What Are the Four Ps of Marketing?

Marketing is the activity, set of institutions, or processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large. The four Ps of marketing is a marketing concept that summarizes the four key factors of any marketing strategy. The four Ps are: product, price, place, and promotion.

The History of the Four Ps of Marketing

The four Ps were popularized by , in the 1950s, as part of the overall marketing mix. The four Ps of marketing are still widely used today by marketers and companies to advertise their goods and services.

Understanding the Four Ps of Marketing

Now that you know what the four Ps of marketing are, let’s break down each one so you can utilize each in your own marketing strategy.

What is Product?

The product is what your company sells. For example, maybe it is smoothies from your juice bar or jewelry from your e-commerce store. Or, it can be a service, like yoga classes or therapy sessions. The product is what you make available to the consumer. Ideally, your product or service should fulfill an existing consumer demand. The type of product or service you offer helps you determine how much to charge for it, where it should be placed, and how it should be promoted. (The other three Ps of marketing!)

To capitalize on successfully marketing your product, you need to identify why it is different or special. So, what sets your product apart from other products like it on the market? How can it win over customers and beat the competition? The key to this P of marketing is determining what it is that makes your product unique or special.

Example of Product

Many successful products and services are the first in their category. For example, that had the ability to do much more than simply make phone calls.

What is Price?

Price is the amount of money you charge customers for the previously determined product or service. The drives up the most amount of sales and the most profit for your company. The price also must be related to the product’s real and perceived value.

Example of Price

There are , like supply costs, seasonal discounts, and competitors’ prices. There are also other reasons to raise or lower prices. For example, some high-end businesses might to appeal to an affluent audience. On the other hand, other businesses might lower the price of a newer product or offer a discount to entice more consumers into buying and trying the product.

What is Place?

The third P of marketing is place. This is the place where you should sell your product and how it should be delivered to the market.

Example of Place

For example, would it be better to sell your product from a brick-and-mortar store or an e-commerce website? Or, it can refer to where you place the product within your store’s display. Place can even refer to where you choose to advertise your product, like on TV, social media, or web pages. The ultimate goal of place is to determine the best place to get products in front of the customers who are most likely to purchase them.

While place might seem irrelevant if , it actually is extremely relevant. For example, on which social media platforms are you promoting your e-commerce website? Place and promotion are tied closely together.

What is Promotion?

The final P of marketing is promotion. Promotion includes all of the advertising and public relations that make up your promotional strategy for your product. The goal of promoting your product is to show consumers why they need it, what problem it will solve for them, and why they should fork over their hard-earned money for it. What is the best way to reach your target market? It might be a social media platform, a PR campaign, or an SEO strategy.

Example of Promotion

There are so many ways to promote your business today and so many tools available to help you do it. The key is choosing the right method of promotion in order to reach your specific target audience. They might include online marketing, SEO, social media, Google Ads, social media advertising, affiliate marketing or influencer marketing, content marketing, or email marketing.

Marketing Strategy Examples

To create an excellent marketing strategy, here is an example:

Step 1: Product: Determine what it is that you sell, whether it is a product, service, consulting, etc.

Step 2: Price: Decide how much you will charge for your product or service that will both help you make a profit, but is realistic for your consumers.

Step 3: Place: Choose where you will sell your product or service.

Step 4: Promotion: Pick the best method of promoting your product or service.

Marketing Analysis Example

If your current marketing strategy doesn’t seem to be working, perform a marketing analysis by working your way through each of the four Ps of marketing to determine the issue. Maybe it lies within the product, price, place, or promotion. For example, maybe you have an excellent product, but the place you are selling it isn’t working.

The Four Ps are Essential for Marketers

If you want to stand out from your competitors, you need to leverage the four Ps of marketing. For more marketing tips, consider joining the ���������! We offer Digital Marketing Certifications and more to help you be the best possible marketer.

The post The Four Ps of Marketing appeared first on ���������.

Teaching Insight:

Exposure to dogs (cats) makes consumers subsequently more promotion- (prevention-) focused, meaning that consumers will become more eager (cautious) in pursuing a goal and more risk-seeking (risk averse) when making decisions.

