The post Research Insight | Sales Contests Are Broken—Here’s How to Make Them Effective appeared first on ���������.

A finds that ALN is effective—but only under specific conditions. Some businesses experience significant increases in sales, while others see little to no impact. The key factors determining success include the nature of the sales cycle, deal complexity, and whether the customer is new or returning.

We uncover a critical insight: ALN enhances lead interactions and improves the quality of sales conversations, but it does not guarantee higher conversion rates across all industries. ALN works best when used for new leads, short sales cycles, and lower-value deals. However, its benefits decline in high-ticket purchases or industries where buyers conduct extensive independent research.

ALN works best when used for new leads, short sales cycles, and lower-value deals. However, its benefits decline in high-ticket purchases or industries where buyers conduct extensive independent research.

This distinction has major implications for businesses investing in ALN. Many companies measure ALN success using vanity metrics—email opens, click-through rates, and engagement levels—without assessing whether those interactions lead to actual sales. Our findings suggest that firms should rethink how they evaluate automation success and shift their focus to measuring ALN’s impact on meaningful outcomes like sales meetings and conversions.

When ALN Works—and When It Doesn’t

For companies selling relatively simple products or services with shorter sales cycles, ALN can be a powerful tool. By delivering targeted content at the right time, ALN reduces uncertainty for potential buyers and ensures that sales teams engage with more informed prospects. Our research finds that in such cases, ALN can lead to a 23 percentage point increase in conversion rates.

However, for industries with long and complex sales cycles, such as B2B enterprise software or industrial equipment, ALN’s impact is less clear. In these cases, buyers rely on detailed research, peer recommendations, and in-depth consultations rather than automated content. ALN may increase engagement but does not necessarily lead to more closed deals.

Returning customers also respond differently to ALN compared to first-time buyers. Since they already have a relationship with the brand, they are less likely to need automated content to guide their purchase decision. This means companies must differentiate how they nurture new versus existing leads, rather than applying a one-size-fits-all approach.

Are Businesses Measuring the Wrong Metrics?

One of the biggest mistakes we observe is companies focusing too much on engagement metrics rather than true business outcomes. Many firms evaluate ALN success on the basis of email opens, website visits, or social media interactions. Although these indicators suggest interest, they do not necessarily translate into revenue.

Our research suggests that businesses should measure ALN effectiveness by tracking:

• Lead-to-sales meeting conversion rates (Does ALN drive actual conversations between buyers and sales teams?)
• Sales cycle speed (Does ALN shorten the time it takes to close a deal?)
• Revenue impact (Does ALN increase the number of closed deals and overall profitability?)

Shifting to these meaningful metrics will help businesses make informed decisions about ALN’s true value.

How Companies Can Use ALN Strategically

We find that ALN works best as an enhancement—not a replacement—for human sales interactions. Companies that rely too heavily on automation risk alienating high-value prospects who expect personalized, consultative selling. Instead of viewing ALN as a standalone solution, businesses should:

• Segment their leads and tailor ALN for different customer groups (e.g., new vs. returning buyers).
• Use ALN to complement human interactions, rather than replace them, particularly for complex sales.
• Refine ALN strategies over time by tracking real business outcomes rather than engagement metrics.

For marketing leaders, the takeaway is clear: ALN can be a powerful tool, but only if it is applied strategically. Businesses should test its impact before fully committing, ensuring that automation aligns with their sales process rather than relying on industry hype.

Source: Johannes Habel, Nathaniel Hartmann, Phillip Wiseman, Michael Ahearne, and Shashank Vaid, “,” Journal of Marketing.

Go to the Journal of Marketing

The post Does Automated Lead Nurturing Really Work? A New Study Challenges the Hype appeared first on ���������.

One of the most commonly used SPM practices involves companies publishing the sales performance rankings of their salespeople on key performance metrics. The goal of publishing performance rankings is to provide feedback to all salespeople by disclosing their performance relative to their peers, thereby creating a competitive motive for performance improvement. However, despite widespread use, the effectiveness of these rankings has not been explored.

In a , we examine how the presentation of performance rankings influences critical outcomes, including salesperson quota attainment and employee turnover.

