Revisiting personalization through customer experience journey
This article investigates personalization with the focus on the customer experience journey and its use for the estimation of customer responses. The need for this focus is based on the necessity to understand customer responses to personalization.
| Рубрика | Маркетинг, реклама и торговля |
| Вид | статья |
| Язык | английский |
| Дата добавления | 27.06.2021 |
| Размер файла | 5,4 M |
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The findings of this research are presented as a set of propositions, which refers to the most central personalization-related issues influencing the customer experience. Introduction of anthropomorphization through interacting messaging (chatbots, conversational, and intelligent agents) and several types of recommendation systems allow firms and customers to experience positive consequences, such as structuring and better processing information, managing the touch points and customer relationships and customer experience, and predicting the customers' responses and intentions. However, these consequences are mitigated by the difficulties related to the increasing amounts of touch points and their specificities as well as customers' increased informational vulnerability as they need more time to develop trust to a firm's online presence, they try to protect their data by reducing informational disclosure, and they expect their choices to be manipulated.
Based on the analysis of customer responses to a firm's offerings and consequent customer-related concepts at purchase steps as well as the most utilized personalization tools, this research identifies positive and negative consequences of personalization both for customers and firms as well as the facilitation and mitigation suggestions, which creates further direction for future empirical research. Therefore, this study contributes to the theory on personalization and customer experience by suggesting propositions for future empirical research. In addition to that, we structure customer responses to personalization and the consequent customer behavior as a purchase stage-based map of customer experience and analyses of the co-evolution of customer experience concepts; we also include approaches to personalization with particular attention to the tools' utilization of the customer experience journey model to the personalization process by structuring its tools based on the touch points. Managerial implications relate to the untangling of complex processes of personalization and accompanying customer experience into practically implementable actions, leading to the creation of a checklist to apply to a firm's operation or estimate potential actions; in addition, firms obtain understanding of the possible customer responses and potential ways to evoke them.
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APPENDIX
An overview of ABS marketing list based articles on personalization with explicit customer focus
|
Author(s) |
Personalization applications and focus |
Customer- related concepts |
Customer's response |
Purchase stage |
Touch point type (owned by) |
||||||||||
|
Cognitive |
Emotional |
Behavioral |
Sensorial | |
Social | |
Prepurchase |
Purchase |
Postpurchase | |
Brand |
Partner |
Customer | |
Social/ External |
||||
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Meuter et al., 2000] |
Self-service technologies use for easier service obtaining |
- Customer interactions with technological interfaces - Customer-firm interaction |
x |
x |
x |
x |
x |
||||||||
|
[Moon, 2000] |
Customer's intimate information obtaining through the consumercomputer context by scripted communication |
- Consumers' selfdisclosure - Customer vulnerability - Ethics |
x |
x |
x |
x |
|||||||||
|
[Moon, 2002] |
Communication tools based on message mass customization based on style based on the personality types |
- Customer communication - Customer persuasion |
x |
x |
х |
x |
x |
||||||||
|
[Fitzsimons, Lehmann, 2004] |
Impact of recommendations through the intelligent agents in the customer decision-making and satisfaction |
- Customer reactance - Customer needs identification - Consumer response - Customer satisfaction |
x |
x |
x |
x |
x |
x |
x |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Montgomery et al., 2004] |
Personalization based on the clickstream data, that provides information about the path (the sequence of pages) that the user followed during the web-site navigation; it is helpful for predicting the future movements at the web-sites (higher result that the benchmark purchase conversion prediction rate without path information) |
- Customer web-site path - Customer behavior - Customer intentions |
x |
x |
x |
||||||||||
|
[Kramer, Spolter- Weisfeld, Thakkar, 2007] |
Cultural traits of the customer (interdependence/ independence; indi- vidualistic/collectivistic; ethnicity) influence on the customer resoince to personalization (product recommendations based on own preferences or collective preferences of similar cultural group) |
- Customer's culture - Customer's response to personalization |
x |
x |
x |
x |
|||||||||
|
[Song, Zinfhan, 2008] |
Impact of interactivity antecedents (number of clicks, response time, message types) and level of personalization in messaging on Web-site interactivity and effectiveness |
- Customer interactivity - Customer perceived interactivity |
x |
x |
x |
x |
x |
||||||||
|
[Chung, Rust, Wedel, 2009] |
Personalization in the digital audio players through the collaborative filtering/adaptive system (as in other personalized applications) and its impact on the effectiveness in terms of number of songs listened to and the listening duration of the recommended songs |
- Customer preferences predictions - Customer behavior - Customer attitudes |
x |
x |
x |
x |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Franke, Keinz, Steger, 2009] |
Use of personalization tools (smart agents) for customization benefits |
- Customer satisfaction - Customer interactivity - Customer preferences |
x |
x |
x |
x |
x |
||||||||
|
[Zhang, Wedel, 2009] |
Granularity levels-based promotions in online and offline stores including the individual-level personalization |
- Customer-cen- tricity - Customer loyalty; - Customer attainment - Customer attraction |
x |
x |
x |
x |
x |
||||||||
|
[Moreau, Herd, 2010] |
