Hoping to appear relevant, businesses love to invoke AI in their marketing every chance they get — but not everyone is impressed. When the hype gets out of control, facts get lost in the haze and the real promise of artificial intelligence regresses into a sci-fi version of snake oil.
As a result, companies — including those most likely to benefit from AI-driven solutions — are starting to doubt the claims of vendors plugging their wares. On the opposite side, marketers tasked with communicating the business utility of their AI products are left with a disinterested or distrusting audience.
So, how do you sell AI without causing your audience to roll their eyes?
Conventional tactics such as customer testimonials have their uses — but only up to a certain point. Textio, a predictive writing platform used in talent recruitment, relies on videos, case studies, and co-branded events with customers like Johnson & Johnson, Vodafone, and Nvidia to demonstrate their business value.
However, customer testimonials aren’t universally applicable. If a client in a competitive industry sees your solution as a true differentiator, they obviously won’t rush to tell everyone about it and might even ask for non-disclosure agreements (NDAs). An earlier-stage startup may very well have exceptional technology, but lack the big name brands to publicly tout its virtues.
To find other effective approaches, we talked with leading marketers and brand communicators who’ve employed unique strategies to stand out in an already saturated marketplace.
1. Educate Customers On Alternative Technical Approaches
You’ve probably heard companies brag about using “machine learning” and its trendy sub-field “deep learning”, but how many companies can you name that use “evolutionary strategies”? Artificial intelligence is comprised of many technical approaches, only a few of which are currently overhyped in the media.
With more than 40 patents in artificial intelligence, Sentient Technologies leverages this fact to educate their customers about their unique approach to evolutionary computation for digital marketing, e-commerce, and finance. In evolutionary strategies (ES), a set of candidate solutions is provided for a given problem and evaluated according to a “fitness” function. The best performing solutions “survive” and the poorly performing ones “die” while increasingly better solutions are created in the next generation.
“Unlike with deep learning, people intuitively understand how evolution works,” says Jeremy Miller, Sentient’s Director of Marketing. Another key benefit that Sentient touts is that, unlike supervised machine learning, which requires large volumes of clearly labeled data, evolutionary strategies don’t have the same dependence. The company uses ES in conversion optimization software that automates website testing with the goal of lifting conversion rates and driving revenue. Rather than use historical data, the product evolves designs based on live, real-time data.
Even though ES algorithms are comparatively unique and don’t have the same high data requirements, Miller cautions against leading with technical differentiation and prefers to highlight utilitarian benefits, such as the fact that their product can be installed on a website with just a single line of javascript code. “Customers care about outcomes. We don’t lead with AI anymore. We lead with what the customer wants,” he emphasizes.
2. Tout Your Hard Science Background In Your Brand Story
Plenty of talented PhDs and postdocs now work at promising AI startups, but how many of them can say they’ve deployed their algorithms on Mars? Deep space is the most unforgiving environment, with zero fault tolerance, limited space for hardware, and a need for autonomous operations.
When Edward Yang, Managing Director at Firecracker PR, started working with Caltech startup Beyond Limits, they didn’t have any corporate use cases. Reporters were confused by early versions of their website and could not figure out what the company did. “Very technical founders are not always the best at communication,” says Yang, who opted for a more compelling storytelling approach.
“Beyond Limits is the only AI solution born in the labs of NASA and battle-tested in deep space missions, including on the Mars Rover,” he shares. “So we opted for the story angle of bringing the most powerful AI from Mars to Earth.”
Now Beyond Limit’s technology is used in numerous sectors, such as energy, finance, autonomous vehicles, healthcare, and internet of things (IoT). While the NASA background certainly gives the company technical street cred, telling the story is not always easy.
“Our biggest challenge is time,” Yang explains. “The best thought leadership comes from the scientists and the technical founders, but in a startup everyone is running in different directions.” Many employees also don’t like to write or aren’t good at writing, so Yang and his team prioritize spending time talking to sources and flushing out their technical expertise.