Questions around Performance Rankings

Our research poses four primary questions:

1. Do performance rankings effectively motivate salespeople to improve their performance?
2. Does this effectiveness vary by the type of information published alongside the ranking?
3. What are the conditions under which publishing certain information with performance rankings is more or less effective?
4. What are the long-term implications of performance rankings on salesperson turnover?

Our research team conducted two studies involving over 27,000 salespeople from more than 170 firms across 83 countries. These studies leveraged extensive field data to examine the effects of three distinct information conditions: anonymized performance rankings, identifiable performance rankings, and identifiable rankings with quotas.

Our findings reveal that while performance rankings can positively influence sales outcomes, their effectiveness—and, by extension, the value derived from the performance ranking dashboard—hinges significantly on the type of information disclosed within the dashboards.

While performance rankings can positively influence sales outcomes, their effectiveness—and, by extension, the value derived from the performance ranking dashboard—hinges significantly on the type of information disclosed within the dashboards.

For instance, anonymized rankings effectively motivate salespeople to increase their quota attainment, yet they also lead to higher turnover rates, which can result in substantial indirect costs related to recruitment, training, and loss of organizational knowledge. As a result, the costs associated with implementing and maintaining anonymized ranking systems may not be justified by the outcomes unless turnover can be effectively managed.

In contrast, identifiable performance rankings have the most substantial positive impact across our two studies, significantly enhancing quota attainment and reducing turnover. Our findings indicate that when salespeople know the identities of their peers in the rankings, they are motivated not only to improve their performance but also to maintain a positive social image. This dual motivation of self-improvement and self-presentation drives better performance and lowers turnover rates. However, when quotas are disclosed alongside identities and performance rankings, we fail to see performance enhancing benefits.

Lessons for Chief Marketing Officers

Our study offers valuable lessons for managers and salespeople:

• More information is not always better. Instead, the strategic selection and combination of performance data are crucial for achieving both immediate and enduring positive outcomes.

• Managers should develop and implement identifiable ranking systems, ensuring transparency in how rankings are determined and communicated.

• Managers should avoid including fixed or objective performance metrics (i.e., quotas) in ranking systems to focus on relative performance evaluations, which is essential for the effectiveness of these systems.

Implementing these recommendations can drive essential behavioral changes among sales managers and executive leadership within sales organizations. Sales managers will be able to adopt a more strategic approach to performance ranking disclosures, emphasizing transparency and leveraging the motivational benefits of identifiable rankings, which should lead to improvements in quota attainment and reduced turnover within their teams.

Furthermore, executive leaders can invest in performance ranking dashboards that are tailored to their organization’s unique characteristics, taking into account their sales force’s compensation structure and size. By doing so, they can ensure the investment in performance dashboards will justify the costs by achieving substantial performance gains and minimizing turnover, thereby enhancing the overall effectiveness of the sales force.

Our research highlights the critical role of transparency and information type in performance rankings. By implementing performance rankings and carefully selecting the information disclosed alongside them, they can create a more motivated and loyal sales force. This approach will not only drive better performance outcomes but also contribute to a more sustainable organizational culture.

We urge scholars to build on our research and explore rankings on team goals and how they interact with individual salesperson rankings. Furthermore, it is important to study factors such as familiarity and social interactions between salespeople, office proximity and location, physical versus virtual contact between peers, and the extent of knowledge sharing. Future studies can also expand our understanding of how performance rankings may differ in effectiveness depending on the motivational orientation of salespeople.

Source: Molly Ahearne, Mohsen Pourmasoudi, Yashar Atefi, and Son K. Lam, “,” Journal of Marketing.

Go to the Journal of Marketing

The post Do Performance Rankings Actually Motivate Salespeople? appeared first on ���������.

In a , we offer a compelling reason for considering brand strength when designing sales incentives in brand-managed retail (BMR) sales settings.

For context: a BMR setting may be a store-within-a-store (SWAS), such as the cosmetics counters in most major U.S. department stores, or a brand-managed standalone store such as Aveda or Gap. Such BMR settings are typically staffed by the brand rather than the retailer, with the brand also having autonomy over inventory and pricing decisions for its products. Although BMR has been historically more prevalent in Europe and Asia than in the U.S., brands are adding SWAS offerings everywhere to reach new customers and to offer additional touchpoints for customers.

