Customer-Experience-Service-Excellence bei E.ON SE: Rolle und Einsatz des Net Promoter Scores

PurposeTo show that retail bank executives across the world are awakening to a realization that long‐term growth and profitability hinge on their ability to attract and retain loyal customers.Design/methodology/approachIn Bain's experience, the best tool for gauging the benefit of fostering customer advocacy is the “Net Promoter™ Score” (NPS), a measurement developed by Satmetrix Systems, a Silicon Valley‐based software and services firm that specializes in customer experience management.FindingsUS banks earn an average net promoter score of just 6 percent – far below those of leaders in the personal computer, property and casualty insurance and car rental industries. The NPS of German retail banks is just 13 percent, on average; meanwhile UK banks rate a dismal minus 6 percent.Research limitations/implicationsA recent global benchmarking study by Bain & Company reveals that bankers recognize they have a problem. In the study, bankers rated the building of strong customer relationships as one of their most important keys to success. Yet they acknowledged that they were not doing a good job of rising to that challenge.Practical implicationsThe Bain study found that banks acknowledged six imperatives as crucial to winning over new customers, deepening relationships with existing account holders and reinforcing all customers' perception that they receive superior value:Originality/valueStrategic managers in banking and other industries must manage to six imperatives: design products and services that offer a truly captivating value proposition and generate genuine consumer enthusiasm; understand their target customer segments in detail and communicate with laser like precision; systematic nurture of new‐customer relationships; manage the customer experience, not just the account; dare to be different; and concentrate on measurements that enable anticipation of customer behavior.

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A Machine Learning Based Classification Method for Customer Experience Survey Analysis

Technologies ◽

10.3390/technologies8040076 ◽

2020 ◽

Vol 8 (4) ◽

pp. 76

Author(s):

Ioannis Markoulidakis ◽

Ioannis Rallis ◽

Ioannis Georgoulas ◽

George Kopsiaftis ◽

Anastasios Doulamis ◽

...

Keyword(s):

Machine Learning ◽

Call Center ◽

State Of The Art ◽

Customer Experience ◽

Survey Analysis ◽

Telecommunication Sector ◽

Data Set ◽

Net Promoter ◽

Experience Survey ◽

Statistical Metrics

Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics.

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Application Programming Interface for a Customer Experience Analysis Tool

10.3233/faia210092 ◽

2021 ◽

Author(s):

George Kopsiaftis ◽

Ioannis Georgoulas ◽

Ioannis Rallis ◽

Ioannis Markoulidakis ◽

Kostis Tzanettis ◽

...

Keyword(s):

Graphical Representation ◽

Application Programming Interface ◽

Customer Experience ◽

Machine Learning Algorithms ◽

Analysis Tool ◽

Net Promoter Score ◽

Application Programming ◽

Net Promoter ◽

User Friendly ◽

Programming Interface

This paper analyzes the architecture of an application programming interface (API) developed for a novel customer experience tool. The CX tool aims to monitor the customer satisfaction, based on several experience attributes and metrics, such as the Net Promoter Score. The API aims to create an efficient and user-friendly environment, which allow users to utilize all the available features of the customer experience system, including the exploitation of state-of-the-art machine learning algorithms, the analysis of the data and the graphical representation of the results.

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The effect of customer experience and emotional value on the net promoter score: Middle-class millennial climber category

Jurnal Manajemen dan Pemasaran Jasa ◽

10.25105/jmpj.v13i2.5763 ◽

2020 ◽

Vol 13 (2) ◽

pp. 199

Author(s):

Syafrida Hafni Sahir ◽

Syafrizal Helmi Situmorang

Keyword(s):

Middle Class ◽

Multiple Regression ◽

Daily Life ◽

Negative Emotion ◽

Customer Experience ◽

Regression Method ◽

Net Promoter Score ◽

Multiple Regression Method ◽

Net Promoter ◽

Emotional Value

<p>This study investigates net promoter score changes due to the emotional value and experience of middle-class customers. A total of 150 respondents wereselected, while 96 brands were observedbased on the respondent's daily life needs, such as clothes, cafes, gadgets and travels, amongothers. Customers experience was measured by analyzing their interactions with the brands they use. The emotional value was measured by the positive or negative emotion score of each brand. The gap between brand Promoter and Detractor is the Net Promoter Score. Thisresearch shows a positive correlation between customer experience, emotional value, and net promoter score. Data were analyzed using the Multiple regression method.</p>

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Towards a Better Measure of Customer Experience

