Explain Algorithms to Affected Individuals

Convincing a consumer to accept an AI-based unfavorable decision about them requires establishing trust in the algorithm by describing why the AI tool made the decision. To address this demand, organizations can provide, along with a decision, an explanation of how the machine logic reached its conclusion.
     Researchers at University of Calgary and Mount Royal University were interested in what characterizes the best of such post-hoc explanations. Their studies established that ones which include concrete, feasible steps the consumer can take to positively influence future outcomes were especially effective in preserving positive attitudes toward the decision and the marketer. The researchers name them sensitivity-based explanations because they describe how much the values of specific variables would have to change in order to alter the decision outcome.
     A scenario used in the studies was for a car insurance applicant who did not receive the best rate. A sensitivity-based explanation was, “If 10% or less of your driving took place at night, you would have qualified for the cheapest tier. If your average miles per month were 700 or less, you would have qualified for the cheapest tier.”
     The positivity of study participants’ ratings of fairness and of intention to do business with the insurance company were higher than for study participants given a case-based explanation: “This decision was based on thousands of similar cases from the past. For example, a similar case to yours is a previous customer: She was 38 years old, with 18 years of driving experience, drove 850 miles per month, occasionally exceeded the speed limit, and 25% of her trips took place at night. Claire was involved in one accident in the following year.”
     The researchers are suggesting that the marketer describe the logic of the particular decision. They are not suggesting that the marketer attempt to explain the entire logic of the AI model. This latter would almost surely overwhelm the customer with details. It also might reveal the marketer’s trade secrets or allow the customer to subsequently game the algorithm.
     I’ll add to those reasons the fact that the entire logic of the AI model may be unknown to the marketer. Aside from the challenge of earning trust from the consumer, the complexity of machine learning—AI systems which learn on their own beyond the data they’re originally fed—readily results in marketers questioning whether to grant trust. Marketers want decision-making transparency, too.

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Relax Guardedness with Gricean Norms 

Scotch Faux Explanations to Explanation Foes

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Utilize Imagination of Consuming Units

Studies at Michigan State University, Baylor University, and Virginia Tech indicate that “A Multipack of Four Body Washes for $16.00” will sell better than “A Multipack of Four Body Washes for $15.30” at an equivalent store in another town.
     A potential explanation for this is how a $16.00 price is simpler for the consumer’s brain to process than is a $15.30 price. But the researcher’s explanation is that the multipack’s easily-divisible price of $16.00 by its quantity of four draws attention from the whole bundle to each unit in the multipack. This facilitates consumption visions, such as each $4.00 body wash being used by multiple family members or in multiple locations, situations, and times. Consequently, buying this multipack is more easily justified than buying a multipack whose price is not easily divisible by its quantity, even though $15.30 is a lower amount.
     The researchers also found this effect with multipacks of six tissue boxes, eight toothbrushes, and eleven bags of cashews.
     Divisibility and consumption visions also play a part in how customers eat from multipacks. People eat less from what’s in a big package than from what’s in a set of small packages containing the same quantity as what’s in the big package. Researchers at Technical University of Lisbon and at Tilburg University in the Netherlands found that people hesitant about eating a food were more likely to overcome their hesitations when presented with small packages than when presented the equivalent amount in a large package.
     In addition, the people who got started on the small packages ended up eating more than did those who dug into the large package. The participants said they had believed small packages would help them limit their consumption. The opposite proved to be true. When faced with an additional small package, the dieter says, “Oh, it would be only a little bit more.” The explanation has less to do with the degree to which eating from a small versus large package sates hunger.
     Still, studies at Hofstra University and Baruch College find that consuming the entire contents of a single serving package gives more of a feeling of fulfillment than consuming the equivalent amount, and therefore not the entire contents, from a multi-serving package. And it applies to medicine, not only food. Patients feel a two-pill dose has been more effective when taking them from a two-pill container than from a twelve-pill container.