Our research team explores this dynamic retail context and investigates the sales incentives used in a variety of BMR settings. In our investigation, we uncover a significant variability in the use of individual and group sales incentives by brands in these settings. Some brands opt for individual incentives to motivate salespeople based on performance, others lean toward group incentives, and a portion adopt a combination of both approaches. This diversity in incentive structures prompted us to explore the underlying factors driving incentive choices by brands.

The literature shows that among other factors, brand strength and sales incentives affect the selling effectiveness of retail salespersons, leading us to conjecture that the differences in incentive choices by brands may be tied to the strength of those brands.

The Secret to Designing Incentives

We use a theoretical principal-agent model to investigate how brand strength may influence the relative profitability of different types of incentives. Our model assumes a BMR setting with salespersons serving a mix of consumers, including some who might be repeat buyers ready to purchase and others who are uninformed about the brand’s value proposition and need to be sold by the salesperson.

In designing incentives, a firm would ideally like to offer incentives only for selling to the uninformed consumer because this sale requires salesperson effort. Firms cannot usually observe whether a sale made by an individual salesperson was to an uninformed consumer; instead, firms have better information on whether the group as a whole sold to an uninformed consumer because the group output in this case would be higher than otherwise. Our analysis suggests that this information advantage of group incentives is more potent for weaker brands, resulting in the main finding that weaker brands may be more profitable with group incentives. Conversely, we find that stronger brands would be better off with individual salesperson incentives.

Weaker brands may be more profitable with group incentives. Conversely, we find that stronger brands would be better off with individual salesperson incentives.

An important qualification to our theoretical results: they apply to somewhat established brands and not to very weak brands. For example, Clarins and Estee Lauder are both established cosmetic brands, but Clarins ranks lower than Estee Lauder in many brand equity rankings and may benefit from using group incentives in its BMR operations, while Estee Lauder may benefit less.

Lessons for Marketing Managers

Our findings underscore the efficiency implications of aligning sales incentives with brand strength. We offer the following lessons to help Chief Marketing Officers make better decisions about individual and group incentives.

• Managers of BMR sales operations need to determine whether their brand falls on the weak or strong end of the spectrum. This empirical question is to be answered by data, which could be from brand equity metrics such as the revenue premium or from surveys that measure consumers’ knowledge, attitude, and emotional connection toward the brand. These data can help managers form a judgement about the strength of their brand and determine the best sales incentives.
• A combination of individual and group incentives can sometimes be better than having just one type of incentive, although this is less so for weaker brands, which may find that offering a group incentive alone is best.
• Our study underscores the importance of adopting a holistic approach when devising marketing strategies. For instance, when a brand allocates substantial resources over time to elevate its brand image, it is imperative for managers to evaluate potential adjustments to the compensation structures of their customer-facing BMR employees.

A limitation of our study is that it focuses on BMR settings, so future research could examine the applicability of our results beyond these settings. As seen from our empirical results, data from two different settings are consistent with our theoretical predictions. Yet, while the literature does not offer an alternative explanation for why brand strength may be related to the choice of group versus individual incentives, new alternative explanations for our results may emerge in the future.

Source: Wenshu Zhang, Jia Li, and Subramanian Balachander, “” Journal of Marketing.

Go to the Journal of Marketing

The post What’s Better for Motivating Salespeople: Group or Individual Incentives? New Research Shows it Depends on the Brand appeared first on ���������.

Compared to more traditional marketplaces, long-established signals of quality such as brand name are less relevant in the peer-to-peer space. Instead, sellers in these marketplaces directly tell potential buyers about themselves and the production processes behind their goods and services.

What should these sellers say in their bios and product descriptions? In a , we find that one of the best things they can mention is that they enjoy their work.

We suspect that many people who choose to sell things through peer-to-peer marketplaces enjoy making their products, yet sellers rarely mention this. For example, in the profiles of 30,000 sellers across various peer-to-peer marketplaces, only about 1% of sellers mentioned production enjoyment. Over 15 experiments, we find consistent evidence that buyers are more interested in, are more likely to choose, and are willing to pay more for products or services that the seller enjoys producing.

We find consistent evidence that buyers are more interested in, are more likely to choose, and are willing to pay more for products or services that the seller enjoys producing.