Related Marketing Courses:

Advertising and Promotion; Consumer Behavior; Marketing Communications; Principles of Marketing, Core Marketing, Intro to Marketing Management

Full Citation:

Jia, Lei, Xiaojing Yang, and Yuwei Jiang (2022), “,” Journal of Marketing,https://journals.sagepub.com/doi/10.1177/00222429221078036

Article Abstract:

Despite the ubiquity of pets in consumers’ lives, scant research has examined how exposure to them (e.g., recalling past interactions with dogs and cats, viewing ads featuring a dog or a cat as the spokescharacter) influences consumer behavior. The authors demonstrate that exposure to dogs (cats) reminds consumers of the stereotypical temperaments and behaviors of the pet species, which activates a promotion- (prevention-) focused motivational mindset among consumers. Using secondary data, Study 1 shows that people in states with a higher percentage of dog (cat) owners search more promotion- (prevention-) focused words and report a higher COVID-19 transmission rate. Using multiple products, Studies 2 and 3 demonstrate that these regulatory mindsets, when activated by pet exposure, carry over to influence downstream consumer judgments, purchase intentions, and behaviors, even in pet-unrelated consumption contexts. Study 4 show that pet stereotypicality moderates the proposed effect such that the relationship between pet exposure and regulatory orientations persists to the extent consumers are reminded of the stereotypical temperaments and behaviors of the pet species. Studies 5- 7 examine the role of regulatory fit and evince that exposure to dogs (cats) leads to more favorable responses toward advertising messages featuring promotion- (prevention-) focused appeals.

Special thanks to Holly Howe and Demi Oba, PhD candidates at Duke University.

Search other Insights in the Classroom​

Read a managerial summary of this article

More from the Journal of Marketing​​�ċ�ċ�ċ�ċ�ċ

The post The Pet Exposure Effect: Exploring The Differential Impact of Dogs Versus Cats on Consumer Mindsets appeared first on ���������.

Teaching Insights:

Quasi-experimental methods are an increasingly important tool for marketing scholars. This work aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing.

Article citation:

Goldfarb, Avi, Catherine Tucker & Yanwen Wang (2022), “,” Journal of Marketing, 86 (3), 1-20. Doi: 10.1177/00222429221082977

Article Abstract:

This article aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing. The purpose of quasi-experimental methods is, in the absence of experimental variation, to determine the presence of a causal relationship. First, the authors explore how to identify settings and data where it is interesting to understand whether an action causally affects a marketing outcome. Second, they outline how to structure an empirical strategy to identify a causal empirical relationship. The article details the application of various methods to identify how an action affects an outcome in marketing, including difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The authors emphasize the importance of clearly communicating the identifying assumptions underlying the assertion of causality. Last, they explain how exploring the behavioral mechanism—whether individual, organizational, or market level—can actually reinforce arguments of causality.

Special thanks to Holly Howe and Demi Oba, PhD candidates at Duke University.

Search other Insights in the Classroom​

Read a managerial summary of this paper.

More from the Journal of Marketing​​�ċ�ċ�ċ�ċ�ċ

The post Conducting Research in Marketing with Quasi-Experiments appeared first on ���������.

For more information, contact Anatoli . For more doctoral student resources, explore our Doctoral Student SIG.

The post Anatoli Colicev: The PhD Journey (and Beyond) appeared first on ���������.

Brands invested more than in 2019 alone. And social media investments are only expected to grow.

Many firms justify investments in social media by showing its effect on brand attitudes, sales, and stock market value. To measure the effects, marketing professionals rely on various metrics. Facebook “likes” have gained prominent status among the metrics, and maximizing likes has become a badge of honor for marketing managers.

However, Facebook likes’ effect on brand performance is still up for debate. What does liking a brand on Facebook mean? It allows users to follow brand updates appearing on their Facebook newsfeed, making it similar to following firms on Twitter or subscribing to their YouTube channel. The term “like” also implies a positive attitude. But if consumers like brands on Facebook, does it truly signal positive attitudes and even improve their perceptions? Moreover, does the feeling apply to all brands and contexts?

Five recent academic articles investigate the phenomenon. They conclude Facebook likes can have positive value for brands if they are augmented with paid advertising and used to drive brand awareness. Otherwise, they have little effect.

Likes Do Not Lead to Buying

In a Journal of Marketing Research-published study, conducted lab and field experiments to investigate whether liking a brand induced positive attitudes and influenced purchasing.

The challenge in understanding likes’ causal effect on performance metrics is creating a counterfactual (i.e., what could happen in a parallel world) to provide evidence beyond what might occur by chance. Consumers who like a brand on Facebook might already be positively predisposed toward it.

To address the issue, John and colleagues used experiments to disentangle consumers’ predispositions from pushing the Facebook like button. Their lab experiments showed liking a brand is not related to positive attitudes or behavior. They found no differences among groups exposed to newer versus older brands nor among consumers with long exposure to a brand’s Facebook page. In another experiment, participants changed their attitudes about brands in response to advertising but not after following them via a Facebook like. The researchers concluded the consumers’ previously-established positive attitudes toward the brands affected the relationship between likes and purchase behavior. Specifically, purchase behavior was the same among consumers fond of the brand, regardless of whether and when they liked it on Facebook. The low cost of clicking a button did not seem to provide a strong enough cue to alter consumer behavior.

