AUTHOR: Francesca Valeri

The history of the field is short but dense. The first secure retail transaction over the web, the 1994 sale of a Sting compact disc through NetMarket, was protected by data-encryption software that transmitted the buyer’s card number safely, a full year before the Secure Sockets Layer protocol became widely available in Netscape’s browser. That single encrypted transfer established the principle that still governs the industry: commerce online is only possible where the customer believes the payment is safe. 

[Source] M. Pawlowski, Data Analytics for E-Commerce (lecture materials, UMCS, 2026); CNET, “E-commerce turns 10.” 

Three decades later, electronic payments in the European Union reached roughly 240 trillion euro in annual value, up from 184 trillion euro in 2017, a scale that has drawn both fierce competition among providers and a comprehensive regulatory response. This report examines the methods consumers use to pay, the European rules that govern those methods, the technologies that secure them, the fraud that targets them, the particular character of the Polish market, and the role that data analytics now plays in defending the payment system. 

[Source] European Commission, Payment Services Regulation proposal, COM(2023)367, citing ECB payment value statistics. 

What are the most common payment methods in e-commerce?

The European checkout is no longer dominated by a single instrument. Payment preference now varies sharply by country, by basket size, and by device, and a merchant that offers the wrong mix loses customers at the final step. The principal methods can be grouped into six families. 

Cards 

Credit and debit cards from Visa and Mastercard remain the most widely accepted instrument and still carry the largest single share of European transaction value. Their strength is universality and consumer protection through chargeback rights; their weakness is exposure to card-not-present fraud, which the security measures described in Section 4 are designed to contain. 

Digital wallets 

Wallets such as PayPal, Apple Pay, and Google Pay now rival cards in many markets. PayPal, founded in 1998 as Confinity, pioneered consumer digital payments and processed close to 40 percent of all eBay transactions at its peak; it has since added cryptocurrency checkout (2021) and its own dollar stablecoin, PayPal USD (2023). Wallets win on speed and on the fact that card data is stored once and tokenized, removing it from the merchant’s reach. 

[Source] Britannica, “PayPal”; M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026). 

What is the role of payment gateways in e-commerce?

Account-to-account and open banking 

Real-time bank transfers, including Poland’s BLIK and the broader category of open-banking payments, move money directly between accounts without a card network in the middle. They are cheap for merchants and increasingly instant for consumers, and the EU’s Instant Payments Regulation (Section 3) is accelerating their growth across the continent. 

What are Buy Now Pay Later (BNPL) services?

Deferred-payment products from providers such as Klarna and, in Poland, PayPo, let customers split a purchase into instalments. They lift conversion and average order value but have attracted regulatory attention over consumer indebtedness and disclosure. 

Cryptocurrency 

Crypto payments remain a small share of European e-commerce. Stripe’s early support for Bitcoin in 2015 illustrated the operational challenge: unlike a card, a Bitcoin payment is initiated by the customer and can take roughly an hour to finalise, which forced a redesign of the settlement flow around webhooks and pending states. 

[Source] Stripe, “Online payment API design”; M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026). 

Cash on delivery and other methods 

Cash on delivery, vouchers, and prepaid instruments persist in certain markets and demographics, particularly where trust in online payment is still developing. 

What is tokenization in payment security?

The EU regulatory landscape 

European payments are among the most heavily regulated activities in the digital economy, and the framework is in the middle of a generational change. Four instruments matter most for e-commerce. 

PSD2 and Strong Customer Authentication 

The Second Payment Services Directive, in force since 2018, introduced Strong Customer Authentication, the requirement that electronic payments be confirmed using at least two independent factors drawn from knowledge (a password), possession (a phone), and inherence (a biometric). PSD2 also opened bank infrastructure to licensed third parties, creating the open-banking market. 

PSD3 and the Payment Services Regulation 

On 27 November 2025 the European Parliament and the Council reached provisional political agreement on the Third Payment Services Directive (PSD3) and the accompanying Payment Services Regulation (PSR), with final publication expected in the first half of 2026. The package tightens authentication, harmonises rules across member states, and introduces a significant shift in liability: a payment service provider that fails to implement appropriate fraud-prevention mechanisms can be held liable for covering a customer’s losses, and large online platforms may share liability where they fail to act on known fraud. 

[Source] Norton Rose Fulbright, “PSD3 and PSR: from provisional agreement to 2026 readiness” (2026); European Parliament, Legislative Train Schedule (March 2026). 

