Return-Path: Delivered-To: andrew@sterlingcal.com Received: from reseller401.webserversystems.com by reseller401.webserversystems.com with LMTP id ILFEJp4Q8Gc/PgAAchKAPA (envelope-from ) for ; Fri, 04 Apr 2025 12:02:22 -0500 Return-path: Envelope-to: andrew@sterlingcal.com Delivery-date: Fri, 04 Apr 2025 12:02:22 -0500 Received: from o170.ptr9231.mail.beehiiv.com ([159.183.140.172]:4078) by reseller401.webserversystems.com with esmtps (TLS1.2) tls TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 (Exim 4.98.1) (envelope-from ) id 1u0kQv-000000005ly-1tUP for andrew@sterlingcal.com; Fri, 04 Apr 2025 12:02:22 -0500 DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=mail.beehiiv.com; h=content-type:from:mime-version:subject:reply-to:list-unsubscribe: list-unsubscribe-post:to:cc:content-type:from:subject:to; s=s1; bh=ihqUcTA+oyzLouFhZzvY7SdvARfvHb5dHgLn5P0m8uk=; b=EC2u+qRoq1SCPNl1uSxrIgGMcDTg/oPEqRj/o8j2KJb5KqEyIq7ZO6ey6ttKCaOAu7vc 4vKsbnHsApNA0HMsiC9eSoCvGchsM0e9k0GrpLV3vsiM6gXUcRPRYHUa5uV24UcPCZH/TQ 8lcQL9eeG+uAT1G2ZcjFAlTOTK2CX/UGl9R+8CjPxZpEbAt+Tm/UnGv82N/UHQrRe5OyuL au1++GrvgqvfkR7PJ7HDRN3wEg+laPMP9Y917xPrl2POe8oVYbVVACILuoDrxjTpkzGAVw /JatauPzRNdGNg/niaE47JRm3q6WJuivwGA1+kuv9gN3K9KCgI5MffC0ew0AJlsQ== Received: by recvd-d8b45558-wl42n with SMTP id recvd-d8b45558-wl42n-1-67F01083-E 2025-04-04 17:01:54.998610138 +0000 UTC m=+12166750.670977877 Received: from NDAyMzQxODk (unknown) by geopod-ismtpd-27 (SG) with HTTP id FHwiK4ZCTtuQo22s9hBvyA Fri, 04 Apr 2025 17:01:54.889 +0000 (UTC) Content-Type: multipart/alternative; boundary=70008ec9f6c0edf41b06773595b8000c89f7b5dbbbb95926bfe09d49419c Date: Fri, 04 Apr 2025 17:02:09 +0000 (UTC) From: Collin Stewart Mime-Version: 1.0 Message-ID: Subject: do you trust the list? Reply-To: Collin Stewart Feedback-ID: =?us-ascii?Q?f524d5fa-faf1-4b34-a4e9-7a5bb75fef81=3Anewsletter=3A3c5b97ff-00a9-?= =?us-ascii?Q?4acc-916d-c137bca24b1d=3A23270855726497d?= List-Unsubscribe: =?us-ascii?Q?=3Cmailto=3A3c5b97ff-00a9-4acc-916d-c137bca24b1d+1d17dc26-c1df-44e?= =?us-ascii?Q?0-812a-883b6ad5b6bc+f524d5fa-faf1-4b34-?= =?us-ascii?Q?a4e9-7a5bb75fef81=40unsub=2Ebeehiiv=2Ecom=3E=2C?= =?us-ascii?Q?_=3Chttps=3A=2F=2Ffoundersedition=2Eco=2Funsubscribe=2FeyJ0eXAiOiJKV1QiLCJhbG?= =?us-ascii?Q?ciOiJIUzI1NiJ9=2EeyJzdWJzY3JpYmVyX2lkIjoi?= =?us-ascii?Q?MWQxN2RjMjYtYzFkZi00NGUwLTgxMmEtODgzYjZ?= =?us-ascii?Q?hZDViNmJjIiwicG9zdF9pZCI6ImY1MjRkNWZhLW?= =?us-ascii?Q?ZhZjEtNGIzNC1hNGU5LTdhNWJiNzVmZWY4MSIsI?= =?us-ascii?Q?mxhc3RfcmVzb3VyY2VfZ3VpZCI6IlBvc3Q6ZjUy?= =?us-ascii?Q?NGQ1ZmEtZmFmMS00YjM0LWE0ZTktN2E1YmI3NWZ?= =?us-ascii?Q?lZjgxIn0=2EuMJPGwUj0wqWHUwNRcdDaPAh2e5aHs?= =?us-ascii?Q?yu9oJ7Y8javkA=3E?= List-Unsubscribe-Post: List-Unsubscribe=One-Click sId: 3c5b97ff-00a9-4acc-916d-c137bca24b1d x-beehiiv-ids: =?us-ascii?Q?{=22account=5Fname=22=3A=22https=3A=2F=2Ffoundersedition=2Eco=2F=22=2C=22campaign=5Fid=22=3A=22f?= =?us-ascii?Q?524d5fa-faf1-4b34-a4e9-7a5bb75fef81=22=2C=22c?= =?us-ascii?Q?ategory=22=3A=22newsletter=22=2C=22email=5Fgenerated=5F?= =?us-ascii?Q?at=22=3A1743786114=2C=22user=5Fid=22=3A=223c5b97ff-00a9?= =?us-ascii?Q?-4acc-916d-c137bca24b1d=22}?= x-beehiiv-type: newsletter x-list-id: 3c5b97ff-00a9-4acc-916d-c137bca24b1d x-list-owner: x-newsletter: https://foundersedition.co/p/do-you-trust-the-list x-newsletter-id: https://foundersedition.co/ x-newsletter-signup: https://foundersedition.co/subscribe X-SG-EID: =?us-ascii?Q?u001=2E7ozyt+tmjDsHnEocxX8pymFA2v5OOh2V4Pdr6hGVjZo99sLHT4OeEg7as?= =?us-ascii?Q?oEFtQzIa0qAOkk1QezFA4qrCcff9558yNK8x6zQ?= =?us-ascii?Q?0aLhxDCyAuB9kgcSLas3IIKR+fvCUJBBugyaOjn?= =?us-ascii?Q?Jt=2FEXGN3Om6FwdYL2AusOgh76X4NNxM8eYbrFbq?= =?us-ascii?Q?80GInguTUe0cp6hiJ1OzVUNsx+ie+MzGNQ3=2Fyzb?= =?us-ascii?Q?qRi7mvF73cETo8JhnJwHEJpQiQkty2PsaE60mz9?= =?us-ascii?Q?lpMZIu1opH8mu3yvNk6rDHIe9Z9=2FODiasqwGT5H?= =?us-ascii?Q?PWytVRYc=3D?= X-SG-ID: =?us-ascii?Q?u001=2ESdBcvi+Evd=2FbQef8eZF3BpTL9BgbK5wfSJMJGMsmprAVgeiC8iaV7rEmQ?= =?us-ascii?Q?