Article 9

Question 1

Who wrote “Unstructured data in marketing”?

Answer 1

Balducci, B., Marinova, D. Unstructured data in marketing. J. of the Acad. Mark. Sci. 46, 557–590 (2018). https://doi-org.ru.idm.oclc.org/10.1007/s11747-018-0581-x

Question 2

What is “Unstructured data in marketing” about?

Answer 2

Despite the rapid increase in unstructured data, most firms are not fully utilizing it, which presents an opportunity for future research. The authors aim to address this gap by providing a unifying framework that defines and conceptualizes UD in marketing, reviews the existing literature, and highlights theoretical, computational, and substantive gaps for further exploration

Question 3

What is “unstructured data”?

Answer 3

information that either does not have a pre-defined data model or is not organized in a pre-defined manner; aka “the dark analytics”; a single data unit in which the information offers a relatively concurrent representation of its multifaceted nature without predefined organization or numeric values

Question 4

What types of UD are utilized in Balducci et al?

Answer 4

can be verbal or nonverbal; types of UD we review include text, video, voice, images, nonverbal (e.g., facial and gestural cues), and select automated methods

Question 5

Balducci et al. characterize the UD in their article based on which three characteristics?

Answer 5

1) non-numeric (lack pre-defined numeric assignments), 2) multi-facetted (ex/ voice data contains many facets (e.g., pitch, speech) rate, intensity), and 3) concurrent (UD can represent multiple facets simultaneously, allowing for dynamic analysis)

Question 6

What challenges are faced when encoding and decoding UD, in Balducci et al.?

Answer 6

1) technical (accuracy of info transmitted, 2) semantic (how the recipient interprets/processes the info), and 3) effectiveness challenges (extent to which the decoded message aligns with the sender’s intended meaning)

Question 7

What is NLP, in Balducci et al?

Answer 7

Natural Language Processing

Question 8

What is LIWC, in Balducci et al?

Answer 8

Linguistic inquiry and word count

Question 9

What are the three key contributions of Balducci et al?

Answer 9

1. Offering a clear definition and conceptualization of UD.
2. Bridging disparate research on UD by synthesizing various subsets of UD for marketing.
3. Identifying research gaps and providing interdisciplinary insights for leveraging UD in marketing research.

Question 10

What are the three stages of categorizing UD, according to Balducci et al?

Answer 10

1. Information Transmission: marketers communicate value by encoding information and transmitting it to customers. example: Advertising and Promotions: Research using unstructured data analyses how content in ads (e.g., vocal cues, facial expressions) affects customer responses and brand evaluations.
2. Information Processing: delivering value by managing customer interactions and providing tailored services. Unstructured data, such as voice or image data, can enhance customer service by enabling real-time adjustments to customer needs.
   Example: Retail Management: Unstructured data, such as gesture recognition and facial cues, helps retailers understand customer behavior and preferences in real time.
3. Information Extraction: extracting insights from unstructured data to create value for customers. Text and image mining, voice analysis, and other machine learning techniques allow firms to generate valuable marketing intelligence from unstructured sources like social media or online reviews. Example: UGC) Analyzing UGC enables firms to understand customer sentiment, predict trends, and inform product development.