We also explore how production enjoyment influences the sellers’ decision to price products and services. Ironically, sellers are willing to accept a lower price—and do indeed charge less—for the products and services they enjoy producing. Although sellers also generally associate production enjoyment with higher quality, they do not rely on this inference in their pricing decisions.

In a field study, we test two ads on Facebook for a search engine optimization (SEO) specialist, one that mentioned production enjoyment (“I really enjoy SEO”) and one that was otherwise identical but did not contain these words. We find that small business owners were more likely to click on the ad that mentioned production enjoyment. The positive reactions that buyers have to sellers’ signals of production enjoyment occur over a broad range of jobs. Across our studies, we examine over 100 different jobs and, in all cases, signaling enjoyment increases buyers’ willingness to pay.

Enjoyment vs. Quality

We find that this positive impact occurs because buyers interpret production enjoyment as a signal of a high-quality product or service. After all, someone who really enjoys making jewelry or loves painting probably spends more time and focus on it than others. When buyers learn of this enjoyment, they then presume the product/service is of high quality and are therefore more likely to buy it.

Notably, signaling production enjoyment works best when the production process requires a lot of skill. Automation has made many production processes much easier. In situations where buyers assume the production process is largely automated (or assume the offering does not require much skill overall), production enjoyment does not impact buyers nearly as much.

But even in high-skill contexts, why do sellers charge less for products and services they enjoy producing? Like buyers, sellers also associate production enjoyment with high quality products and services, which should increase prices. We think sellers instead charge lower prices because the joy that they experience during the production process already compensates them for their work. In any case, it seems that production enjoyment carries signals for sellers that lead to different pricing decisions.

Taken together, these findings are somewhat contradictory: sellers charge less money for products and services they enjoy producing, even though buyers are willing to pay more for them.

Lessons for Sellers

• When sellers mention production enjoyment in their profiles and marketing, buyers are more interested in their products and services.
  
• By comparing production enjoyment to a variety of other established cues of product quality (e.g., production effort) and identifying several moderators that determine the impact of this signal (e.g., required skill), we give sellers a useful framework for when and how to signal production enjoyment.
  
• Sellers should reconsider their willingness to charge for different products/services. This knowledge can also benefit buyers, who can recognize production enjoyment as a signal of a potential discount, because sellers charge less when they enjoy the production process.
  
  Overall, if you enjoy the work you do, make sure you tell people! They will think you do better work and should be willing to pay you more for it.

Source: Anna Paley, Robert W. Smith, Jacob D. Teeny, and Daniel M. Zane, “,” Journal of Marketing.

Go to the Journal of Marketing

The post Enjoy Your Work? Make It Known! Buyers Are Willing to Pay More for Products You Enjoy Producing appeared first on ���������.

As the importance of data in decision making continues to grow, predictive sales analytics have gained immense popularity in business. Companies are currently using predictive analytics for numerous functions such as predicting conversion likelihood to prioritize clients and improving consumer retention by predicting consumer churn. However, salespeople may be averse to using predictive analytics, as they may mistrust technology or lack the knowledge to use it. Still, it is unclear under what circumstances predictive analytics can be effective.

In a , researchers Johannes Habel, Sascha Alavi, and Nicolas Heinitz address this challenge by focusing on customer churn prediction in a business-to-business context. Their extensive study, which analyzed 9.7 million transactions, revealed that the success of predictive analytics tools hinges significantly on the characteristics of both customers (like churn probability and previous revenue) and salespeople (such as their perceptions of technology and abilities to use it).

The research further demonstrates that the effectiveness of these tools is enhanced when users have realistic expectations about the algorithm’s accuracy, though this was found to be effective only in specific circumstances. These findings, derived from a meticulously conducted field experiment conducted by collaborating with 12 companies for more than two years and reinforced by two follow-up experiments, provide critical insights into the nuanced interplay between predictive models and their real-world application in sales. 

For practitioners, policymakers, consumers, and other stakeholders, the key takeaways from this research are:

1. The success of predictive analytics in sales, specifically for customer churn prediction, is highly dependent on both customer characteristics (like churn probability and prior revenue) and salesperson traits (such as technology perceptions and abilities).
2. Implementing strategies like fostering realistic expectations about the algorithm’s accuracy can improve the effectiveness of predictive analytics tools, but this is highly situational.
3. This study underscores the importance of understanding the nuances of predictive models in sales, encouraging a more informed and strategic application of these tools in various business contexts. These insights are crucial for managers looking to optimize their use of analytics in decision-making processes and for stakeholders interested in the practical implications of predictive analytics in the business sector.

