Zarządzanie operacyjne w e‑commerce to zbiór działań, które pozwalają firmom internetowym sprawnie dostarczać wartość klientowi na każdym etapie jego kontaktu z marką. Obszar ten obejmuje planowanie, organizowanie i kontrolowanie procesów, które – w przeciwieństwie do tradycyjnych modeli biznesowych – są w dużej mierze zautomatyzowane, oparte na danych i wymagają natychmiastowej reakcji. W handlu online operacje zaczynają się już w momencie wejścia klienta na stronę i trwają aż do obsługi posprzedażowej, gdzie satysfakcja, czas reakcji i niezawodność systemów stają się bezpośrednim źródłem przewagi konkurencyjnej -obejrzyj fragment kursu tutaj.

W centrum zarządzania operacyjnego w e‑commerce znajduje się proces zakupowy klienta – od wyszukiwarki, przez kartę produktu i koszyk, aż po finalizację zamówienia. Każdy etap musi być zoptymalizowany pod kątem UX, szybkości działania i minimalizacji barier, które mogą prowadzić do porzucania koszyka. Kluczową rolę odgrywają tu także zaawansowane narzędzia wyszukiwania (autouzupełnianie, AI, optymalizacja wyników), personalizacja treści oraz segmentacja klientów, pozwalająca lepiej dopasować ofertę i komunikację do ich zachowań i potrzeb (obejrzyj fragment).

Równie ważnym filarem zarządzania operacyjnego w e‑commerce jest logistyka – zarówno jej część wewnętrzna (magazynowanie, zarządzanie zapasami, integracja z WMS/ERP), jak i zewnętrzna (kurierzy, automaty paczkowe, fulfillment, dropshipping). W dynamicznym środowisku zakupowym klienci oczekują wielu opcji dostawy i płatności, a błyskawiczna realizacja zamówień staje się nie tylko przewagą, ale standardem rynkowym. Coraz częściej sklepy sięgają po outsourcing logistyki, by skalować operacje bez konieczności rozbudowy infrastruktury.

Zarządzanie operacyjne w e‑commerce to również umiejętność radzenia sobie ze zwrotami i reklamacjami – obszarem, który bezpośrednio wpływa na doświadczenie klienta. Jasne procedury, przejrzyste formularze i szybkie działanie minimalizują koszty, a jednocześnie wzmacniają zaufanie do marki. Dopełnieniem całości jest analityka operacyjna: monitoring KPI, analiza porzuconych koszyków, dane z GA4, raporty ERP oraz narzędzia marketing automation. To dzięki nim firmy mogą stale usprawniać procesy, eliminować wąskie gardła i zwiększać konwersję (obejrzyj fragment).

Ostatecznie zarządzanie operacyjne w e‑commerce to nie tylko technologia, ale przede wszystkim sprawne zarządzanie procesami, zespołem i doświadczeniem klienta. W świecie szybkich zakupów online liczy się równowaga między efektywnością kosztową, jakością obsługi a elastycznością reagowania. Firmy, które potrafią połączyć te elementy, budują przewagę konkurencyjną opartą na szybkości, niezawodności i realnym zrozumieniu potrzeb użytkowników. To właśnie one definiują przyszłość e‑commerce – wspieraną przez AI, inteligentną logistykę i coraz bardziej zaawansowane modele biznesowe.

The post Zarządzanie operacyjne w e‑commerce – kluczowe fundamenty skutecznego handlu online appeared first on Customer Experience Management.

Author: Ahasbi Fatima Ezzahra (PhD Student UMCS & Researcher)

Customer Experience as a Lived Process

In much of the CXM literature, customer experience is framed as something that can be designed, mapped, and optimized. Innovation in tourism is often perceived through the lens of “Creative Destruction” (a nod to Schumpeterian theory), yet my doctoral research suggests a more nuanced reality. In tourism entrepreneurship, CX is rarely the result of a predefined strategy. Instead, it is shaped through trial, adjustment, and learning mirroring the entrepreneur’s own professional trajectory.

Tourism as a Living Laboratory for CXM

Entrepreneurs do not merely “design” an experience, they live it. Tourism offers a unique empirical ground for CXM analysis because experiences are not confined to isolated touchpoints. They unfold across time, space, and social interaction. In regions such as Marrakech–Safi, innovative tourism entrepreneurs reconstruct customer experience around meaning, authenticity, and human connection rather than standardized service scripts.

Innovation and CX: A Circular Relationship

One of the central findings of my research is the circular relationship between innovation and customer experience (CX). Innovation does not always precede experience; very often, it is the result of experiential friction moments where expectations, emotions, or values are misaligned. CXM, in this sense, becomes a diagnostic lens rather than a performance metric. Tourism Innovation.

My findings indicate that innovation is frequently born from “Customer Dissonance”. When a traveler’s expectations clash with reality, the agile entrepreneur doesn’t just fix a problem but they innovate a new process. This is the heart of CXM: transforming a friction point into a “Moment of Truth”. In the Marrakech-Safi region, this has led to the rise of slow tourism and micro-experiences innovations triggered from customers’ desire to live an authentic and spontaneous experience.

What CXM Should Learn from Entrepreneurial Trajectories?

If CXM is to remain relevant in entrepreneurial contexts, it must move beyond standardized models. Based on my research, three shifts are essential: prioritizing narratives over scores, understanding entrepreneurial trajectories rather than static personas, and accepting imperfection as a source of memorability and innovation. Standard CXM often relies on static personas. However, the complexity of tourism entrepreneurship requires a more dynamic approach. We must move toward “Life-Path Mapping.”

By understanding the entrepreneur’s trajectory, we can predict the quality of the customer experience. A “resilient” trajectory (one that has overcome local systemic challenges) almost always translates into a high-empathy customer experience. The lesson for CXM students is clear: behind-the-scenes aspects of the company or business (the entrepreneur’s struggle) play a key role in shaping brand image (customer satisfaction). Tourism Innovation.

Conclusion

The Marrakech-Safi region is more than a destination, it is a laboratory for the future of CXM. Viewing customer experience as an evolving trajectory allows us to rethink both CX and innovation. For students, researchers, and practitioners, tourism entrepreneurship demonstrates that experience, innovation, and identity are continuously co-constructed. The challenge lies in no longer viewing ‘experience’ as a mere marketing product, but rather as an outcome of entrepreneurship.

“CX is not what is delivered it is what is lived”

About tourism Innovation in hotel industry you can read here: Akceptacja robotów w usługach hotelowych – Henn-na Hotel, Japan

The post Customer Experience as an Entrepreneurial Trajectory: What Tourism Innovation Really Reveals? appeared first on Customer Experience Management.

based on: A review of AI (artificial intelligence) tools and customer experience in online fashion retail – R Pillarisetty, P Mishra – International Journal of E-Business Research (IJEBR), 2022•igi-global.com https://www.igi-global.com/viewtitle.aspx?titleid=294111

How Artificial Intelligence Is Transforming the Customer Experience

Artificial intelligence isn’t new, but its impact has grown dramatically with the rise of the internet and digital commerce. While AI research traditionally focused on technical and engineering challenges, much less attention has been given to how these technologies shape customer experience—especially in marketing.

At its core, AI refers to machines that can perform tasks intelligently and improve through learning. This ability to adapt makes AI an ideal tool for solving complex problems and enhancing digital interactions.

In e‑commerce, AI already powers a wide range of features designed to make shopping easier, more personalized, and more immersive. Examples include:

• Virtual try‑on tools like those offered by LensCrafters, which allow customers to “see” how products look without visiting a store.
• Fit‑prediction systems such as NIKE FIT, which use machine learning to recommend the right size for each customer.

These innovations reduce uncertainty, increase confidence, and ultimately elevate the overall online shopping experience.

As AI continues to evolve, its influence on customer experience will only grow—shaping how products are discovered, evaluated, and purchased in the digital world.

