Nate, one of our founders, actually started doing cold outbound 25 years ago at the age of 9. He did not yet have access to the Internet, but to support the launch of a pet-sitting venture, he rode his bike to every house in the neighborhood to place a promotional flier in mailboxes. This yielded 4 clients from ~150 houses, a strong success rate that he has attempted to replicate ever since.

We believe that entrepreneurs at their core are much more like painters, DJs, chefs, photographers, directors, etc. In the woefully gender-biased words of Christopher Walken playing Paul Rayburn in Man on Fire, "A man can be an artist... in anything, food, whatever." More eloquently: Human creativity is universal. Starting a company is a creative act. Early business models are surrealist ideas and startups are blank canvases - not color-by-numbers books.

Go-to-market is the fuel for these creative acts. We love cold outbound because it unlocks this creative self-actualization for people creating businesses. There was nothing there. Then someone thought of something new and shared it with others. Now there's something there. The world was a certain way, but then someone did something and it will never be the same. What we like best is facilitating people's “dents in the universe.”

Cold outbound absolutely has been bastardized by turn-and-burn spam shops peddling YouTube clickbait growth rates, but that's not what it's really about. We care about what we do because it facilitates a certain type of human creativity.

This is the framing that we bring to our considerations of how cold outbound is affected by the rise of AI. For all of its advantages (and there are many), even generative AI is unable to replicate the creative spirit at the core of our work. We are not worried that it can replicate the work that we do, but we are excited about the possibilities that it affords.

With that in mind, how are we finding AI useful in our work so far? There are three primary functions we’ve found, ordered below from the least to most impactful:

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1. Writing email copy: ChatGPT can write a solid cold outbound email (you may have heard!). Maybe not great email copy, but we don’t think that’s the most important part of the job anyway. Given the lack of importance that we place on email text in our methodology, this capability is at present a minor time-saver, but it has proven to be helpful for A/B testing.
2. As part of a lead generation value chain: Finding leads is a challenge, always and forever. This is no longer because we do not have a way to find millions of email addresses - tools like LinkedIn & Apollo have taken care of that - but sourcing frames for targeting can be a challenge. By way of example, we often work with clients to reach out to prospects who attended a particular conference or are members of a specific association, but it can be difficult to source a complete list of conferences to begin the search. We’ve used ChatGPT to make a list of “up-and-coming e-commerce brands,” etc. This saves a great deal of time on the front end when creating leads, though it still requires a good database to turn the raw list into names and emails that can be contacted.
3. Big data analysis: Incendium works diligently to adapt and iterate our targeting based on the results we see in our outbound campaigns. We are, however, constrained by our (human) ability to assess causality across a wide range of variables. We have yet to fully actualize this use case and are on the lookout for tools that help in this way. We have a hunch that, because most sales organizations do not approach go-to-market the same way that we do, products are not being built to serve this function. But if you know any companies looking at this, or any products that could effectively be repurposed as simulated annealing machines for cold email, we’re all ears!

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We expect that AI will continue to offer transformative efficiencies in nearly every industry. We are no exception. Our role is and will continue to be to serve as a sherpa for clients in order to help them identify real value vs. noise. We’re excited to keep experimenting.

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