+cYi2k72nnltDKqZrnPVQEtOuBK+pIk2HG84dc9?= =?us-ascii?Q?7hUUAShu2ZDS96WlCi+r3aQucEaI521RDmdScjP?= =?us-ascii?Q?y+uuMViBLAGfeRXEJDtsK3r5AttBSxM2CVL8VE5?= =?us-ascii?Q?5wzcANl6u6t8GCRmTB4Zo+9zQt6ZdsrBzVnoMql?= =?us-ascii?Q?5kgl+Q46qIFmGpzULuJ=2FVDPXZb1fejpT6V6YUll?= =?us-ascii?Q?J2khxddR8ZbhtyWRFahX0RhUJQ=3D=3D?= To: "andrew@sterlingcal.com" X-Entity-ID: u001.RuZ2+X9IBGtjUb8qq3teHQ== X-Spam-Status: No, score=-3.3 X-Spam-Score: -32 X-Spam-Bar: --- X-Spam-Flag: NO --70008ec9f6c0edf41b06773595b8000c89f7b5dbbbb95926bfe09d49419c Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 Mime-Version: 1.0 Hey Andrew,=20 _**Rev Roundtable Update**__: Our founder revenue roundtable is happening a= gain next week (April 11th at 9:30am PT) and we have a few more spots left,= hit me up if you=E2=80=99d like to join us. This is specifically for found= ers under $1m in revenue that are looking for help unblocking a particular = revenue obstacle. We pick 3-4 companies/struggles as a group and share what= =E2=80=99s worked for us to help them find a way forward. _**_Hit reply if = you=E2=80=99re interested_**_._ I built a list this week that gave me that cold, sinking feeling =E2=80=93 = the realization I=E2=80=99ve been approaching list building with a level of= risk recently that borders on negligent. Like =E2=80=98driving your GTM st= rategy without a seatbelt=E2=80=99 dangerous. Here=E2=80=99s the gist: I was working with a founder trying to answer a critical question: Is there= enough runway in their current market segment to hit their revenue goals, = or do they _need_ to expand into new verticals? Standard top-down research = reports showed their industry had roughly twenty thousand companies and Apo= llo only showed ~600 companies that specifically met the employee range we = were looking for.=20 This founder wants to move slightly upmarket, shifting focus from smaller c= ompanies to those with a minimum 50 employees and max 200. Why? Because the= se larger companies derive significantly more value from their software, me= aning bigger deals, better adoption, and longer client lifetimes.=20 **The Initial Plan & The Nagging Doubt** We started by building a bottoms-up sales development forecast model. (Hit = reply if you'd like my template for this =E2=80=93 happy to share). This mo= del helped us map out how long it would take to penetrate the _assumed_ mar= ket size and what profitability could look like based on various inputs (co= nversion rates, sales cycle length, etc.). It felt like a solid start. But something was nagging at me. The entire model =E2=80=93 the hiring plan, the GTM investments, the sales = commission structure =E2=80=93 hinged precariously on _one_ number: the tot= al number of accounts with 50-200 employees. My client was about to bet a s= ignificant chunk of their next year's strategy and budget on this figure. A= ny major error would ripple through the entire company. We had cross-referenced our target number with data from a couple of reputa= ble providers, and their figures generally aligned. Yet, my spidey sense wa= s tingling. I couldn't shake the feeling that trusting these numbers implic= itly for such a high-stakes decision wasn't good enough. **Digging Deeper: Validating Assumptions the Hard Way ** We agreed that a decision this fundamental deserved extra diligence. It was= time to validate our market size assumptions from the ground up. Here=E2= =80=99s what I did: 1. **Data Collection via Clay:** I built a Clay worksheet leveraging the Go= ogle Maps API. The goal was to identify _any_ company whose online presence= included keywords relevant to the founder's specific niche within Industry= N. 2. **AI-Powered Filtering:** Crucially, I then created an AI classifier pro= mpt within Clay. This prompt instructed an LLM to analyze each company's de= scription and determine if it was _truly_ the right type of company for my = client =E2=80=93 not just tangentially related. **(Sidebar: Why Standard Data Providers Can Fall Short Here)** This process highlights a common challenge with third-party data providers.