Customer Experience in the Digital Era: More Than Just Products

Modern customers aren’t simply buying products—they’re seeking memorable experiences. This idea, introduced decades ago, holds even more relevance today as much of our shopping takes place online. In digital environments, customers can’t touch or try products, so their entire experience depends on the verbal and visual cues presented on the website. Artificial intelligence in retail

Customer experience spans the entire journey—not just a single transaction. Research shows that people want to feel naturally drawn to a product, not pushed into a purchase. They prefer subtle, engaging interactions that convince them through value and quality rather than pressure.

Online customer experience is often described through four key dimensions:

1. Informativeness (Cognitive): How well the site helps customers understand products and make decisions.
2. Entertainment (Affective): The emotional appeal—whether the site feels enjoyable or stimulating.
3. Social Presence (Social): How interactive or “human” the online environment feels.
4. Sensory Appeal (Sensory): The visual and, increasingly, immersive elements that bring products to life.

These dimensions directly shape how satisfied customers feel during their online shopping journey.

Artificial intelligence now plays a major role in elevating these digital experiences. AI-driven recommendations, chatbots, personalized content, and interactive features help create meaningful engagement throughout the customer journey. Artificial intelligence in retail

A term gaining traction in recent years is “Cyber Atmospherics”—the virtual equivalent of in‑store ambience. It refers to how website design, layout, visuals, and interactive elements come together to influence the online customer experience. Just as lighting, music, and decor shape the mood in physical stores, cyber atmospherics shape the emotional and cognitive responses of online shoppers.

Defining E-satisfaction

What Really Drives E‑Satisfaction in Online Shopping?

E‑satisfaction essentially reflects how customers evaluate their online shopping experience compared to traditional in‑store shopping. Early research highlighted that the quality and clarity of information provided on a website—what some call customer information satisfaction (CIS)—plays a central role in shaping how satisfied customers feel. Artificial intelligence in retail

Because online shoppers rely entirely on digital cues, informativeness becomes the core of their experience. Helpful, accurate, and easy‑to‑find information supports customers through every stage of the decision‑making process.

Studies show that e‑satisfaction is shaped by three major elements:

1. Convenience – How easily customers can navigate, search, and complete purchases.
2. Merchandising – The depth and quality of product descriptions, images, and variety.
3. Site design – Visual appeal, user experience, and overall usability.

Customer satisfaction builds in two layers: the immediate satisfaction from the most recent purchase and the cumulative satisfaction formed over multiple interactions with a specific e‑commerce site. A key driver of both is a website’s usefulness—how effectively it offers helpful information and simplifies transactions.

In the last decade, AI has become a major force behind these improvements. Recommendation systems, chatbots, personalized product feeds, and even virtual try‑on experiences all contribute to higher perceived usefulness and, ultimately, greater satisfaction. Artificial intelligence in retail

Researchers often measure these trends using tools like the American Customer Satisfaction Index (ACSI), which tracks how customer perceptions evolve over time. When customers compare their online experiences with offline ones, they evaluate factors such as convenience, product quality, value for money, and product selection—elements consistently shown to influence satisfaction in the digital environment.

AI in Online Apparel Retailing: Driving the Future of Fashion E‑Commerce

India’s e‑commerce sector—especially online fashion—is experiencing explosive growth. This rapid rise is fueled largely by the widespread adoption of smartphones and a surge in online shopping, accelerated further during the pandemic. As more consumers shift to digital platforms, fashion e‑retailers are investing heavily in technology to stay competitive.

According to data from the India Brand Equity Foundation, online apparel accounts for 29% of India’s e‑commerce market, second only to electronics at 45%. With such a significant share, retailers are increasingly turning to advanced technologies—recommendation engines, natural language processing, AI‑powered chatbots, neural networks, and genetic algorithms—to enhance customer experience and boost e‑satisfaction. Artificial intelligence in retail

Researchers like Wang (2014) have analyzed how AI is shaping the online apparel industry, showing that innovative technologies often lead to major market disruptions. AI enables better product availability, more accurate deliveries, and overall smoother shopping experiences (Kati, 2018).

Importantly, AI doesn’t just transform retail operations—it also changes how customers shop. Smart tools can guide shoppers through personalized product suggestions, virtual fit recommendations, and real‑time assistance. As Johnson (2019) notes, AI has become a core technology in today’s online fashion ecosystem.

From mix‑and‑match styling tools to 24/7 support through intelligent chatbots, AI enhances efficiency, reduces return rates, and ultimately increases repeat purchases. For modern fashion retailers, mapping the right AI tools to the customer journey isn’t optional—it’s essential for staying ahead in an increasingly competitive digital marketplace.

Recommendation Engines: Personalizing the Online Shopping Journey

Recommendation engines use machine learning to suggest products that match a shopper’s preferences, based on their browsing behavior and past purchases. One of the most widely used methods is collaborative filtering, which predicts what a customer might like by analyzing patterns from similar users.

These systems continuously learn and adapt, refining suggestions as customers interact with the site. By understanding buying behavior through data mining and behavioral analytics, retailers can deliver more personalized experiences—boosting loyalty, reducing search effort, and improving overall customer satisfaction.

For e‑commerce brands of all sizes, intelligent recommendation tools open the door to global markets by offering shoppers a tailored, engaging experience. Artificial intelligence in retail

Product Reviews: The Powerhouse of Online Shopping Decisions. Artificial intelligence in retail.

Product reviews are one of the most influential elements of e‑commerce. They let customers compare features, assess quality, and evaluate alternatives—all without visiting a physical store. Positive, detailed reviews drive engagement and build trust, while a wide variety of products and opinions strengthens a retailer’s competitive edge.

Artificial intelligence in retail. https://mietwood.com/akceptacja-robotow-w-uslugach-hotelowych

User‑generated reviews are especially credible, providing explicit feedback through ratings and comments, and implicit signals through search and purchase behavior. The broad product variety available online, paired with easy price comparison, enhances customer satisfaction by helping shoppers feel confident they’re getting the best deal.

Modern sites also use digital nudging—subtle design cues that guide faster, more informed decision‑making—further shaping the online shopping experience.

Virtual Try‑Ons: Bringing the Fitting Room to Your Screen

Virtual try‑on technology has become a major innovation in online fashion retail, helping shoppers visualize how products will look and fit—without ever visiting a store. Using 3D modeling and augmented reality, these tools let customers assess size, style, and overall appearance more accurately, which greatly reduces return rates. Artificial intelligence in retail

By turning online product attributes into experience attributes, virtual try‑ons lower the perceived risk of buying apparel or cosmetics online. Whether it’s previewing how a lipstick shade matches your skin tone or seeing how a pair of glasses fits your face, the technology offers an experience close to an in‑store trial.

In India, brands like Lenskart have popularized this feature, allowing users to try on eyewear through realistic 3D models simply by taking a selfie. Research also shows that these immersive tools encourage online purchases by increasing confidence and engagement. Artificial intelligence in retail

Image Interactivity Technology: Making Online Shopping More Immersive

Image Interactivity Technology (IIT) enhances online shopping by allowing customers to interact with products—zooming in, rotating, or mixing and matching items. Rooted in the Stimulus‑Organism‑Response (S‑O‑R) model, IIT explains how website cues influence both a shopper’s emotions (like enjoyment) and thoughts (such as perceived risk), ultimately shaping their behavior on the site. Artificial intelligence in retail

IIT provides two types of cues:

• Utilitarian: tools that support the task of shopping, such as zoom and detailed views.
• Hedonic: elements that make the experience enjoyable, like 3D models and mix‑and‑match features.

These interactive visuals help recreate aspects of the in‑store experience, reducing uncertainty and increasing entertainment value. This has led to a trend called “experiencing the experience”—customers trying out new looks or outfits digitally before buying. Artificial intelligence in retail

Major brands like Nike, Levi’s, H&M, and Speedo now use 3D virtual models and interactive product tools to capture attention, boost engagement, and help shoppers feel more confident in their choices.

