customer satisfaction research methodology pdf

Customer satisfaction: a mathematical framework for its analysis and its measurement

• Original Paper
• Published: 09 May 2018
• Volume 17 , pages 23–45, ( 2020 )

Cite this article

• R. Ferrentino 1 &
• C. Boniello 1  

907 Accesses

3 Citations

Explore all metrics

The customer satisfaction, an important concept in the field of general marketing and in the management of firms, is the measure of the degree of customer satisfaction. More precisely, the customer satisfaction provides an assessment of the discrepancy between the perceived performance by the customer (i.e. all the sensations and impressions after use of a product or the fruition of a service) and the expectations (expectations and desires) of the same customer but also represents the company’s ability to anticipate and to manage the expectations of the customer, to satisfy its needs with competence and responsibility. The company resources, in fact, must be organized taking account of the demands of customers who represent a precious commodity for the company, even if their value does not appear in the financial statements, the fulcrum around which revolves the whole system business. To have satisfied customers is, therefore, fundamental for a company and, therefore, the customer satisfaction can be defined as a touchstone for the management of customer relationships, as a key to create the competitive advantage as of a company. In this article, the authors present some of the main processes and detection systems required for a scrupulous and reliable process of measuring of the customer satisfaction or better they present a mathematical framework to determine and to improve the customer satisfaction and to demonstrate how it has a great impact on corporate performance. In particular, the authors propose a model of structural equations with latent variables which is the most rigorous methodology currently available for the evaluations of the customer satisfaction and they hope that the paper, which at first sight has little in common with mathematics, is a very useful for stimulating research at the interface of mathematics and marketing and that it builds a link that can become beneficial for both disciplines involved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Measurement of Customer Satisfaction

Defining Customer Satisfaction: A Strategic Company Asset?

Customer Satisfaction in Services Marketing : An Analysis of the Antecedents of Satisfaction and the Linkage with Service Quality

Bentler PM (1986) Structural equation modeling with EQS on a microcomputer. In: Gaul W, Schader M (eds) Classification as a tool of research. Elsevier Science, Amsterdam, pp 11–17

Google Scholar  

Busacca B, Bertoli G (2009) Customer value. Soddisfazione, fedeltà, valore, Egea

Chin WW (1998) The partial least squares approach for structural equation modeling. In: Marcoulides GA (ed) Modern methods for business research. Lawrence Erlbaum Associates, London, pp 295–336

Cronin JJ Jr., Taylor SA (1994) Servperf versus Servqual: reconciling performance-based and perceptions-minus-expectations measurement of service quality. J Mark 58:125–131

Article   Google Scholar  

Ferrentino R, Cuomo MT, Boniello C (2016) On the customer lifetime value: a mathematical perspective. Comput Manag Sci 13(4):521–539

Fornell C (2008) Clienti soddisfatti vincitori e vinti nella battaglia per la preferenza dei consumatori. F. Angeli, Milano

Fornell C, Anderson W, Lehmann DR (1994) Customer satisfaction, market share, and profitability: findings from Sweden. Am Mark Assoc 58(3):53–66

Fornell C, Mithas S, Morgeson F, Krishnan MS (2006) Customer satisfaction and stock prices: high returns, low risk. J Mark 70(1):3–14

Gruca TS, Rego LL (2005) Customer satisfaction, cash flow, and shareholder value. J Mark 69(3):1–130

Guido G, Bassi F, Peluso A (2010) La soddisfazione del consumatore. La misura della customer satisfaction nelle esperienze di consumo. Franco Angeli, Milano

Gupta S, Lehmann DR (2003) Customers as assets. J Interact Mark 17(1):9–24

Gupta S, Zeithaml V (2006) Customer metrics and their impact on financial performance. Mark Sci 25(6):718–739

Helsen K, Schmittlein DC (1993) Analyzing duration times in marketing: evidence for the effectiveness of hazard rate models. Mark Sci 11:395–414

Heskett JL, Sasser WE, Hart CWL (1990) Breakthrough service. The Free Press, New York

Heskett JL, Jones TO, Loveman GW, Sasser WE Jr., Schlesinger LA (1994) Putting the service profit chain to work. Harv Bus Rev 105–111

Hughes A (1997) Customer retention: integrating lifetime value into marketing strategies. J Database Mark 5(2):171–178

Hughes A (2005) Strategic database marketing, 3rd edn. McGraw-Hill, New York

Hughes A, Wang P (1995) Media selection for database marketers. J Direct Mark 9(1):79–84