= They often get criticized for data quality, but it's partly by design. The= ir incentive is usually to maximize the _volume_ of data matching broad cri= teria =E2=80=93 they don't want to risk leaving out a potential lead for th= eir customers. In casting this wide net, they inevitably pull in companies = that technically fit the filters (industry, employee size) but aren't actua= lly a _good fit_ for your specific solution or value proposition. The issue= isn't the raw data itself, but the lack of sufficiently _granular filters_= to precisely define _your_ ideal customer profile based on qualitative fac= tors. **The Alarming Results** The outcome of our deep dive was both validating and alarming. * **The Good:** The total _potential_ number of companies in the space loos= ely matched the top-down reports. The market _was_ roughly that size on pap= er. * **The Bad:** The number of companies that were a good fit _and_ within th= e target employee range was nearly 50% lower than the initial data suggeste= d. **The "Near Miss" and Its Potential Fallout** I couldn't shake that "near miss" feeling =E2=80=93 like watching a potenti= al car crash unfold and realizing a slightly different decision could have = put you right in the middle of it. Fortunately, my intuition kicked in, and the client readily invested the ex= tra time to get the foundation right. But let's imagine we hadn't: 1. **Over-Hiring:** They would have built their hiring plan based on a mark= et twice as large as reality, likely recruiting double the necessary salesp= eople. 2. **Missed Quotas & Morale:** Sales reps would have struggled immensely, f= acing a scarcity of viable accounts. At best, the team might have hit 50% o= f their target, leading to frustration, demotivation, and eventual turnover= . 3. **Wasted Resources:** Think of the management overhead spent hiring peop= le they didn't need, the months spent managing underperforming (through no = fault of their own) reps, and the painful process of rightsizing the team l= ater. 4. **Strategic Risk:** This wouldn't just burn cash and time; it could have= jeopardized their ability to hit milestones crucial for their next funding= round or achieving profitability. Burning through resources based on flawe= d data can quickly deplete a startup's limited "matches." While perhaps not= fatal in this specific instance, it would have been dangerously close =E2= =80=93 all stemming from one unchecked assumption in a spreadsheet. **The Real Hero: Accessible AI + Clay ** The real star here is 03-mini. Before some of the more recent models, havin= g an AI look at all the relevant details and make an assessment _that I wou= ld trust_ my GTM program felt like science fiction. We used to have a big t= eam of folks to manually process lists like these, it was so expensive that= it only made sense to run for really small lists of really high value targ= ets. With 03 mini + Clay, it=E2=80=99s so reliable and cheap that it=E2=80= =99s almost irresponsible not to do this for every list I=E2=80=99m running= .=C2=A0 Before LLMs, having an AI reliably analyze qualitative details (like a comp= any description) and make a nuanced judgment call you could confidently bas= e your GTM strategy on felt like science fiction, or at least were prohibit= ively expensive.=20 I've built top-down/bottoms-up forecasts before, often relying on subscript= ions to expensive data vendors. While those provide a starting point, anyon= e who's run serious outbound campaigns knows list quality degrades quickly.