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Dynamic pricing can optimize profits but alienate customers

In addition to taking supply or production costs into account, companies increasingly use customer-level data to make pricing decisions, often with the help of artificial intelligence.

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If you’ve ever seen a steep increase in the fare for an Uber to the airport on a Friday, or you’ve checked an item’s cost on Amazon, only to see it has changed hours later, you might have experienced algorithmic pricing.

That’s the practice of using algorithms to automatically adjust the price of goods or services based on factors such as demand, competitor pricing, inventory levels, or data about the customer.

While such pricing practices can squeeze out extra profit, they can also carry a marketing risk if not carefully implemented, according to Gizem Yalcin Williams, assistant professor of marketing at Texas McCombs. In 2012, Uber was widely criticized for raising ride prices during Hurricane Sandy. More recently, customers have expressed outrage over concert ticket surge pricing.

In a paper, co-written with an interdisciplinary group of 12 other researchers, Williams examines algorithmic pricing and the challenges companies can face when integrating it with their other objectives. The researchers offer some preliminary dos and don’ts for aligning pricing with marketing strategy, regulations, and avoiding customer backlash.

One potential factor in customer backlash, Williams says, is driven by feelings of unfairness.

“Let’s say that I just got myself something from Amazon, for my dorm, and then a couple of days later, I saw that the price changed,” she says. “I now feel like I overpaid for it, regardless of how good the product is.”

By the same token, seeing a price increase later might trigger elation, she says. “If I feel like I bought it at a lower price, I feel like I was smart.”

When Prices Get Personal

If pricing sometimes feels a bit more personal when algorithms are involved, Williams says, that’s because it is.

In addition to taking supply or production costs into account, companies increasingly use customer-level data to make pricing decisions, often with the help of artificial intelligence.

The exact data that go into the algorithm might not be always known, Williams says. “But what if the price I receive is different than others because of my own data, such as my shopping history, demographics, or location? Shoppers might react to the same price differently, depending on which data they think affected the price set by the company’s algorithm.”

Besides eroding customer loyalty, companies can face regulatory or legal attention when dynamic or surge pricing goes awry. Last year, the grocery chain Kroger was scrutinized by members of Congress over its plans to introduce algorithmic pricing at its stores.

Practical advice on pricing

As part of its research, Williams’ team surveyed pricing managers and conducted in-depth interviews with five strategic-pricing experts. They offered several pieces of advice.

  • Companies should be aware of how accepting their customers are — or are not — of dynamic pricing to avoid potential reputational damages.
  • Opening the “black box” and increasing transparency about how algorithms work can help managers and employees adopt and oversee them effectively.
  • Companies need guardrails to make sure they can effectively and carefully navigate the competitive and regulatory environment.

For Williams, one takeaway, she notes, is clear: Many companies slap the AI label on their operations, to cut costs or boost efficiency, without comprehensive planning for its design, integration, and monitoring.

 “Managers need to be deliberate about when, where, and whether to integrate AI into their operations,” she says. “And even when decisions are automated, it’s critical to have mechanisms that keep humans in the loop.”

Algorithmic Pricing: Implications for Marketing Strategy and Regulation” is published in International Journal of Research in Marketing.

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Structure of online reviews shapes their helpfulness

Reviews that grow increasingly positive are most helpful to readers, while those that turn negative are least helpful. For average-rated products, progressively negative trajectories enhance helpfulness, whereas reviews that start negative and grow positive are least effective.

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A study of nearly 200,000 Amazon reviews shows that the usefulness of online product reviews depends not only on what is said, but on how the information is structured.

The researchers, from the Universities of Cambridge and Queensland, studied Amazon reviews for products ranging from clothing to food to electronics. They found that how the information is organised matters as much as what is said, and that different review structures are more or less helpful, depending on how highly the reviewer has rated the product.

Their results, published in the journal Scientific Reports, could help companies and third-party review platforms design their review pages to prompt the sort of reviews that will be most helpful to potential customers.

For example, a reviewer assessing a laptop might praise its performance and design while criticising its battery life, so how should such information be structured to be most useful to the reader? Should the review begin with criticism and end on a positive note, or start positively before turning to drawbacks?

“Any target of evaluation typically has both positive and negative aspects, which makes crafting evaluative messages challenging,” said co-author Dr Yeun Joon Kim from Cambridge Judge Business School. “The key question is how to structure these elements within a single message. For example, one might present criticism upfront and then move to praise, or instead integrate negative points within an otherwise positive evaluation. Yet research has paid little attention to this structural dimension.

“We wanted to understand whether certain structures are consistently more effective, or whether their effectiveness depends on the performance of the target being evaluated.”

The study was based on 195,675 reviews of 5,487 distinct products, and assessed performance and related factors, and a helpfulness score as measured by reader votes.

The researchers identified nine possible structures of online reviews ranging from Type A reviews that start positive and become more positive as they go along, to Type I reviews that start negatively and become even more negative – with lots of variance in between.

For highly-rated products, reviews that grow increasingly positive are most helpful to readers, while those that turn negative are least helpful. For average-rated products, progressively negative trajectories enhance helpfulness, whereas reviews that start negative and grow positive are least effective. For low-rated products, reviews are judged most helpful when they open constructively before introducing criticism.

“The results are nuanced but very clear,” said co-author Dr Luna Luan from the University of Queensland, who carried out the research while earning her PhD at Cambridge Judge Business School. “Looking at the overall sentiment of reviews does not fully translate into message effectiveness. It is the broader structure of sentiment – how positivity and negativity evolve throughout the review – that shapes how readers interpret online reviews.”

