Article

Uniting the Tribes: Using Text

for Marketing Insight

Jonah Berger, Ashlee Humphreys, Stephan Ludwig, Wendy W. Moe,

Oded Netzer, and David A. Schweidel

Abstract

Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing com-

munications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an

overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text

subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.

Keywords

computational linguistics, machinelearning, marketing insight, interdisciplinary, natural language processing, text analysis, text mining

Online supplement: https://doi.org/10.1177/0022242919873106

The digitization of information has made a wealth of textual

data readily available. Consumers write online reviews, answer

open-ended survey questions, and call customer service repre-

sentatives (the content of which can be transcribed). Firms

write ads, email frequently, publish annual reports, and issue

press releases. Newspapers contain articles, movies have

researchers must be able to extract underlying insight—to mea-

sure, track, understand, and interpret the causes and conse-

quences of marketplace behavior.

This is where the value of automated textual analysis comes

in. Automate d textual analysis

1

is a computer-assisted

methodology that allows researchers to rid themselves of mea-

surement straitjackets, such as scales and scripted questions,

and to quantify the information contained in textual data as it

“how” it is said, using both qualitative and quantitative inqui-

Dean’s Professor of Marketing, and Co-Director of the Smith Analytics

cies), and the more genera l topics discussed in the text.

Techniques range from computerized word counting and

applying dictiona ries to s upervised or automated machine

learning that helps deduce psychometric and substantive prop-

erties of text.

Within this emerging domain, we aim to make four main

contributions. First, we illustrate how text data can be used for

both prediction and understanding, to gain insight into who

produced that text, as well as how that text may impact the

people and organizations that consume it. Second, we provide a

ships, and themselves. Whereas previous treatments of text

analysis have looked specifically at consumer text (Humphreys

2016), or psychological processes (Tausczik and Pennebaker

2010), we aim to provide a framework for incorporating text

into marketing research at the individua l, firm, market, and

societal levels. By necessity, our approach includes a wide-

ranging set of textual data sources (e.g., user-generated content,

annual reports, cultural artifacts, government text).

Fourth, and most importantly, we discuss how text analysis can

related questions (Moorman et al. 2019a, b). In reality, however,

the field often seems fragmented. Rather than different rowers all

simultaneously pulling together, it often feels more like separate

Although everyone is theoretically working toward similar goals,

“languages” (e.g., psychology, sociology, anthropology, statis-

tics, economics, organizational behavior) and use different tools,

making it increasingly difficult to have a common conversation.

We start by discussing two distinctions that are useful when

thinking about how text can be used: (1) whether text reflects or

impacts (i.e., says something about the producer or has a down-

stream impact on something else) and (2) whether text is used

for prediction or understanding (i.e., predicting something or

understanding what caused something). Next, we explain how

text may be used to unite the tribes of marketing. Then we

provide an overview of text analysis tools and methodology

and discuss key questions and measures of validity. Finally,

we close with a future research agenda.

cations generate text or can be transcribed into text.

think about producers and receivers—the person or organi-

zation that creat es the text and the person or orga nizat ion

who consumes t he text (Table 1). While there are certainly

other parties that could be listed, some of the main

producers a nd receivers are cons umers , fi rms, investors, and

society at large. Consumers write online reviews that are

read by other consumers, firms create annua l reports tha t

are read by investors, and cultural producers represent soci-

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Berger et al. 3

These same approaches can also be used to understand

leaders, orga niz at ions, o r cultu ral elites through the text they

produce. For example, the words a leader uses reflect who

they are as an individual, their leadership style, and their

attitudes toward va rious sta keho lders . The lang uage use d in

ads, on websites, or by customer serv ice ag ents refle cts inf or -

mation about the company those pieces of text represent.

Aspects s uch as bra nd personali ty (Opoku, Abratt, and Pit t

2006), how mu ch a firm is thinking about its cu stome rs (Pack-

ard and Berger 2019), or ma nage rs ’ orienta tion tow ard e nd

institutions. Given that texts reflect information about the peo-

organization s t ogether on th e basis of shared characteristics

can provide insight into the nature of such groups and differ-

ences between them. Analyzing blog posts, for example, can

shed light on how older and younger people view happiness

Kamvar, and Aaker 2011). In a comparison of newspaper arti-

cles and press releases about different business sectors, text can

be used to understand the creation and spread of globalization

culture, U.S. culture values high-arousal positive affective

states more than East Asian cultures (Tsai 2007), and these

differences may show up in the language these different groups

use. Similarly, whereas members of individualist cultures tend

to use first-person pronouns (e.g., “I”), members of collectivist

cultures tend to use a greater proportion of third-person pro-

nouns (e.g., “we”).