Johnson SG, Joannopoulos JD (2001) Block-iterative frequency-domain methods for Maxwell’s equations in a planewave basis. Opt Express 8(3):173–190

Jöreskog KG (1970) A general method for analysis of covariance structures. Biometrika 57:239–251

Kotler P, Keller K (2007) Marketing management. Pearson, London

Lambin JJ (2008) Market-driven management. Marketing strategico e operativo. McGraw-Hill, Milano

Lohmoller J (1989) Latent variable path modeling with partial least squares. Physica-Verlag, Heidelberg

Book   Google Scholar  

Mulhern F (1999) Customer profitability analysis: measurement, concentration, and research directions. J Interact Mark 13:25–40

Myers RA, Bowen KG, Barrowman NJ (1999) The maximum reproductive rate of fish at low population sizes. Can J Fish Aquat Sci 56:2409–2419

Oliver RL (1999) Whence customer loyalty? J Mark (Special Issue):33–44

Parasuraman A, Zeithaml V, Berry L (1985) A conceptual model of service quality and its implications for future research. J Mark 41–50

Payne A, Frow P (2006) Customer relationship management: from strategy to implementation. J Mark Manag 22:135–168

Rossetto F, Franceschini S (1998) Quality function deployment: how to improve its use. Total Qual Manag 9(6):491–500

Schvaneveldt SJ, Enkawa T, Miyakawa M (1991) Consumer evaluation perspectives ofservice quality: evaluation factors and two-way model of quality. Total Qual Manag 2(2):149–161

Straub DW, Gefen D, Boudreau MC (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst 4(7):1–70

Teas RK (1993) Expectations, performance evaluation and consumers’ perceptions of quality. J Mark 57(4):18–34

Valdani E (1995) Marketing strategico. Un’impresa proattiva per sviluppare capacità market driving e valore. Etas Libri, Milano

Valdani E, Ancarani F (2009) Marketing strategico. I risultati delle strategie di marketing, vol 3. Egea, Milano

Valdani E, Busacca B (1999) Customer-based view. Finanza Marketing e Produzione 2:95–131

Wheaton P (2000) The life cycle view of customers. U.S. Bank 110:77–78

Download references

Author information

Authors and affiliations.

Department of Economic and Statistics Sciences, University of Salerno, Fisciano, Salerno, Italy

R. Ferrentino & C. Boniello

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to R. Ferrentino .

Rights and permissions

Reprints and permissions

About this article

Ferrentino, R., Boniello, C. Customer satisfaction: a mathematical framework for its analysis and its measurement. Comput Manag Sci 17 , 23–45 (2020). https://doi.org/10.1007/s10287-018-0305-1

Download citation

Received : 07 May 2017

Accepted : 26 March 2018

Published : 09 May 2018

Issue Date : January 2020

DOI : https://doi.org/10.1007/s10287-018-0305-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Structural equations models
• Customer satisfaction
• Interpretation models
• Path diagram
• Partial least squares
• Find a journal
• Publish with us
• Track your research

McKinsey technology trends outlook 2024

Despite challenging overall market conditions in 2023, continuing investments in frontier technologies promise substantial future growth in enterprise adoption. Generative AI (gen AI) has been a standout trend since 2022, with the extraordinary uptick in interest and investment in this technology unlocking innovative possibilities across interconnected trends such as robotics and immersive reality. While the macroeconomic environment with elevated interest rates has affected equity capital investment and hiring, underlying indicators—including optimism, innovation, and longer-term talent needs—reflect a positive long-term trajectory in the 15 technology trends we analyzed.

What’s new in this year’s analysis

This year, we reflected the shifts in the technology landscape with two changes on the list of trends: digital trust and cybersecurity (integrating what we had previously described as Web3 and trust architectures) and the future of robotics. Robotics technologies’ synergy with AI is paving the way for groundbreaking innovations and operational shifts across the economic and workforce landscapes. We also deployed a survey to measure adoption levels across trends.

These are among the findings in the latest McKinsey Technology Trends Outlook, in which the McKinsey Technology Council  identified the most significant technology trends unfolding today. This research is intended to help executives plan ahead by developing an understanding of potential use cases, sources of value, adoption drivers, and the critical skills needed to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, investment, and talent to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into the underlying technologies, uncertainties, and questions surrounding each trend. (For more about new developments in our research, please see the sidebar “What’s new in this year’s analysis”; for more about the research itself, please see the sidebar “Research methodology.”)

