ChatGPT wasn't built for writing personalized sales emails
Writing personalized and effective B2B sales emails is a major challenge every SDR (Sales Development Representative) faces. It’s time consuming, costly, and difficult to address the correct audience. OpenAI’s ChatGPT has burst onto the scene, wowing the world with its conversational text-generating capabilities.
B2B sales and marketing has been all over new generative text use cases. ChatGPT is helping avoid writer's block when it comes to generating content such as blogs, emails, meeting summaries, social media posts, and more.
However, one of ChatGPT’s biggest shortcomings is that it’s only “pretty good” at everything, and isn’t great when it comes to specific tasks. That’s why users find themselves editing the AI generated content prior to sending it out.
That’s where Autobound comes in—a “ChatGPT specifically for writing personalized B2B sales emails.” Since Autobound includes real-time information in its algorithm, it helps ensure that your email copy will be relevant and up-to-date when it reaches the customer—something that ChatGPT and other generative AI tools can’t offer.
ChatGPT’s limited knowledge of real-time events
ChatGPT was trained on information just up until 2021 (see the image below). Because of this, emails generated through ChatGPT are painfully generic and lack any level of real personalization (such as mentioning a company’s initiatives, news events, etc). Because of a “limited knowledge of world events”, ChatGPT is unable to incorporate critical insights that SDRs use inside of sales emails to generate a high response rate.
Since Autobound includes real-time information in its algorithm, it helps ensure that your email copy will be relevant and up-to-date.
What Makes Autobound Different?
Autobound uses custom machine learning models and millions of insights from across the web to generate personalized emails for SDRs and AEs instantly.
Unlike other generative AI tools like ChatGPT, Lavender, Regie.ai, or Jasper.ai, Autobound is able to create bespoke sales messaging specific to each user that integrates “insights” plucked from thousands of sources across the web (news events, social media, etc).
In the above example, the CEO (whose name has been redacted) mentioned their love for ultra marathon running on their LinkedIn profile. By threading this information through custom machine learning models, Autobound was able to create a compelling message with more of a “human touch” than other generative AI tools.
To better illustrate the point, we created a prompt for ChatGPT in an effort to produce a similarly high quality email for CloudHedge’s CEO on behalf of an LTSE (Long Term Stock Exchange) seller.
ChatGPT is great for streamlining general tasks, such as generating email copy. Its algorithms are trained on historical information from the web, so it can generate text that is stylistically similar to what a human might write. However, it does not include any real-time information that can be used to drive personalization. Nor can it position your company’s value proposition around the unique pain points of the prospect you’re reaching out to. In short, while ChatGPT may help you get to 60% of what a good sales email looks like, it won’t take you all the way there.
Conclusion:
Whether you’re selling, recruiting, marketing, customer-supporting, or fundraising, cold emailing is a way of life in the workspace. Unfortunately, actually generating a reply is more difficult and time consuming than ever before. Anyone can sniff out a templated, spammy email from a mile away.
Though painfully monotonous, writing a personalized and unique message is an absolute must-have. Generative AI is revolutionizing this process.
While ChatGPT offers a great starting point for automating your sales emails, it falls short in providing personalization based on real-time data points like current news events or job changes. That’s where Autobound shines: by utilizing custom machine learning models and millions of insights from across the web to drive personalization at scale.