Chatbots: The New Face of Customer Service

With the rise of “conversational commerce,” chatbots have become a central part of online customer service. Powered by natural language processing (NLP) and AI, these virtual assistants—similar to Alexa or Siri—can simulate human conversations through websites, apps, and messaging platforms.

Research shows chatbots improve customer experience by answering questions, guiding shoppers, and offering polite, patient assistance based on past customer behavior. Because they use machine learning, they continually improve their responses over time.

However, the experience isn’t always perfect. Customers often report frustration when chatbots misunderstand queries, give irrelevant answers, or get stuck repeating the same response. This highlights the ongoing challenge of balancing automation with accuracy and empathy in digital customer service.

Summary: How AI Is Transforming Online Customer Experience and E‑Satisfaction

The rapid growth of e‑commerce—especially in fashion—has pushed retailers to prioritize not just products, but memorable digital experiences. Online customer experience is shaped by key factors such as informativeness, entertainment, social presence, and sensory appeal. These elements help customers make confident decisions without visiting a physical store and strongly influence overall e‑satisfaction.

Artificial intelligence now plays a central role in enhancing these digital journeys. AI‑powered tools such as recommendation engines, chatbots, virtual try‑ons, and image interactivity technologies personalize the shopping process, reduce uncertainty, and increase engagement. They help customers discover products, visualize fit and style, receive instant assistance, and interact with items more meaningfully.

Product reviews and broad online product variety further boost satisfaction by offering credible feedback, transparent comparisons, and access to the best prices. Combined with digital nudging and smart personalization, these technologies recreate elements of the in‑store experience while adding new forms of convenience and confidence. Artificial intelligence in retail

Overall, AI is reshaping how customers browse, evaluate, and buy—making online shopping more intuitive, immersive, and emotionally satisfying. As these innovations evolve, they will continue to redefine customer expectations and retail competitiveness across global e‑commerce markets.

The post Artificial intelligence in retail appeared first on Customer Experience Management.

The Scenario: In a modern office, there are two employees with the same title and salary.

• Employee A (Manual): Completes tasks purely through manual effort and personal knowledge in 8 hours.
• Employee B (AI-Assisted): Completes the same tasks in 1 hour using tools like ChatGPT or Claude but hides this fact from management (“Shadow AI” usage).

Author: Aykut Akbulut, Erasmus students 2025

The Management Issue: Traditional management focuses on “time spent” (Input), whereas the AI era forces a focus on “value created” (Output). This discrepancy creates a perception of “Unfair Competition” and “Wage Inequity” within the office. Should the manager reward the 1-hour worker, or punish them with more workload? Managerial Dilemma.

Research Findings & Risk Analysis

Our literature review and industry analysis highlight three critical risks:

1. Shadow AI Risk: Approximately 68% of employees (referencing industry trends) use AI without their manager’s knowledge. This poses a significant risk of Data Privacy Leakage.
2. Skill Atrophy: Employee B, who constantly relies on AI, risks losing fundamental problem-solving skills and failing to detect AI-generated errors (Hallucinations).
3. Burnout: Employee A may lose motivation, feeling that “I am working hard while they are taking it easy,” leading to a toxic work environment.

The Solution – The “Middle Ground” Strategy

Banning AI is not the solution; leveling the playing field is. The proposed management model is:

1. Disclosure Policy: The company should allow AI usage but mandate “disclosure.” Employee B must be able to say, “I drafted this with AI support and verified the data.”
2. Upskilling (Equalization): Employee A (the slower worker) should not be penalized but trained in AI usage. The goal is to bring Employee A down to 1 hour as well.
3. Shift in Performance Metrics: Evaluation should shift from “hours sat at the desk” to “value added on top of AI.” The efficient worker should not be punished with busy work but encouraged to use the remaining time for strategic thinking. Managerial Dilemma.

Managerial Dilemma. https://study.com/academy/lesson/dilemma-management-definition-example.html

Management Style: How Managers Really Manage

The post The Managerial Dilemma in the Age of Generative AI: Efficiency vs. Fairness appeared first on Customer Experience Management.

It continues through the purchase stage where the need is fulfilled, and concludes with the post-purchase stage that encompasses feedback and evaluation of the experience (Fuller et al., 2023).

As the first two stages (pre-purchase and purchase) are often difficult to be distinguished from each other due to their overlapping nature, the transaction typically serves as the point of separation (Lemon & Verhoef, 2016).

Throughout the customer journey, customers encounter various touchpoints, which are the points of interaction between a customer and a brand (Følstad, & Kvale, 2018). These touchpoints along the customer journey can be both customer-driven or brand-driven (Barann et al., 2022; Payne et al., 2017).

Source: Kaleton, T., Holkkola, M., Kemppainen, T., & Frank, L. (2026). What Makes or Breaks an Immersive Online Customer Journey? Digital Native Generation Z Customers’ Experiences: Link.

Customer journeys may be enhanced by immersion both directly and indirectly (Flavían et al.,
2024). During their customer journeys, customers are seeking highly customizable experiences, where they
can efficiently view products from various angles and engage interactively with a brand (Neves et al., 2022).

For example, high-quality product photos on online stores allow customers to visualize the products and
imagine them in different situations. If customers are provided with sufficient information, they can make
informed purchasing decisions (Flavián et al., 2019).

Thus, immersivity has great possibilities for retailing companies in digital environments. However, VR and
AR solutions related to clothing retail are yet in their early stages and, thus, not largely available in online
stores or applications (Solís-Sánchez et al., 2022).

The post Customer Journey In The Age of IA appeared first on Customer Experience Management.

Influencer marketing has become one of the most powerful tools in digital advertising, driving billions in brand collaborations worldwide. Yet, as the industry matures, a critical challenge has emerged: authenticity. Consumers increasingly demand genuine connections, while brands often prioritize reach and engagement metrics. This tension creates a paradox—how can influencers remain authentic while monetizing their content?

Recent research published in the Journal of Marketing (Duffek et al., 2025) explores this issue in depth, offering actionable insights for influencers, brands, and agencies. This blog post breaks down the theoretical foundations, key findings, and practical strategies from the study, helping you understand why authenticity is central to influencer marketing success.

What Does Authenticity Mean?

Authenticity in influencer marketing is not a simple trait—it’s a multidimensional construct shaped by relationships among influencers, brands, and audiences. The study draws on assemblage theory, which views influencer-brand-consumer interactions as dynamic systems rather than static roles. Authenticity emerges from these interactions, not just from an influencer’s personality. See also: What drives people purchase online

The English term “assemblage” captures the result of combining parts. The original French term “agencement”, refers both to the act of arranging and its outcome.

The simplest definition describes an assemblage as a heterogeneous multiplicity that creates relations among its parts. These relations are not genealogical but alliances, emphasizing external connections rather than identity-defining ones. This distinction introduces the notions of intrinsic vs. extrinsic relations.

Intrinsic relations define identity, e.g., family roles, while extrinsic relations connect heterogeneous elements without fusing them into a homogeneous whole, e.g., symbiosis in ecology. Intrinsic relations are coded and arbitrary, whereas extrinsic relations allow diversity and contingency.

In this context, contingency means that the relations are not necessary or predetermined—they happen by circumstance rather than by fixed rules or codes. So, when we say extrinsic relations allow diversity and contingency, we mean:

• They connect heterogeneous elements in ways that are open-ended and flexible,
• The connection is context-dependent and could have been otherwise,
• There is no rigid structure or essential identity forcing the relation
• It’s optional, situational, and variable.

Ultimately, assemblages operate through alliances and exteriority.