New and notable

The two trends that stood out in 2023 were gen AI and electrification and renewables. Gen AI has seen a spike of almost 700 percent in Google searches from 2022 to 2023, along with a notable jump in job postings and investments. The pace of technology innovation has been remarkable. Over the course of 2023 and 2024, the size of the prompts that large language models (LLMs) can process, known as “context windows,” spiked from 100,000 to two million tokens. This is roughly the difference between adding one research paper to a model prompt and adding about 20 novels to it. And the modalities that gen AI can process have continued to increase, from text summarization and image generation to advanced capabilities in video, images, audio, and text. This has catalyzed a surge in investments and innovation aimed at advancing more powerful and efficient computing systems. The large foundation models that power generative AI, such as LLMs, are being integrated into various enterprise software tools and are also being employed for diverse purposes such as powering customer-facing chatbots, generating ad campaigns, accelerating drug discovery, and more. We expect this expansion to continue, pushing the boundaries of AI capabilities. Senior leaders’ awareness of gen AI innovation has increased interest, investment, and innovation in AI technologies, such as robotics, which is a new addition to our trends analysis this year. Advancements in AI are ushering in a new era of more capable robots, spurring greater innovation and a wider range of deployments.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend.

Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents, where the data highlight the number of granted patents.
• Research. Data on research publications are sourced from Lens.
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises (venture capital and corporate and strategic M&A, including joint ventures), private equity (including buyouts and private investment in public equity), and public investments (including IPOs) are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data are drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s report to reflect the evolution of technology trends:

• The future of robotics trend was added since last year’s publication.
• Data sources and keywords were updated. For data on the future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

Finally, we used survey data to calculate the enterprise-wide adoption scores for each trend:

• Survey scope. The survey included approximately 1,000 respondents from 50 countries.
• Geographical coverage. Survey representation was balanced across Africa, Asia, Europe, Latin America, the Middle East, and North America.
• Company size. Size categories, based on annual revenue, included small companies ($10 million to $50 million), medium-size companies ($50 million to $1 billion), and large companies (greater than $1 billion).
• Respondent profile. The survey was targeted to senior-level professionals knowledgeable in technology, who reported their perception of the extent to which their organizations were using the technologies.
• Survey method. The survey was conducted online to enhance reach and accessibility.
• Question types. The survey employed multiple-choice and open-ended questions for comprehensive insights.
• 1: Frontier innovation. This technology is still nascent, with few organizations investing in or applying it. It is largely untested and unproven in a business context.
• 2: Experimentation. Organizations are testing the functionality and viability of the technology with a small-scale prototype, typically done without a strong focus on a near-term ROI. Few companies are scaling or have fully scaled the technology.
• 3: Piloting. Organizations are implementing the technology for the first few business use cases. It may be used in pilot projects or limited deployments to test its feasibility and effectiveness.
• 4: Scaling. Organizations are in the process of scaling the deployment and adoption of the technology across the enterprise. The technology is being scaled by a significant number of companies.
• 5: Fully scaled. Organizations have fully deployed and integrated the technology across the enterprise. It has become the standard and is being used at a large scale as companies have recognized the value and benefits of the technology.

Electrification and renewables was the other trend that bucked the economic headwinds, posting the highest investment and interest scores among all the trends we evaluated. Job postings for this sector also showed a modest increase.

Although many trends faced declines in investment and hiring in 2023, the long-term outlook remains positive. This optimism is supported by the continued longer-term growth in job postings for the analyzed trends (up 8 percent from 2021 to 2023) and enterprises’ continued innovation and heightened interest in harnessing these technologies, particularly for future growth.

Future frontiers: Navigating the next wave of tech innovations

Join Lareina Yee and Roger Roberts on Tuesday, July 30, at 12:30 p.m. EDT/6:30 p.m. CET as they discuss the future of these technological trends, the factors that will fuel their growth, and strategies for investing in them through 2024 and beyond.

