Making Marketing More Effective with AI
Increased efficiency closes more deals.
Email. (source: PublicDomainPictures.net)
Marketing teams have been using machine learning for more than a decade. In the early days of big data, it was common to hear people say that marketing was data’s killer app. As data science has evolved into artificial intelligence, people in marketing and sales have discovered a variety of ways of using data to make them more productive: helping to find the right audiences in their ad targeting, predicting just the right time to land an email in a recipient’s inbox to maximize the chances of getting an open, and even personalizing their company’s web experience or advertising to make it most appealing to their customers. Now we’re starting to see these same teams make the use of generative AI in their marketing and sales programs to continue to drive greater productivity and efficiency from their efforts.
Three generative AI products that have made our marketing and sales teams more productive: LinkedIn’s Sales Navigator and 6sense’s AI Email Assistant and Conversational Email products.1 Sales Navigator highlights useful information about an organization in ways that are easily useable by sales staff. This is a classic application of generative AI: it’s excellent at digesting and summarizing information, doing a lot of basic research for you. It looks at press releases, product information, LinkedIn (of course), and other sources to give an accurate, up-to-date picture of what’s important to an organization. Who are the key people? What partnerships are they involved in? What are their goals? What are their financials (to the extent that they’re public)? Salespeople need this information at every stage of a sale, from writing an initial email to closing the deal. Now it’s at their fingertips, without requiring hours of research.
Learn faster. Dig deeper. See farther.
The AI Email Assistant, which incorporates Conversational Email, is more complex. Conversational Email helps to automate the early stages of finding qualified prospects. An AI writer drafts messages to be used with prospects. The draft is created from information in the assistant’s knowledge base and suggestions from the salesperson. Prompting is familiar to anyone who has tried prompt engineering with ChatGPT: “Imagine you are a friendly salesperson sending an email to an attendee of O’Reilly’s Strata Conference…”
The draft emails generated by the assistant tend to be too long and focus on our products too soon in the conversation rather than addressing the client’s needs. But editing a draft is much easier and faster for a human than starting with a blank page. Our staff often compares the assistant’s initial draft with output from other generative AI tools like ChatGPT, combining and mixing in ideas. They also edit for length; short emails are more effective than verbose messages, and anyone who has played with generative AI knows that it is verbose. The best way to use AI, as Ethan Mollick has written, might be to read AI’s suggestions and then write the message yourself. That way, it won’t sound like AI output, and it will incorporate the salesperson’s own thoughts and ideas. It’s essential to keep the human in the loop.
Drafting emails is useful, but that’s not where the real value lies. The assistant is capable of classifying and routing responses. A virtual inbox manages the conversation flow—and this ability to manage the early stages of a conversation is more important than creating draft messages. It allows a user to define different campaigns for different kinds of prospects, each with a distinct “cadence” for contacts: an initial email, followed by an email to set up a call if the response is positive or other emails to answer initial questions.
Responses to each message are analyzed and classified into one of several categories: uninterested, interested later, serious prospect, and others. The assistant also classifies leads on the basis of their role, assigning different leads to different campaigns. Our salespeople work with many different kinds of people: technical staff, technical leads, operations, HR, executives, and more. They all have different interests—but it’s all too easy for a human to make judgments based on preconceived ideas rather than facts (for example, “programmers aren’t interested in management skills”; they are). Based on the reply, the assistant could automatically notify a salesperson of a serious prospect from the HR department and start the process of setting up a meeting. It can mark a prospect as “not interested” or “possibly interested later” and initiate a closing sequence. It can manage a referral to another potential client. And it can give potential users who don’t have decision-making authority tools to advocate for our products within their company. If the assistant has trouble classifying a message, it notifies a human. Someone can then make the classification, and the AI assistant uses that information for future emails.
AI pays other dividends too. Every response—even “not interested” or no response at all—yields information. AI can tell us which campaigns are more effective, which emails are more likely to generate a positive response, and why: What issues do prospects respond to? What do they care about?
If provided with appropriate training data, the assistant can reframe a conversation. For example, if one of the prospect’s problems is “the difficulty of hiring qualified talent” (for example, developing AI products), the assistant can attempt to reframe the conversation around developing their current staff’s skills: the demand for AI talent is huge and the supply is limited, so the fastest and most reliable way to acquire AI talent is to augment your current employees’ skillsets. Again, it’s important to keep humans in the loop so that the conversation doesn’t go off the rails—but the ability to reframe a conversation appropriately saves a lot of a salesperson’s time.
Saving time is ultimately what these tools are about, but it’s important to understand why we want to save time. We want to make our salespeople more productive, to free their time to do things that an AI can’t do—or, more appropriately, not to spend time doing things that an AI can automate. AI can’t close deals. While AI can make some simple statements about a customer’s needs, it’s not able to explore the customer’s situation deeply, help them see what the real issues are, and make suggestions about how our products fit requirements that they didn’t realize that they had. Reframing is important, but it’s only a start.
So what can AI do, and what parts of the sales process can it take over? This is where classifying responses plays a huge role. Much of a salesperson’s job involves processing leads through the prospect funnel. The first few steps of that job are fairly mechanical. For example, you might send a standard email to every attendee of a conference—maybe 1,000 or 2,000 attendees. Most of them won’t reply, but you’ll still have a few hundred replies, which need to be sorted into categories. Leads can also be assigned to different campaigns, all managed through conversational email: for example, former customers can be assigned to a campaign that is designed to win them back. Managing this filtering process requires a fair amount of time-consuming work, especially if it has to be done manually: ending a conversation on a positive note, adding possible prospects to a database, and scheduling calls with the most serious prospects. That kind of filtering is an excellent job for AI.
So conversational email is really about scale: filtering 1,500 show attendees, all of whom are possible leads, down to two or three highly qualified leads, 20 or 30 possible, a few hundred to try again later, and a thousand who showed no interest. Salespeople still need to be the “humans in the loop” who edit messages, prevent conversations from going astray, and help the AI sort responses, but they have much more time to spend closing deals with the most serious prospects. In turn, AI’s ability to classify email at scale increases the number of early-stage prospects with whom you can engage. More prospects enter the funnel and in turn, that means that there will be more high-quality leads for the salespeople to work with.
So, what have we learned?
- Salespeople need to remain in the loop at every stage.
- Rewriting AI-generated messages to ensure that they have a human voice is a best practice.
- The biggest gains in efficiency come from classifying responses and managing the response pipeline, not automated email generation.
- The goal is closing more deals, not minimizing headcount.
People have used tools ever since we lived in caves, and AI is just another tool that marketing and sales can use to become more productive. We are still in the early stages of figuring out what this particular tool can do and how we can use it effectively. We’re still making the mistakes that are part of learning a new technology. But we have already seen that AI makes our salespeople more effective, makes them better at the jobs they are already doing. Is this a revolution or just incremental growth? It doesn’t matter; in either case, we are part of it.
Footnotes
- LinkedIn is a customer of O’Reilly Media. 6sense is not. This article discusses O’Reilly’s experiences with these products. It is not sponsored by either LinkedIn or 6sense.