“Our findings have practical implications for how platforms and companies can design review pages in order to elicit the sort of reviews that will be most helpful to readers based on how highly products are rated,” said Kim. “For example, instead of simply asking ‘Write your review here’, the online review form could instead include micro-prompts that guide how reviewers structure feedback in a way recipients find most helpful.”

The researchers found the most commonly used review styles are not necessarily the most helpful to readers. In particular, for average- and low-rated products, the structures that reviewers tend to adopt often differ from those that readers find most useful.

This mismatch likely reflects different underlying motivations. Reviewers are not always writing to maximise usefulness for others, but may instead be expressing their own experiences, frustrations or emotions – especially when evaluating products of moderate or poor quality. As a result, review writing often serves both as information sharing and as a form of self-expression. This helps explain why widely used review styles do not always align with what readers perceive as most informative or helpful.

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Reversible words can lower consumer disbelief in ads

A simple word choice in marketing messages can significantly impact how confident consumers feel about believing – or not believing – a claim.

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It’s estimated that consumers experience hundreds if not thousands of marketing messages daily. While the exact number can depend, how much someone believes the message can be more important for marketing success than the number of messages they see. 

A new study reveals that a simple word choice in marketing messages can significantly impact how confident consumers feel about believing – or not believing – a claim. Researchers found that when words differ in their “reversability,” or how easily people can think of their opposites, it can trigger different mental processes when consumers evaluate marketing language. 

Imagine the messaging options for a new sunscreen designed specifically for those who like a strong scented product. The first product description reads, “The scent is prominent,” while the second notes, “The scent is intense.” The word “prominent” is uni-polar, meaning people tend to negate it by adding “not” to the original statement.

“Intense,” though, is a bi-polar word, meaning readers can easily come up with its opposite meaning and negate the statement by replacing it with its antonym. In this example, “The scent is mild,” instead of, “The scent is intense.” 

“When people encounter easily reversible words, like ‘intense’, in messages processed as negations (mild), they experience lower confidence in their judgements compared to words that are hard to reverse, like ‘prominent,’” explained Giulia Maimone, a postdoctoral scholar in marketing at the University of Florida Warrington College of Business. 

Across two experiments of more than 1,000 participants, the research demonstrated that this effect occurs because negations of bi-polar, or reversible, words engage a more elaborate cognitive process requiring additional mental effort, resulting in lower confidence of the statement’s truthfulness. 

Based on their findings, the researchers suggest that marketers take this advice when crafting language: for new products, use affirmative statements with easily reversible words, like ‘The scent is intense’ in the sunscreen example, which most consumers will judge as true with high confidence. Importantly, this language would also minimize the confidence of consumers who will be skeptical about the message, as they will process it via a more complex cognitive process that reduces confidence in those consumers’ disbelief. 

“This simple lexical choice could help companies maximize confidence in their desired messaging and minimize confidence among the doubters,” Maimone explained. 

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If you’re a perfectionist at work, your boss’ expectations may matter more than your own, research finds

Help your employees by clarifying expectations through regular feedback and performance conversations to reduce role ambiguity, as doing so can provide employees with a better understanding of role expectations and enhance mutual understanding of those standards.

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If you’re among the 93% of people who struggle with perfectionism at work, new research suggests that your experience may depend less on your own high standards and more on whether those standards meet your supervisor’s expectations. 

Researchers from the University of Florida Warrington College of Business found that whether perfectionism helps or harms employees depends largely on whether employees’ personal standards align with their supervisors’ expectations. 

Specifically, they looked at the connection between employees’ self-oriented perfectionism, or the expectations of flawlessness they set for themselves, and supervisors’ other-oriented perfectionism, which reflects the extent to which they set excessively high standards for and critically evaluate their employees’ performance. 

Using data from more than 350 employees and about 100 supervisors, the researchers found that perfectionism’s impact depends on whether employees’ standards align with what their supervisors expect and how clearly those expectations are understood. 

When employees’ personal standards are aligned with their supervisors’ expectations, they tend to experience less role ambiguity, meaning they have less uncertainty about the expectations and standards for their role, why those standards matter and the consequences of not meeting them. This clarity in their work is linked to better performance, lower burnout and higher job satisfaction. 

“Problems between employees and their supervisors are more likely to arise when these expectations don’t match,” explained Brian Swider, Beth Ayers McCague Family Professor.

The most difficult situation occurs, Swider and his colleagues found, is when supervisors expect higher levels of perfectionism than employees expect from themselves. In these cases, employees reported greater uncertainty about their roles, along with worse work outcomes including higher burnout and lower job satisfaction.

“If you’re an employee who struggles with perfectionism at work, our findings suggest that understanding your supervisor’s expectations may be just as important as managing your own tendencies towards perfectionism,” Swider said. “Talking to your supervisor about priorities, standards and how your performance will be evaluated can help reduce uncertainty and ensure you both share a clear understanding of what success looks like.”

The researchers have similar recommendations for employers: help your employees by clarifying expectations through regular feedback and performance conversations to reduce role ambiguity, as doing so can provide employees with a better understanding of role expectations and enhance mutual understanding of those standards.

The researchers also recommend that organizations should consider how employees and supervisors are paired, as mismatched expectations can increase stress, reduce job satisfaction and ultimately impact performance. 

The research, “The influence of employee-supervisor perfectionism (in)congruence on employees: a configurational approach,” is published in Organizational Behavior and Human Decision Processes

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