In 2023, technology equity investments fell by 30 to 40 percent to approximately $570 billion due to rising financing costs and a cautious near-term growth outlook, prompting investors to favor technologies with strong revenue and margin potential. This approach aligns with the strategic perspective leading companies are adopting, in which they recognize that fully adopting and scaling cutting-edge technologies is a long-term endeavor. This recognition is evident when companies diversify their investments across a portfolio of several technologies, selectively intensifying their focus on areas most likely to push technological boundaries forward. While many technologies have maintained cautious investment profiles over the past year, gen AI saw a sevenfold increase in investments, driven by substantial advancements in text, image, and video generation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Despite an overall downturn in private equity investment, the pace of innovation has not slowed. Innovation has accelerated in the three trends that are part of the “AI revolution” group: gen AI, applied AI, and industrializing machine learning. Gen AI creates new content from unstructured data (such as text and images), applied AI leverages machine learning models for analytical and predictive tasks, and industrializing machine learning accelerates and derisks the development of machine learning solutions. Applied AI and industrializing machine learning, boosted by the widening interest in gen AI, have seen the most significant uptick in innovation, reflected in the surge in publications and patents from 2022 to 2023. Meanwhile, electrification and renewable-energy technologies continue to capture high interest, reflected in news mentions and web searches. Their popularity is fueled by a surge in global renewable capacity, their crucial roles in global decarbonization efforts, and heightened energy security needs amid geopolitical tensions and energy crises.

The talent environment largely echoed the investment picture in tech trends in 2023. The technology sector faced significant layoffs, particularly among large technology companies, with job postings related to the tech trends we studied declining by 26 percent—a steeper drop than the 17 percent decrease in global job postings overall. The greater decline in demand for tech-trends-related talent may have been fueled by technology companies’ cost reduction efforts amid decreasing revenue growth projections. Despite this reduction, the trends with robust investment and innovation, such as gen AI, not only maintained but also increased their job postings, reflecting a strong demand for new and advanced skills. Electrification and renewables was the other trend that saw positive job growth, partially due to public sector support for infrastructure spending.

Even with the short-term vicissitudes in talent demand, our analysis of 4.3 million job postings across our 15 tech trends underscored a wide skills gap. Compared with the global average, fewer than half the number of potential candidates have the high-demand tech skills specified in job postings. Despite the year-on-year decreases for job postings in many trends from 2022 to 2023, the number of tech-related job postings in 2023 still represented an 8 percent increase from 2021, suggesting the potential for longer-term growth (Exhibit 1).

Enterprise technology adoption momentum

The trajectory of enterprise technology adoption is often described as an S-curve that traces the following pattern: technical innovation and exploration, experimenting with the technology, initial pilots in the business, scaling the impact throughout the business, and eventual fully scaled adoption (Exhibit 2). This pattern is evident in this year’s survey analysis of enterprise adoption conducted across our 15 technologies. Adoption levels vary across different industries and company sizes, as does the perceived progress toward adoption.

Image description:

A graph depicts the adoption curve of technology trends, scored from 1 to 5, where 1 represents frontier innovation, located at the bottom left corner of the curve; 2 is experimenting, located slightly above frontier innovation; 3 is piloting, which follows the upward trajectory of the curve; 4 is scaling, marked by a vertical ascent as adoption increases; and 5 is fully scaled, positioned at the top of the curve, indicating near-complete adoption.

In 2023, the trends are positioned along the adoption curve as follows: future of space technologies and quantum technologies are at the frontier innovation stage; climate technologies beyond electrification and renewables, future of bioengineering, future of mobility, future of robotics, and immersive-reality technologies are at the experimenting stage; digital trust and cybersecurity, electrification and renewables, industrializing machine learning, and next-gen software development are at the piloting stage; and advanced connectivity, applied AI, cloud and edge computing, and generative AI are at the scaling stage.

Footnote: Trend is more relevant to certain industries, resulting in lower overall adoption across industries compared with adoption within relevant industries.

Source: McKinsey technology adoption survey data

End of image description.

We see that the technologies in the S-curve’s early stages of innovation and experimenting are either on the leading edge of progress, such as quantum technologies and robotics, or are more relevant to a specific set of industries, such as bioengineering and space. Factors that could affect the adoption of these technologies include high costs, specialized applications, and balancing the breadth of technology investments against focusing on a select few that may offer substantial first-mover advantages.

As technologies gain traction and move beyond experimenting, adoption rates start accelerating, and companies invest more in piloting and scaling. We see this shift in a number of trends, such as next-generation software development and electrification. Gen AI’s rapid advancement leads among trends analyzed, about a quarter of respondents self-reporting that they are scaling its use. More mature technologies, like cloud and edge computing and advanced connectivity, continued their rapid pace of adoption, serving as enablers for the adoption of other emerging technologies as well (Exhibit 3).