= It's like a Google search =E2=80=93 page one looks promising, but by page = three or four, the relevance often nosedives. **Applying This Yourself: AI-Powered List Qualification** The only robust way to combat this decay and ensure high list quality is to= have an AI individually assess each potential account _before_ it makes it= into your strategic plans or your reps' outreach sequences. The best way t= o do this is to use Clay to inject the Company Name and Description variabl= es into a prompt and run it at scale. It=E2=80=99s the same idea as sending= a cold email to =E2=80=9CHi First Name=E2=80=9D but instead of sending an = email, you=E2=80=99re injecting variables into the prompt. =20 A simple method involves: 1. Pulling company names and descriptions (e.g., from Google Maps, LinkedIn= , or other sources) in Clay. 2. Feeding the company name & description into an LLM via a "classifier" pr= ompt. 3. Filtering based on the output of the classifier 4. Sourcing contacts for =E2=80=9Cgood fit=E2=80=9D companies This is excellent for basic categorizations like "Software vs. Services," "= B2B vs. B2C," or, as in my example, "Is this company actually in my specifi= c niche based on what they do?" Here=E2=80=99s a [simple Clay template](https://app.clay.com/shared-table/s= hare_ttS3oW3A3bPj/?via=3D418abf) that does just that.=20 To use it: 1. Sign up for Clay (if you haven't already). 2. Add this template to your account.=20 3. Add your OpenAI API key in Clay's integrations settings. 4. Adjust the classifier prompt for your business.This is key. You need to = tell the AI _exactly_ how to judge the companies based on the data it will = receive (in this basic template, primarily the company description). **How to Create an Effective Classifier Prompt (Using ChatGPT or similar):*= * You can ask an AI like ChatGPT to help you draft the prompt. Here=E2=80=99s= a prompt I used yesterday: ``` =E2=80=9Cwrite a classifier prompt for me, the inputs will be company name = and linkedin description. I'm looking for companies that but not . Here ar= e 3 examples of companies that and 3 examples of companies that .=20 ``` **Key Tips for Your Prompt:** * **Be Specific:** Clearly define what makes a company a good or bad fit _b= ased on information likely found in a description_. * **Use Examples:** Provide clear examples of both good and bad fit descrip= tions to guide the AI. * **Constrain the Output:** Tell the AI _exactly_ how to format its answer = (e.g., just 'Good Fit' or 'Bad Fit'). This makes it easy to use the output = in tools like Clay. **The Takeaway** Don't let seemingly solid top-down numbers or basic list filters lull you i= nto a false sense of security when making critical GTM planning decisions. = Validate your _actual addressable market_ with a level of granularity that = reflects your _ideal customer fit_. Thanks to accessible AI, this deeper va= lidation is no longer a luxury; it's a crucial step to de-risk your strateg= y and build your growth plans on solid ground. Have you faced similar challenges with market sizing or list building? Hit = reply =E2=80=93 I=E2=80=99d love to hear your experiences.=20 Collin=20 PS - if you made it this far, let me know if you want more geeky-gtm posts = like this or less. Just hit reply with =E2=80=9Cmore=E2=80=9D or =E2=80=9Cl= ess=E2=80=9D.=20 PPS - if you=E2=80=99re struggling with the sheet, I can help but my time c= osts $ =F0=9F=98=89.=20 PPPS - I=E2=80=99m thinking of switching from publishing on Fridays to Wedn= esdays because newsletters get better engagement during the week. Good idea= ? Bad? I=E2=80=99d love to hear from you.=20 =E2=80=94=E2=80=94=E2=80=94 You are reading a plain text version of this post. For the best experience,= copy and paste this link in your browser to view the post online: https://foundersedition.co/p/do-you-trust-the-list --70008ec9f6c0edf41b06773595b8000c89f7b5dbbbb95926bfe09d49419c Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 Mime-Version: 1.0 do you trust the list?
<= td align=3D"center" valign=3D"top" style=3D"border-width:0px 0px 0px 0px;bo= rder-style: solid; border-color: #FFFFFF;border-radius:10px 10px 0px 0px;ba= ckground-color:#FFFFFF;" class=3D"c">

