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Cracking Customer Churn: Insights from 300 Revenue Leaders
91% of revenue leaders have reported that their deals have become more competitive this year. Learn how top leaders are navigating competitive markets and closing their competitive revenue gap.
We spoke with over 300 revenue leaders to understand how they’re navigating difficult market conditions. Deals and renewals are heating up. There are more people chasing less pie.
Their top 3 priorities of the year? 1. Hitting revenue targets 2. Retaining customers 3. Improving sales performance
If your business isn’t preparing to compete effectively today, your revenue numbers are going to look grim at your next board meeting. Read and listen to how these leaders are navigating a tough, competitive market to hit their targets in our latest report: When the Pie Shrinks.
With the exponentially increasing volumes of rich, written textual data generated via consumer–brand interactions in online spaces, harnessing this data to understand and measure its marketing implications has become an exciting opportunity for marketing managers. However, given the increasing sophistication and complexity of written communication in online spaces, it is becoming harder to analyze and comprehend the impact of written consumer communication for brands. Now, a cutting-edge new Journal of Marketing Research article provides an easy-to-use tool that is grounded in nonverbal and sensory marketing theory.
The researchers developed and validated a paralanguage classifier (called PARA) using social media data from over 1.2 million Twitter, YouTube, and Instagram posts. Whereas the currently popular text analysis tools explore verbal aspects of what is being conveyed, this research focuses on the subtleties of how the text is written. “Textual paralanguage” (TPL) is the measure of nonverbal characteristics of a text that extend beyond the literal meanings of words and parts of speech. The PARA software provides output variables at a superordinate sensory level, such as auditory TPL (which includes elements denoting tone, rhythm, stress, emphasis, and tempo of the text), tactile TPL (which includes tactile emojis and emoticons), visual TPL (bodily/nonbodily emoticons/emojis), and aggregate variables (emoji count, emoticons index, and TPL index). The PARA tool uses a rule-based algorithm to analyze textual data.
Implications for Marketing Managers
Feedback forums are rich sources of customers’ opinions and emotions. By applying PARA to measure the intensity of sentiment valence on these platforms, managers can prioritize responses and devise better communication strategies for faster and more effective resolution. Moreover, PARA can also help brands to convey persuasive messages with nonverbal cues that enhance the appeal of their marketing content. Social media is not only a powerful tool for firms but also for individuals such as artists, celebrities, and influencers who seek to attract consumer engagement. PARA can enable such emerging brands to create captivating content with nonverbal cues that inspire people to interact. As the authors suggest, PARA can also serve as a valuable tool for influencer vetting to ensure that the content produced by influencers is consistent with brand communication.
Overall, the research shows that paralingual text analysis can help us understand how TPL elements communicate the visual as well as auditory aspects of a text and can be used to study the influence on consumer perception and response. Although extrapolating the exact meaning of the language used in a textual content has been a tough nut to crack, this study provides an easy-to-use tool to understand the tough layer of non-verbal cues in the text.
We interviewed the authors to learn more about this exciting project:
Q. What drove your motivation to analyze auditory, visual, and tactile elements in online textual content? Could you explain the methodology used to develop the PARA tool in layperson’s terms?
A: In this work, we developed a text analysis tool called PARA to identify nonverbal communication cues in text. We realized that many text tools focus on what is said—the verbal content of a message or the words themselves. What makes PARA different is that it identifies 19 features of nonverbal parts of speech—focusing on how something is said—or what’s called textual paralanguage. Simply put, PARA has been built on a set of rules as well as dictionaries that identify keywords.
Q. How is nonverbal communication in online text different from that of traditional face-to-face interactions and are there any potential ethical considerations for using the PARA tool, especially in the context of social media privacy?
A: In-person communication is riddled with nonverbal cues (e.g., facial expressions, vocal tone). In text-based communication, communicators alter the message to add this nuance. People are creative with how they bring the text to life: they might add capitalizations, elongation of words, repeated punctuation, rhythmic punctuation, emojis, emoticons, asterisks, etc. PARA captures these elements.
As with any online content, privacy is always a consideration. It is important to realize that content posted on social media is often public and depends on the privacy settings of a particular user or platform.