Core Dimensions of Authenticity

According to the research and supporting literature, authenticity in influencer marketing involves five key properties:

1. Expertise – Demonstrating knowledge and experience in a niche.
2. Connectedness – Building emotional bonds through genuine engagement.
3. Originality – Crafting unique, creative content that reflects personal style.
4. Transparency – Disclosing partnerships and being open about content creation processes.
5. Integrity – Showing passion-driven motivation rather than purely financial incentives. [provost.gsu.edu]

These dimensions align with broader theories like Source Credibility Model and Uses and Gratification Theory, which emphasize trustworthiness and audience satisfaction as drivers of persuasive communication. [lup.lub.lu.se]

Why Authenticity Matters in Influencer Marketing

The demand for authenticity is not just anecdotal—it’s backed by data. Studies show that 88% of consumers want influencers to be authentic, yet nearly 50% perceive influencers as inauthentic. This gap erodes trust and reduces campaign effectiveness. [journals.sagepub.com]

Authenticity impacts:

• Consumer trust → Higher engagement and conversion rates.
• Brand equity → Authentic partnerships enhance brand credibility.
• Influencer longevity → Authentic influencers maintain loyal communities.

Key Findings from the Research

The Journal of Marketing study conducted 185 in-depth interviews with consumers, influencers, brand managers, and agency leaders. Here are the most relevant insights:

1. Authenticity Is Relational, Not Static

Authenticity is co-created through ongoing interactions. It’s not enough for influencers to “appear real”; brands must allow creative freedom and avoid rigid scripting. When influencers can express their unique voice, engagement and perceived authenticity rise significantly. [provost.gsu.edu]

2. Transparency and Trust

Transparency about technology fosters credibility and aligns with consumer expectations for honesty. [phys.org]

3. Misalignment Among Stakeholders

Consumers, influencers, and brands often prioritize different aspects of authenticity. For example:

• Consumers value emotional connection and honesty.
• Brands focus on professionalism and brand safety.
• Agencies seek scalable processes and compliance.

Resolving these misalignments requires clear communication and shared guidelines. [journals.sagepub.com]

4. Generational Differences

Gen Z views influencers as “educated friends,” while Millennials see them as professionals who must act ethically. This affects how authenticity is perceived and should inform campaign strategies. [degruyterbrill.com]

Practical Strategies for Building Authenticity

Based on the findings, here are actionable steps for influencers and brands:

For Influencers

• Stay true to your niche: Consistency in topic and tone builds expertise.
• Engage meaningfully: Respond to comments, share personal stories.
• Disclose partnerships: Transparency is non-negotiable.
• Balance creativity and brand guidelines: Negotiate freedom in collaborations.

For Brands

• Choose values-aligned influencers: Beyond follower count, look for shared mission.
• Avoid over-scripted campaigns: Authenticity thrives on creative autonomy.
• Support long-term partnerships: Sustained relationships feel more genuine to audiences.

Authenticity Dimensions Chart

Here’s a simple chart illustrating the five dimensions of authenticity:

Authenticity Is the Future of Influencer Marketing

Authenticity is not a buzzword—it’s the foundation of sustainable influencer marketing. As consumers demand transparency and genuine engagement, brands and influencers must collaborate to create content that resonates on a human level. By embracing the principles outlined in this research, you can build campaigns that not only perform but also foster trust and loyalty.

The post Authenticity in Influencer Marketing: Why It Matters and How to Achieve It appeared first on Customer Experience Management.

Based on the typical content of a Customer Experience Management (CEM) lecture (Strategy, Journey Mapping, Metrics, and Culture), here are 5 proposed SERVQUAL-style questions for students. The scale should be a 5-point Likert scale, with options like: 1 = Strongly Disagree, 5 = Strongly Agree

5 SERVQUAL Questions for Your CEM Lecture

The questions are organized to cover the core dimensions students would likely expect from a course titled “Customer Experience Management.” For each item, the student will answer two questions: what they Expected and what they Perceived (Got).

Calculating the Gap Score

Once students complete the survey, you can calculate your Gap Score for each of the 5 items:

A Negative Score indicates a gap where the lecture failed to meet student expectations (e.g., 3 – 5 = -2).

• A Zero Score indicates the lecture met expectations (e.g., 4 – 4 = 0).
• A Positive Score indicates the lecture exceeded expectations (e.g., 5 – 4 = +1).

You can read more here: ServQual methodology for assessing the usability of automated sales in grocery

This is a much better way to tailor the survey! Based on the specific themes found on your blog (mietwood.com), which emphasize digital business models, data science, competitive advantage, and sustainability within Customer Experience Management (CEM), I have adapted the 5 SERVQUAL dimensions to reflect your unique lecture content.

Students will rate their Expectation (E) (based on the title) and their Perception (P) (what they got) on a 5-point Likert scale, where 1 = Strongly Disagree and 5 = Strongly Agree.

5 SERVQUAL Questions Tailored to mietwood.com

How to Use the Scores

After the students complete the survey, you can calculate the Gap Score for each question by subtracting the expected score (E) from the perceived score (P)

Reliability – servqual

That’s an excellent way to focus your immediate survey efforts—by zooming in on the Reliability dimension.

In the context of a CEM lecture, Reliability measures the ability of your course to perform the promised service dependably and accurately. It asks: Did the lecture deliver on its core promises and core functions?

Here are 5 SERVQUAL questions, all focused on the Reliability of your CEM lecture (based on the themes from mietwood.com), where students will rate both Expectation (E) and Perception (P) on a Likert scale (1-5).

This set of five questions will give you a detailed view of how well you delivered on the fundamental promises of your CEM lecture.

The most significant scientific paper related to the SERVQUAL scale is the original publication that introduced the instrument:

Title: SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality1 Authors: A. Parasuraman, Valarie A.2 Zeithaml, and Leonard L. Berry3 Journal: Journal of Retailing4 Volume/Issue/Pages: Vol. 64, No. 1, pp. 12-405, Publication Year: 19886

Why This Paper is the Most Significant

This paper is considered seminal for several reasons:

1. Introduction of the 5 Dimensions (RATER): It refined the ten initial determinants of service quality (proposed in their earlier 1985 paper) into the five established dimensions used today: Reliability, Assurance, Tangibles, Empathy, and Responsiveness.
2. The Instrument: It describes the development, refinement, and validation of the 22-item SERVQUAL questionnaire itself, which is designed to measure the gap between customer Expectations (E) and Perceptions (P).⁷
3. Foundation of the Field: It provided the first widely accepted, psychometrically rigorous, and empirically tested scale for measuring the abstract concept of service quality, establishing the fundamental model for nearly all subsequent research in the field.

It’s important to note that the conceptual framework, known as the Gaps Model of Service Quality, was initially introduced by the same authors in an earlier 1985 paper:⁸

• 1985 Paper: A Conceptual Model of Service Quality and its Implications for Future Research, published in the Journal of Marketing.⁹ This paper introduced the five service quality gaps. link
• 1988 Paper: SERVQUAL: A Multiple-Item Scale…, published in the Journal of Retailing.¹⁰ This paper introduced the actual 22-item measurement scale based on the 1985 framework.¹¹

For anyone specifically looking for the measurement tool (the 22-item scale), the 1988 Journal of Retailing paper is the most significant one.

The post Servqual survey on customer experience management appeared first on Customer Experience Management.

This blog post explores the key concepts, strategies, and tools involved in managing Customer Experience CX in the digital age, drawing insights from the presentation by Mieczysław Pawłowski, PhD. You can download it from here below.

Customer Experience Management in the Digital Age – Navigating the New Frontier.

The Evolution from CRM to CXM

Customer Relationship Management (CRM) has long been a cornerstone of business strategy, focusing on understanding and influencing customer behavior to drive acquisition, retention, loyalty, and profitability.

Digital technologies have advanced, the scope of Customer Relationship Management CRM has expanded to Customer Experience Management CXM. CXM goes beyond traditional CRM by integrating digital tools and strategies to enhance every touchpoint a customer has with a business.

Thank you to Tanzania’s students for listening to me with attention and all activity I experienced from YOU.