April 04= , 2025   |   Read Online

do you trust t= he list?

<= td align=3D"left" valign=3D"middle" class=3D"mob-stack">
3D"share
 
3D"share
 
3D"share
 
3D""
<= /tr>

Key Tips for Your Prompt:

  • Be = Specific: Clearly define = what makes a company a good or bad fit based on information likely found in a description<= span style=3D"color:rgb(44, 62, 80);">.

  • Use Examples:= Provide clear examples of bo= th good and bad fit descriptions to guide the AI.

  • Constr= ain the Output: Tell the = AI exactly how to format its answer (e.g., just = 9;Good Fit' or 'Bad Fit'). This makes it easy to use the output= in tools like Clay.

= Hey Andrew,

Rev Roundtabl= e Update: Our foun= der revenue roundtable is happening again next week (April 11th at 9:30am P= T) and we have a few more spots left, hit me up if you=E2=80=99d like to jo= in us. This is specifically for founders under $1m in revenue that are look= ing for help unblocking a particular revenue obstacle. We pick 3-4 companie= s/struggles as a group and share what=E2=80=99s worked for us to help them = find a way forward. Hit reply if you=E2=80=99re interested.

I bu= ilt a list this week that gave me that cold, sinking feeling =E2=80=93 the = realization I=E2=80=99ve been approaching list building with a level of ris= k recently that borders on negligent. Like =E2=80=98driving your GTM strate= gy without a seatbelt=E2=80=99 dangerous.

Here=E2=80=99s the gist:

I was working with a founder trying to answer a critical question: Is ther= e enough runway in their current market segment to hit their revenue goals,= or do they need to expand into new verticals? Stan= dard top-down research reports showed their industry had roughly twenty tho= usand companies and Apollo only showed ~600 companies that specifically met= the employee range we were looking for.