The researchers’ field experiments showed Facebook likes did not provide firms network effects. Partnering with a new cosmetics brand , Grace Choi, John and colleagues tested whether participants’ Facebook likes affected coupon redemption rates among their friends. The researchers found redemption rates were higher (5.2%) for those who did not see their friends’ Facebook likes than for those who did (3.7%). They found more meaningful endorsements external to Facebook, such as expressing brand preference face-to-face, increased redemption rates to 5.9%.

Similarly, behavioral research by found publicly supporting a charitable initiative did not reliably predict real-world engagement with the cause. The researchers found participants wearing pins honoring veterans donated $0.34 to the cause on average, while people privately owning the pins donated $0.86, a statistically significant difference. Furthermore, individuals wearing the pins in public did not donate more than those not engaged in any act of support. In other words, public commitments to charitable initiatives might even decrease individuals’ propensity to provide the cause meaningful support.

How Likes Can Be Effective

Are Facebook likes ever effective for a brand?

A study by examined specific conditions under which liking a brand could lead to positive outcomes. The authors conducted a field experiment in collaboration with a wellness program, Discover Vitality, on a sample of 7,470 new customers. They investigated whether incentivizing social media likes for current customers could affect offline behaviors (e.g., exercising, purchasing healthy groceries) that accumulated points in a health program. The researchers did not invite customers in the control condition to like the Vitality Facebook page. They combined customer survey data with aggregate Facebook activity data and Vitality program points, which reward customers for participating and constituted the study’s primary dependent variable. In line with John and colleagues’ 2017 findings, the researchers saw no increase in points per month when consumers liked the Vitality Facebook page.

However, the researchers reported a 43.91-point increase per month when experiment participants liked Vitality during a “boosted” period, one in which the firm paid to extend its Facebook reach. In other words, when the brand bought reach via Facebook Insights and used it to spread firm-relevant information, brand likes had a positive and significant effect on the dependent variable. The effect was more substantial when consumers were minimally involved with the brand prior to the experiment.

found social media fandom (likes, followers, and subscribers) had a pronounced effect on the bottom of the marketing funnel. The effect on brand awareness (1.1966) was smaller than on purchase intent (.6154) and customer satisfaction (.3366). It appears likes can help spread a brand name through the Facebook network but may not drive financial outcomes.

A study by showed social media users engaged more via Facebook likes and Twitter retweets with content shared by individuals following few other users. Following fewer others seemed to imply autonomy, influence, and prestige. Thus, brands risk limiting follower engagement by following and liking many other users and brands.

Summary

Exclusive reliance on Facebook likes to measure social media effectiveness can be counterproductive for marketing professionals. To benefit from likes, brands should spend on paid reach and keep their objectives modest by focusing on increasing awareness. Firms should consider following few other brands and users to signal autonomy, influence, and prestige and boost engagement.

In the end, marketing managers relying on social media might be best served by focusing on meaningful metrics like valence and brand-consumer engagement to reap rewards.

Author Bio

Anatoli Colicev is an Assistant Professor of Marketing at Bocconi University in Milan, Italy.

Citation

Colicev, Anatoli (2021), “The Real Value of Facebook Likes,” Impact at JMR, (January), Available at: /2021/01/26/the-real-value-of-facebook-likes/

References

Colicev, Anatoli, Ashwin Malshe, Koen Pauwels, and Peter O’Connor (2018), “Improving Consumer Mindset Metrics and Shareholder Value through Social Media: The Different Roles of Owned and Earned Media,” Journal of Marketing, 82(1): 37-56.

John, Leslie K., Oliver Emrich, Sunil Gupta, and Michael I. Norton (2017), “Does ‘Liking’ Lead to Loving? The Impact of Joining a Brand’s Social Network on Marketing Outcomes,” Journal of Marketing Research 54(1): 144-55.

Kristofferson, Kirk, Katherine White, and John Peloza (2014), “The Nature of Slacktivism: How the Social Observability of an Initial Act of Token Support Affects Subsequent Prosocial Action,” Journal of Consumer Research 40(6): 1149-66.

Mochon, Daniel, Karen Johnson, Janet Schwartz, and Dan Ariely (2017), “What Are Likes Worth? A Facebook Page Field Experiment,” Journal of Marketing Research 54(2): 306-17.

Valsesia, Francesca, Davide Proserpio, and Joseph C. Nunes (2020), “The Positive Effect of Not Following Others on Social Media,” Journal of Marketing Research, forthcoming.

Zote, Jacqueline (2020), “55 critical social media statistics to fuel your 2020 strategy,” SproutSocial.

The post What Facebook Likes Really Mean for Your Brand appeared first on ���������.

The business-to-business economy is almost twice as large as the business-to-consumer economy. Marketing scholars have studied the B2B sector for decades, investigating the impact of firms’ strategic decisions on critical outcomes. For example, show investments in online sales channels increase salesperson productivity, and in turn, company profits.