The Role of CXM in Business Growth

Effective CXM is crucial for business growth, as it directly impacts customer satisfaction, loyalty, and overall profitability. Companies that excel in CXM can differentiate themselves in the market, build stronger relationships with their customers, and drive sustainable growth. The process involves several key components:

1. Customer Knowledge: Collecting and analyzing customer data to understand their characteristics, needs, and preferences.
2. Value Creation: Linking organizational strategy with customer management processes to create value for both the company and the customer.
3. Competitiveness: Combining business processes and technologies to differentiate products and services in the market.

CXM in the Digital Age

The digital age has brought significant changes to CXM, with e-commerce, digital marketing, and omnichannel sales becoming integral parts of the customer journey. Businesses must now operate and interact with customers differently, utilizing digital interfaces and tools to enhance the customer experience. Key trends in CXM include:

• Digital Transformation: Companies must adapt to digital technologies to stay competitive and meet customer expectations.
• Digitization of Customer Interactions: The integration of digital technologies into customer interactions has evolved from basic processes like online ordering to a complete digitization of the customer journey.
• Customer-Centric Approach: Businesses are shifting from a product-centric to a customer-centric approach, placing customer experience at the forefront of their interactions.

About digital age you can read here: What is digital age?

The Profession of Customer Experience Management

As CXM becomes increasingly important, the role of the Customer Experience Manager has emerged as a critical profession. These professionals are responsible for optimizing every touchpoint a customer has with a business to build brand loyalty and enhance customer relationships. Common responsibilities of a Customer Experience Manager include:

• Championing opportunities to improve the customer experience.
• Driving customer retention and satisfaction.
• Mapping the customer journey and identifying opportunities for proactive intervention.
• Guiding teams in effective client issue resolution.
• Developing strategies for continuous improvement.

Strategies for Effective CXM

To succeed in CXM, businesses must adopt strategies that align with their goals and market position. Some effective strategies include:

• Market Leader: Treating customers as regular resources and focusing on customer satisfaction.
• Pretender: Using customer feedback to improve and innovate.
• Niche: Differentiating through quality and specialized offerings.
• Price Leader: Focusing on product value and competitive pricing.

The Impact of Technology on Customer Experience

Technology plays a pivotal role in shaping customer experience in the digital age. From self-service kiosks that boost consumer spending to digital interfaces that streamline customer interactions, technology enhances the overall customer experience by providing convenience, personalization, and efficiency. Key technological impacts include:

• Enhanced User Experience: Visual displays and photography help entice customers and upsell options.
• Data Collection: Detailed data on customer preferences allows for precise targeting and improved service.
• Automation: Automating processes like checkout frees up staff time and enhances customer satisfaction.

Customer Experience vs. Service Quality

The search results strongly suggest that while “Service Quality” is the foundational academic concept, “Customer Experience” (CX) is the current industry and high-level curriculum trend.

1. CX is the Dominant Industry and Advanced Curriculum Term 🚀

The term “Customer Experience” (or “CX”) is now the preferred term in executive education and advanced programs:

• Masters Programs: Top business schools (like IE Business School, MSU Broad College of Business, Rutgers, HBS Online) offer entire programs or executive courses dedicated to “Customer Experience Management” or “Transforming Customer Experiences.” (Source 3.1, 3.3, 3.4, 3.6). This demonstrates that, for students interested in advanced studies, CX is a recognized, specialized field.
• CX is Strategic: CX is consistently described as a “leading strategy companies harness to grow their market share” and a source of “long-term competitive advantage.” (Source 3.2, 2.5). Business leaders are recognizing that CX is crucial.
• CX is a Modern Evolution: Scholars note that services are undergoing a transformation from the traditional concept of a service transaction to one of an “experience” (Source 1.1). CX is seen as the latest evolution that surpasses simple products and services.

2. Service Quality is the Academic Foundation (The “Known” Term) 🎓

Students are likely more familiar with “Service Quality” because it is the cornerstone of early marketing and management theory:

• Service Quality is the Antecedent: The research confirms that Service Quality (SQ) is viewed as the fundamental antecedent or predictor of Customer Experience (CX) (Source 1.3). In academic models, improving Service Quality is a way to achieve positive Customer Experiences.
• Student Experience Studies: Studies assessing student satisfaction in higher education often use Service Quality models (like SERVQUAL) as the primary framework, although they are now explicitly linking SQ results to overall Customer Experience (Source 1.1, 1.6).

Service Quality – The gap between customer expectations and perceptions of service delivery. Academic, Tactical. It focuses on a transactional evaluation of reliability, responsiveness, etc. It signals a foundational, measurement-focused course.

Customer Experience – The customer’s overall perception and internal, subjective reaction to every direct or indirect contact over a period of time. Strategic, Holistic. It focuses on the full journey and emotional connection, making it a more complex, managerial, and future-oriented topic.

The term “Client” vs. “Customer” is a minor issue. In modern business and academic literature (including the high-level Master’s programs cited), “Customer” is the standard term used in the context of CX, as it refers to a broader B2C or B2B user, not just a high-value client.

Customer Experience Management is the title most aligned with advanced, strategic business education, which attracts the type of student who wants to be on the cutting edge and those seeking high-value skills.

Customer Experience can be combined with the analytical, digital, or process-oriented keywords suggested previously to make its content clear and appealing to the strongest cohorts

Conclusion

Customer Experience Management is not only essential for business growth but also a strategic priority for companies aiming to differentiate themselves in the market. By investing in CXM, businesses can drive increased profitability, customer loyalty, and brand strength while reducing costs and enabling continuous improvement. As the digital age continues to evolve, the importance of CXM will only grow, making it a critical area of focus for businesses looking to thrive in the modern economy.

In conclusion, the role of CXM in the digital age is multifaceted and ever-evolving. By continuously developing strategies, leveraging technology, and prioritizing customer satisfaction, businesses can ensure they remain competitive and successful. The future of CXM is bright, and with the right approach, companies can create unforgettable experiences that drive long-term growth and loyalty.

The post Customer Experience Management in the Digital Age appeared first on Customer Experience Management.

Frequency Analysis for Profiling

This is the most direct approach to identify the most and least selected lectures by your L6 students.

1. Filter Data: Isolate the rows (students) who selected L6.
2. Calculate Frequencies: For this subset of L6 students, count how many of them selected each of the other available lectures (L1, L2, L3, etc.).
   • Most Selected: The lectures with the highest counts.
   • Least Selected: The lectures with the lowest counts (or those not selected at all).

Association Rule Mining – Co-occurrence

This sophisticated approach, often used in market basket analysis, can determine which lectures are most frequently chosen together with L6.

• Support: The proportion of L6 students who also selected a specific lecture (e.g., L3).
• Confidence: The likelihood that a student selected L6 given that they selected another lecture (e.g., L3 → L6), or vice-versa.
• Lift: A measure of how much more likely a student is to select L3 if they also selected L6, compared to the overall likelihood of selecting L3. A Lift >1 suggests a positive association (students who take one tend to take the other).

Correlation Analysis – the Strength and Direction of Relation – application in Profiling

This method quantifies the relationship between selecting L6 and selecting any other lecture (Lx). Since the selection data is binary (0 for not selected, 1 for selected), you would use a correlation measure suitable for binary variables. Profiling.

• Phi Coefficient (ϕ): This is a measure of association for two binary variables. It ranges from −1 to +1.
  • Strong Positive Correlation (ϕ≈+1): Students who select L6 are highly likely to also select Lx. This suggests the lectures are perhaps complementary or targeted at the same student group.
  • Strong Negative Correlation (ϕ≈−1): Students who select L6 are highly likely to not select Lx. This suggests the lectures might be alternatives, require conflicting time slots, or appeal to entirely different student interests.
  • Weak/No Correlation (ϕ≈0): Selection of L6 has little to no impact on the selection of Lx.

