Introduction
Is it possible to believe that the e-mail below is 100% automated?
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Yes, the era where the dynamic variables in an automated e-mail were {first_name}, (account_name}, and {job_title} are long gone. As you can see in the e-mail above, the cold email campaign goes into the specifics on a post that the prospect has made and what was its content. We may not notice but even the tasks of “planning, scheduling, and tracking your posts across various channels” was personalized to Max.
This is the original snippets structure of the e-mail above with all its dynamic variables:
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This is a simple example, but you could go way beyond that and insert all kinds of professional, personal and emotional connections. You could, for instance, identify what university your prospect went to and make a very personalized reference: “Noticed you went to University of Virginia”. Or even quote any kind of achievements, projects and certifications your prospects have made: “Just read your podcast interview on supporting non-traditional career paths on the Portfolio Carrer Podcast. Loved your point about the lack of resources for non-traditional career paths after college.” Yes, all that can be 100% automated, and this is just the beginning.
Meet Clay - The mastermind behind the AI Revolution
Clay is a GTM (Go-To-Market), sales and marketing platform founded in 2017 that had an astonishing growth in revenue and valuation. The company achieved a tenfold increase in revenue during 2022 and 2023, followed by a sixfold growth in 2024. After securing its first Series A funding in 2019, Clay reached a valuation of USD $1.25 billion five years later, during its most recent Series B funding round in 2024. According to Clay’s website, the platform is already utilized by over 5,000 customers, including prominent companies such as OpenAI, Canva, Anthropic, Ramp, and Rippling.
Yes, you heard it right. The AI founders themselves – OpenAI – uses Clay. As mentioned in this Forbes Article:
The Head of GTM systems in OpenAI, Keith Jones, said that his team used Clay in its workflows to launch and test new business-to-business sales tactics on a daily basis. “In my professional opinion they have one of the most practical and exciting applications of AI, in a decades-old practice that has long been stale,” Jones wrote in an email.
Clay is disrupting the way sales operates and it has potential to top up with the biggest sales and marketing platforms, like Hubspot and Salesforce, in a really (and let’s emphasize this “really”) short time.
Quoting the Forbes Article once again:
Already handling the licensing and collection of outside, third-party data for customers, Clay has capitalized on improvements in AI to supercharge those efforts. Clay’s software can now do some of the work for the user, predicting what data points and patterns might be valuable. Its AI agent, called a ‘Claygent’, can take a potentially valuable but complicated question, such as finding every previously disclosed customer of a company, or every person on LinkedIn who worked in certain roles at that company over a specific time period, and return just the results.
Let’s dive deeper.
Clay revolutionizes the automation of processes that were previously impossible to automate. Before Clay, SDRs and sales professionals conducting cold outreach faced two primary approaches:
- Sending bulk automated cold emails with highly templated messages, such as, “Hi {first name}, I wanted to reach out to {company_name} because we can help you achieve X result.” Unsurprisingly, 98% of these emails are ignored, as they are clearly generic and lack any real relevance to the recipient.
- Employing a large team of SDRs and BDRs to manually research companies and craft personalized one-to-one emails. While this approach is more effective, it is costly, time-intensive, and heavily reliant on the size of the team—making it challenging for smaller teams to scale their outbound efforts.
Now, everything has changed. Clay enables sending one-to-one personalized messaging at the scale of high-volume automated emails. This shift is transformative. Instead of needing a team of five SDRs, a company can now deploy one Go-To-Market (GTM) Engineer (more on this role later) who, with Clay’s setup, can send up to 30,000 emails per month. These emails are not only high in volume but also hyper-personalized, heavily researched, and highly relevant - appearing as though they were crafted by a person.
However, it’s important to note that fully AI-generated messages are not yet entirely viable for outreach. With Clay, users can create a hybrid approach: combining human input with AI-generated snippets to enhance templated content.
Beyond email personalization, Clay eliminates the tedious research process traditionally required of SDRs and BDRs. Previously, sales professionals would individually research prospects—scouring Google, analyzing 10-K filings, and leveraging LinkedIn Sales Navigator to gather data. This manual process was time-consuming and labor-intensive. Clay automates this entire research component by using AI agents to source publicly available information about individuals or companies, at scale. This allows sales teams to focus their time on selling rather than researching and writing.
Clay also excels in managing inbound lead generation. It centralizes all inbound leads from various marketing initiatives—such as LinkedIn ads, organic LinkedIn content, newsletters, lead magnet campaigns, webinars, and in-person events—into a single hub for scoring, enrichment, and normalization.