While researchers can use many methods to study a strategic decision’s effect on an outcome, experiments are the gold standard for establishing causality. In B2B domains, however, we cannot randomly assign firms or buyers to experimental conditions with ease. Instead, we must randomly assign B2B managers to experimental conditions and study each context’s effect on managerial decision making and/or downstream outcomes.

Understanding B2B Experiments

When can we use experiments? Whenever we consider using a survey.

In a survey of 511 industrial buyers, for example, studied how customer loyalty to salespeople improved vendor profits but increased defections – when such salespeople left their company, they took their customers with them. The researchers showed the vendors could reduce the defection risk by giving their customers special treatment or status.

An experiment could augment Palmatier and colleagues’ findings by investigating why the customers left their suppliers. Was it because of loyalty to salespeople and indifference about the firms’ and competitors’ products? Or was it because they feared new salespeople would not understand their needs? Experiments can establish such causal relationships.

To be effective, experiments must capture context richness and complexity. Researchers can find it difficult to incorporate B2B contexts in the experimental stimuli due to the multiple actors involved, purchasing process timelines, and long post-purchase assessments. They must carefully select B2B experiment participants to ensure they have experience making complex decisions in contexts varying from selecting software as a service provider to recruiting franchisees.

But the complexity of B2B experiments is not necessarily a bug or obstacle. It is an opportunity to enrich insights—if researchers remember a two-step process.

Step One: Match Study Design to Purpose

B2B experimental research predominantly uses two designs: between-subjects and conjoint. A between-subjects design involves A/B testing with participants randomly assigned to two or more groups and exposed to different experimental conditions.

For example, wanted to explore how B2B suppliers engaged in low-stakes opportunism, such as mildly overstating costs and concealing information. The researchers created two groups, each with 93 randomly assigned executives. Half the executives were in a “low-stakes with low-rapport” condition; the other half experienced a “low-stakes with high rapport” condition. The researchers found suppliers were actually more likely to indulge in small forms of opportunism when they had a good rapport with their customers.

Conjoint studies use a fractional-factorial design, which allows researchers to simultaneously manipulate many variables for each experiment participant. Conditions are randomized for each individual (i.e., within-subjects), and each individual provides multiple responses which increases the size of the final dataset. , for example, wanted to understand what drives firms to outsource systems integration. To measure the many factors affecting the decision, the researchers used a conjoint experiment among actual and prospective telecommunications system buyers. They studied seven variables with 55 managers. Each manager saw different variable level combinations and decided to outsource system integration or bring it in-house: providing valuable insight into managers’ preferences in outsourcing IT activities.

Experiments can provide additional insights by incorporating blocking factors, similar to externally determined segmentation variables. For example, researchers may restrict conditions based on firm type (e.g., public versus private, small versus large) or purchasing situation (e.g., new versus rebuy) to study differences among company profiles, customer types, product lines, geographic regions, or industries. A 1994 paper by is a starting point for understanding the strategy.

Step Two: Match Context to Respondent

B2B experimenters can use scenario-based role-playing vignettes to simulate realistic decision making (e.g., ). To develop a realistic scenario, researchers must carefully describe industry setting (e.g., health insurance versus software), the customer interaction process (e.g., online versus in person), and participant titles (e.g., salespeople versus key account managers), among other factors. They must construct scenarios so participants believe they are real and create stimuli and respondent samples synergistically.

Finding managers to participate in B2B studies is expensive. Firms or industry associations (e.g., the ) may allow researchers to survey their members. Other firms, like Dynata and Qualtrics, provide dedicated respondent panels at a cost of  $21-$50 per completed response. Before contracting with the panel provider, researchers must ensure they can describe their panels in terms of size, industry, manager titles, etc. Understanding a panel’s composition allows researchers to select sub-samples and create appropriate stimuli.

Researchers can also find experiment participants among evening MBA or executive MBA students, with work experience being a critical factor. Jap and associates accessed executive MBA students for their 2013 experiment, while Stremersch and colleagues mailed their stimuli to managers from a telecommunications license database.

How B2B Experiments Benefit Theory and Practice

Every B2B experiment should pass a crucial litmus test: Does it improve our understanding of an issue beyond merely establishing causality?

Experiments assess the combined effect of multiple variables, which may not co-occur frequently in the real world. Consider the effect of economic shocks like COVID-19 on firms’ franchising capabilities. Researchers can use experiments to examine franchisees’ reactions to franchisors’ contract changes with or without the shock’s presence.

Experiments allow researchers to measure the underlying process by which causal factors affect an outcome, indicating the thought process of suppliers, customers, investors, and other stakeholders. In their 2013 experiment, Jap and colleagues showed why suppliers behaved opportunistically. One participant said, “This is part of the game, and these other sales reps will do the same to me.”