This founder wants to move slightly upmarket, shifting focus from = smaller companies to those with a minimum 50 employees and max 200. Why? Be= cause these larger companies derive significantly more value from their sof= tware, meaning bigger deals, better adoption, and longer client lifetimes. =

The Initial Plan & The Naggin= g Doubt

We started by build= ing a bottoms-up sales development forecast model. (Hit reply if you'd = like my template for this =E2=80=93 happy to share). This model helped us m= ap out how long it would take to penetrate the assumed market size and what profitability could look like based on various inp= uts (conversion rates, sales cycle length, etc.). It felt like a solid star= t.

But something was nagging at m= e.

The entire model =E2=80=93 the= hiring plan, the GTM investments, the sales commission structure =E2=80=93= hinged precariously on on= e number: the total numbe= r of accounts with 50-200 employees. My client was about to bet a significa= nt chunk of their next year's strategy and budget on this figure. Any m= ajor error would ripple through the entire company.

We had cross-referenced our target number with data from= a couple of reputable providers, and their figures generally aligned. Yet,= my spidey sense was tingling. I couldn't shake the feeling that trusti= ng these numbers implicitly for such a high-stakes decision wasn't good= enough.

Digging Deeper: Valid= ating Assumptions the Hard Way

We agreed that a decision this fundamental deserved extra diligence. It = was time to validate our market size assumptions from the ground up. Here= =E2=80=99s what I did:

<= div style=3D"margin-left:0px;" class=3D"edm_outlooklist">

    = Data Collection via Clay:= I built a Clay worksheet leveraging the Google Maps API. The goal was to i= dentify any company whose online presence included = keywords relevant to the founder's specific niche within Industry N.

  1. AI-Powered Filtering: Crucially, I then created an AI classifier prompt within Clay.= This prompt instructed an LLM to analyze each company's description an= d determine if it was trul= y the right type of compa= ny for my client =E2=80=93 not just tangentially related.

(Sidebar: Why Standard Data Pro= viders Can Fall Short Here)

T= his process highlights a common challenge with third-party data providers. = They often get criticized for data quality, but it's partly by design. = Their incentive is usually to maximize the volume o= f data matching broad criteria =E2=80=93 they don't want to risk leavin= g out a potential lead for their customers. In casting this wide net, they = inevitably pull in companies that technically fit the filters (industry, em= ployee size) but aren't actually a good fit for= your specific solution or value proposition. The issue isn't the raw d= ata itself, but the lack of sufficiently granular filters to precisely define your ideal customer prof= ile based on qualitative factors.

The Alarming Results

The = outcome of our deep dive was both validating and alarming.

  • The Good: The tota= l potential number of companies in the space loosel= y matched the top-down reports. The market was roug= hly that size on paper.

  • = The Bad: The number of companies that were a good fit and within the target employee range was nearly 50% low= er than the initial data suggested.

The "Near Miss" and Its Potential Fallout

I couldn't shake that &quo= t;near miss" feeling =E2=80=93 like watching a potential car crash unf= old and realizing a slightly different decision could have put you right in= the middle of it.

Fortunately, = my intuition kicked in, and the client readily invested the extra time to g= et the foundation right. But let's imagine we hadn't:

  1. Over-Hiring: They would have built their hiring plan based = on a market twice as large as reality, likely recruiting double the necessa= ry salespeople.

  2. Missed Quotas & Morale: Sales reps would have struggled immensely,= facing a scarcity of viable accounts. At best, the team might have hit 50%= of their target, leading to frustration, demotivation, and eventual turnov= er.

  3. Wasted Resources: Think of the management overhead spent hiring people they di= dn't need, the months spent managing underperforming (through no fault = of their own) reps, and the painful process of rightsizing the team later.<= /span>

  4. Strategic Risk: This wouldn't just burn cash and time; it could have jeopardiz= ed their ability to hit milestones crucial for their next funding round or = achieving profitability. Burning through resources based on flawed data can= quickly deplete a startup's limited "matches." While perhaps= not fatal in this specific instance, it would have been dangerously close = =E2=80=93 all stemming from one unchecked assumption in a spreadsheet.