The Instant Payments Regulation and Verification of Payee 

The Instant Payments Regulation (IPR) entered into force on 8 April 2024 and requires that euro credit transfers settle within ten seconds, around the clock. Its most visible consumer protection is Verification of Payee (VoP), a mandatory check that compares the payee’s name against the IBAN before a transfer is authorised and returns one of four results: match, close match, no match, or other. VoP became mandatory for euro-area providers on 9 October 2025, with providers outside the euro area required to comply by 9 July 2027. Industry estimates suggest the measure could prevent on the order of 2.4 billion euro in fraud each year. 

[Source] European Central Bank, Instant Payments Regulation overview; European Payments Council, Verification of Payee Scheme Rulebook (2025); Finextra, “Europe’s Instant Payments Regulation and the 9 October deadline” (2025). 

The EUDI Wallet mandate 

Under the revised eIDAS framework, every member state must make at least one certified European Digital Identity (EUDI) Wallet available to citizens by November 2026. The wallet will allow the secure storage of identity and payment credentials, and in Poland it is expected to build on the existing mObywatel application. For e-commerce, the EUDI Wallet promises a future in which a customer can prove identity and authorise payment from a single, state-backed credential. 

[Source] M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026), “Payment security in Poland.” 

Payment security: the layered defence 

No single control secures an online payment. Modern systems rely on several overlapping layers, each addressing a different point of weakness, so that the failure of one does not expose the customer. 

Transport encryption: SSL and TLS 

The foundation is encryption of the connection itself. Transport Layer Security (TLS), the successor to the Secure Sockets Layer that made web commerce possible in 1995, encrypts data as it travels between the customer’s browser and the merchant’s server, so that an eavesdropper cannot read card details in transit. 

Tokenization 

Tokenization replaces sensitive card data with a unique reference, a token, that is useless if intercepted. Stripe built its platform on this idea: card details are sent directly to the processor and the merchant receives only a token that points to data held in a secure vault, which means the merchant never stores the real card number and avoids the most demanding parts of PCI DSS compliance. Network tokenization in services such as Visa Mobile and Click to Pay extends the same protection across the card networks. 

[Source] Stripe, “The token and the charge”; M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026). 

The CVV and two-factor authentication 

The card verification value (CVV), the short code printed on the card, proves that a buyer physically holds the card and is never stored after authorisation. Two-factor authentication, the practical form of PSD2 Strong Customer Authentication, adds a second proof of identity such as a one-time SMS code or an in-app biometric confirmation, so that a stolen password alone cannot complete a purchase. 

3D Secure 2.0 

3D Secure 2.0 is the protocol that ties these elements together at the card checkout. Rather than challenging every customer, it shares up to 150 data elements about the device and transaction with the card issuer, which scores the risk and decides whether to approve the payment silently (the frictionless flow, used for the large majority of transactions) or to request a step-up challenge. When authentication completes, liability for fraud shifts from the merchant to the issuer. Figure 2 sets out the full flow. 

Fraud typology 

The controls of Section 4 exist because online payment has been a target for fraud since its earliest days. As early as 2004 a Gartner survey found that at least 30 million Americans had been targeted by phishing, with phishing alone costing card companies and banks more than 1.2 billion dollars in a single year. The threats have grown more organised since, but they still fall into recognisable categories. 

[Source] Gartner phishing survey, reported in M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026). 

The main categories 

• Card-not-present (CNP) fraud. The dominant category in e-commerce: stolen card details are used to make purchases where the card need not be physically presented. 3D Secure and tokenization are the primary defences. 

• Phishing and social engineering. Fraudulent messages impersonate a trusted brand to trick a customer into revealing credentials or authorising a payment. The weakness exploited is human, not technical. 

• Account takeover. An attacker gains control of a legitimate customer account, often using credentials leaked from another breach, and then transacts as the genuine user. 

• Identity theft and new-account fraud. Stolen personal data is used to open accounts or obtain credit in a victim’s name, a risk that the EUDI Wallet and stronger identity verification aim to reduce. 

• Friendly fraud and chargeback abuse. A genuine customer disputes a legitimate charge to obtain a refund while keeping the goods, shifting cost onto the merchant. 

Figure 3. Approximate distribution of reported e-commerce fraud cases by type. Card-not-present fraud and social-engineering attacks together account for the majority of incidents. 

[Source] Visa and Mastercard fraud reports (2024 to 2025); European Central Bank, Report on card fraud. 

The Polish context 

Poland is one of Europe’s most distinctive payment markets, shaped by a domestic mobile scheme that most consumers prefer over cards and by a strong tradition of state-backed digital identity. 