Across time, researchers were able to examine whether the

national mood changed after the September 11 attacks by

studying linguistic markers of psychologic al chang e in online

provide a window into the evolution of topics within acade-

everything from cultural differences in customs to changes in

values over time.

Consequently, text analysis can provide insights that may

methods. Companies and organizations can use social listening

(e.g., online reviews and blog posts) to understand whether

consumers like a new product, how customers feel about their

brand, what attributes are relevant for decision making, or what

the performance of the stock market (Bollen, Mao, and Zeng

2011; Tirunillai and Tellis 2012).

Text’s Impact on Receivers

In addition to reflecting information about the people, organi-

zations, or society that created it, text also impacts or shapes the

attitudes, behavior, and choices of the audience that consumes

it. For example, take the language used by a customer service

agent. While that language certainly reflects something about

that agent (e.g., their personality, how t hey are feeling that

day), h ow they feel toward the customer, and what type of

world. It has consequences.

(Humphreys and LaTour 2013), trade publications and con-

sumer magazines shift product category pe rceptions (e.g.,

Rosa et al. 1999), movi e scripts shape audience reactions

(Berger, Kim, and Meyer 2019; Eliashberg, Hui, and Zhang

2014; Reagan et al. 2016), and song lyrics shape song market

reflects information about the actor or actors that created it,

impacts that audience.

versus impact tend to use text differently. Research that exam-

ines what the text reflects often treats it as a dependent variable

and investigates how it relates to the text creator’s personality,

the social groups they belong to, or the time period or culture in

which it was created.

Research that examines how text impacts others often treats

Berger et al. 5

is that text can generate hundreds and often thousands of fea-

tures (words) that are all potential predictors for the outcome of

interest. In some cases, the number of predictors is larger than

the number of observations, making traditional statistical pre-

researchers often resort to machine learning–type methods, but

overfitting needs to be carefully considered. In addition, infer-

ence with respect to the role of each word in the prediction can

be difficult. Methods such as feature importance weighing can

help extract some inference from these predictive models.

Understanding

understandi ng. How does the lan guag e consumers us e shape

brands engender greater loyalty? How do cultural attitudes

is drawing causal infere nces from observati onal data. Con-

experiments t o allow key indepe ndent variables to be

manipulated. An ot her c ha lleng e is i nte rpre ting r ela tions hips

section). Songs that use more second-person pronouns are

more popul ar (Packar d a nd Berger 2019), for exa mple, but

this relat ionship alone does not nec essa rily explain why this

what text reflects could use it t o predict states or traits of

the text creator such as customer satisfaction, likelihood of

churn, or brand personality. Researchers interested in the

such as reading behavior, sharing, or purchase among con-

sumers of that text.

reflects could use it to shed light on why peopl e might use

certain personal pronouns when they are depressed or why

customers might use certain types of emotional language

aspects. Netzer, Lemaire, and Herzenstein (2019), for example,

both use a range of available textual features to predict whether

a given person will default on a loan and analyze the specific

language used by people who tend to default (e.g., language

used by liars).

Uniting the Tribes of Marketing

Regardless of whether the focus is on text reflection v ersus

impact, or prediction versus understanding, doing text analysis

Given concerns about replication, researchers have started to

look for new tools that enable them to ensure validity and

increase relevance to external audiences. Previously, use of

“what” but not the “why” (i.e., what people bought or did, but

not why they did so). But text can provide a window into the

underlying process. Online reviews, for example, can be used

another. Blog posts can help marketers understand consider-

ation sets (Lee and Bradlow 2011; Netzer et al. 2012) and the

researchers. Text opens a large-scale window into the world

of “why” in the field and does so in a scalable manner. Quan-

titative modelers are always looking for new data sources and

tools to explain and predict behavior. Unstructured data pro-

vides a rich set of predictors that are often readily available, at

large scale, and able to be combined with structured measures

as either dependent variables or independent variables. Text,

through product reviews, user-driven social media activity, and

firm-driven marketing efforts, provides data in real time that

LeCun, Bengio, and Hinton 2015; Liu et al. 2019).