Customers' comparisons of their self-designed (user-designed) products with those of others and experts-recommended |
- Customer evaluation of self-designed products - Customer valuecreation |
x |
x |
x |
x |
x |
x |
x |
x |
|||||
|
[Puligadda et al., 2010] |
Influence of idiosyn- cratically evaluated (i.e., personalizable) attributes on satisfaction with a customization platform |
- Customer satisfaction - Customer knowledge - Customer evaluation |
x |
x |
x |
x |
x |
x |
|||||||
|
[Zhang, 2011] |
Personalized pricing and its impact on behaviorbased price discrimination with respect to the revealed customer preferences and competition in the competitive market |
- Customer loyalty - Customer purchase history - Customer selfselection |
x |
x |
x |
x |
|||||||||
|
[Acquisti, John, Loewenstein, 2012] |
Trade-offs between privacy and (for example) personalization, which has been described as the future of interactive marketing [Deighton, 1996, p. 173] |
- Customer welfare - Customer disclosure behavior - Customer vulnerability perception - Customer propensity to disclose |
x |
x |
x |
x |
x |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Dellaert, Haubl, 2012] |
Recommendations as a common form of decision assistance and its impact on recommending the customer the products with attractive features for a particular customer, in contrast with an unassisted search by customer |
- Customer search in “choice mode” - Customer decisionmaking |
x |
x |
x |
x |
|||||||||
|
[Hennig- Thurau, Marchand, Marx, 2012] |
Recommendation systems (through adaptive personalization systems) impact on customer choice |
- Customer decisionmaking - Customer preferences - Customer need identification |
x |
x |
x |
x |
x |
x |
x |
x |
x |
x |
|||
|
[Feld et al., 2013] |
Personalizing the e-mails in direct marketing to customers thus enhancing the effectiveness; however, this result is marginal |
- Customer response behavior |
x |
x |
|||||||||||
|
[Lambrecht, Tucker, 2013] |
Specificities of dynamic retargeting through recommendation agents in online advertising |
- Customer decisionmaking - Customer choice - Consumer response |
x |
x |
x |
x |
|||||||||
|
[Sonnier, 2014] |
Personalized pricing for the customer: “how to aggregate consumer valuations to assess the overall profitability of attribute improvements under price personalization” (p. 168) |
- Customer attributes - Customer's product valuation - Customer choice - Customer willing- ness-to-pay |
x |
x |
x |
x |
|||||||||
|
[Yadav, Pavlou, 2014] |
E-mail personalization for online advertisement and customer acquisition and retentions; AR for products and recommendations for individualized customer experience |
- Customer experience - Customer acquisition - Customer retention - Customer satisfaction |
x |
x |
x |
x |
x |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Aguirre et al., 2015] |
Even though the greater personalization increases service relevance and customer adoption, it may have strong impact on the customer perceived vulnerability and hence decrease adoption rates, thus creating the personalization paradox. This effect is mitigated if the data collection is conducted not covertly, and if the trust-building activities are accomplished with the inclusion of other platforms the customers trust (but duality with the incorporation the social networks information is recognised) |
- Customer vulnerability - Customer privacy concern - Customer trust - Customer loyalty |
x |
x |
x |
x |
x |
x |
|||||||
|
[Bleier, Eisenbeiss, 2015] |
The impact of depth and breadth of ad banner personalization on the trust and reactance of the customer. |
- Customer reactance - Customer privacy concern - Customer trust - Customer attitude |
x |
x |
x |
x |
x |
x |
|||||||
|
[Fong, Fang, Luo, 2015] |
Mobile targeting through the location-targeting (competitive locational targeting) for attracting the customer in close proximity |
- Customer's location awareness - Customer clickthrough - Customer responsiveness |
x |
x |
x |
x |
x |
x |
x |
||||||
|
[Chung, Wedel, Rust, 2016] |
“Repeatedly adapting to the customer's observed behavior improves personalization performance”; “personalizing automatically, using a personalization algorithm, results in better performance than allowing the customer to self customize”; “using the customer's social network for personalization results in further improvement” |
- Customer data - Customer preferences in offerings - Customer's social networks |
x |
x |
x |
x |
x |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Wedel, Kannan, 2016] |
Personalization of marketing mix to individual consumers; online and mobile personalization of marketing mix; recommendations to the consumers to fill absent data; adaptive personalization approaches to learn and adapt to users' preferences' changes; evaluation of personalization effectiveness (profitability) |
- Capturing customer heterogeneity - Customer privacy concern - Customer data accumulation |
x |
x |
x |
x |
x |
x |
x |
x |
x |
||||
|
[Martin, Borah, Palmatier, 2017] |
Enchancing firm's offerings through the personalized experience with a sufficient level of transparency; duality of consumer data collection |
- Customer perception of vulnerability - Customer reactance - Customer data collection - Customer satisfaction |
x |
x |
x |
x |
x |
x |
x |
x |
|||||
|
[Kim, Barasz, John, 2019] |
Personal information collection for generating and showing the ad (behavioral targeting); information transparency and changes in its levels based on the trust to the platform |
- Customer vulnerability - Customer privacy concern - Customer need for personalization - Customer loyalty - Customer information disclosure |
x |
x |
x |
x |
x |
x |
x |
||||||
|
[Kumar, 2018] |
Importance of “machine learning algorithms used in areas such as data security, health care, natural language processing, marketing personalization, and online recommendations” (p. 6) |
- Customer needs and expectation reshaping to more niche |
x |
x |
x |
x |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
|
|
[Matz et al., 2019] |
Prediction of an image's personality appeal -- the personality of consumers to which the advertisement image appeals most |
- Customer emotions - Customer attention seeking - Customer first impression; - Customer attitude - Customer purchase intentions |
x |
x |
x |
x |
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