BLIK and the leading providers 

BLIK is the national mobile payment system and the market leader. It works through single-use, six-digit codes that are valid for two minutes and require the customer to approve each transaction inside the banking app, which combines convenience with built-in two-factor security. Around BLIK sits a mature ecosystem of gateways: Przelewy24 (part of the Nexi Group), PayU with its one-click and instalment options, Tpay, and Polish ePayments in the in-store terminal segment. 

[Source] M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026), “Payment security in Poland.” 

Security standards in practice 

Table 1. Selected payment-security measures applied in the Polish market. 

[Source] M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026); NBP, payment-system reports. 

mObywatel, the EUDI Wallet, and Verification of Payee 

Poland’s mObywatel application already gives citizens a trusted digital identity, and it is the natural foundation for the certified EUDI Wallet that the country must provide by November 2026. In parallel, Polish banks and payment institutions implement Verification of Payee under the Instant Payments Regulation, checking the IBAN against the recipient’s details before a transfer is executed. Together these measures position Poland to combine a popular domestic payment method with continent-wide identity and anti-fraud infrastructure. 

[Source] M. Pawlowski, Data Analytics for E-Commerce (UMCS, 2026); European Payments Council, VoP Scheme (2025). 

Data analytics applied to payment security 

Because fraud adapts faster than any fixed rule, the defence of the payment system has become a data problem. The same analytical methods used elsewhere in e-commerce, applied here to transaction streams, allow providers to separate legitimate behaviour from attack in real time. 

Anomaly detection 

Each customer and merchant generates a behavioural baseline: typical purchase amounts, times, locations, and devices. Anomaly-detection models flag transactions that deviate sharply from that baseline, for example a high-value purchase from a new device in an unfamiliar country, so that they can be challenged or held for review. 

Transaction scoring 

Rather than a simple approve-or-decline decision, modern systems assign each transaction a risk score derived from many signals at once. This score feeds directly into the 3D Secure 2.0 decision: low-risk transactions pass through the frictionless flow, while high-risk ones trigger a step-up challenge, which keeps friction low for the honest majority while concentrating scrutiny where it is needed. 

Machine-learning fraud models 

Supervised machine-learning models learn from labelled histories of genuine and fraudulent transactions to recognise the subtle patterns that characterise emerging fraud. Their value lies in adaptation: as fraudsters change tactics, the models are retrained on fresh data and continue to detect what static rules would miss. The forthcoming PSR strengthens this approach by allowing payment providers to share fraud-related information among themselves, enlarging the data on which detection models can be trained. 

[Source] European Banking Authority, guidance on fraud detection; Norton Rose Fulbright, PSD3 and PSR briefing (2026). 

Conclusion and future outlook 

Payment in e-commerce has matured from a single encrypted card transfer in 1994 into a layered system in which method, regulation, technology, and analytics reinforce one another. The customer who chooses BLIK, a wallet, or a card at checkout is relying, often unknowingly, on transport encryption, tokenization, strong authentication, risk-based 3D Secure, and a machine-learning model scoring the transaction in milliseconds. 

The near future is unusually well defined. The Instant Payments Regulation has already made Verification of Payee mandatory across the euro area, PSD3 and the Payment Services Regulation will reshape liability and authentication once published in 2026, and the EUDI Wallet mandate arrives in November 2026 to give every European a state-backed digital identity. The clear direction of travel is toward payments that are faster and frictionless for honest customers, yet harder for fraudsters, with the balance held by data analytics. 

For merchants the strategic lesson is consistent: security and conversion are not opposites. The same risk scoring that blocks a fraudulent transaction also waves a genuine customer through without a challenge. Treating payment security as a data-driven discipline, rather than a compliance burden, is what allows an e-commerce business to be both safe and easy to buy from. 

REFERENCES 

[1]  [Source] Pawlowski, M. Data Analytics for E-Commerce, lecture materials, UMCS Faculty of Economics, 2026. 

[2]  [Source] European Central Bank. Instant Payments Regulation and Verification of Payee, ecb.europa.eu, 2024 to 2025. 

[3]  [Source] European Payments Council. Verification of Payee Scheme Rulebook, 2025. 

[4]  [Source] European Commission. Proposal for a Payment Services Regulation, COM(2023)367, 2023. 

[5]  [Source] Norton Rose Fulbright. PSD3 and PSR: From Provisional Agreement to 2026 Readiness, 2026. 

[6]  [Source] Finextra. Europe’s Instant Payments Regulation and the 9 October Deadline, Explained, 2025. 

[7]  [Source] Statista. European E-Commerce Payment Methods, 2025. 

