Target Account Modeling in 2 Easy Steps

Stop Fishing: Treat Your TAM as an Account List

My close friend owns a commercial fishing boat. The stories he tells are incredible, from catching WWII mines to carelessly discarded ammunition by the NAVY in the 60s. But that’s another post altogether. What caught my attention more was how their strategies are very much the same as most sales and marketing do nowadays.

Broad industry category plus titles, maybe employee counts, and the fishing expedition starts: email and social outreach. Then load keywords, buy ads, publish online, and hope for inbound. Casting wide nets and seeing if it works. If not, change it, and do it over. Inefficient? Rhetorical question.

AI hasn’t solved the fishing problem yet. It’s the same as it was 60 years ago for the most part. Better nets, GPS, and a few other small things, but nothing groundbreaking. Now imagine if every fish in the ocean would radio back to your boat its weight, location, and kind? You could quickly build a strategy for getting the most out of each trawl.

This is the change that we are pursuing for modern sales and marketing organizations: let AI identify your client personas from your existing customer list and then define the full Total Addressable Market (TAM) as an Account List for each persona, every single company that fits. All of a sudden, you don’t have a need to go on fishing expeditions from the last century.

Instead, work the list and nobody else outside of it. No more spamming tens of thousands with cold emails or showing your ads to unrelated people. No more MQLs or other questionable metrics.

Step 1: Create Personas Through Customer Segmentation

The first step in Target Account Modeling is to segment your customers based on shared traits. This process helps you create detailed personas that represent different types of accounts you want to target.

Start by analyzing your existing customer base to identify common characteristics, such as:

Industry
Company size
Revenue
Product usage patterns
Customer pain points

Once you have defined these personas, you can use them as a foundation for finding new prospects that match these profiles. However, traditional keyword-based approaches to segmentation are limited. To achieve more precise results, you need to leverage large language model (LLM) embeddings.

Step 2: Leverage Embedding-Based Search for Better Prospecting

To make search and segmentation truly effective, it’s essential to go beyond basic keywords and categories. Embeddings allow you to understand the full context of a website, capturing its meaning, intent, and relationships. This deeper understanding helps you:

Segment businesses based on their goals, customers, and market positioning
Identify high-fit prospects more accurately
Exclude irrelevant accounts to create cleaner lists

Embedding-based search offers significantly better similarity matching and effective negations. These capabilities are crucial for refining your target account lists. By excluding accounts that don’t align with your ideal customer profile (ICP), you can focus on the prospects most likely to convert.

Combining Segmentation and Search for Optimal Results

Here’s how to combine segmentation and search for incredible results:

Run Segmentation on Existing ProspectsAnalyze both closed-won and closed-lost accounts to understand what works and what doesn’t.
Use this data to refine your personas and target account lists.
Use Embedding-Based Search to Find and Negate AccountsTarget high-fit prospects by searching for accounts that match your personas.
Negate low-fit accounts to remove irrelevant prospects from your list.

This combination ensures that your prospecting efforts are focused on accounts with the highest potential, leading to cleaner lists and higher conversion rates.

Measure Market Penetration, Then Layer In Signals

Start measuring your marketing and sales performance as market penetration. What percent of the market is yours? Who are the top next 100 to go after? We have built the tech and precision company data to make this happen, no gaps, no false positives.

With your TAM defined, it is easy to layer in other signals: personal connections or introductions, hiring signals, related LinkedIn posts, people in your network, influencers who can get you to your targets, and events where you can meet them. This becomes a methodical operation instead of the random trawling of an endless ocean.

Why Embedding-Based Target Account Modeling Works

Traditional keyword-based approaches often miss the mark because they rely on exact matches and surface-level attributes. In contrast, embedding-based modeling goes deeper, understanding the context and intent behind each account’s digital presence. This allows you to:

Identify businesses that align with your ICP based on nuanced similarities
Avoid irrelevant matches that could waste your sales team’s time
Continuously refine your target lists based on real-world results

At DiscoLike, we provide the tools to help you achieve this level of precision through our technologies. Our embedding-based segmentation and search capabilities offer a strong foundation for Target Account Modeling that you can further enhance with additional parameters and filters.

Conclusion

Target Account Modeling doesn’t have to be complicated. By breaking it down into two simple steps, creating personas through segmentation and leveraging embedding-based search, you can dramatically improve your prospecting efforts.

Ready to take your account modeling to the next level? DiscoLike’s tools can help you achieve cleaner lists, higher conversion rates, and better overall results. Embrace the future of Target Account Modeling with embedding-based technologies today.

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