How to build an actionable Total Available Market (TAM) and Total Obtainable Market (TOM) dataset

I was listening to a Podcast called ‘Smooth Scaling’, where there was a high level discussion on Total Available Market (TAM) and its actionable derivative, which I call Total Obtainable Market (TOM). TAM data is essential during the fund-raising process, but that number is highly aspirational, and beyond the funding process, it does not have much use on its own.  But if calculated correctly, it can be a goldmine in sales and marketing. In this post, I will share my thoughts, based on my prior experience, on how to build an actionable TAM and TOM dataset.

TAM can be calculated top down or bottoms up.  For top-down calculation, you typically start with a number from an analyst firm and then apply some assumptions against that number to come up with your TAM number.  For example, if an analyst says that the Digital Experience Market in US is 100 billion dollars a year and your assumption is that 20% of that is small business (under 25 employees), and 25% of the remaining is custom software (and the rest is packaged software), then your TAM is $60B. But there is not a lot you can do with that number to drive sales or marketing efforts.

The second approach is to build the TAM model bottoms up.  You can use tools such as ZoomInfo to identify all the companies in the US that have over 25 employees and capture that data in a spreadsheet. Now using the same assumptions as above and assuming an Average Selling Price (ASP), you can come up with the bottom-up TAM number.  It is a better number than the top-down number, but still not very actionable by your sales and marketing.  But having gone through this exercise, you now have the data in the spreadsheet that allows you to come up with Total Obtainable Market (TOM) number, as well as make it highly actionable.  How do you do that?

• Apply filters that matter: The first thing you need to do is to identify all the filters you would need in your TAM spreadsheet to make the data actionable.  These are the filters that are relevant to your business. For example, industry information, company revenue, geography etc. are three key firmographic/demographic data points that could be a part of your spreadsheet.  With that information, you can now filter your dataset.  For example, if you are targeting companies above a certain company size in a certain set of verticals this year, you can now come up with an actionable list of companies you want to target.  All this info is either provided by the data service you used to create the TAM dataset (such as ZoomInfo) or is available from other providers. But don’t stop there.  I have found other fields, such as technology used, to be very helpful.  For example, if your Ideal Customer Profile is someone who uses Salesforce and/or NetSuite for business systems (because you integrate well with these systems) and uses Microsoft Azure (because they are a Microsoft shop), make sure you enrich your spreadsheet with ‘technology used’ data from a third-party data provider such as TechTarget or Built With, so you can apply those filters.  Also use the ‘technology used’ data to filter out anyone who uses your competitor’s product today, since they are not likely to buy your solution in the next 12 to 24 months  Add any other filters that are relevant to your business based on patterns you have seen in your sales cycles.  This filtered dataset is what I call Total Obtainable Market (TOM).
• Bring this information into your CRM system:  To make this spreadsheet actionable, bring this TOM dataset information into your CRM system. Then use these filters to score each account accordingly in your CRM system.  This score will help you assign territories/accounts better and feed into your marketing automation system for marketing/ABX campaigns. You will also be able to ensure that this data, once in CRM, is continuously enriched with third party datasets so a) you can add more filters to suit your needs b) you can keep it current on an ongoing basis. If your CRM system is connected to LinkedIn, you can also see the contacts in those selected accounts you want to target, based on the personas you sell to.
• Review the selected accounts on a quarterly basis: Your competitive dynamics change, your product evolves, or you simply learn more about why you win and lose.  This can help you sharpen your target customer profile. Use this information to revise your filters (and scores), so that you can help your sales reps tweak their focus on the accounts they are spending their time on, so they can maximize their chances of hitting their targets. It will also help you refresh your ABX targets/programs to maximize MQLs/SQLs.

Be relentless about fine tuning the filters and scores on an ongoing basis.  It is not a ‘one and done’ item.  If you do this right and invest time in it, you will see the benefits quickly. I have successfully used this methodology in my past to increase pipeline by over 15% with a flat marketing spend.  If you have any questions, please message me at https://www.linkedin.com/in/applicationsmarketing/