Personalized Communication

by

Ewa Maslowska

• LAST REVIEWED: 12 January 2023
• LAST MODIFIED: 12 January 2023
• DOI: 10.1093/obo/9780199756841-0284

Introduction

Personalization is not a new idea in communication—media, marketers, political strategists, health communication specialists have been always looking for ways to increase their messages’ relevance; however, it was the arrival of highly connected digital technologies that enabled personalization on a big-scale. The prevalence of vast amounts of digital-trace data and the development of methods facilitating the analysis of large data sets, often in (near) real time, as well as the optimization of content based on analytical insights made various forms of personalization ubiquitous. When participating in digital activities (e.g., texting, location tagging, browsing websites, sharing content on social media, tracking activities on a smartwatch, interacting with a voice assistant, etc.), people produce digital footprints that can be used to tailor messages, services, and products to them. The digital footprints data are also used to infer higher-order constructs, allowing for personalized communication to build on information about people’s interests, needs, motives, political views, personalities, etc. This article defines personalization as a strategy of providing content selected, adjusted, or created for the specific recipient based on information about them with the aim of increasing the recipient’s perceived relevance of and openness to the content. While in the early days, personalization was based on surveys and the variation in the message content was rather limited, today personalization is increasingly fueled by digital-trace data and developed automatically via algorithms, allowing for dynamic content optimization.

Definitions of Personalized Communication

As a construct studied by various domains, such as (health, marketing, political) communication, advertising, psychology, computer science, journalism and more, there is no one broadly agreed upon definition of personalization. Also, various similar terms have been used to describe personalization and personalization strategies, such as tailoring, customization, micro-targeting, etc. (see Personalization Strategies and Closely Related Terms). In the context of health communication, Dijkstra 2008 discusses personalization as one of tailoring strategies, next to adaptation and feedback. Here, as Hawkins, et al. 2008 explains, the otherwise generic message includes self-referent signals, such as the recipient’s name or a city in which they live. In marketing communication, Vesanen and Raulas 2006 discusses how personalization has a different meaning for different marketers and scholars, showing that even within fields, there is often no consensus regarding what personalization means. In web personalization, Tam and Ho 2005 defines personalization as a strategy “to provide the right content in the right format to the right person at the right time” (p. 271) and includes such elements as preference matching, recommendation set size, and sorting cue. More recently, in the context of digital culture and algorithms, Soffer 2019 defines personalization as “matching content to an individual user through algorithmic calculations and association between entities” (p. 14). In a similar vein, Bol, et al. 2018 proposes a slightly broader definition that seems to reconcile many of the existing disagreements. Bol, et al. 2018 defines personalization as “the strategic creation, modification, and adaptation of content and distribution to optimize the fit with personal characteristics, interests, preferences, communication styles, and behaviors” (p. 373). This definition allows us to treat personalization as a dynamic process as Vesanen and Raulas 2006 suggests we should, and to account for the role of data, technology, and the various forms personalization can take, and hence refer not only to the content of communication, but also other elements of the communication process like channel, context, source, etc. Following this definition, personalization may be applied to the format of the message (framing, writing style), the content of the message (e.g., tailored health tips, a recommended product, song, political party), or the context (e.g., website, timing).

• Bol, N., T. Dienlin, S. Kruikemeier, et al. 2018. Understanding the effects of personalization as a privacy calculus: analyzing self-disclosure across health, news, and commerce contexts. Journal of Computer-Mediated Communication 23.6: 370–388.

  DOI: 10.1093/jcmc/zmy020

  This paper proposes a new definition of personalized communication. It also applies the privacy calculus theory to analyze how people make cost–benefit trade-offs in personalized media environments across the contexts of health, news, and commerce. In an experiment, the authors find that personalization reduces trust, expected benefits, and it may reduce the willingness for self-disclosure.

• Dijkstra, A. 2008. The psychology of tailoring‐ingredients in computer‐tailored persuasion. Social and Personality Psychology Compass 2.2: 765–784.

  DOI: 10.1111/j.1751-9004.2008.00081.x

  Dijsktra focuses on health messages and discusses three ways in which persuasive information can be tailored to individual characteristics using computer technology: adaptation, personalization, and feedback. Here personalization is defined as “the incorporation of one or more recognizable individual characteristics (e.g., one’s first name) in a persuasive text” (p. 765). The article builds on different psychological processes, such as self-referent information encoding, self-affirmation, and depth of information processing, to explain how tailoring influences persuasion.

• Hawkins, R. P., M. Kreuter, K. Resnicow, M. Fishbein, and A. Dijkstra. 2008. Understanding tailoring in communicating about health. Health Education Research 23.3: 454–466.

  DOI: 10.1093/her/cyn004

  Personalization (next to feedback and content matching) is one of tailoring strategies, which “attempts to increase attention or motivation to process messages by conveying, explicitly or implicitly, that the communication is designed specifically for ‘you’” (p. 458). The authors argue that a personalized health message attracts attention and makes the message more relevant by implying that the message is “for you.” This is usually done using one of the three personalization strategies: raising expectation, identification, and contextualization.

• Soffer, O. 2019. Algorithmic personalization and the two-step flow of communication. Communication Theory 31.3: 1–19.

  DOI: 10.1093/ct/qtz008

  The author compares personalization in the mass communication versus algorithmic era and discusses it in the context of the two-step flow theory of communication. Here personalization is defined as occurring “through matching content to an individual user through algorithmic calculations and association between entities” (p. 14).

• Tam, K. Y., and S. Y. Ho. 2005. Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information Systems Research 16.3: 271–291.

  DOI: 10.1287/isre.1050.0058

  The authors define personalization as a strategy “to provide the right content in the right format to the right person at the right time. The objective is to provide customized services and to maximize business opportunities” (p. 271). Based on the Elaboration Likelihood Model, the authors investigate several elements of a web personalization strategy: level of preference matching, recommendation set size, and sorting cue and find that matching users’ preferences heightens elaboration and affects users’ choice of personalized offerings.

• Vesanen, J., and M. Raulas. 2006. Building bridges for personalization: A process model for marketing. Journal of Interactive Marketing 20.1: 5–20.

  DOI: 10.1002/dir.20052

  The authors focus on marketing communication and propose a different perspective on personalization—they see it as a process with different linked phases: customer interactions, analyses of customer data, customization based on customer profiles, and targeting of marketing activities.