[8]  [Source] ECDB. Payment Methods in European E-Commerce, 2025. 

[9]  [Source] Stripe. Online Payment API Design, stripe.com/blog, 2017. 

[10]  [Source] Britannica. PayPal, britannica.com/money, 2026. 

[11]  [Source] Visa and Mastercard. Fraud and Authentication Reports, 2024 to 2025. 

[12]  [Source] Narodowy Bank Polski. Payment System Reports, nbp.pl, 2025. 

The post Payment Methods and Security in E-Commerce  appeared first on Customer Experience Management.

Oto konkretne dane i wnioski, z czym dokładnie przyjdzie Ci się zmierzyć.

Konkurencja i Ceny: Z czym wchodzisz na ring?

Rynek w Lublinie jest wysoce konkurencyjny. Działa tu kilkadziesiąt bardzo mocnych i dobrze pozycjonujących się pracowni (m.in. Ubrane Wnętrza, Miliart Studio, Ka Studio czy Interior Art Studio), a klient ma w czym wybierać.

• Średnia cena na rynku: Obecnie wynosi około 210 zł/m² za kompleksowy projekt wnętrza.
• Rozstrzał cenowy: Zaczyna się od około 70 zł/m² u początkujących freelancerów, a sięga powyżej 250 zł/m² w renomowanych biurach.
• Przewaga “eksportowa”: Lubelskie biura są średnio o ok. 90 zł/m² tańsze niż te w Warszawie czy Krakowie. Z tego powodu znaczna część lokalnych firm rozwija usługi projektowania online, obsługując klientów z bogatszych regionów, zachowując przy tym marżę, która w Lublinie jest wysoce opłacalna.

Siła napędowa: Lubelski rynek nieruchomości (2026)

Popyt na usługi projektowe bezpośrednio wynika z tego, co dzieje się w nieruchomościach. A dzieje się dużo — Lublin plasuje się w krajowej czołówce pod względem przyrostu zasobów mieszkaniowych.

1. Dojrzewający rynek pierwotny: Średnia cena nowego mieszkania to obecnie ponad 11 800 zł/m². Podaż lekko się kurczy w stosunku do lat ubiegłych, co oznacza, że kupujący są bardziej zamożni i wymagający. Płacąc tak dużo za sam lokal, nie oszczędzają na wykończeniu i częściej sięgają po architekta, oczekując maksymalnej funkcjonalności i standardu premium (szczególnie w modnych lokalizacjach, takich jak dynamicznie rozbudowujący się Czechów Południowy).
2. Królowa “Wielka Płyta”: Aż 49% transakcji na rynku wtórnym w Lublinie to wciąż mieszkania w starych blokach. Wymagają one trudnych generalnych remontów i całkowitej zmiany układu funkcjonalnego — to idealny obszar dla sprawnego projektanta.
3. Rynek pod wynajem: Średnia cena najmu to ok. 49 zł/m². Jako duże miasto akademickie i rosnące centrum usług biznesowych, Lublin ma ogromny rynek mieszkań inwestycyjnych, na którym liczy się każdy centymetr kwadratowy.

Potencjał dla nowych firm: Gdzie szukać niszy?

Wejście na rynek z komunikatem “projektuję wszystko dla każdego” szybko zderzy Cię ze ścianą silniejszej konkurencji. Potencjał dla nowej firmy leży w wąskiej specjalizacji na start.

Twarda rzeczywistość: Zanim zaczniesz liczyć zyski, pamiętaj, że stałe koszty działalności (ZUS, licencje na pakiety takie jak SketchUp czy V-Ray, księgowość, ubezpieczenie i dojazdy) pochłoną na start około 20-30% Twoich miesięcznych przychodów.

Werdykt: Czy jest sens zakładać nową firmę?

Tak, pod warunkiem, że podejdziesz do tego biznesowo, a nie tylko artystycznie.

Sens istnieje, jeśli nie zamierzasz konkurować z rynkiem wyłącznie najniższą ceną. Zaniżanie stawek poniżej 100 zł/m², aby “tylko złapać klienta”, to w Lublinie najszybsza droga do wypalenia zawodowego. Praca przy skomplikowanej dokumentacji technicznej, nadzorze i niekończących się poprawkach sprawi, że będziesz pracować poniżej płacy minimalnej.

Jeśli jednak dysponujesz dobrym warsztatem oprogramowania, przygotujesz świetne portfolio (nawet opierające się na projektach koncepcyjnych, a nie realizacjach) i celnie wejdziesz w jedną z nisz rynkowych — rynek deweloperski w Lublinie wciąż pomieści ambitnych profesjonalistów.

