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AI intermediary at department store

When AI Does the Shopping, Every Price Is Negotiable

When customers’ AI assistants are making the purchases, sellers need to be ready to haggle on everything with an algorithm.

By David Jonker, Christopher Koch | 8 min read

A home is the most expensive item most of us will ever buy. That’s why we invest so much time in negotiating the details of the purchase, often with the help of an experienced real estate agent. We haggle our hearts out to reach an agreement that satisfies everyone, and we hope for a buyer’s market, in which there are more sellers than buyers, to give us the leverage to drive our price down.


By comparison, we perceive bottled water as cheap, even though the markup on water is many times that of any house. In fact, consumers regularly pay US$2 for something that costs the manufacturer only five cents to produce, a 4,000% increase that makes water one of the most heavily marked-up products in the world.


Yet we don’t bargain for bread (or toothpaste or other common, inexpensive items) even when we know the markup is high. In large part, this is because time is money. Negotiating is only worthwhile when the amount we save by bargaining down the price of something is more than the value we place on our time spent doing it.


It makes sense for someone earning the median U.S. hourly wage of $18 an hour to invest, say, 10 hours to research and negotiate 5% off the purchase of a used vehicle with an average price of $20,000. The buyer spent $180 worth of time to save $1,000.


By comparison, imagine that same person trying to negotiate a discount on a $2 bottle of water. We already know the huge markup should leave room for a win-win outcome for both parties. However, a buyer who can persuade the seller to drop the cost by 50% in just five minutes has still spent 1/12 of an hour, or $1.50, to save $1. So for now, the time it would take to haggle over most purchases makes it not worth the effort.


But artificial intelligence (AI) intermediaries will soon change that. Consumers happily accept algorithm-generated recommendations for music to play, movies to watch, places to eat – even clothes to wear.


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Research published in 2019 found, for example, that when creating a music playlist, two-thirds of study subjects chose at least one recommended song to include. In addition, more than half of a control group of people who built their playlists without help from algorithms said they would have liked it if recommendations had been offered. When AI intermediaries are able to also factor in price, location, sustainability, and myriad other purchase considerations, there is every reason to believe that consumers will rely on them even more, including for purchases that, like homes, cars, and clothing, have emotional resonance.


For companies, AI intermediaries threaten time-honored strategies for attracting customers by appealing to their emotions. When consumers can outsource all but the most personally meaningful tasks to shopping bots, brands will need to capture the attention of – and negotiate with – the bots.




AIs gang up to buy

What takes humans hours or minutes to do, computers can accomplish in seconds or less. Instead of taking five minutes to negotiate a deal individually or 50 hours to organize a group to negotiate collectively, a human can give a command and let an AI quickly handle the negotiations in the background. And when a negligible time to negotiate is part of the equation, AI-assisted haggling will be common for many products in the future.


To see what that might mean for the future of customer experience, consider a widely viewed YouTube demonstration of the Google Assistant AI’s ability to contact a local business to schedule an appointment. The AI placed a phone call to a business that doesn’t have an online booking system, spoke to a human receptionist, responded with pauses and phrasing that mimicked human speech, and parsed the nuances and context of the receptionist’s comments to complete the transaction exactly as a person would. Now imagine eliminating the humans entirely in favor of negotiations between and among AIs that can negotiate at lightning speed.


As more of our shopping moves online, we’ll trust AIs to organize more of it. Eventually, groups of personal AI intermediaries will band together to barter and buy collectively without our intervention, creating economies of scale to ensure the best deals on whatever they’re purchasing for us. Imagine hundreds of AI bots, each representing an individual customer, instantly banding together to negotiate a reduced price on a bulk purchase of dish detergent from a major retailer.


In essence, this will create temporary virtual marketplaces where AIs cooperate with each other to meet a group of human buyers’ needs without involving them in the negotiations. This AI cooperative buying will have the benefit of numbers; 500 AIs negotiating as one will have more influence than each one negotiating individually. Just as critically, the individual AIs will be able to leverage the combined historical and real-time data from each buyer’s purchasing habits and insights into market trends to influence pricing and product availability before they ever begin to negotiate.




Sellers fight back with AI engine optimization

Of course, sellers could have their own AIs on the opposite side of the negotiating table, and they wouldn’t necessarily be at a disadvantage. When buyers have delegated to an AI the purchase of a low-volume, highly customized product or in situations where demand is high but supplies are limited, the buyers’ AIs may decide to compete with others instead of joining forces, thus temporarily shifting negotiating power to the sellers’ AIs instead.


Either way, expect AI, whether working for one buyer or cooperating for many, to complete hundreds or even thousands of simultaneous, automated transactions each day, with participating AIs gathering data all the while. Sellers will need AI engine optimization (AIEO) strategies to attract attention and present the bots with winning offers.


However, brand managers must not make the mistake of assuming that optimizing for AI is the same as optimizing for search.


AIEO builds on search engine optimization (SEO) in three critical ways that companies must consider as they prepare for a future customer experience increasingly mediated by machine learning algorithms.

  1. Emotion is 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 an 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.

    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 quality. 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.

  2. Transparency will make or break sales. 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, ethical management practices, brand partnerships, corporate political donations, and hiring practices. A product that doesn’t meet these criteria, or that doesn’t say whether it does, is likely to be passed over.

  3. Content optimization will be a constant challenge. To get to the top of AI rankings, organizations will have to understand how shopping algorithms make decisions and align their content strategy accordingly. The same has been true for SEO, but AI is already leading to more frequent updates of search algorithms. 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.



Breaking through the bias

Companies will need to account for bias in AI intermediaries that can be difficult to discern. Owners of AI recommendation engines have been accused of privileging recommendations that are most profitable for them, at the expense of consumers and competitors.


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 fair competition. The European Union, for example, is developing proposals aimed at ensuring that digital platforms can be accountable for their content and that businesses of all sizes can compete equally.


There will still be 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 algorithm-generated recommendations.


The future store shelf is inside the machine. To sell products, companies will need AI to enable customers to reach them.

Meet the Authors

David Jonker
Vice President and Chief Analyst | SAP Insights research center

Christopher Koch
Senior Editorial Director | SAP Insights research center

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