6 Journal of Marketing XX(X)

these researchers is to obtain reliable and generalizable survey

or field data about factors that lie deep in the firm’s culture and

structure or that are housed in the mental models and beliefs of

marketing leaders and employees. Text analysis offers an objec-

tive and systematic solution to assess constructs in naturally

occurring data (e.g., letters to shareholders, press releases, patent

text, marketing messages, conference calls with analysts) that

may be more valid. Likewise, marketing strategy scholars often

struggle with valid measures of a firm’s marketing assets, and

text may be a useful tool to understand the nature of customer,

¨

tent analysis. Consumer c ulture theory–oriented marketing

researchers are primarily interested in understanding underly-

ing meanings, norms, and values of consumers, firms, and

markets in the marketplace. Text analysis provides a tool for

quantifying qualitative information to measure changes over

time or make comparisons between groups. Sociological and

anthropological researchers can use automated text analysis to

identify important words, locate themes, link them to text seg-

ments, and examine common expressions in their context. For

example, to understand consumer taste practices, Arsel and

tative and quant itative marketing research. Like qualitative

research, it is rich, exploratory, and can answer the “why,” but

like quantitative research, it benefits from scalability, which

often permits modeling and statistical testing. Textual analysis

enables researchers to explore open-ended questions for which

they do not know the range of possible answers a priori. With

text, scholars can answer questions that they did not ask or for

which they did not know the right outcome measure. Rather

than forcing on participants a certain scale or set of outcomes

from which to select, for example, marketing researchers can

likely to be realized when researchers work across traditional

subgroups. That is, the benefits of computer-aided text analysis

are best realized if we include both quantitative, positivist

analyses of content and qualitative, interpretive analyses of

discourse. Quantitative researchers, for example, have the

skills to build the right statistical models, but they can benefit

words to underlying psychological or social processes as well

as marketing strategy researchers’ understanding of organiza-

tional and marketing activities driving firm performance. This

is true across all of the groups.

Thus, to really extract insights from textual data, research

teams must have the interpretative skills to understand the

meaning of words, the behavioral skills to link them to under-

Given the recent work using text analysis to derive marketing

section reviews methodologies often used in text-based

research. These include techniques needed to convert text into

constructs in the research process as well as procedures needed

to incorporate extracted textual information into su bsequen t

modeling and analyses. The objective of this section is not to

provide a comprehensive tutorial but, rather, to expose the

reader to available techniques, discuss when different methods

are appropriate, and highlight some of the key considerations in

tive. Table 2 provides a summary of the different steps

involved in the text analysis process from preprocessing to com-

monly used tools and measures and validation approaches. Table

2 can serve as a starter kit for those taking their first steps with

text analysis.

Data Preprocessing

Text is often unstructured and “messy,” so before any formal

analyses can take place, researchers must first preprocess the

text itself. This step provides structure and consistency so that

the text can be used systematically in the scientific process.

Tausczik and Pennebaker 2010) and WordStat (Peladeau 2016),

require minimal preprocessing. We detail the data preprocessing

steps next (for a summary of the steps, see Table 3).

Berger et al. 7

Data acquisitio n. Data acquisition can be well defined if the

researcher is provided with a set of documents (e.g., emails,

quarterly reports, a data set of product reviews) or more open-

ended if the researcher is using a web scraper (e.g., Beautiful

Soup) that searches the web for instances of a particular topic

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8 Journal of Marketing XX(X)

ming, the entities “car” and “cars” are stemmed to “car,” but

“automobile” is not. In lemmatization, the words “car,” “cars,”

and “automobile” are all reduced to the lemma “automobile.”

Several prepackaged stemmers exist in most text-mining tools

(e.g., the Porter stemmer). Similar to stop words, if the goal of the

analysis is to extract the writing style, one may wish to skip the

stemming step, because stemming often masks the tense used.

Text Analysis Extraction

Once the data has been preprocessed, the researcher can start

analyzing the data. One can distinguish between the extraction