Experiments obviate the problem of “searching under the lamp post,” which often occurs when researchers rely exclusively on pre-existing or secondary data. Conjoint studies can assess multiple factors researchers cannot study with secondary data. For example, a firm’s CRM data might provide information on an outsourcing decision, but the raw numbers would not allow analysts to assess the impact of internal company knowledge on the decision.

B2B firms often carefully select existing customers to influence potential customers. For example, software provider SAS Inc. reported asking reference customers to tell potential customers “the good, the bad, and the ugly” about the firm. SAS Inc.’s intent was to improve its reference customers’ credibility. But what if the firm wanted to determine whether the strategy was effective? SAS could run an experiment using a panel of supplier-facing managers. used the strategy in their 2014 experiment. They found negative information indeed made referrals seem more credible, but the overall effect was that potential customers fixated on the negatives.

Summary

Experimental designs like A/B testing and conjoint studies are invaluable tools for answering questions about corporate strategy, decision making, sales processes, key account management, cross-selling, and other topics of interest to B2B companies. Practitioners and scholars can deploy experiments to gain insights complementing secondary data and providing a holistic picture.

Author Bio

Mahima Hada is Associate Professor of Marketing and Director of Marketing Analytics Programs at Baruch College, City University of New York.

Citation

Hada, Mahima (2021), “Two Steps: A Primer on B2B Experiments,” Impact at JMR, (January), Available at: /2021/01/26/two-steps-a-primer-on-b2b-experiments/

References

Hada, Mahima, Rajdeep Grewal, and Gary L. Lilien (2014), “Supplier-Selected Referrals,” Journal of Marketing 78(2): 34-51.

Jap, Sandy D., Diana C. Robertson, Aric Rindfleisch, and Ryan Hamilton (2013), “Low-Stakes Opportunism,” Journal of Marketing Research, 50(2): 216-227.

Kuhfeld, Warren F., Randall D. Tobias, and Mark Garratt (1994), “Efficient Experimental Design with Marketing Research Applications,” Journal of Marketing Research, 31(4), 545-557.

Lawrence, Justin M., Andrew T. Crecelius, Lisa K. Scheer, and Ashutosh Patil (2019), “Multichannel Strategies for Managing the Profitability of Business-to-Business Customers,” Journal of Marketing Research 56(3): 479-497.

Palmatier Robert W., Lisa K. Scheer, and Jan-Benedict E.M. Steenkamp (2007), “Customer Loyalty to Whom? Managing the Benefits and Risks of Salesperson-Owned Loyalty,” Journal of Marketing Research 44(2):185-199.  

Stremersch, Stefan, Allen M. Weiss, Benedict G.C. Dellaert, and Ruud T. Frambach (2003), “Buying Modular Systems in Technology-Intensive Markets,” Journal of Marketing Research 40(3): 335-350.

The post Two Steps: A Primer on B2B Experiments appeared first on ���������.

The internet has enabled many business developments, but it has turned media allocation and planning on its head. In traditional mass media like television, advertisers can purchase a commercial slot and expect large audiences.

However, many of those reached are not interested in the advertised product or service, so a large percentage of those exposed to advertising do not respond to the message. In digital advertising, websites containing specialized content (e.g., model airplanes) allow advertisers to display their products to loyal and attentive audiences. In the social media space, Facebook delivers ad content to ideal target audiences by examining the web activity of users and their networks. Paid search advertising sends firms customers who are already “in the market” for their products, as indicated by their keyword use.

Over the past 15 years, television channels have grown in number. But the more significant change has been the exponential growth in websites supporting themselves with advertising, not to mention the rapid uptake of paid search advertising.

Advertisers have moved to new digital media outlets not only because of their ability to target customers, but also their lower cost compared to traditional media. Furthermore, digital media allows firms to connect ad exposures and search clicks to downstream sales, a feature suggest eludes traditional media. show the most convincing way for firms to demonstrate advertising’s effectiveness is by linking the effort to sales. In turn, researchers can use two methods to assess advertising effectiveness: field experiments and econometric models.

Field Experiments

Targeting and retargeting customers who are more likely to respond to offers, an increasingly common practice, makes advertising appear more effective than it is. , in an award-winning Journal of Marketing Research paper, reported a comparison of advertising response between customers exposed to standard banner ads and retargeted banner ads showed the ads displaying products previously viewed were six times more effective at generating sales. However, the consumers receiving retargeted ads had already demonstrated product predilection. The researchers therefore randomly assigned consumers to a treatment group seeing retargeted, product-specific ads and a control seeing generic product category ads. They found the retargeted ads were less effective than the generic ads, as the customers were in different stages of the purchase funnel, and while retargeted ads work well near purchase, they are not effective for the larger group of customers embarking on their search.