We had a chance to contact the authors to learn more about their study and gain additional insights.

Q: What inspired you to delve into the topic of predictive analytics in sales, particularly in the context of customer churn?

A: The origin of this research project arose from two observations of our team in machine learning and firm practice. Regarding machine learning, we noted that the capabilities and access to machine learning models through the statistics software R vastly improved at that time. At the same time, firms grew increasingly excited about the potential of using such ML for their analytics in sales. In the workshop, we decided to cooperate with a B2B firm to explore the opportunities, potentials, and dangers of using predictive sales analytics. We discussed various use cases with the management of the company for predictive analytics applications in their sales force. Since customer churn often constitutes a key issue in B2B industries, we decided to focus on a predictive tool that could assist the sales force in this respect.

Q: Could you discuss the significance of your research in the broader context of business and technology? How does understanding customer churn through predictive analytics impact the field at large?

A: In the broader context of business and technology, our study points to a key tension: AI-based technologies exhibit tremendous potential to improve productivity and diverse other outcomes. But these are only promises of potential, which are not automatically and easily realized. Rather, all too often, implementations of such new technologies fail and may even backfire. Our study underlines the enormous nuances and contingencies that need to be considered when implementing such new AI-based tools. Such implementations never constitute low-effort quick fixes but major change management projects that require massive attention, effort and resources of management to succeed. Findings of our study contribute to marketing research by underlining the fundamental role of contingent effects of such technology implementations and uncovering the most important categories of contingencies.

Q: Are there potential extensions of your work that you find particularly exciting? How could other researchers build on your findings to explore new dimensions in predictive analytics or customer behavior?

A: Our study uncovered that salespeople deal very differently and develop different strategies in response to the implementation of predictive sales analytics tools. At the same time, the firm communication strategy to foster effective tool use did not unconditionally provide the wished for results. Consequently, an intriguing endeavor for future research, as we see it, is to develop a taxonomy of AI implementation strategies in sales and examine the differential impacts of different strategies to foster adoption.

Q: What were the biggest challenges and the most significant lessons you learned from doing a large-scale field study collaborating with multiple companies?

A: The first—and most important—lesson, in our view, is to first identify the right company, that is, the right and suitable cooperation partner. This lesson may sound trivial, but its significance cannot be overemphasized. This is because many companies initially intend to cooperate and join a research project on such an important and promising topic. Yet, after the initial enthusiasm, we noted that many companies realize the high effort and necessary resources which establish barriers. Moreover, sometimes companies are not suitable for compelling field study due to various issues such as lack of data, insufficient commitment or resistance by employees, or organization structural aspects. Therefore, it is important first to ensure the firm partner is eligible and committed. Otherwise, one may face enormous losses of time and energy.

Q: Do you think that predictive analytics also have negative effects in the long term? If so, how could managers mitigate this?

A: Yes, predictive analytics might exhibit negative long-term effects for companies and their employees. Such harmful effects may occur, for instance, if the new technology is initially implemented in an ineffective way such that employees’ fears and aversion had not been properly addressed and resolved. Then, such an implementation might cause disruption in teams, harming interpersonal trust and triggering negative long term social dynamics in the organization. The seed for positive long-term use and development of predictive analytics needs to be set with the initial implementation and introduction. Managers need to:

1. accompany the implementation with a deliberate change management project,
2. select an appropriate tool which indeed provides value and usability to the employees,
3. give them freedom and time to learn and experiment with the tool, and
4. make sure that the tool is employed in the right customer and market situations.

Q: Beyond asking people to have realistic expectations toward predictive analytics tools, what other strategies could be used to trigger people’s trust in predictive analytics even when they can fail sometimes?