If your marketing efforts generate leads through multiple channels but you fail to act on them on a timely manner, you are wasting both time and resources. Clay ensures that all leads from these campaigns are automatically consolidated in one place, enabling you to act on them - fast and at scale. Tasks like enriching leads, gathering additional data, scoring, and qualifying can be seamlessly automated for both outbound and inbound efforts. Once organized, these leads can be transferred to platforms like Apollo, HubSpot, or Salesforce for outreach sequences.
The true power of Clay lies in its role as a connective platform that integrates with other go-to-market tools rather than competing with them. This eliminates the need for additional subscriptions while enhancing cost-efficiency.
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Clay represents an unprecedented evolution in the application of AI within sales frameworks. If this innovation excites you as much as it did when I first encountered it, just wait a bit. There is much, much more. Let's take a deeper dive into AI-powered lead generation and automation possibilities - not just with Clay but also with AI GTM Agents - along with real-world case studies showcasing these tools in action.
Precision Targeting
Buying Intent Signals
Signals provide insights into a prospect’s “buying intent”. In other words, they help determine not only whether a prospect is a suitable match for the solution you are offering but also their “sales readiness”—whether they are likely prepared to engage with your sales team and if the timing is right for you to make a move. Examples of effective buying intent signals include:
- Interactions on your website: content conversions, number of pages viewed, specific pages viewed (ex: pricing or contact page), forms submitted, etc
- Email engagement: emails open, emails click, emails reply, and negative metrics such as “unsubscribes”.
- Social media engagement: following your company on various channels, engaging with social content (shares, comments, etc)
The examples above represent signals derived from traditional marketing analytics and tracking codes—commonly referred to as “first-party intent data.” This data is collected directly from your own digital environments, such as your website, CRM, social media efforts, or offline interactions. Now, we can explore a broader range of signal possibilities through “third-party intent data.” This data is gathered from external sources, offering a wider perspective on buyer intent by leveraging methods such as cooperative data from publishers or web scraping. Essentially, any publicly available information on the internet can be transformed into a buying intent signal. Examples of such signals include website content updates, financial filings, Google Maps reviews, or social media activity.
One commonly used example is “hiring intent.” If a company has open job postings in a specific area, it may indicate a need for related services. For instance, a company hiring a digital marketing analyst might require marketing services, while hiring for the HR department could signal the need for recruiting agency support. More complex examples are also possible: if you offer a compliance platform, you might monitor when prospects announce plans to pursue SOC 2 certification or open offices in highly regulated regions. Successful teams often combine multiple signals - for example, layering third-party signals (e.g., a company doubling its engineering team) with first-party signals (e.g., a trial user signing up). The possibilities are limited only by your creativity.
The image below illustrates the vast range of buying intent signals available within Clay. Additionally, Clay integrates with other data providers to further enhance its capabilities. For instance, the platform Delivr offers buying intent insights at an individual level rather than just at the company level. Common Room has compiled an impressive list of 100 signal ideas in a Google Sheets document - in case you need some inspiration.
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Account and Persona Prospection
We are accustomed to filtering both companies and personas when building a list of prospects for outbound efforts, typically using criteria such as industry and company size for account-level information, and job titles for persona-level information. With Clay, however, we now have access to an expanded array of filters for both individuals and companies, along with an entirely new category for precise targeting, using "signals".
With the ability to access and retrieve virtually any publicly available data point on the internet, we can now leverage our creativity to develop new filters when searching for a company or an individual. For instance, when searching for a person, you can not only filter by job titles but also by their years of experience or specific keywords in their profile. If you have an Industrial Technology (IndTech) startup, you might search for terms like “Industry 4.0” or “Innovation” within a prospect's LinkedIn profile. Additionally, you could use an AI-powered Boolean logic formula to determine whether the individual is genuinely a decision-maker within their organization.
When searching for a company, you could create a prompt for an AI assistant to identify whether a business operates as B2B or B2C (a feature already available on platforms like Apollo) or even narrow it down further to determine if they are a SaaS company. For example, you could use a prompt such as:
“Is the company with the name {{Name}} and domain {{Domain}} a Software-as-a-Service company? Return two fields: the first as a boolean (\true\ for yes or \false\ for no), and the second field with the reasoning behind the response"
The real deal, though, is the new ability we have now to search for small businesses using Google Maps. These are companies that are not on LinkedIn and up until now to scrape the web for this information was a painstaking task. Now we can prospect bakeries and hair salons just as easily as prospecting fortune 500 companies.