The use of field experiments to determine ad effectiveness has subsequently blossomed, with studies using “ghost ads” on Google () and Facebook ( to create randomized control groups. For example, used a field experiment to show digital ads for one restaurant increased sales at competing restaurants offering similar cuisine.

In every case, these field experiments have shown that advertising effects are often difficult to detect. For example, the study of Facebook ads by examined 15 campaigns and found that only eight produced a statistically significant lift in sales.

Econometric Models

The studies by Johnson, Lewis, and Nubbemeyer and Gordon and colleagues also highlight the challenges of designing an experiment to assess digital ad effectiveness. Individual customers use the internet in different ways, and providers deliver digital ads via unique online auction processes. Econometric models therefore provide a versatile approach to gauging advertising effectiveness. And while field experiment studies have been limited to examining one medium at a time, econometric models allow researchers to compare effectiveness across several media.

Researchers can use econometric models to examine time series data, such as weekly or monthly advertising and sales records. studied traditional and digital advertising’s effects on in-store and online sales for an upscale clothing retailer across 103 weeks. The retailer made about 85% of its sales in-store, and the researchers examined three media: traditional (i.e., total spend on newspapers, magazines, radio, television, and billboards), online banner advertising, and paid search. They found online display and paid search were more effective than traditional advertising. Although firms might expect digital advertising to influence only online sales, the researchers found it also influenced in-store sales.

Researchers can also use econometric models to examine single-source data linking individual-level ad exposure to sales, the strategy employed by Danaher and Dagger in 2013. They examined 10 media types employed by a large retailer: television, radio, newspaper, magazines, online display ads, paid search, social media, catalogs, direct mail, and email. The researchers found traditional media and paid search effectively generated sales, while online display and social media advertising did not.

Multimedia, Multichannel, and Multibrand Advertising

also used single-source data but extended it to multiple retailer-brands, two purchase channels, and three media (email, catalogs, and paid search). They collected the data from a North American specialty retailer selling mostly apparel, where 80% of sales were in-store. The parent retailer owned three relatively distinct brands operating independently. They collected customer data in a combined database, giving them information on sales for each retailer-brand over a two-year period.

The researchers found emails and catalogs from one retailer-brand negatively influenced competing retailer-brands in the category. Paid search influenced only the focal retailer-brand. However, competitor catalogs often positively influenced focal retailer-brand sales among omni-channel customers. The researchers also segmented customers by retailer-brand and channel usage, revealing customers shopping across multiple retailer-brands and both purchase channels were the most responsive group to multimedia advertising.

Summary

In the contemporary business environment of ever-increasing media channels but static advertising budgets, firms must be able to measure advertising effectiveness. Many businesses have shifted their advertising expenditure toward digital media, but multiple studies show traditional media remain effective.

How do marketing managers decide what is best for their companies? Digital media firms like Google and Facebook offer in-house field experiment methods of examining advertising effectiveness. For multimedia studies, analysts can apply econometric models in any setting where time series or single-source data are available.

Author Bio

Peter Danaher is Professor of Marketing and Econometrics and Department Chair at Monash Business School in Melbourne, Australia. He was recently appointed a co-editor of the Journal of Marketing Research.

Citation

Danaher, Peter J. (2021), “Advertising Effectiveness,” Impact at JMR, (January), Available at: /2021/01/26/advertising-effectiveness/

References

Danaher, Peter J., and Tracey S. Dagger (2013), “Comparing the Relative Effectiveness of Advertising Channels: A Case Study of a Multimedia Blitz Campaign,” Journal of Marketing Research, 50(4): 517-534.

Danaher, Peter J., Tracey S. Danaher, Michael S. Smith, and Ruben Laoizo-Maya (2020), “Advertising Effectiveness for Multiple Retailer-Brands in a Multimedia and Multichannel Environment,” Journal of Marketing Research, 57(3): 445-467.

Dinner, Isaac, Harald J. van Heerde, and Scott A. Neslin (2014), “Driving Online and Offline Sales: The Cross-channel Effects of Traditional, Online Display, and Paid Search Advertising,” Journal of Marketing Research, 51(5): 527-545.

Gordon, Brett R., Florian Zettelmeyer, Neha Bhargava, and Dan Chapsky (2019), “A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook,” Marketing Science, 38(2): 193-225.

Johnson, Garrett A., Randall A. Lewis, and Elmar I. Nubbemeyer (2017), “Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness,” Journal of Marketing Research, 54(6): 867-84.

Lambrecht, Anja, and Catherine Tucker (2013), “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research, 50 (October): 561-576.

Sahni, Navdeep S. (2016), “Advertising Spillovers: Evidence from Online Field Experiments and Implications for Returns on Advertising,” Journal of Marketing Research, 53(4): 459-78.

Sethuraman, Raj, Gerard J Tellis, and Richard A. Briesch (2011), “How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities,” Journal of Marketing Research, 48 (June): 457-471.