International Journal of Market Research ◽

10.2501/ijmr-2013-021 ◽

2013 ◽

Vol 55 (2) ◽

pp. 227-246 ◽

Cited By ~ 146

Author(s):

Philipp ‘Phil’ Klaus ◽

Stan Maklan

Keyword(s):

Customer Satisfaction ◽

Market Research ◽

Predictive Power ◽

Customer Experience ◽

Multiple Channels ◽

Total Quality ◽

Customer Interaction ◽

Net Promoter Score ◽

Net Promoter ◽

Marketing Service

Defining and improving customer experience is a growing priority for market research because experience is replacing quality as the competitive battleground for marketing. Service quality is an outgrowth of the total quality management (TQM) movement of the 1980s and suffers from that movement's focus on the provider rather than the value derived by customers. Researchers today state that customer experience is generated through a longer process of company–customer interaction across multiple channels, generated through both functional and emotional clues. Our research with practitioners indicates that most firms use customer satisfaction, or its derivative the Net Promoter Score, to assess their customers' experiences. We question this practice based on the conceptual gap between these measures and the customer experience. In IJMR 53, 6 (2011), we introduce a new measure appropriate for the modern conceptualisation of customer experience: the customer experience quality (EXQ) scale. In this article we extend that work and compare EXQ's predictive power with that of customer satisfaction. We establish that EXQ better explains and predicts both, loyalty and recommendations, than customer satisfaction.

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Defining customer experience and measuring its impact on financial performance of US publicly traded companies

10.31235/osf.io/mzepq ◽

2020 ◽

Author(s):

Michele Bennett ◽

Anthony Molisani

Keyword(s):

Customer Loyalty ◽

Business Performance ◽

Customer Experience ◽

Publicly Traded ◽

Public Companies ◽

Publicly Traded Companies ◽

Confirmatory Factor ◽

Net Promoter ◽

Individual Variables ◽

The Individual

Customer experience (CX) is an important contributor to business performance. Yet, studies vary in defining CX, variables and constructs, and measurement to performance. This study defined CX and the direct impact to market capitalization (MC) of US public companies. The CX definition combined Customer Experience Quality (CEQ) with Customer Satisfaction (CSAT), Customer Loyalty (CLY), and Net Promoter Score (NPS). Each dimension was measured to the extent to which they impact MC. The sample consisted of 1605 US customers of US publicly traded companies. Using correlation analysis, multiple linear regression, and confirmatory factor analysis, the individual variables of CEQ were positively and significantly correlated with MC and with each other. CEQ, CSAT, CLY, and NPS were also positively and significantly correlated with MC, and the holistic CX was predictive of MC. Companies can apply the insights of the study to improve their customer experience to deliver measurable business and shareholder value.

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Measurement Of Customer Loyalty On Products Indonesian Brands With Net Promoter Score (NPS) Method In Medan City Middle Class Consumers

Journal of Management Science (JMAS) ◽

10.35335/jmas.v4i4.117 ◽

2021 ◽

Vol 4 (4) ◽

pp. 118-123

Author(s):

Natashya Situmorang

Keyword(s):

Middle Class ◽

Economic Status ◽

Descriptive Analysis ◽

Secondary Data ◽

Customer Experience ◽

Quantitative Descriptive Analysis ◽

Highly Educated ◽

Net Promoter Score ◽

Net Promoter ◽

Quantitative Descriptive

The increasing number of graduates from universities who already have jobs have resulted in the rapid growth of one of the categories of middle class society in Indonesia. Indirectly it has an impact on increasing income which results in changes in lifestyle and purchasing power as well as changes in the choice of brands used by a person. Various kinds of products that are classified as luxury goods have now become a necessity for supporting the lifestyle of the middle class in this modern era. This middle class consumer is included in the Climber Middle Class category which is the largest segment among the 8 Middle Class Faces. And generally Climbers are known as hard-working figures who try to improve their economic status, are highly educated, do not have much experience (just got a job or are still building their careers) and tend to be the backbone of the family. This research was conducted using the Net Promoter Score (NPS) method which was then continued by measuring the customer experience (Customer Experience) through the touch point (moment of truth) of each brand studied. The number of population in this study is unknown, so the sample used is 125 respondents. Secondary data were also collected through books, magazines, and literature to support the analysis in this study. This method uses quantitative descriptive analysis. The results of this study indicate that there are 25 brands out of 96 brands studied that have a negative Net Promoter Score (NPS).

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