3. Ditch Buzz Words & Meet Customers Where They Are
You might be a savvy techie who genuinely understands and uses words like “neural networks” or “deep learning” in everyday conversation, but some people — including Protagonist’s early prospective customers — might find that uncool.
“Protagonist started by trying to hammer home their unique technology by pioneering the buzzword ‘narrative analytics’, but this just wasn’t resonating with customers,” recalled VP of Marketing Damon Waldron.
A veteran B2B marketing expert, Waldron currently heads the team at Protagonist, a company which leverages AI to capture the stories and beliefs which drive people’s behavior. Fortune 500 companies like Wells Fargo, Starbucks, and Microsoft use the product to assess how consumers really feel about their brands and develop a communication strategy. Asked to distill the biggest challenge in communicating AI innovation, Waldron responded: “The tech news cycles are overly saturated with frilly buzzwords. It’s been a challenge to truly communicate the significance of a product via pen and paper when competing against so much noise.”
Talking about “narrative analytics” did not connect with buyers, so Waldron opted instead to position Protagonist against well-known, commonly used marketing tactics that his customers did understand. These included traditional market surveys, which are limited in scope and introduce bias, and social media monitoring tools, which are often quite shallow and don’t extend beyond positive or negative sentiment analysis.
He also uses Protagonist to market Protagonist. “We recently did an analysis of Harley Davidson as an exercise in how to reinvigorate a stale brand. We did that in three days. What we want is for the CMO of Brooks Brothers or another brand in the same situation to think about how they could use Protagonist.” Similarly, Waldron and his team can quickly put together educational content on any trending topic, such as whether traditional investment managers are under threat from robo-advisors. “We can easily host a webinar on the topic or throw together an infographic on how millennials are investing and insert them into our sales and marketing emails.”
4. Personify Your Platform & Make AI Interactive
People rarely understand how enterprise software works just by staring at screenshots or reading spec sheets. Within the right context, personifying your AI-driven software can demonstrate the interactivity of your platform in a clear but friendly way. If this tactic results in clients finally seeing the benefits and understanding how your product works, the extra branding effort may be well worth it.
Tradeshift did exactly that. The company’s customers are executives who work in the relatively humdrum worlds of supply chain management, procurement, and accounts payable. “There is a disconnect between supply chain and procurement professionals and IT,” explains CMO David Ahrens. “Since executives don’t work too much on the tech side, this starting gap makes it even harder for them to understand AI. Yet many of them realize that AI is a technology they must get their arms around quickly.”
To overcome this “familiarity gap”, as Ahrens put it, Tradeshift personified their AI platform by putting a human face — Ada — to their solutions. Giving their technology a conversational persona enabled customers to interact with Tradeshift’s platform in a more natural, intuitive way and quickly grasp the business use cases. When customers go to make bulk business purchases, they can easily ask Ada for help finding the best prices in a category or ensuring they stay under their procurement limits. Ada also helps them analyze their spending behavior from past periods in a multi-dimensional way.
Named after Ada Lovelace, Ada was born from an internal company hackathon. While Tradeshift’s capabilities extend far beyond what Ada can do, Ahrens cautions that you must take your customers on a buyer’s journey step by step. “If you say you have AI in everything, customers won’t know what to do with that information. Instead, they’ll get confused and won’t return. We decided to start from a specific use case and build from there.”
Stop Telling Tall Tales About AI
A wide chasm exists between those who build AI technologies and those who buy it. Communicating with technical and marketing jargon will not only confuse customers, but also scare them away. In each of these unique marketing cases, success was achieved after tailoring conversations to the level of the customer by referencing concepts they already understand, like evolution or social monitoring or space missions, and by making AI approachable with friendly interfaces that clearly demonstrate important use cases.
Thanks for writing this. It’s difficult to find resources indicating the strategies that AI companies are using to sell their products.