Attracting customers through emotional resonance is a time-honored marketing tradition. Back in 1930, when every housewife listened to serial radio dramas in the afternoon, a soap company came up with the idea of sponsoring a radio show with commercials and mentions in the script. The “soap opera” was a brilliant marketing innovation for its time: a way to attract the attention of a brand’s target market by putting it in a context that tugged at the listeners’ heartstrings and gave them something to discuss with their friends.
But what are marketing departments to do in the era of artificial intelligence (AI) engines, when consumers can outsource all but the most personally meaningful shopping tasks to algorithms that have no heartstrings to pluck?
As consumers increasingly include subscription boxes of goods chosen by algorithms as well as AI assistants like Alexa, Siri, Cortana, and Google Home in their buying process, AI engine optimization (AIEO) will become every bit as important to marketers as search engine optimization (SEO). However, brand managers must not make the mistake of assuming that optimizing for AI is as easy as optimizing for search.
Every marketing department today is familiar with SEO’s tools and tricks for improving content placement in search-result rankings. Tomorrow’s marketers will also have to dedicate resources to brand and product placement within general purpose AI systems, not just search engines.
AIEO builds upon SEO in three critical ways that companies must consider as they prepare for a future customer experience increasingly mediated by machine learning algorithms.
1. Takes emotion out of the equation
SEO teams use keywords, page structure, and other logical elements to boost search engine rankings, but this quantified approach is still intended to create a channel for delivering brand content to consumers, who can be swayed by emotion.
An AI assistant, on the other hand, doesn’t care whether a brand of coffee evokes nostalgia for Grandma’s Sunday dinners – not unless the consumer teaches it to care. As a result, the strategy for marketing to the AIs that are making decisions on behalf of customers will de-emphasize emotion-based brand experience. Instead, the AIEO team will focus almost entirely on the rational and logical aspects of brand offers, such as product features, customer reviews, and price, with one major exception: situations in which consumers have already stated a preference beyond those criteria.
2. Requires greater transparency from companies
Even when consumers trust an AI to make purchasing decisions for them, they are still likely to have at least a few product preferences in addition to price, features, and capabilities. An AI choosing among products will consider much more information about every choice from many more sources, simply because it can, and the more information an AI has about a product, the more accurately it can match that product to the consumer’s preferences. Therefore, it’s in a company’s best interests to offer up as much information about its products, brands, or services as possible.
For example, as consumers increasingly care about the ethical impact of their buying decisions, they’re likely to insist that their AI assistants shop based on previously unknowable attributes like recyclability, materials sourcing, carbon footprint, brand ownership, brand partnerships, corporate political donations, and hiring practices. A product that doesn’t meet these criteria, or doesn’t say whether or not it does, is likely to be passed over.
3. Demands content optimization at the speed of AI
To get to the top of AI rankings, organizations will have to understand how these algorithms are making decisions and align their content strategy accordingly. Still, that won’t be enough because the algorithms will regularly evolve. To stay at the top, therefore, organizations will need to constantly monitor how they’re faring in AI recommendations and be ready to make content adjustments swiftly in reaction to how AI algorithms are changing.
AIs and the art of the sale
There’s still room for emotion in meaningful experiences that cement consumers’ brand loyalty and convince them to tell their AI intermediaries not to buy anything else. But in situations where that isn’t effective or possible, companies will need to optimize content for general-purpose AI systems so their brand floats to the top of the machine learning algorithms.
Finally, it should shock no one that AI systems are designed to benefit their owners first and foremost (see John Battelle’s recent exploration of how Amazon’s recommendation engine guided him to a purchase that was less about his needs than Amazon’s profits). As we start figuring out how to market to AIs, we may also want to discuss steps to regulate the AI-based customer experience to ensure competition and minimize unfair advantage.
SAP worked with more than a dozen industry experts to uncover five trends that will determine the customer experience over the next decade. “The Future Customer Experience: 5 Essential Trends” examines each of these trends and offers recommendations for how brands should respond now to prepare.