The Real Hero: A= ccessible AI + Clay

The real= star here is 03-mini. Before= some of the more recent models, having an AI look at all the relevant deta= ils and make an assessment that I would trust my GT= M program felt like science fiction. We used to have a big team of folks to= manually process lists like these, it was so expensive that it only made s= ense to run for really small lists of really high value targets. With 03 mi= ni + Clay, it=E2=80=99s so reliable and cheap that it=E2=80=99s almost irre= sponsible not to do this for every list I=E2=80=99m running.=C2=A0

Before LLMs, having an AI reliably analyz= e qualitative details (like a company description) and make a nuanced judgm= ent call you could confidently base your GTM strategy on felt like science = fiction, or at least were prohibitively expensive.

I've built top-down/bottoms-up forecasts before, oft= en relying on subscriptions to expensive data vendors. While those provide = a starting point, anyone who's run serious outbound campaigns knows lis= t quality degrades quickly. It's like a Google search =E2=80=93 page on= e looks promising, but by page three or four, the relevance often nosedives= .

Applying This Yourself: AI-P= owered List Qualification

The= only robust way to combat this decay and ensure high list quality is to ha= ve an AI individually assess each potential account before it makes it into your strategic plans or your reps' outreach se= quences. The best way to do this is to use Clay to inject the Company Name = and Description variables into a prompt and run it at scale. It=E2=80=99s t= he same idea as sending a cold email to =E2=80=9CHi First Name=E2=80=9D but= instead of sending an email, you=E2=80=99re injecting variables into the p= rompt.

A simple method involves:=

  1. Pulling company names and= descriptions (e.g., from Google Maps, LinkedIn, or other sources) in Clay.=

  2. Feeding the company name & description into an LLM via a &q= uot;classifier" prompt.

  3. <= p style=3D"line-height:24px;padding:0px;text-align:left;word-break:break-wo= rd;">Filtering based on the output o= f the classifier

  4. Sourcing contacts for =E2=80=9Cgood fit=E2= =80=9D companies

= This is excellent for basic categorizations like "Software vs. Service= s," "B2B vs. B2C," or, as in my example, "Is this compa= ny actually in my specific niche based on what they do?"

Here=E2=80=99s a simple Clay template= that does just th= at.

To use it:

  1. Sign up for Clay (if you haven't alread= y).

  2. Add this template to your account.

  3. Add y= our OpenAI API key in Clay's integrations settings.

  4. Adju= st the classifier prompt for your business.This is key. You need to tell th= e AI exactly how to judge the companies based on th= e data it will receive (in this basic template, primarily the company descr= iption).

How t= o Create an Effective Classifier Prompt (Using ChatGPT or similar):

= You can ask an AI like ChatGPT to he= lp you draft the prompt. Here=E2=80=99s a prompt I used yesterday:

=E2=80=
=9Cwrite a classifier prompt for me, the inputs will be company name and li=
nkedin description. I'm looking for companies that <blank_a> but not =
<blank_b>. Here are 3 examples of companies that <blank_a> and =
3 examples of companies that <blank_b>.

The Takeaway

Don't= let seemingly solid top-down numbers or basic list filters lull you into a= false sense of security when making critical GTM planning decisions. Valid= ate your actual addressabl= e market with a level of = granularity that reflects your . = Thanks to accessible AI, this deeper validation is no longer a luxury; it&#= 39;s a crucial step to de-risk your strategy and build your growth plans on= solid ground.

Have you faced sim= ilar challenges with market sizing or list building? Hit reply =E2=80=93 I= =E2=80=99d love to hear your experiences.

Collin

PS - if you m= ade it this far, let me know if you want more geeky-gtm posts like this or = less. Just hit reply with =E2=80=9Cmore=E2=80=9D or =E2=80=9Cless=E2=80=9D.=

PPS - if you=E2=80=99re struggl= ing with the sheet, I can help but my time costs $ =F0=9F=98=89.=C2=A0

PPPS - I=E2=80=99m thinking of switching from publishing on Friday= s to Wednesdays because newsletters get better engagement during the week. = Good idea? Bad? I=E2=80=99d love to hear from you.

3D"yt"3D"in"
 

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