Jak przeanalizować rynek projektantów wnętrz w Lublinie?

Projektowanie wnętrz: Firmy rejestrowane vs. freelancerzy

Analiza rynku projektowania wnętrz wymaga połączenia danych twardych (oficjalne rejestry państwowe) z danymi miękkimi (aktywność w sieci, social media, portale zleceniowe). Pozwala to na mapowanie konkurencji na dwa segmenty: oficjalne, widoczne biura projektowe oraz “niewidzialną” konkurencję w postaci freelancerów i studentów dorabiających do studiów.

Jak przeanalizować liczbę oficjalnych firm?

Oficjalnie działające firmy to podmioty posiadające wpis do CEIDG (jednoosobowe działalności gospodarcze) lub KRS (spółki z o.o., spółki komandytowe itp.).

Analiza baz CEIDG i KRS za pomocą kodów PKD

Głównym narzędziem do wyszukiwania zarejestrowanych firm są państwowe wyszukiwarki biznesowe. Kluczem do sukcesu jest filtrowanie po odpowiednich kodach PKD (Polska Klasyfikacja Działalności).

• Kluczowe kody PKD dla tej branży:
  • 74.10.Z – Działalność w zakresie specjalistycznego projektowania (to pod tym kodem kryje się większość projektantów wnętrz, choć obejmuje on również projektantów mody czy grafików).
  • 71.11.Z – Działalność w zakresie architektury (często wybierany przez architektów z wykształceniem kierunkowym, którzy projektują zarówno bryły budynków, jak i wnętrza).
• Jak to zrobić technicznie?
  1. Wejdź na oficjalną wyszukiwarkę CEIDG (aplikacja.ceidg.gov.pl) lub skorzystaj z agregatorów danych typu Aleo.com, Rejestr.io czy MGBiM.
  2. Ustaw filtry wyszukiwania:
     • Miejscowość / Powiat: Lublin / powiat lubelski.
     • Status: Aktywny (odrzuć firmy zawieszone i wykreślone).
     • Kod PKD: 74.10.Z (jako przeważająca działalność) oraz 71.11.Z.
     • Eksportuj dane do pliku Excel.
• Ważne ograniczenie metody: Nie każda firma z PKD 74.10.Z projektuje wnętrza (może to być projektant stron WWW lub ubrań). Uzyskany wynik należy traktować jako “górną granicę” rynku rejestrowanego, którą trzeba przefiltrować ręcznie.

Weryfikacja aktywności rynkowej (Google Maps i Katalogi)

Samo istnienie firmy w CEIDG nie oznacza, że jest ona Twoim realnym konkurentem (część to martwe działalności). Musisz sprawdzić, kto realnie walczy o klienta. Projektowanie wnętrz.

• Narzędzia:
  • Google Maps: Wpisz frazy takie jak: “projektowanie wnętrz Lublin”, “architekt wnętrz Lublin”, “biuro projektowe Lublin”. Zlicz unikalne pinezki w granicach miasta. To Twoi najgroźniejsi konkurenci, ponieważ dbają o lokalne pozycjonowanie (SEO).
  • Portale branżowe: Przeanalizuj profile na portalach Houzz.pl oraz Homebook.pl z filtrem lokalizacji “Lublin”. Są to portale skupiające wyłącznie aktywnych i zweryfikowanych profesjonalistów z portfolio.

Jak przeanalizować liczbę freelancerów?

Freelancerzy to najtrudniejsza do policzenia grupa. Część z nich prowadzi JDG (jednoosobową działalność), ale nie posiada biura ani strony www, a część działa w szarej strefie (np. studenci architektury dorabiający bez rejestracji działalności) lub rozlicza się przez portale pośredniczące (np. Useme).

Aby ich oszacować, musisz przeszukać miejsca, w których szukają zleceń i pokazują swoje prace.

Portale zleceniowe i ogłoszeniowe

To tam klienci szukają najtańszych wykonawców, a początkujący projektanci oferują swoje usługi.

• Fixly.pl i Oferteo.pl: Załóż konto jako klient szukający projektu mieszkania w Lublinie lub po prostu przejrzyj katalog wykonawców w kategorii “Projektowanie wnętrz” dla regionu lubelskiego. Zobaczysz tam setki profili osób fizycznych (często bez firm), które licytują zlecenia.
• OLX.pl: Wpisz “projektowanie wnętrz” i wybierz lokalizację Lublin (+ ewentualnie +15 km). Policz ogłoszenia od osób prywatnych. Zwróć uwagę na ceny – to pokaże Ci stawkę minimalną na rynku.
• Useme.com: Przeszukaj bazę freelancerów w kategorii “Projektowanie 3D / Architektura i Wnętrza” wpisując lokalizację “Lublin”.