The post Key Factors That Drive Advertising Effectiveness appeared first on ���������.

The nonprofit sector contributed more than $1 trillion to the U.S. economy in 2016, 5.6% of the country’s gross domestic product, according to a recent National Center for Charitable Statistics . In 2019, . The funds support nonprofit hospitals, nonprofit professional theaters, public schools, and food banks, among other organizations.

Yet with rising demand for nonprofit services, 62% of the sector’s leaders say . Thus, nonprofits must find ways to grow their revenues. Three revenue sources nonprofits might consider are: 1) offering free and paid services by client segment, 2) investing in premium services, and 3) aligning product innovation to market needs.

Offering One Segment Free Services and Another Paid Services

Nonprofit hospitals in low- and middle-income countries serve poor patients with limited healthcare access. To fill the access gap, the hospitals have historically spent money on outreach efforts targeting poor patients. The organizations sustain their outreach efforts while serving the poor patients for free by charging well-off patients market rates.

Many therefore assume serving poor patients for free is a pure cost that must be subsidized by paying patients. challenged the assumption. The researchers analyzed data on outreach “camps” and patient visits at Aravind Eye Hospital from 2006 to 2014. The India-based eyecare network operates vision screening camps for patients around the country, preventing and treating eye problems for those with no other access to healthcare.

Gupta and colleagues examined Aravind’s outreach camp marketing activities, targeted only at poor patients, to determine whether they also drew paying patients to their eyecare centers. Their results indicate that when nonprofits market free services for poor patients (e.g., outreach camps), they can bring in more paying patients and earn more revenue. Specifically, Aravind’s outreach camps increased revenue by $2,273 on average, or three times each camp’s total cost.

Nonprofits’ social mission outreach efforts may therefore offer a standalone revenue stream. By implication, nonprofits should consider devoting more funds to the efforts, which also communicate their tangible value to the community.

Investing More in Premium, Differentiated Services

Nonprofit and for-profit U.S. hospitals generate revenue by offering both basic (e.g., diagnostics, nursing) and premium, specialty services (e.g., transplants). found nonprofit hospitals actually earn more than for-profit hospitals. Why? The nonprofits focus more on premium specialty services than the for-profits.

Moon and Shugan examined the for-profit healthcare industry and $1 trillion nonprofit sector by collating data from public sources. Through analytical and empirical analyses, they determined which marketing strategies led to the nonprofit hospitals’ superior performance, as measured by output and profits.

The study showed nonprofit and for-profit hospitals chose different competitive marketing strategies to maximize their objectives. The nonprofit hospitals they examined invested more in premium services to increase both their output (i.e., number of treatments) and profits. In contrast, they found for-profit hospitals could increase profitability by charging higher prices for basic services. They determined the difference was even more pronounced in situations where competition was intense.

Moon and Shugan’s findings provide a useful framework to guide nonprofits in selecting marketing strategies. By differentiating themselves from competitors, particularly by providing premium services, they can improve their financial sustainability.

Aligning Innovative Products with Market Receptiveness to Innovation

examined how nonprofit professional theaters could use product mix to grow revenue. Studying 124 theaters across the United States, the researchers examined how innovation, product exploration experience, promotion, and market sophistication impacted objective measures of financial performance.

Nonprofit professional theaters usually offer a variety of plays, such as new productions, modified productions, and classics. The theaters earn revenue from single and season ticket sales. Thus, they must understand the optimal production mix for each customer segment. For example, single ticket holders might be more willing to experiment on new-to-the-world plays. Should nonprofit theaters therefore offer a higher proportion of new productions to single ticket holders than season ticket holders?

Voss, Montoya-Weiss, and Voss found the answer depends on how receptive each market is to innovation. They found innovation had a positive impact on revenue from single ticket holders only in markets valuing their community arts program.

Based on the research, nonprofits must be mindful of two issues when trying to grow revenues through innovative new products and services. First, radical innovation might provide value only to some customer segments. And second, innovation’s effectiveness depends on market characteristics.

Author Bio

Yixing Chen is an Assistant Professor of Marketing at the Mendoza College of Business at the University of Notre Dame.

Citation

Chen, Yixing (2021), “Three Ways Nonprofits Grow Revenues,” Impact at JMR, (January), Available at: /2021/01/26/three-ways-nonprofits-grow-revenues/

References

Gupta, Sachin, Omkar D. Palsule-Desai, C. Gnanasekaran, and Thulasiraj Ravilla (2018), “Spillover Effects of Mission Activities on Revenues in Nonprofit Health Care: The Case of Aravind Eye Hospitals, India,” Journal of Marketing Research, 55 (6), 884-99. ()

Moon, Jihwan, and Steven M. Shugan (2020), “Nonprofit Versus For-Profit Health Care Competition: How Service Mix Makes Nonprofit Hospitals More Profitable,” Journal of Marketing Research, 57 (2), 193-210. ()

Voss, Glenn B., Mitzi Montoya-Weiss, and Zannie Giraud Voss (2006), “Aligning Innovation with Market Characteristics in the Nonprofit Professional Theater Industry,” Journal of Marketing Research, 43 (2), 296-302. ()

The post How Nonprofits Can Increase Their Income [3 Funding Strategies] appeared first on ���������.

