructured

In a perfect world, you’d define a data point that marks an aspect of a solid customer and track that automatically for every customer. Over a series of steps, this process lets you build factors that score accounts for nearness to “ideal”.

Here’s what’s available to research about an arbitrary company with some web domain history:

  • Firmographic traits (number of employees, industry, headquarters location, address, revenue estimates, year of founding)

  • Technographic items (what software do they use on their website, what do surveys say they use internally)

  • Popularity (if the site is popular enough you can get estimates of site ranking, web ad spend)

  • Job Listings (date stamps of available jobs from various job listing sites)

  • Content from a company’s web site (the public site)

  • For public companies, SEC information and filings give you additional information

There’s also good old web search – but it can be challenging to triangulate information about a company with the validation of that information. Simply put, there be information dragons. (LLMs don’t make this easier, as they will often confidently state information as facts without providing receipts.)

For an individual company, research is possible

Demonstrating to a seller that an account is an ideal customer is a repeatable process. You might be looking for a specific product or service on their website, an employee characteristic (they have more than a certain number of people working for them), or perhaps a competitive software that they use.

Now, scale this effort to hundreds or thousands of potential accounts.

The first thing you need is a list of these accounts and a unique ID or key to map future enrichment to these accounts. It would be nice to have consistent information about every account and key fields populated to enable you to research them.

The reality is often different. Most accounts have middling information based on what the prospect provides. The difference is often the ability of a seller to find that hidden piece of information.

Request for product: instant research

There’s a lot of news about “AI-automated” SDRs recently, or processes that take over the initial research and qualification of accounts based on a standard operating procedure.

I’m not bullish on letting AI handle tier 1 contact with a customer. However, I love the idea of having an AI agent conduct research using a very specific procedure on lots of accounts. What would make that promising result great? Being able to validate the result.

I’d love to see a data automation product that did the following:

  • define a hypothesis that can be proved, e.g. “does a company have a staff or “about us” page listing listing 5 or more employees”

  • search the available site and produce a yes or no answer to the question, including supporting information to validate the information with a date stamp

  • create a place to validate this observation, which when overruled removes that data point from the account

  • let you tune your observation to include “possibly correct” through “must be validated by a human”

With a system like this, it would be a lot easier to find information needles in a haystack of accounts at scale, even if there is a novel way to identify a data point.

What’s the takeaway? Account research is hard to do at scale. AI combined with humans can help process automation to find specific information on lots of accounts. This would make it possible to harness the intuition of sellers yet operationalize that search across all the accounts.

gregmeyer
gregmeyer
Articles: 566