Social Media i Platformy Portfolio

Współczesny freelancer rzadko inwestuje we własną stronę internetową. Jego portfolio to profil na Instagramie lub Behance.

• Instagram (Analiza hashtagów): Wyszukaj lokalne hashtagi i przeanalizuj profile, które pod nimi publikują:
  • #projektantwnetrzlublin
  • #architektwnetrzlublin
  • #projektowanie-wnetrz-lublin Wypisz unikalne konta osobiste (niebędące dużymi biurami). Zwróć uwagę na dopiski w bio typu “freelancer”, “student architektury”, “przyjmuję zlecenia”.
• Behance.net i Dribbble.com: Użyj filtrów wyszukiwania twórców: Creative Field: Interior Design oraz Location: Lublin, Poland. To tutaj gromadzi się społeczność młodych projektantów o bardzo wysokich umiejętnościach wizualizacyjnych, którzy często pracują jako podwykonawcy dla większych biur lub realizują własne, pojedyncze tematy.

Analiza środowiska akademickiego (Ukryty potencjał)

Lublin to silny ośrodek akademicki. Co roku mury uczelni opuszczają dziesiątki potencjalnych freelancerów i przyszłych konkurentów.

• Sprawdź liczbę studentów i absolwentów kierunków takich jak Architektura (Politechnika Lubelska), Architektura Wnętrz (WSPA, KUL, wydziały artystyczne UMCS).
• Młodzi absolwenci w pierwszych 2-3 latach po studiach zazwyczaj działają jako freelancerzy, oferując bardzo niskie stawki, aby zbudować portfolio.

Podsumowanie

Aby uzyskać pełny obraz, stwórz tabelę z podziałem konkurencji na 4 segmenty:

Przeprowadzenie takiego badania zajmie Ci około 2-3 dni roboczych, ale da Ci pełną, opartą na danych odpowiedź na pytanie, z kim dokładnie będziesz rywalizować o klientów w Lublinie.

Kiedy będziesz już miał klientów przyda ci się analiza: Customer portfolio profitability calculation based on ABC model

The post Projektowanie wnętrz – rynek w Lublinie appeared first on Customer Experience Management.

Edukacja to też E-commerce: 3 tematy na licencjat z marketingu uczelni wyższych (CX, Analityka i Strategia)

Zastanawiasz się nad tematem pracy licencjackiej? Szukasz zagadnienia, które połączy twardą analitykę biznesową, zarządzanie doświadczeniami klienta (CX) i realia rynkowe? Jako wykładowca akademicki i praktyk biznesu, często widzę, jak studenci uciekają w stronę ogólnikowych i wtórnych tematów. Tymczasem tuż pod Waszymi nosami rozgrywa się fascynująca, oparta na danych walka o klienta. Mowa o rynku szkolnictwa wyższego.

Zarządzanie doświadczeniem kandydata, a później studenta, wymaga dogłębnego zrozumienia Customer Experience Management (CXM). Jak pisałem w mojej publikacji “Customer Experience Management in Era of AI”, wygrywają te organizacje, które potrafią personalizować komunikację w czasie rzeczywistym w oparciu o dane. Dotyczy to w równym stopniu sklepów internetowych, jak i uniwersytetów.

Poniżej przygotowałem dla Was 3 propozycje tematów prac licencjackich z digital marketingu. Najlepsze z tych prac chętnie opublikuję tutaj, na łamach mietwood.com.

Dlaczego warto badać marketing instytucji edukacyjnych?

Zanim przejdziemy do konkretów, musimy spojrzeć na uczelnię przez pryzmat wskaźników biznesowych. Wdrożenie takiego myślenia do pracy dyplomowej to gwarancja najwyższej oceny.

• Student to Klient (o wysokim LTV): Wskaźnik Customer Lifetime Value (LTV) w edukacji jest ogromny. Obejmuje nie tylko czesne za studia I i II stopnia, ale także studia podyplomowe i MBA.
• Decyzja zakupowa jest złożona: Cykl decyzyjny (Customer Journey) trwa miesiącami, angażuje emocje, logikę i często osoby trzecie (rodziców). To idealne środowisko do badania wielokanałowych punktów styku (omnichannel touchpoints).
• Łatwy dostęp do danych: Strony uczelni są ogólnodostępne, a narzędzia takie jak SimilarWeb czy Senuto pozwalają na głęboki audyt ich działań digitalowych bez posiadania wewnętrznych dostępów.