Q. Does the textual paralanguage tool capture the subtle usage of linguistic elements (such as the use of wordplay), which are crucial for content success yet marginally captured by traditional textual analysis methods?
A: While PARA does not directly target the use of wordplay, its primary focus is on identifying textual paralanguage in online written communication, which is often overlooked and minimally detected using conventional text analysis methods. In our article, we present empirical evidence highlighting the role that PARA plays in successful online communication. This is demonstrated through its ability to shape content sentiments and predict user engagement, thereby serving as an important driver of content success. It is important to note that while PARA does not explicitly identify wordplay, it excels at detecting the textual paralanguage used alongside it. PARA provides a rich, multidimensional view of online communication by enhancing our understanding of text sentiment.
For example, consider this dialogue:
Person A: “Why did the bicycle fall over? 🚲😉”
Person B: “Hmm… I’m not sure, why did it fall over? 🤔”
Person A: “*drumroll* Because it was two-tired!!!”
In this instance, while PARA would not directly recognize the wordplay (‘two-tired’), its ability to capture textual paralanguage cues (e.g., emojis, emphasis, vocalizations, etc.) helps inform us about the playful, joking context in which the wordplay is used, enhancing the holistic understanding of the text.
Q. How does the tool treat the following categories: (1) only text, (2) only emojis, and (3) text + emojis? Can the tool be used to analyze sentiments in a specific linguistic category? If so, how should researchers deal with these limitations?
A: PARA is a linguistic tool designed primarily to detect and classify textual paralanguage. The paralinguistic features it detects include not only emojis but also alphanumeric (keyboard-based) nonverbal cues, with 15 features that can be operationalized using text alone. In our article, we demonstrate that disregarding these textual paralanguage features can result in missing significant explanatory and predictive power when analyzing sentiment valence and intensity. This suggests that PARA is an indispensable tool for sentiment analysis, regardless of whether the data comprises only text, only emojis, or a combination of the two. Moreover, when used in conjunction with traditional sentiment methods such as VADER or LIWC, PARA can enhance the accuracy of sentiment analysis. This is because it captures variations in sentiments that might be overlooked when relying solely on text-based verbal cues. Of course, with any methodology, there are limitations. PARA uses human-generated rules and dictionaries based on training data to detect textual paralanguage. Such rule-based detection may not be exhaustive, and there are likely rare and nuanced forms of paralanguage that remain undetected, which could be further investigated in conjunction with generative AI. In addition, the features detected by PARA are rooted in the English language. There are many future research opportunities to expand PARA to detect nonverbal features in other languages.
Q. Given the highly subjective nature of emotions, how do you approach the potential ambiguity of nonverbal cues in the text, such as emojis, and the challenge of accurately determining their precise emotional implications?
A: In our study, we focus primarily on sentiment valence (i.e., the positivity/negativity of a message) and sentiment intensity (i.e., the degree of positivity/negativity of a message). PARA is not designed to detect specific emotions or make emotional inferences from emojis. In other words, our approach allows us to identify the influence of nonverbal cues across various contexts and social media platforms, independent of the specific emotion being expressed.
A natural next step is to further investigate whether certain nonverbal features convey specific emotions better. For example, certain paralinguistic features may better indicate happiness, whereas others convey pride. It is important to consider the impact of each nonverbal feature in different contexts, particularly how verbal and nonverbal text features interact to produce emotional effects. We consider this to be an important area for future research.
Q. Could you share any specific applications or cases with marketing researchers and professionals where PARA can effectively predict consumer engagement? What is the easiest way for marketing researchers to access these tools?
A: In our article, we demonstrate the effectiveness of PARA in predicting consumer engagement using social media data from platforms such as Twitter, YouTube, and Instagram (N = 1,241,489 posts). PARA proves valuable in predicting consumer engagement metrics, including the number of likes and retweets, even after controlling for a wide range of verbal features using existing text tools. PARA will be particularly relevant when analyzing text content that has been generated on mobile devices. Text from cell phones as opposed to computers is more likely to include paralanguage because individuals have easy access to emoji keyboards, and on phones, people generally write more brief, more emotional, and more “gist-based” content. Textual paralanguage is particularly relevant for marketing managers involved in content creation and consumer listening. Whether replying to customers on Twitter, managing a brand’s Instagram page, or composing emails to notify customers of important updates, marketing managers should consider the way content is written as it significantly impacts consumer interpretation and engagement. To access the tool, visit https://www.textualparalanguage.com/. You can download the PARA software for free, and it is compatible with both Mac and Windows systems. Researchers or practitioners interested in the PARA Python source code can contact us for access (firstname.lastname@example.org).