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Hit Shoppers with a Two-by-Four 

Stimulate Consumption Visions with Ads 

Serve Singular Satisfaction 

Bargain for Good Value via Price Primacy

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Spend Consideration on Money versus Points

Many customer loyalty programs allow a member to use accumulated points to acquire items which also could be purchased from the marketer with money. Researchers at University of Calgary, The University of Western Ontario, and University of Georgia were interested in what determines which of the two currencies the customer will choose to spend.
     This matters because there are arguments for preferring our customers use their points. For instance, the researchers report prior studies showing that the action of redeeming points motivates further sales. But there are circumstances where we’d prefer to have customers build their point total for future use and give us additional cash income now.
     The researchers hypothesized that what is central to a customer’s decision whether to use their points or their money is the difference in how people conceptualize the two. Consumers tend to think of specific, relatively small dollar amounts in concrete terms, with a focus on the feasibility of the expenditure and on the gain in the short term. We’re accustomed to spending money and are able to define its value across a range of contexts.
     By contrast, people conceptualize loyalty program points in more abstract ways. For one thing, the value of a point is determined by the specific marketer so is hard for the consumer to concretely define. For another thing, the accumulation of points usually occurs in ways less direct than with how money is obtained. We get points while our mind is on making a purchase, not on the fruits of our labor, and we might be given bonus points by the marketer for reasons we don’t fully understand. Thinking about points carries the hypotheticals of thinking about the future.
     This reasoning, validated by the researchers’ studies, leads to recommendations: 

• When you prefer customers spend their money rather than their points, offer items which are available promptly and are easy to use. 
• When you prefer customers spend their points, offer items which are of highly desirable quality, with less attention to the practicality of the item. 
• When you prefer customers spend their money, offer abundant concrete details about the items. 
• When you prefer customers spend their points, offer experience items, such as concert tickets and hotel stays.

     Clearly, there are additional considerations for consumers. For example, they would be much more likely to use up their points if learning those points will expire very soon.

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Profit from Status with Loyal Customers 

Grace with Loyalty Point Expiration Extensions 

Vary Velocity for Loyalty Program Motivation 

Contract with Near-Term Use via Contractions

Realize Realities of Cause-Related Marketing

In cause-related marketing (CRM) promotions, a marketer pledges to donate a small portion of sales revenues to a nonprofit organization or cause. Past studies say such promotions improve brand attitudes and purchase intentions. But while taking note of those conclusions, researchers at Vienna University of Economics and Business, University of Hamburg, Vrije Universiteit Amsterdam, and University of Groningen say that the conditions under which the studies were conducted are not sufficiently representative of the typical conditions under which marketers conduct these promotions.
     The most common use of CRM promotions is by marketers of fast-moving consumer goods (FMCGs), such as in grocery stores, where a shopper is choosing from a multitude of alternatives under time pressure. The vast majority of past studies, however, have been conducted in lab or field settings where participants are choosing among a few alternatives with ample time to carefully consider them all.
     In their own study, the researchers analyzed 63 actual CRM promotions including a range of 20 grocery and drugstore product categories. The average duration of the promotions was 11 weeks, and the average donation amount was about 3% of the product price.
     The average sales lift from the promotions was about 5% per week. But the most successful of the promotions accounted for the realities of FMCG contexts: The shopper should want to buy the particular product for reasons other than the charitable contribution. Sales lift can more than double when the brand is a market leader in the category or the item is priced below the alternatives in the category. The sales lift also was greater for categories with few alternatives and when the price differences for equivalent items were small.
     The combination of contribution to a charity with discount for the customer was also explored in a Temple University, Aalto University, Hanken Swedish School of Economics, and Sichuan University study. Analysis of results showed that a moderate discount in this situation works better than no discount or a deep discount. The moderate discount used for the studies was 30% and the deep discount was 50%. In discussing their results, the researchers say a discount in the range of 10% to 30%—more than only 3%—would be best for increasing sales.
     Why doesn’t a deep discount boost item sales as much as a moderate one? Because the deep discount erases the motivation of feeling charitable. People are buying for the price alone.

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Double Down on Cause Marketing 

Cause Donations to Enhance Business Credit

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