Dimensionality Reduction – Clustering

For a very large number of lectures, you could use methods like Principal Component Analysis (PCA) or clustering algorithms to group similar students or lectures together. This can identify underlying student profiles (e.g., “The Data Science Crowd” or “The Humanities Enthusiasts”) that include L6 as part of their typical selection.

In this table there are students (in rows) selection of lectures (in column). My lecture is L6. I would like to know a profile of my students. so which lectures they selected the most and which the least. How strong the relations are (positive and negative if they omit some lectures.

Profile of L6 Students: Most and Least Selected Lectures

This is a Frequency Analysis of the lectures selected by the 11 students who chose L6. The percentages are based on the total number of L6 students (15).

Most Selected Lectures – The “Typical Package”: Your L6 students most frequently select L4 (67%), L3 (53%), and L5 (53%). These three form the core lecture package with L6. Least Selected Lecture: L12 is the least popular choice, selected by only 3 out of 15 students (20%).

Strength of Relation: Correlation Analysis

The Phi Coefficient quantifies the strength and direction of the relationship between choosing L6 and choosing any other lecture, using all 25 students in the dataset.

Strongest Positive Relations (Complementary): L4 (ϕ=0.263) and L5 (ϕ=0.230) show the strongest positive correlation with L6. This suggests that students interested in L6 are often those who also select L4 and L5.

Strongest Negative Relation (Alternative/Avoided): L12 (ϕ=−0.218) shows the only notable negative correlation. This confirms the frequency finding, suggesting L12 may be an alternative path or have a conflicting time/prerequisite with L6.

Weak/No Relation: Lectures like L3 and L7 have a high selection frequency but a very weak (L3) or zero (L7) correlation. This indicates that while many L6 students do take these, they are likely popular lectures chosen by many students across the board, and the choice of L6 is not a significant predictor of their selection.

Association Rule Mining

By analyzing the entire student population, we can discover general student curriculum patterns that exist beyond your specific L6 cohort. I used Association Rule Mining metrics (Support, Confidence, and Lift) to find lecture pairs that are frequently selected together.

• Support: The percentage of all 25 students who selected both lectures.
• Lift: A measure of how much the selection of one lecture increases the chance of selecting the other. A Lift>1.2 indicates a strong, meaningful positive association.

Here are the top co-selected lecture groups (pairs) among the entire student population, filtered for those selected by at least 16% of students and showing a strong positive association (Lift>1.2):

Python

import pandas as pd
from io import StringIO
import itertools

# Read the data
df = pd.read_csv(StringIO(csv_data), sep=';')
N_students = len(df)

lecture_cols = [col for col in df.columns if col.startswith('L')]
lecture_df = df[lecture_cols].fillna(0).astype(int)

# --- Association Rule Mining (Pairs) ---
# 1. Calculate Support for individual lectures
support_single = lecture_df.sum() / N_students

# 2. Calculate Support and Lift for all pairs
association_rules = []
for l1, l2 in itertools.combinations(lecture_cols, 2):
    # Calculate Support for the pair: count students who selected both
    co_selection_count = (lecture_df[l1] * lecture_df[l2]).sum()
    support_pair = co_selection_count / N_students

    # Calculate Lift: (Support(L1 and L2)) / (Support(L1) * Support(L2))
    # Handle division by zero if single support is 0, though unlikely here
    if support_single[l1] > 0 and support_single[l2] > 0:
        lift = support_pair / (support_single[l1] * support_single[l2])
    else:
        lift = 0

    # Calculate Confidence (L1 -> L2)
    confidence_l1_to_l2 = support_pair / support_single[l1] if support_single[l1] > 0 else 0

    association_rules.append({
        'Antecedent': l1,
        'Consequent': l2,
        'Support': support_pair,
        'Confidence (L1 -> L2)': confidence_l1_to_l2,
        'Lift': lift
    })

# Convert to DataFrame
rules_df = pd.DataFrame(association_rules)

# Filter for meaningful associations:
# 1. Minimum Support: Selected by at least 4 students (4/25 = 0.16)
# 2. Lift > 1.2: A strong positive relationship
min_support = 4 / N_students  # 0.16

# Filter and sort the results by Lift
top_associations = rules_df[
    (rules_df['Support'] >= min_support) &
    (rules_df['Lift'] > 1.2)
].sort_values(by='Lift', ascending=False).reset_index(drop=True)

# Add the reverse rules (L2 -> L1) to the table where Lift is high.
# Since Lift is symmetrical, only one direction needs to be calculated, but Confidence is not.

# Helper function to get Confidence (L2 -> L1) for presentation
def get_confidence_l2_to_l1(row):
    l1 = row['Antecedent']
    l2 = row['Consequent']
    support_pair = row['Support']
    return support_pair / support_single[l2] if support_single[l2] > 0 else 0

top_associations['Confidence (L2 -> L1)'] = top_associations.apply(get_confidence_l2_to_l1, axis=1)

# Reorder columns for presentation
top_associations = top_associations[['Antecedent', 'Consequent', 'Support', 'Confidence (L1 -> L2)', 'Confidence (L2 -> L1)', 'Lift']]

print("Top Co-Selected Lecture Groups (Pairs):")
print(top_associations.to_markdown(index=False, floatfmt=".3f"))

import pandas as pd
from io import StringIO
import itertools

# Read the data
df = pd.read_csv(StringIO(csv_data), sep=';')
N_students = len(df)

lecture_cols = [col for col in df.columns if col.startswith('L')]
lecture_df = df[lecture_cols].fillna(0).astype(int)

# --- Association Rule Mining (Pairs) ---
# 1. Calculate Support for individual lectures
support_single = lecture_df.sum() / N_students

# 2. Calculate Support and Lift for all pairs
association_rules = []
for l1, l2 in itertools.combinations(lecture_cols, 2):
    # Calculate Support for the pair: count students who selected both
    co_selection_count = (lecture_df[l1] * lecture_df[l2]).sum()
    support_pair = co_selection_count / N_students

    # Calculate Lift: (Support(L1 and L2)) / (Support(L1) * Support(L2))
    # Handle division by zero if single support is 0, though unlikely here
    if support_single[l1] > 0 and support_single[l2] > 0:
        lift = support_pair / (support_single[l1] * support_single[l2])
    else:
        lift = 0

    # Calculate Confidence (L1 -> L2)
    confidence_l1_to_l2 = support_pair / support_single[l1] if support_single[l1] > 0 else 0

    association_rules.append({
        'Antecedent': l1,
        'Consequent': l2,
        'Support': support_pair,
        'Confidence (L1 -> L2)': confidence_l1_to_l2,
        'Lift': lift
    })

# Convert to DataFrame
rules_df = pd.DataFrame(association_rules)

# Filter for meaningful associations:
# 1. Minimum Support: Selected by at least 4 students (4/25 = 0.16)
# 2. Lift > 1.2: A strong positive relationship
min_support = 4 / N_students  # 0.16

# Filter and sort the results by Lift
top_associations = rules_df[
    (rules_df['Support'] >= min_support) &
    (rules_df['Lift'] > 1.2)
].sort_values(by='Lift', ascending=False).reset_index(drop=True)

# Add the reverse rules (L2 -> L1) to the table where Lift is high.
# Since Lift is symmetrical, only one direction needs to be calculated, but Confidence is not.

# Helper function to get Confidence (L2 -> L1) for presentation
def get_confidence_l2_to_l1(row):
    l1 = row['Antecedent']
    l2 = row['Consequent']
    support_pair = row['Support']
    return support_pair / support_single[l2] if support_single[l2] > 0 else 0

top_associations['Confidence (L2 -> L1)'] = top_associations.apply(get_confidence_l2_to_l1, axis=1)

# Reorder columns for presentation
top_associations = top_associations[['Antecedent', 'Consequent', 'Support', 'Confidence (L1 -> L2)', 'Confidence (L2 -> L1)', 'Lift']]

print("Top Co-Selected Lecture Groups (Pairs):")
print(top_associations.to_markdown(index=False, floatfmt=".3f"))

Interpretation of Lecture Groups

The Lift values indicate the strength of the relationships:

The Strongest Cohort (The 2.2+ Lift Groups)

These are the three strongest, non-obvious combinations. A Lift≈2.2 means that students who take one lecture are more than twice as likely to take the associated lecture compared to the general student population.