A: Employees’ fears and anxieties when asked to work with the new predictive analytics tools often arise from deep-seated uncertainties regarding the potential consequences of the tools. For instance, they might feel uncertain about how to effectively work with the tools, perceive them as an intrusion into their work practices, fear being outperformed by more tech-savvy peers, or, most prominently, might dread being replaced by effective AI tools. Manager strategies to foster trust in predictive analytics must tackle and resolve those fears and uncertainties. A major prerequisite for such strategies to be effective is that a healthy relationship exists between the manager and their employees. For that, managers need to consistently demonstrate to employees that the purpose of the tool is to assist them and help them, and not replace them or monitor them.

Managers need to consistently demonstrate to employees that the purpose of the tool is to assist them and help them, and not replace them or monitor them.

Ideally, managers succeed in establishing a trust-based company culture that prioritizes employee well-being. In such a culture, fears of AI should be less pronounced, and employees should be more open to effectively leverage the opportunities provided by the new technology.

Read the full article:

Johannes Habel, Sascha Alavi, and Nicolas Heinitz, “,” Journal of Marketing Research. doi:.

Go to the Journal of Marketing Research

The post Predicting the Unpredictable: How Sales Managers Can Get Better ROI from Predictive Sales Analytics Tools appeared first on ���������.

Imagine you’re walking into a tech store to purchase a new phone. As you’re browsing through the phones, a salesperson approaches you asking if you need any help. You’re not sure what accessories you need, but the salesperson suggests a phone case and screen protector. The salesperson explains how the accessories are specifically designed to complement the features of the phone, keeping it safe from scratches and drops. They also mention how these accessories can save you money in the long run by preventing costly repairs. Feeling more confident in your decision, you agree to purchase not only the phone but also the phone case and screen protector. As you leave the store, you feel satisfied with your choice of products.

How did the salesperson convince you to buy not just the phone but also the additional items? A recent  by Jennifer K. D’Angelo and Francesca Valsesia provides an answer. When an individual, such as a salesperson, stylist, waiter, designer, shop assistant, or influencer, suggests combining two products for purchase or use, they are perceived as an expert in the product category. As a result of this higher level of credibility, consumers are more likely to follow such individuals’ recommendations and make a purchase. This effect holds true regardless of whether the combination is encouraged or discouraged, whether the customer requires the advice, or whether it applies to other products in the category. Furthermore, the more explicit the combinatory recommendation, the stronger the effect.

When an individual, such as a salesperson, stylist, waiter, designer, shop assistant, or influencer, suggests combining two products for purchase or use, consumers perceive them as an expert in the product category and are more likely to follow their recommendations and make a purchase.

We had the opportunity to contact the authors, who kindly provided interesting insights into this article. Read on to discover more details about this fascinating research.

Q: Providing combinatory recommendations is a novel cue that consumption advisers can use to signal expertise. What sparked off the initial research idea of combinatory recommendations as being more impactful?

A: Anecdotally, when browsing images of clothing outfits and interior design, we noticed ourselves thinking, “I would never have thought to put those pieces together, but it totally works.” Those observations sparked the idea that people who have the ability to curate such outfits and designs might possess a novel kind of expertise that the lay consumer doesn’t have.

Jennifer has some ongoing work in the customization domain that finds consumers tend to limit their number of choices (e.g., food ingredients) when these choices are to be combined (e.g., mixed together in a dish). Consumers limit themselves less when making choices that are intended to be consumed individually. This led us to suspect that many consumers have difficulty combining things together. The know-how to combine things requires some higher level of expertise. We later discovered through our research that “higher level of expertise” was greater depth of knowledge.

Q: The real-world persuasive value of combinatory recommendations was tested with a field study (Study 6). Did you face any related challenges? How would you advise scholars or practitioners who are thinking of adopting a similar research approach?

A: The biggest challenge was finding a company that offered a suitable context to operationalize both our combinatory and control conditions. We think that smaller, startup stage companies may be more amenable to experimentation, giving researchers more leeway to better operationalize their constructs in a field study. Many business schools host startup venture competitions. One idea would be to attend these competitions to forge partnerships with these companies for future field experiments. On another note, we think it’s important to read up on Facebook Ad Manager’s latest best practices regarding target market specifications, campaign length, ad formats, etc. as the platform’s guidelines are ever-changing.

Q: Can you elaborate on the potential extensions of your research findings? In particular, what would be the effect of recommending more than two products for joint consumption?

