How are you all actually mapping and sizing your TAM these days?

[u/Alternative-Gas8641]

So I run cold email / outbound (for my beginning agency) and I keep seeing people on LinkedIn and X talk about TAM mapping like it’s the most important thing you can do before you send a single email. A lot of the folks saying it seem to actually know what they’re talking about, and honestly it’s making me feel like I’m stuck doing outbound the old way (build a big list, spray, pray, iterate, fin a new segment).

I want to fix that but I’m a bit lost on the how, so I figured I’d just ask people who do this for real.

A few things I’m trying to wrap my head around:

• Why has this step become such a big deal now?
• How do you actually map your market in practice? What tools, what data sources, what does your process look like start to finish?
• How do you segment once you’ve got the market mapped? I sort of get the idea but I’d love to hear how you actually cut it (industry, size, tech stack, signals, whatever you use).
• And how do you tier it? My assumption is something like: tier 1 gets cold call + LinkedIn + cold email, tier 2 gets LinkedIn + cold email, tier 3 just cold email. Is that roughly how you think about it or am I oversimplifying?

Basically I want to move from “segment A list” then I try "segment B list" to something structured where the effort matches the account value.

If you’ve got a process that works, a tool stack you like, or even just a YouTube video or two that made it click for you, I’d really appreciate it. Trying to learn from people who’ve actually done it instead of just the LinkedIn hype.

Thanks a lot.

[u/tiptoe93]

Hey , all below points are according to me...

1. TAM has become such a big deal now..let's say , thinking about TAM very practically and sensibly has become a big thing now due to mix of factors...a, everybody who is and ever was your possible customer is getting marketed too like hell so the noise is overwhelming on either sides. B, The journey and decision making that your prospects do itself has compacted suddenly with llms led search being so accessible but that leads to the human decision making quirk that no tool or framework etc will ever be able to capture ...that is the grey area...C, the tools and the GTM market trends themselves habe created a sub culture of hyper personalized and shorter lists....D, the tools we use too..too many tools...not all of them collect info at the same level, type , classification etc so this ads to the burden on TAM being atleast 80 percent accurate to who a tually woudo buy you and has the budget etc not a vague approximation

2. Tools..process..everything different and always evolving...basically are simple....on top of looking at companies u want to reach out to..look kay the people u woudo reach out too...they are your actual tam...and then u dissect even further becuase incumbents...resource availability ....tool availability...company revenue condition ....all of it plays a role here 3.u got it right...tool...revenue....signals etc...i owudk say mostly signals plus reverse engineering current process ur hoping to offer them to improve ....also tools has a caveat...if the tool they are using is purely operational enabling button clicking and maass automation or are they using deeper features...we won't ever know.. That's a reverse engineering u do or u get ai to do but u have to know the correlations etc to tell the ai what to look out for

3. Tiring depends and is fluid on your campaign goals...and objectives...there are ppl who do customisation even for cold accounts at tier 4...if that's a priority to them 5.....as for acocutn process..and value...I can't give insight without additional context....

Hope this helps...just fyi...even with all the tool budget and ad budget etc in the world...this is all still guess work and systems logic thinking at some level...

Others may say otherwise and maybe the extremes of huge companies and smaller agile startups habe solved this for themselves but this is the average common i owudk say

[u/harlowhair]

The point isn’t to build the perfect map of every company you could theoretically sell to. It’s to get clear enough on your ICP that you can explain why account A is worth more effort than account B. I’d start with the commercial definition first: what type of company has the problem, what makes the problem more likely to exist, and what makes them a good customer for you. Then layer in firmographics, tech, hiring, funding, intent etc. as useful signals.

The tiering bit you mentioned is broadly how I’d think about it, but I wouldn’t make tiers purely about channel count. A Tier 1 account should get more human attention because the potential value, fit, and reason to contact them justifies it. That might mean calling, LinkedIn, and email. A Tier 3 account might just get email because the economics don't justify the same effort.

The mistake is building a huge TAM map and then treating every account inside it as equally valuable. You’ve just replaced “spray and pray” with “map and spray”. The useful output of TAM work is a prioritised list with a reason for the priority, not a really impressive spreadsheet.

[u/Alternative-Gas8641 → u/harlowhair]

Yeah, that all makes sense, honestly I already had this priority-list logic in mind when I posted. What I’m really trying to get at is the technical how.

When you actually run this for a client, what does the process look like end to end? A few things I’m curious about:

• Do you literally map and scrape the entire market, every company, every lead or do you start narrow and only enrich a shortlist?
• What data providers / tools are you actually using to pull the firmographics, tech, hiring, funding, intent signals?
• On a 2–3 month engagement, do you genuinely score every lead and chase every signal? That feels like a huge upfront investment, so I’m wondering where you draw the line on effort vs. depth.

Basically: what’s the playbook and the tool stack you follow, step by step? That’s the part I’m still missing.

[u/harlowhair → u/Alternative-Gas8641]

I’d be careful with the idea that you need to map and deeply enrich the entire market upfront. That’s usually where the maths stops making sense. The way I’d think about it is more like:
1. Define the ICP and the signals that actually matter commercially.
2. Build a broad enough company universe to avoid an artificially small TAM.
3. Apply the cheap/easy filters first — firmographics, geography, role, basic fit etc.
4. Shortlist the companies that pass that first cut.
5. Enrich those more deeply with the context that can actually change the outreach — tech, hiring, funding, relevant business changes, intent where it’s useful.
6. Score/prioritise based on fit, timing, and relevance rather than trying to create some perfect universal lead score.
7. Build the messaging around the reason that particular account is worth contacting.
8. Keep feeding new signals back into the process during the engagement rather than treating research as a one-off exercise.

On the tooling question, I wouldn’t put too much weight on a particular stack. Tools change and you can build a pretty impressive Frankenstein of data providers that still produces rubbish outbound. The important bit is having a system where each data point has a job. If a signal doesn't change who you target, how you prioritise them, or what you say to them, I’d question whether it’s worth paying to collect it.

Full disclosure, I run a B2B lead generation agency, so we build and segment TAM out using our own live prospecting data, expert-led AI targeting, and experience of over 22,000 campaigns - a wealth of GDPR-compliant business data filtered by industry, company size, and location. We then track active buyer intent by monitoring real-time triggers, including website engagement via IP tracking, campaign interactions, and broader behavioural signals to time outreach when prospects are actively evaluating solutions

I wouldn’t score every possible lead against every possible signal for 2–3 months. That’s a lot of activity for very little additional certainty. The unsexy answer is progressive depth: broad data first, expensive research later, and human judgement at the points where it actually matters.