A segmented bar graph shows the adoption levels of tech trends in 2023 as a percentage of respondents. The trends are divided into 5 segments, comprising 100%: fully scaled, scaling, piloting, experimenting, and not investing. The trends are arranged based on the combined percentage sum of fully scaled and scaling shares. Listed from highest to lowest, these combined percentages are as follows:

• cloud and edge computing at 48%
• advanced connectivity at 37%
• generative AI at 36%
• applied AI at 35%
• next-generation software development at 31%
• digital trust and cybersecurity at 30%
• electrification and renewables at 28%
• industrializing machine learning at 27%
• future of mobility at 21%
• climate technologies beyond electrification and renewables at 20%
• immersive-reality technologies at 19%
• future of bioengineering at 18%
• future of robotics at 18%
• quantum technologies at 15%
• future of space technologies at 15%

The process of scaling technology adoption also requires a conducive external ecosystem where user trust and readiness, business model economics, regulatory environments, and talent availability play crucial roles. Since these ecosystem factors vary by geography and industry, we see different adoption scenarios playing out. For instance, while the leading banks in Latin America are on par with their North American counterparts in deploying gen AI use cases, the adoption of robotics in manufacturing sectors varies significantly due to differing labor costs affecting the business case for automation.

As executives navigate these complexities, they should align their long-term technology adoption strategies with both their internal capacities and the external ecosystem conditions to ensure the successful integration of new technologies into their business models. Executives should monitor ecosystem conditions that can affect their prioritized use cases to make decisions about the appropriate investment levels while navigating uncertainties and budgetary constraints on the way to full adoption (see the “Adoption developments across the globe” sections within each trend or particular use cases therein that executives should monitor). Across the board, leaders who take a long-term view—building up their talent, testing and learning where impact can be found, and reimagining the businesses for the future—can potentially break out ahead of the pack.

Lareina Yee is a senior partner in McKinsey’s Bay Area office, where Michael Chui  is a McKinsey Global Institute partner, Roger Roberts  is a partner, and Mena Issler is an associate partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Aakanksha Srinivasan, Ahsan Saeed, Alex Arutyunyants, Alex Singla, Alex Zhang, Alizee Acket-Goemaere, An Yan, Anass Bensrhir, Andrea Del Miglio, Andreas Breiter, Ani Kelkar, Anna Massey, Anna Orthofer, Arjit Mehta, Arjita Bhan, Asaf Somekh, Begum Ortaoglu, Benjamin Braverman, Bharat Bahl, Bharath Aiyer, Bhargs Srivathsan, Brian Constantine, Brooke Stokes, Bryan Richardson, Carlo Giovine, Celine Crenshaw, Daniel Herde, Daniel Wallance, David Harvey, Delphine Zurkiya, Diego Hernandez Diaz, Douglas Merrill, Elisa Becker-Foss, Emma Parry, Eric Hazan, Erika Stanzl, Everett Santana, Giacomo Gatto, Grace W Chen, Hamza Khan, Harshit Jain, Helen Wu, Henning Soller, Ian de Bode, Jackson Pentz, Jeffrey Caso, Jesse Klempner, Jim Boehm, Joshua Katz, Julia Perry, Julian Sevillano, Justin Greis, Kersten Heineke, Kitti Lakner, Kristen Jennings, Liz Grennan, Luke Thomas, Maria Pogosyan, Mark Patel, Martin Harrysson, Martin Wrulich, Martina Gschwendtner, Massimo Mazza, Matej Macak, Matt Higginson, Matt Linderman, Matteo Cutrera, Mellen Masea, Michiel Nivard, Mike Westover, Musa Bilal, Nicolas Bellemans, Noah Furlonge-Walker, Obi Ezekoye, Paolo Spranzi, Pepe Cafferata, Robin Riedel, Ryan Brukardt, Samuel Musmanno, Santiago Comella-Dorda, Sebastian Mayer, Shakeel Kalidas, Sharmila Bhide, Stephen Xu, Tanmay Bhatnagar, Thomas Hundertmark, Tinan Goli, Tom Brennan, Tom Levin-Reid, Tony Hansen, Vinayak HV, Yaron Haviv, Yvonne Ferrier, and Zina Cole.

They also wish to thank the external members of the McKinsey Technology Council for their insights and perspectives, including Ajay Agrawal, Azeem Azhar, Ben Lorica, Benedict Evans, John Martinis, and Jordan Jacobs.

Special thanks to McKinsey Global Publishing colleagues Barr Seitz, Diane Rice, Kanika Punwani, Katie Shearer, LaShon Malone, Mary Gayen, Nayomi Chibana, Richard Johnson, Stephen Landau, and Victor Cuevas for making this interactive come alive.

Explore a career with us

Related articles.

Rewired and running ahead: Digital and AI leaders are leaving the rest behind

False friends or good ends? The CIO’s four-point guide to navigating technology trends