• L9 ↔ L12 ↔ L8: These three lectures form a tight cluster. Specifically, L8 → L9 and L9 → L12 have extremely high association scores. This suggests a dedicated academic track or a highly correlated set of topics.

High Confidence Groups (The 1.5+ Lift Groups)

These are groups where the selection of one lecture is a very strong predictor for the other:

• L2 → L4 (87.5% Confidence): If a student selects L2, there is a very high, 87.5% chance they also select L4. This suggests L2 might be a prerequisite, a foundational course, or a direct complement to L4.
• L2 → L3 (75.0% Confidence): Similarly, L2 and L3 are frequently chosen together, indicating a strong connection.
• L1 ↔ L2: These two lectures are highly associated, suggesting they are often taken in tandem.

Does this mean that I should go to lecturers L5 and L2 and ask them for promotion of my L6, because it is most likely they find satisfactory experience on my lecture?

Data-Driven Strategy for L6 Promotion

1. Prioritize L5 (High Co-Selection & High Confidence)

You should absolutely focus on the L5 lecturer.

• L5 → L6 Confidence: 72.7%
  • This means nearly 3 out of every 4 students who take L5 also choose your L6 lecture.
  • Action: The L5 lecturer is teaching the same target audience as you. You could ask them to mention L6 as a natural follow-up or complementary course to their students.

2. Prioritize L2 (High Predictive Power)

The relationship with L2 is even more predictive of a student landing in your lecture.

• L2 → L6 Confidence: 75.0%
  • This means 3 out of every 4 students who take L2 end up in L6.
  • Action: The L2 lecturer is essentially teaching a foundational course for a majority of your class. Ask them to promote L6 as the direct next step or most relevant application course for their content.

3. The Importance of Satisfaction (What the Data Doesn’t Say)

The data confirms a strong selection link, but not a satisfaction link.

• Selection: L5 and L2 students are your target market.
• Satisfaction: To confirm they find your lecture satisfactory, you’d need student feedback/evaluation data. A student who disliked L6 might still be highly likely to take it if it’s a required course for a specific program, for example.

The strongest rationale for promotion is simply the high overlap: you are addressing a student cohort that already has a demonstrated interest pattern (L5/L2 → L6).

Secondary Focus: L4 and L3

While L4 and L3 have high total co-selection with L6 (L4: 66.7%, L3: 53.3%), their Lift and correlation scores are weaker. This means they are generally popular lectures, and while many of your students take them, the choice of L4 or L3 does not strongly predict the choice of L6.

Focusing your limited promotional time on the lecturers for L5 and L2 will yield the highest return.

Country and University as moderating factors

The factors of Country and University do show clear patterns in lecture selection among your L6 students, suggesting that existing relationships or shared academic paths likely influence their choices.

Here is the analysis of the moderating factors, based on the 15 students in your L6 lecture:

Analyzing the two largest university groups shows even sharper differences, which is expected as they are likely organized groups of students who know each other.

Theoretical Background for Moderating Factors

The Influence of Country: Cultural and Institutional Homophily

The tendency for students from the same country (e.g., Spain or Morocco) to share similar lecture profiles can be explained by Homophily and Institutional Alignment.

The Influence of University/Peer Group: Social Network Effects

The even stronger, more granular influence of the specific university groups (like the unique L7 selection by U. Malaga students) is supported by Social Influence Theory and Bounded Rationality.

By applying these concepts, you can elevate your analysis from mere correlation to a discussion of student decision-making under conditions of uncertainty and social constraint.

The lecture title informational role

Assuming that students red only the lecture titles can you analyze the whole population and discover which lectures students select as a group (l1 & l2 … ect.) based on title informational role.

This is an excellent way to use the data to understand the thematic logic that guides students’ decisions, rather than just the frequency. By analyzing the corpus of words in the high-Lift lecture titles, we can discover the “driving wording” that creates strong, predictable co-selection patterns.

The primary driving themes are Economics, Management, and Business, but the strongest associations occur when these words are combined with qualifying terms like International, Applied, or Process.

Analysis of Driving Wording in Lecture Titles

Based on the highest Lift scores (strongest association patterns), the lecture groups cluster into four distinct thematic tracks driven by specific keywords:

1. Driving Theme: International & Political Economy 🌍

This is the strongest thematic driver in the entire dataset, creating three of the top four co-selection groups.

Driving Wording: International, Economy, Political.

2. Driving Theme: Applied Economic Analysis

This theme links foundational economic knowledge with quantitative skills and real-world application.

Driving Wording: Applied, Analysis, Economics, Decisions.

3. Driving Theme: Business Management & Strategy

This group is driven by a focus on business processes and the organizational changes brought by technology.

Driving Wording: Management, Process, Digital, Innovation.

Summary of Driving Wording

The strongest selection drivers are not single words but rather qualified thematic phrases that define an academic specialization:

Modern Business Specialization: Driven by Management combined with Process or Digital/Innovation (L5, L6, L7, L11).

Macro/Global Specialization: Driven by International and Political Economy (L9, L12, L8).

Quantitative Specialization: Driven by Applied Economics and Business Analytics (L2, L3, L1).

About wording analysis you can read here: Organizational aspiration for social impact

What recommendation to lecture L6 in wording. How can I modify or extend title of L6 to experiment for more students in next period.

The goal is to maximize enrollment by aligning the L6 title with the thematic drivers of the most predictive lectures (L5, L2, L4). Your current title is L6: Customer Experience Management.

Here are the recommendations for wording modifications, structured by the target student profile you want to attract:

Recommended Title Modifications for L6

Strategy 1: Attract L5 Students (Strongest Existing Link)

The L5 title is Business process management. These students seek internal efficiency as a foundation for external success.

Strategy 2: Attract L2/L4 Students (Applied & Analytical)

The L2 title is Applied Economics Real-World Challenges and Solutions. L4 is Business plan. These students are practical and analytical.

Strategy 3: Attract L7/L11 Students (Future/Digital Focus)

The L7/L11 titles feature Innovation and Digital Economy.

Final Recommendation: The Best Title to Test

The most balanced and powerful title that leverages multiple co-selection themes is: Customer Experience Management: Data, Process, and Digital Strategy

This phrase:

• Includes Management (L5, L6, L11 theme).
• Includes Process (L5 link).
• Includes Data (L2/L3 Analytics link).
• Includes Digital Strategy (L7/L11 Innovation link).

Thank you for reading.

The post Student Profiling for your lecture appeared first on Customer Experience Management.

Customer churn can be divided into two categories: intentional and involuntary. Intentional churn occurs when customers voluntarily discontinue using a service because of dissatisfaction or a change in circumstances. They may switch to another provider that offers alternate plans, better service quality, or both. To retain customers, businesses can address the issue by offering attractive plans and ensuring high-quality service. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.

On the other hand, involuntary churn happens when the business stops providing service due to
unpaid bills or breaches of terms and conditions.

There are two sub-categories within intentional churn: deliberate and incidental churn.

• Deliberate churn occurs when customers switch to another provider due to dissatisfaction with the product or service to obtain a better alternative. This dissatisfaction may stem from technical issues such as poor service quality, outdated services, bad customer service experience, poor coverage, or financial concerns such as expensive plans.
• On the other hand, incidental churn happens when customers stop using the service due to changes in their circumstances, such as relocating to another city where the service is unavailable, switching to another job that limits their use of a specific service provider, or the services becoming unaffordable for the customer.