Architektura Customer Experience (CX) w procesie rekrutacji online. Analiza porównawcza wybranych warszawskich uczelni wyższych (UW, SGH, Akademia Leona Koźmińskiego)

Opis problemu badawczego

W dobie cyfryzacji, system rekrutacyjny (IRK/ISR) to najważniejszy punkt styku (touchpoint) na ścieżce kandydata. To wirtualny “koszyk zakupowy”. Z perspektywy zarządzania operacyjnego w e-commerce, proces ten powinien być maksymalnie bezbłędny, intuicyjny i wspierający. Praca ta skupia się na audycie użyteczności (UX) oraz całkowitym doświadczeniu kandydata (CX) podczas aplikacji na studia w Warszawie, porównując uczelnię publiczną (Uniwersytet Warszawski), uczelnię publiczną o profilu biznesowym (SGH) oraz lidera prywatnej edukacji biznesowej (Akademia Leona Koźmińskiego).

Jak przeprowadzić analizę (Data-driven approach)?

Nie opieraj się na subiektywnych odczuciach. Zbuduj matrycę punktów styku.

1. Mapowanie Customer Journey: Stwórz szczegółową mapę podróży kandydata od momentu wpisania w Google frazy “studia zarządzanie Warszawa”, aż po kliknięcie “Złóż aplikację”.
2. Ocena lejków konwersji: Policz, ile kroków (kliknięć) dzieli kandydata od znalezienia programu studiów do założenia konta rekrutacyjnego. Gdzie występuje największe ryzyko porzucenia procesu (tzw. drop-off rate)?
3. Audyt użyteczności (Heurystyki Nielsena): Przeanalizuj interfejsy systemów rekrutacyjnych pod kątem responsywności mobilnej, jasności komunikatów i łatwości nawigacji.
4. Badania symulacyjne (NPS / CSAT): Przeprowadź badanie symulacyjne wskaźnika Customer Effort Score (CES) – jak wiele wysiłku kosztowało by kandydatów przejście przez proces na tych uczelniach?

Przykład analityczny do pracy

Hipoteza: Uczelnie niepubliczne (ALK) posiadają krótszy, bardziej zoptymalizowany pod kątem mobile lejek konwersji (M-commerce approach) w porównaniu do rozbudowanych systemów uczelni publicznych (UW), co przekłada się na wyższy wskaźnik satysfakcji z procesu rekrutacji (CSAT) i niższy współczynnik odrzuceń na stronie lądowania.

Strategia Digital Marketing Mix jako narzędzie budowania przewagi konkurencyjnej na regionalnym rynku edukacyjnym. Studium przypadków uczelni lubelskich (KUL, PL, WSPA)

Opis problemu badawczego

Marketing Mix (tradycyjne 4P, czy zmodernizowane 7P) ewoluował. Dziś mówimy o cyfrowym miksie marketingowym, w którym kluczową rolę odgrywa content marketing, SEO oraz social media. Temat ten pozwala na zbadanie, jak uczelnie o różnym profilu pozycjonują się na hermetycznym, lokalnym rynku. Jak Katolicki Uniwersytet Lubelski (KUL) walczy o uwagę z uczelnią techniczną (Politechnika Lubelska) oraz nowoczesną szkołą niepubliczną (WSPA)?

Jak przeprowadzić analizę (Business Analytics)?

W tej pracy powinieneś wykorzystać ogólnodostępne narzędzia analityczne, by zamienić “miękki” marketing w twarde dane.

1. Analiza widoczności (SEO): Wykorzystaj narzędzia takie jak Senuto lub Ahrefs. Sprawdź, na ile słów kluczowych widoczne są w TOP10 poszczególne uczelnie. Jaki generują szacunkowy ruch organiczny (Organic Traffic)? To wskaźnik ich pasywnej siły pozyskiwania leadów.
2. Audyt Content Marketingu: Jak uczelnie prowadzą sekcje blogowe i informacyjne? Czy stosują strategię Inbound Marketingu (np. artykuły typu “Jakie studia po profilu mat-fiz?”), aby przechwytywać użytkowników na wczesnym etapie świadomości (Top of the Funnel – ToFu)?
3. Social Media Engagement Rate: Zamiast liczyć “lajki”, oblicz wskaźnik zaangażowania (ER – Engagement Rate). Kto tworzy lepszą społeczność? Czy WSPA zyskuje przewagę na TikToku pokazując kulisy życia studenckiego, podczas gdy KUL dominuje w formalnej komunikacji na Facebooku?