Teaching Insights

The value of creativity is subject to longstanding debate and recent reports highlight how marketers are increasingly growing skeptical of advertising creativity and decreasing their investments in it. This paper outlines why doing so is a mistake and how marketers can get the most out of their investments in advertising creativity.

Related Marketing Courses: ​
Marketing Communications; Marketing Strategy

Full Citation: ​
Rosengreen, Sara, Martin Eisend, Scott Koslow, and Micael Dahlen (2020), “” Journal of Marketing

Article Abstract: 
Although creativity is often considered a key success factor in advertising, the marketing literature lacks a systematic empirical account of when and how advertising creativity works. The authors use a meta-analysis to synthesize the literature on advertising creativity and test different theoretical explanations for its effects. The analysis covers 93 data sets taken from 67 papers that provide 878 effect sizes. The results show robust positive effects but also highlight the importance of considering both originality and appropriateness when investing in advertising creativity. Moderation analyses show that the effects of advertising creativity are stronger for high- (vs. low-) involvement products, and that the effects on ad (but not brand) reactions are marginally stronger for unfamiliar brands. An empirical test of theoretical mechanisms shows that affect transfer, processing, and signaling jointly explain these effects, and that originality mainly leads to affect transfer, whereas appropriateness leads to signaling. The authors also call for further research connecting advertising creativity with sales and studying its effects in digital contexts..

Kelly Gullo and , Ph.D. candidates at Duke University supported the authors teams with submissions to this program. 

Search other Insights in the Classroom​

Read a managerial summary of this paper.

More from the Journal of Marketing​​�ċ�ċ�ċ�ċ�ċ

The post A Meta-Analysis of When and How Advertising Creativity Works appeared first on ���������.

Teaching Insights

Persistent high failure rates of new product alliances call for identification of factors that might improve alliance outcomes. We studied how asymmetries in prealliance network ties between a firm and its alliance partner affect the focal firm’s financial performance and financial performance uncertainty. We discovered that direct tie asymmetry has an inverted U-shaped effect on the focal firm’s abnormal returns and a U-shaped effect on its risk. Indirect tie asymmetry also has a U-shaped effect on the focal firm’s risk. However, the focal firm’s innovation quality and preexisting ties with its partner flatten these curvilinear effects. This study highlights the need to assess a potential alliance partner’s direct ties and indirect ties. We alert managers that indirect ties of alliance partners are important to assess, and that direct and indirect ties assessed relative to an alliance partner directly affect the focal firm’s market capitalization.

More specifically, firms should definitely avoid selecting a partner with whom it has high direct tie asymmetry and low total interdependence. Further, it is best to avoid selecting a partner with whom it has high indirect tie asymmetry and low total interdependence

Related Marketing Courses: ​
Business-to-Business Marketing, Innovation/New Product Development; Marketing Strategy; Technology Marketing

Full Citation: ​
Chakravarty, Anindita, Chen Zhou, and Ashish Sharma (2020), “Effect of Alliance Network Asymmetry on Firm Performance and Risk” Journal of Marketing.

Article Abstract: 
Persistent high failure rates of new product alliances call for identification of factors that might improve alliance outcomes. In this research, the authors identify two attributes of alliance network asymmetry that affect alliance performance and performance uncertainty: differences in the number of prealliance direct ties, which can create asymmetry in the volume of resources of the two firms, and differences in the interconnectivity among prealliance indirect ties, which leads the firms to possess different types of resources. The authors theorize that absolute levels of such asymmetries have curvilinear effects on alliance performance and performance uncertainty, which materialize as a focal firm’s abnormal returns and risk, respectively. They demonstrate that direct tie asymmetry has an inverted U-shaped effect on the focal firm’s abnormal returns and a U-shaped effect on its risk. Indirect tie asymmetry also has a U-shaped effect on the focal firm’s risk. However, the focal firm’s innovation quality and preexisting ties with its partner flatten these curvilinear effects. The findings have implications for partner selection in new product alliances.

and , Ph.D. candidates at Duke University along with Adam Mills, Assistant Professor of Marketing, Loyola University supported the authors teams with submissions to this program. 

Search other Insights in the Classroom​

Read a managerial summary of this paper.

More from the Journal of Marketing​​�ċ�ċ�ċ�ċ�ċ

The post Effect of Alliance Network Asymmetry on Firm Performance and Risk appeared first on ���������.