Follows some other examples of searches we can do now with Clay:
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Data Enrichment
Until recently, I relied on two separate tools, Apollo and Lusha, to retrieve prospect phone numbers. I noticed that Lusha often provided alternative numbers for the same prospects, increasing the likelihood of reaching someone during cold calls. However, this process was highly time-consuming, as it required manually navigating each LinkedIn profile. Fortunately, those days are over. Clay offers a streamlined solution by allowing users to search for work emails and phone numbers across multiple data providers simultaneously, including Prospeo, DropContact, Datagma, Hunter, PeopleDataLabs, Nimbler, Apollo, Lusha, Snov, and others.
Well, maybe not simultaneously. Clay operates using a waterfall system, sequentially querying each source and stopping once a valid email is found. Users can decide whether to stop the search after finding an email - ideal when email addresses are the primary goal - or to continue enriching the data table with all available options, such as multiple phone numbers.
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As illustrated in the image above, Clay’s email waterfall not only identifies emails but also validates them. Additionally, the waterfall system can enrich other data points, such as company-related information (industry, company size, revenue, funding stage) or other valuable insights like job openings, recent news, and additional data that provide a more comprehensive understanding of our prospects.
One of the most notable innovations in data enrichment is Clay’s ability to utilize its “AI Clay Agent.” If a specific data provider app is unavailable for certain information, users can create a custom prompt for the AI to search for the required data across the web. Anything, anywhere. In the example below, we are instructing AI to locate case studies on a prospect’s website. This information can then be leveraged to craft personalized emails, which is the topic we will see next.
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Personalization at scale
The idea of this article is not to be a Clay tutorial. So let’s keep it simple just so you can understand the logic behind how this personalization at scale works. Once we have enriched our prospect’s data with the necessary details for personalization, we proceed to create “AI snippets” for each sentence in our email. Rather than generating full paragraphs or extensive content (as AI is not yet fully reliable for that), we carefully craft individual sentences or smaller segments of text, one at a time.
When developing these snippets, we provide clear, step-by-step instructions to guide the AI on how to construct a sentence using the relevant data from your enriched table. Example:
“Always begin the subject line with 'Congrats on.' Ensure the final subject line does not exceed 10 words. Always use second-person pronouns. We are drafting a subject line for {FullName}, who works at {CompanyName}.”
Here, dynamic variables such as {FullName} and {CompanyName} are utilized in the prompt to direct the AI to the specific data it needs from the Clay table. These dynamic variables enable the reuse of data across various datasets.
There are essentially two methods for generating AI snippets for personalized emails. The first and simpler method is outlined above. In this approach, we instruct the AI to generate a sentence by specifying how it should begin, what its purpose is (e.g., summarizing, commenting, praising), and its length. This method is best suited for short paragraphs containing no more than 15 words. Below are some examples:
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To create a more advanced personalization snippet, we utilize the second method: designing an email template and incorporating placeholders (dynamic variables) enhanced with AI-generated snippets within the content.
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We will then create various snippets for each section of our email, following the method outlined above. This includes the subject line, opening sentence, subsequent sentences, and even a PS at the end, if applicable. By leveraging AI formulas, we will combine all these elements into one cohesive email, customized for each prospect. It’s hard to believe that both emails below originated from the same template.
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Note that the e-mails above start with a different opening. The first one starts with “I wanted to reach out because I saw your post…”, while the second one starts with “I saw the news about…”. Don’t panic. It’s still 100% automated. What we did here is a conditional formula, instructing the AI to select the appropriate opening sentence based on the information available for that specific prospect (such as a post, news, or case studies) using an “if-else” sequence statement.
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Follow some examples. The copies below were provided by Jed Mahrle, from Practical Prospecting. If it wasn’t for him, I believe I would still be lost in the darkness, since he was the one that introduced me to Clay. In one raining morning, he woke-up with the right foot and decided to publish his “sales playbook” as a lead magnet in his website. A very simple pdf, without any formatting, but very, very informative. I spent an entire day thoroughly studying his strategies when I came across an email copy containing a dynamic variable that initially didn’t make any sense to me. I sad to myself, how on earth can he insert that information on an automated email? And there it was, in a footnote: “all that can be 100% automated with Clay”. Thanks Jed. You changed my life.
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