Przykład analityczny do pracy

Możesz przeprowadzić badanie sentymentu (Sentiment Analysis) komentarzy pod postami rekrutacyjnymi wybranych uczelni. Na tej podstawie udowodnisz, jak konkretny styl komunikacji wizualnej i tekstowej (Tone of Voice) wpływa na percepcję marki. Zwróć uwagę na spójność wielokanałową (Omnichannel) – czy doświadczenie kandydata przeglądającego profil Politechniki Lubelskiej na Instagramie jest spójne z tym, co widzi na oficjalnej stronie www? Wniosek może stanowić rekomendację optymalizacyjną dla działu promocji.

Podsumowanie

Pisanie pracy licencjackiej z marketingu nie musi sprowadzać się do nudnego przypisywania definicji Kotlera. Przemysł edukacyjny w Polsce przechodzi obecnie potężną transformację cyfrową. Instytucje te stały się platformami usługowymi, które muszą zarządzać potężnymi zasobami danych, optymalizować swoje lejki sprzedażowe i nieustannie poprawiać Customer Experience.

Jeśli zdecydujecie się na jeden z powyższych tematów, obiecuję Wam, że proces badawczy będzie nie tylko fascynujący, ale przede wszystkim dostarczy Wam “twardych” umiejętności pożądanych na współczesnym rynku pracy. Analityk, który potrafi przełożyć ruch na stronie www, wskaźniki użyteczności oraz dane z social mediów na realne rekomendacje biznesowe optymalizujące proces konwersji, nigdy nie będzie musiał martwić się o zatrudnienie.

Masz już pomysł na badanie? Zbierasz dane, ale utknąłeś przy ich interpretacji? Zapraszam do zgłębiania artykułów w sekcji Analityka Biznesowa na mietwood.com oraz do przesyłania draftów swoich najlepszych tekstów naukowych – najciekawsze analizy chętnie udostępnię w ramach gościnnych wpisów na blogu.

Powodzenia w badaniach!

Słowa kluczowe (Keywords): marketing edukacyjny, customer experience management (CXM), analityka biznesowa w edukacji, e-commerce, uczelnie wyższe, praca licencjacka marketing, user journey, marketing mix, konwersja (CR), optymalizacja lejka, LTV, churn rate, SGH, UW, KUL, Akademia Leona Koźmińskiego.

The post Customer Experience w procesie rekrutacji na studia appeared first on Customer Experience Management.

AI Influencers Case Studies

Lil Miquela — The Pioneer
Created by Los Angeles startup Brud in 2016, Lil Miquela (Miquela Sousa) is widely credited with proving that virtual influencers could achieve mainstream commercial success. Before releasing a single sponsored post, Brud invested over a year building her persona — a 19-year-old Brazilian-American musician with strong views on social justice, digital identity and pop culture. This character depth proved critical: by the time brands approached Brud, Miquela had an audience that genuinely cared about her narrative. Her 2019 Calvin Klein campaign — shot alongside Bella Hadid — marked the inflection point for the entire industry, demonstrating that consumers would engage with a virtual personality in a luxury fashion context at the level of an A-list celebrity.

Aitana López — The Agency Model
Aitana López (link) represents the next evolutionary stage: a commercially motivated, generative-AI-built persona created explicitly to solve a business problem. The Clueless agency (Barcelona) built her in late 2023 after client campaigns were disrupted by unreliable human models. She was engineered from the outset with commercial viability in mind — pink hair, tattoos, fitness and gaming niche, Spanish-speaking audience, and a Fanvue subscription tier at €15 per month. Her success led The Clueless to pivot its core offering to an AI influencer-as-a-service model, building bespoke virtual personas for other brands — demonstrating the B2B extension of the concept. AI influencers.

Lu do Magalu — The Corporate Model
Brazilian retail giant Magazine Luiza launched Lu in 2009 as a virtual assistant. Over 15 years she evolved
into the world’s most-followed brand character, amassing over 30 million followers across platforms. Lu
simultaneously handles product reviews and customer service, demonstrating that AI influencers can function
as integrated marketing-plus-operations assets. Her ROI extends beyond marketing KPIs to include
measurable reductions in customer service load — a dual-function model increasingly adopted by retailers
globally.

Authenticity in Influencer Marketing: Why It Matters and How to Achieve It

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. AI influencers.

The post AI influencers appeared first on Customer Experience Management.

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.