Attracting customers through emotional resonance is a time-honored marketing tradition.
Back in 1930, when housewives listened to serial radio dramas in the afternoon, a soap company came up with the idea of sponsoring a radio show, both with commercials and with 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?
As consumers increasingly include subscription boxes of goods chosen by algorithms and 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.
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 what emotions a brand evokes—not unless the consumer teaches it to care. As a result, the strategy for marketing to AI assistants 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, except in situations that consumers have already stated a preference beyond those criteria.
Even when consumers trust an AI assistant to make purchasing decisions for them, they are still likely to have a few product preferences in addition to price, features, and capabilities. An AI assistant choosing among products will consider more information from more sources about every choice simply because it can. And the more information an AI assistant 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 the criteria, or doesn’t say whether or not it does, is likely to be passed over.
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. And 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 the AI’s algorithms themselves are changing.
There’s still room for emotion in the 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.
It should shock no one that AI systems are designed to benefit their owners first and foremost. As we start figuring out how to market to AI assistants, 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 report, examines each of these trends and offers recommendations for how brands should respond now to prepare.
David Jonker is head of Global Thought Leadership at SAP.
Dan Wellers is global lead of Digital Futures at SAP.