Tejaswi Pant is a doctoral student in marketing, Indian Institute of Management Udaipur, India.
When Less Is More: The Power of Simple Packaging for Consumable Products
Lan Anh N. Ton, Rosanna K. Smith and Julio Sevilla
Designing products is both an art and a science. Companies have found that bringing together many visual elements in product design—with multiple colors, text, and illustrations incorporated in the packaging—can lead to enhanced brand engagement. However, in the last few years, consumers have increasingly desired more minimalist aesthetics.
In a recent Journal of Marketing study, we examine this consumer trend toward minimalist packaging in consumable products. We theorize that consumers tend to assume that the simplicity of the product package suggests that the product contains few ingredients, which in turn increases the belief that the essential ingredients of the product are undiluted. With customers increasingly seeking product purity, there is an increase in a willingness to pay for consumable products with simple packaging.
We define simple packaging design as the extent to which a product package contains few design elements that lack detail, are similar to one another, and are arranged in regular ways. Complex packaging design refers to the extent to which a product package contains many design elements that are highly detailed, different from one another, and arranged in irregular ways. We examined over 1,000 consumable product packages from the largest supermarket chain in the U.S. and find that the simplicity of the packaging design is positively associated with price. A series of experiments show that increased willingness to pay for products in simple packaging is due to consumers often assuming that simple packaging signals few ingredients, which enhances perceived product purity.
Increased willingness to pay for products in simple packaging is due to consumers often assuming that simple packaging signals few ingredients, which enhances perceived product purity.
However, simple packaging does not always enhance consumers’ willingness to pay. We find that store-brand products are not likely to experience the same benefits of simple packaging as non-store brand products. This is likely because the simplicity of the product package aligns with consumers’ default assumption that store brands invest less in product quality. Thus, the simplicity of store brand packaging likely signals a lack of investment in the product rather than few ingredients and product purity.
The Role of Consumer Goals
We also find the preference for simple packaging depends on consumers’ goals. When consumers have a health goal, they are more likely to pay for a product with simple packaging. This is because simple packaging conveys that the product contains few ingredients and high product purity—attributes that tend to be associated with healthy products. In contrast, when consumers seek to indulge, they are less willing to pay for products with simple packaging. This is because complex packaging signals many ingredients and low product purity—attributes that tend to be associated with unhealthy (and, by extension, tasty) products.
Our research extends the understanding of consumer interest in minimalist aesthetics by showing conditions under which design simplicity can be less desirable. Visual simplicity often conveys the idea of “less is more,” but there are situations when it can simply signal “less is less.” Our work also broadens the understanding of the concept of purity in the context of consumer research. While explicit illustrations, such as a drawing of a mountain spring, can enhance consumer judgments of product purity, we find that product purity can be inferred from more subtle visual cues (that is, the lack of visual design elements). Relatedly, we dig deep into the concept of product purity, which can hold a variety of meanings, and differentiate purity from its related construct of naturalness, which typically refers to products that are not man-made.
Lessons for CMOs
Our research provides several insights for chief marketing officers:
Simplifying package design can be an effective way for brands to visually (and nonverbally) communicate key product information to consumers. Simple packaging can lead consumers to infer that the product has fewer ingredients and is purer, thereby enhancing their willingness to pay.
Aligning the visual design of the product package with ingredient information is essential to make a positive impact on consumers.
Managers may consider the specific brand when using simple packaging because positive inferences are less likely to occur for store-brand products.
When managers want to signal that their products are indulgent, opting for more complex designs could be more effective.
Our work could be extended to durable goods such as technology products. For instance, Apple products are well-known for their simple packaging and are often seen as easier to use than their competitors. It may be fruitful to explore how inferences derived from simple packaging of technology products align or differ from those of consumable goods.