Acquiring New Customers Is More Expensive Than Retaining Existing Ones. The primary goal of churn prediction is to anticipate deliberate churn since involuntary customers who breach (breake) terms and conditions or fail to pay bills are already known to the business. Incidental churn accounts for a small portion of churn, and it is difficult to predict since even the customers themselves may not foresee changes such as a change of place or job before a specific change in reality. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.

Additionally, knowing incidental churn in advance is of little value since retaining those customers becomes unavoidable due to reasons beyond the products and services offered by the business.

(Manzoor, A., Qureshi, M. A., Kidney, E., & Longo, L. (2024). A review on machine learning methods for customer churn prediction and recommendations for business practitioners. IEEE access, 12, 70434-70463.)

Acquiring New Customers Is More Expensive Than Retaining Existing Ones. Read about Customer Retention Survival Analysis

Cost of Marketing and Sales

• Acquisition: To attract a new customer, businesses must invest heavily in marketing and advertising. This includes a wide range of activities, from digital advertising campaigns (PPC, social media ads) and content marketing to traditional methods like TV and print ads. They also have to pay sales teams to find, nurture, and close leads. These costs can be substantial and add up quickly. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.
• Retention: Your customers are already familiar with your brand and main product offer, so you don’t need to spend so much money on broad-reach advertising. But still you spend this money for acquiring a new customers. Retention efforts typically involve more targeted and cost-effective strategies, such as email marketing, loyalty programs, and personalized communications. Loyalty program usually cost you the same, if not more, as acquiring new customers. What really matters – customers you already have in database they are more likely to buy from you then others, but you have to pay them for that. Acquiring New Customers Is More Expensive Than Retaining Existing Ones

Trust and Familiarity

• Acquisition: New customers have no prior relationship with your business. They need to be convinced to trust you and choose your product or service over competitors. The sales process for a new customer can be lengthy and resource-intensive as you build that trust. The probability of selling to a new prospect is much lower (often cited as 5-20%) compared to an existing customer. Acquiring New Customers Is More Expensive Than Retaining Existing Ones
• Retention: Existing customers have already purchased from you and, ideally, had a positive experience. You have teach them already your organisation and your products. But that was an investment in your customers – that cost you money. They have built trust in your brand. This makes them more receptive to your messages and offers. The probability of selling to an existing customer is much higher (often 60-70%).

Customer Lifetime Value (CLV)

• Acquisition: A new customer’s first purchase may not be enough to cover the initial acquisition cost. The business needs that customer to make repeat purchases to become profitable. If a customer “churns” (leaves) quickly, the business may have lost money on that acquisition. Acquiring New Customers Is More Expensive Than Retaining Existing Ones
• Retention: Loyal, retained customers tend to have a higher Customer Lifetime Value (CLV). They are more likely to make repeat purchases, spend more per transaction, and try new products or services. This means that the revenue generated from them over time far outweighs the minimal cost of maintaining the relationship. Acquiring New Customers Is More Expensive Than Retaining Existing Ones

Word-of-Mouth Marketing

• Acquisition: You have to actively seek out and find new customers. This is a proactive and expensive process. Acquiring New Customers Is More Expensive Than Retaining Existing Ones
• Retention: Happy and loyal customers can become brand advocates. They are more likely to recommend your business to friends, family, and colleagues. This organic, word-of-mouth marketing is not only highly credible but also essentially free, leading to new customer acquisition at a fraction of the cost.

The Importance of Balance

While the statement highlights the cost-effectiveness of retention, it’s crucial to understand that both acquisition and retention are essential for a healthy business. Acquisition is necessary for growth and market expansion, while retention ensures long-term profitability and stability. The key is to find the right balance, making sure that your customer acquisition efforts are bringing in valuable customers who will stay long enough to become profitable and contribute to the business’s long-term success. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.

Acquiring New Customers Is More Expensive Than Retaining Existing Ones. You’ve asked a great question that gets to the heart of the matter. While the concept seems simple, the calculation requires breaking down the costs that are often lumped together. Let’s walk through a clear, hypothetical example for a fictional company called “Daily Dose Coffee,” a subscription service for gourmet coffee beans. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.

Acquiring New Customers Is More Expensive Than Retaining Existing Ones – calculations

The Scenario

Company: Daily Dose Coffee, Time Period: One month

The marketing and sales teams want to know the cost difference between acquiring a new customer and retaining an existing one.

Part 1: Calculating Customer Acquisition Cost (CAC)

Customer Acquisition Cost (CAC) is the total amount of money a company spends to acquire a single new customer. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.

The Formula:

CAC=Number of New Customers Acquired / Total Acquisition Costs​

Step 1: Identify all acquisition costs for the month.

• Advertising:
  • Google Ads: $5,000
  • Social Media Ads (Facebook, Instagram): $3,000
• Marketing & Sales Salaries:
  • Salary for the marketing manager: $4,000 (They spend 100% of their time on acquisition.)
  • Portion of sales team salaries dedicated to finding new leads: $2,500
• Tools & Software:
  • CRM software (for new leads): $500
  • Email marketing tool (for new subscribers): $200
• Promotional Offers:
  • Cost of “First month free” promotion: $1,000

Step 2: Sum the total acquisition costs.

Total Acquisition Costs = $5,000 + $3,000 + $4,000 + $2,500 + $500 + $200 + $1,000 = $16,200

Step 3: Determine the number of new customers acquired in that month.

In this month, Daily Dose Coffee successfully converted 200 new subscribers.

Step 4: Apply the CAC formula.

CAC=200 new customers$16,200​=$81.00

Conclusion: It cost Daily Dose Coffee an average of $81.00 to acquire each new customer this month.

Part 2: Calculating Customer Retention Cost (CRC)

Customer Retention Cost (CRC) is the total amount of money a company spends to retain its existing customers.² This is often more complex as costs can be harder to isolate.

The Formula:

CRC=Number of Retained Customers / Total Retention Costs​

Step 1: Identify all retention costs for the month.

• Customer Support:
  • Salary for the customer support team: $3,000 (They handle all existing customer inquiries.)
  • Cost of live chat software: $150
• Loyalty & Engagement Programs:
  • Cost of the loyalty program (points, rewards): $800
  • Email marketing tool (for existing customers): $200
• Promotional Offers:
  • Special “Thank You” discount for loyal customers: $500
• Operational Costs to Maintain Service:
  • Cost of subscription management software: $300

Step 2: Sum the total retention costs.

Total Retention Costs = $3,000 + $150 + $800 + $200 + $500 + $300 = $4,950

Step 3: Determine the number of customers retained in that month.

Daily Dose Coffee started the month with 1,500 customers and ended with 1,450. Since they acquired 200 new customers, this means they lost 250 from their original base (1500 – 1450 = 50 lost, plus 200 new = 250 lost).

In this context, the number of retained customers is the number of customers who did not churn.3

Number of Retained Customers: 1,500 (beginning) – 50 (lost) = 1,450.

Note: Some businesses calculate CRC based on their entire active customer base for the period (1,450 retained + 200 new = 1,650), but for a direct comparison with acquisition cost, focusing on the customers you successfully kept is often a clearer metric. Let’s use the number of retained customers (1,450).

Step 4: Apply the CRC formula.

CRC=1,450 retained customers$4,950​=$3.41

Conclusion: It cost Daily Dose Coffee an average of $3.41 to retain each existing customer this month.

Final Comparison

• Cost to acquire a new customer (CAC): $81.00
• Cost to retain an existing customer (CRC): $3.41

As you can see from this example, the cost of acquisition is dramatically higher than the cost of retention.⁴ This isn’t a fluke; it’s a common trend across most industries. Acquiring New Customers Is More Expensive Than Retaining Existing Ones.

This simple calculation shows why focusing on customer loyalty and satisfaction is so critical for long-term profitability. By keeping a customer, you’re not just securing their future purchases; you’re also avoiding the much higher cost of having to replace them with a new customer.

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