A: This is a great question and one that we have thought about ourselves. Based on our theorizing, a person who can recommend many compatible products exhibits a greater ability to process interactions among products, which might signal even greater expertise than only recommending two products for joint consumption. We find initial support for this prediction from Smart Closet, an online platform where users post clothing pieces that one could wear together: the greater the number of clothing pieces in the post, the more likes the post received. It is nonetheless possible that this effect has an upper boundary: consumers might have a hard time believing an advisor who recommends 20, 50, or 100 products for joint consumption. What this upper boundary is exactly might depend on the product category.    

Q: What do you think would happen if the recommendations are created by artificial intelligence or algorithms instead of human advisors?

A: Prior research has demonstrated consumers’ resistance to adopting AI recommendations (e.g., algorithm aversion; see ). This resistance might translate into consumers having a hard time believing a combinatory recommendation made by nonhuman advisors. This is, of course, if consumers are aware that the recommendation came from artificial intelligence or an algorithm.

Q: What do you think are the key takeaway points of this research that could be of particular interest to practitioners?

A: On the one hand, retailers should consider using personalized communication, such as direct mail, to provide combinatory recommendations. In this vein, fashion retailers like Mango and StitchFix have recently started sending follow-up emails that contain suggestions of clothing pieces to wear with items consumers already own. On the other hand, retailers could also prompt combinatory recommendations when asking consumers to review their products, as well as asking them to post photos of their outfits. Finally, the influencer marketing industry is set to grow to approximately $21.1 billion in 2023. Marketers should encourage influencers they collaborate with to use combinatory recommendations and consider the use of combinatory recommendations as a selection criterion when evaluating influencers with which to collaborate.

Read the full article:

Jennifer K. D’Angelo and Francesca Valsesia (2023), “,” Journal of Marketing Research, 60 (1), 155–69. doi:

References:

Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey (2015), “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err,” Journal of Experimental Psychology: General, 144 (1), 114–26. doi:.

The post “You Should Try These Together”: How Combinatory Recommendations Shape Purchase Decisions appeared first on ���������.

Conference participants receive a digital copy of the conference proceedings approximately one week ahead of the conference.

Non-members may purchase digital proceedings by contacting customerservice@ama.org or purchase a print copy via the ���������’s official .

Winter Academic Conference | February

Marketing & Public Policy Conference | June

Summer Academic Conference | August

The post ��������� Academic Conference Proceedings appeared first on ���������.

Important Timeline for SIG Leaders

• July 1: Start of the New Fiscal Year
• August: ��������� Summer Academic Conference
• September 15: SIG Leadership Updates are Due to ��������� Support Center
• December: Winter ��������� Award Orders + Reception Details Due
• February: MA Winter Academic Conference
• June: Summer ��������� Award Orders Due
• June 15: Due Date for Fiscal Year Spending

SIG Guidelines and Reporting | SIG Processes | SIG Documents | SIG and ��������� Branding Guidelines | Pop-Up SIGs

Can’t find what you are looking for? Please do not hesitate to reach out to membersupport@ama.org for help.

SIG Guidelines and Reporting

• 1 Year, 3 Year and 5 Year Guidelines Overview
• 1 Year Guidelines (July 2023 – June 2024)
• 3 Year Guidelines (July 2025 – June 2026)
• 5 Year Guidelines (July 2027 – June 2028)

SIG Processes

SIG Documents

Every SIG has a personalized SIG Overview Document. These documents include important dates, a live budget, a list of active academic members, and a list of contacts who have previously been members or affiliated for wider outreach. Since these documents have individual budget information, they are not linked on this page. If you are a SIG Leader and need access, please contact rfickett@ama.org to resend you your SIG Document link.

SIG and ��������� Branding Guidelines

Pop-Up SIGs

Pop-up SIGs are temporary entities (formed for a period of one to three years) created to address emerging topics in marketing that typically fall at the intersection of existing SIGs or around new substantive/thematic developments in the field. The Organizational Frontlines group has been successful in holding programming and attracting members from diverse SIGs in the past and that is the pilot as a Pop-up SIG. These may transition into SIGs of their own if they have enough members at the end of three years or may be folded into existing SIGs (provided the SIG agrees to merge with the Pop-Up). It is a way for ��������� to promote new topic areas within marketing, and to highlight topics at the intersection of different sub-fields in marketing.

Pop-Up SIG Applications are approved by the Academic Council, and Pop-Up SIGs may receive a budget of up to $1,000 per fiscal year to support their efforts and programming. 

The Academic Councils reviews at their quarterly meetings.

The post SIG Leader Resources appeared first on ���������.

The United States has a sugar problem. Excessive sugar consumption induces severe illnesses that increase health care costs. Not surprisingly, about 58% of U.S. adults indicate a desire to cut back on sugar to avoid obesity, diabetes, and heart conditions. Research shows that reducing sugar in consumer packaged goods by a modest 8%–10% could lead to nationwide savings of more than $110 billion in health care costs.

However, despite clear evidence of the negative consequences of sugar consumption, consumers’ intake has steadily increased over the years. This suggests that it is not sufficient for consumers to want to decrease their sugar intake: Companies need to offer appealing products that can help reduce sugar consumption. Soda manufactures such as PepsiCo have been reducing sugars in their products over the years and are increasingly launching smaller package sizes of well-known sugary products to appeal to health-conscious consumers. However, soda companies have to strike a delicate balance between sugar reduction and protecting and increasing their sales—two motives that will conflict if consumers reject reduced-sugar alternatives. As the war on sugar rages on, soda manufacturers seek to find the best solution to maintain sales without harming society.

Sugar Reduction or Package Size Reduction?

In a , we explore if (and how) such sugar reduction strategies affect new product sales. We study two sugar-reduction efforts:

• Sugar content reduction that involves launching a new product containing less sugar (or no sugar) compared to current products. This tactic is currently being implemented by all major players in the soda sector. For example, in 2011, PepsiCo introduced a new product called Pepsi Next, which contains about half the amount of sugar of Pepsi’s regular products.

• Package size reduction involving brands introducing smaller package sizes that help consumers cut back on their sugar intake. In this case, the brand’s average (relative) sugar content remains the same, but consumers’ absolute intake diminishes. Prominent use of this tactic was made evident in the introduction of 7.5-ounce sizes by many soda brands.

We examine the direct effects of these sugar reduction strategies while also proposing that their effectiveness depends on three sets of product-related strategy decisions involving labeling, branding, and packaging. These decisions have important moderating effects on how the sugar reduction strategy affects sales.

• First, with respect to labeling, brand manufacturers must decide whether to feature claims of the presence or absence of (un)healthy ingredients, which can signal enjoyment and/or healthiness. For example, Pepsi emphasizes enjoyment and highlights the use of sugar in some cases (e.g., “Made with Real Sugar”), whereas Mountain Dew has highlighted the absence of sugars in several others (e.g., “Zero Sugar”).

• Second, branding decisions determine whether reduced-sugar products are launched under a mini or diet sub brand or the main brand. For example, Coca-Cola recently launched zero-sugar products under the Coca-Cola name, not a sub brand such as Coke Zero.

• Third, packaging decisions, such as the number of products per pack, also matter. Single items limit consumption, which is consistent with package size reduction, whereas multipacks give consumers stock for continued consumption.

Health vs. Enjoyment

Our analysis of almost 130,000 product additions by nearly 80 brands over 11 years in the U.S. soda category shows that, on average, sugar content reductions perform comparably to similar, nonreduced products, while smaller package sizes perform better than regular sizes. We also find that sugar-reduction efforts work substantially better if they do not overemphasize the reduced sugar content in new additions; that is, sugar reductions perform better without a dedicated sub brand and with enjoyment-oriented claims rather than health claims. As an example, Coca-Cola’s Zero Sugar product was redesigned in 2021 to closely resemble “regular” Coca-Cola rather than the earlier “Coke Zero.” We also find that package size reductions perform better if presented as a fun, high-quality product rather than a stern, healthy alternative. Using single items rather than multipacks further supports this positioning.

How does sugar reduction contribute to society? An average package size reduction reduces incremental category sugar sales by more than 20%. With the average soda product being close to 50 fluid ounces in size, there is ample room for product (size) adjustments that can reduce consumers’ average sugar exposure.

From: Kristopher O. Keller and Jonne Y. Guyt, “,” Journal of Marketing.

Go to the Journal of Marketing

The post Balancing Health and Profit: Reducing Sugar in Soda Without Losing Sales appeared first on ���������.