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.

Below I’ll (1) summarize what manuals say, (2) show the best formulas for your exact data (date, product_id, sales value, quantity), (3) explain what to avoid, and (4) give ready-to-use SQL/Python patterns.

Why this matters

With your data you can compute transaction unit prices (value/quantity) and then construct a category price index. The “right” average depends on what you mean:

• “How much would last year’s basket cost at this year’s prices?” → Laspeyres (base-period weights).
• “How much would this year’s basket have cost at last year’s prices?” → Paasche (current weights).
• Best symmetric compromise (often recommended in theory) → Fisher Ideal = √(Laspeyres × Paasche).

Practical recipe

Assumptions:

• You have a mapping product_id -> category_id
• You want YoY for a given month t vs t-12
• You can compute p_it = sum(value)/sum(qty) at product-month

Step-by-step

1. Aggregate transactions to product-month:
   • value_it = SUM(sales_value)
   • qty_it = SUM(quantity)
   • p_it = value_it / qty_it
2. Join month t with month t-12 on product_id.
3. Compute weights and index:
   • For Laspeyres: weight = value_i0 / SUM(value_i0) within category
   • Index = SUM(weight * (p_it/p_i0))
4. Convert to percent:
   • %Δ = (Index - 1) * 100

This matches the weighted aggregation logic used in official price indices. Price index.

Calculation

For each product i, month t, you can compute a unit value price:

$$p_{i,t}=\frac{\sum \text{SalesValue}_{i,t}}{\sum \text{Quantity}_{i,t}}$$

This “unit value” approach is standard when you have value + quantity, and it is widely used as a proxy for price movement in official contexts (e.g., trade statistics). Then define the product price relative (YoY for the same month):

$$r_i=\frac{p_{i,t}}{p_{i,t-12}}$$

Price index on category level

Let the category contain products $i \in C$. You need a single number $I_C$​ such that:

$$\%\Delta_C = (I_C – 1)\times 100\%$$

This index will show price changes on category level, as just a one figure, ex 10% – easy to interpret.

Price index calculation example of 3 products is demonstrated here

and a SQL code for Price index calculation

WITH t1 AS (
    /* 1) Product-level aggregation (within Channel + Category)
          - unit prices p1, p0 as unit values (value/qty)
          - price relative r = p1/p0
          - base-valued current quantity: p0*q1 (needed for Paasche weights in Σ(w*r) form)
    */
    SELECT
        a.ProdIdx,
        ISNULL(a.ORDERBYCUST,0) AS Channel,
        p.Category_top,

        SUM(a.QUANTITY)  AS qty_akt,       -- q1
        SUM(b.QUANTITY)  AS qty_py,        -- q0

        SUM(a.LINEVALUE) AS sales_val_akt, -- p1*q1
        SUM(b.LINEVALUE) AS sales_val_py,  -- p0*q0

        SUM(a.LINEVALUE) / SUM(a.QUANTITY) AS price_akt, -- p1
        SUM(b.LINEVALUE) / SUM(b.QUANTITY) AS price_py,  -- p0

        (SUM(a.LINEVALUE) / SUM(a.QUANTITY))
        / (SUM(b.LINEVALUE) / SUM(b.QUANTITY)) AS price_rel, -- r = p1/p0

        -- p0*q1 : base price times current quantity (for Paasche weights in Σ(w*r) form)
        (SUM(b.LINEVALUE) / SUM(b.QUANTITY)) * SUM(a.QUANTITY) AS base_val_q1p0

    FROM DB_sales_stat a WITH (NOLOCK)
    JOIN DB_sales_stat b WITH (NOLOCK)
      ON  a.PRODIDX = b.PRODIDX
      AND ISNULL(a.ORDERBYCUST,0) = ISNULL(b.ORDERBYCUST,0)
      AND b.ORDERDATE = DATEADD(year,-1,a.ORDERDATE)
    JOIN Products p WITH (NOLOCK)
      ON p.ProdIdx = a.ProdIdx
    WHERE
        a.ORDERDATE >= CAST(DATEADD(day,-10,GETDATE()) AS date)
        AND (a.ORDERBYCUST IS NULL OR a.ORDERBYCUST = 1)  -- manual + ecom
        AND p.Category_top IS NOT NULL
        AND a.QUANTITY  > 0 AND b.QUANTITY  > 0
        AND a.LINEVALUE > 0 AND b.LINEVALUE > 0
    GROUP BY
        a.ProdIdx, ISNULL(a.ORDERBYCUST,0), p.Category_top
),
chcat AS (
    /* 2) Totals per Channel + Category
          - needed to compute shares/weights
    */
    SELECT
        Channel,
        Category_top,
        SUM(sales_val_py)  AS total_sales_py,   -- Σ(p0*q0)
        SUM(base_val_q1p0) AS total_p0q1        -- Σ(p0*q1)
    FROM t1
    GROUP BY Channel, Category_top
),
weights AS (
    /* 3) Weights (structures)
          w_L : Laspeyres weights = base period value shares (p0*q0 share)
          w_P : Paasche weights  = base-valued current shares (p0*q1 share)
          Both sets sum to 1 within Channel + Category
    */
    SELECT
        t1.Channel,
        t1.Category_top,
        t1.ProdIdx,

        t1.sales_val_py  / chcat.total_sales_py AS w_L,   -- w0 = (p0*q0)/Σ(p0*q0)
        t1.base_val_q1p0 / chcat.total_p0q1     AS w_P    -- wP = (p0*q1)/Σ(p0*q1)
    FROM t1
    JOIN chcat
      ON chcat.Channel = t1.Channel
     AND chcat.Category_top = t1.Category_top
),
idx AS (
    /* 4) Indices per Channel + Category
          L = Σ(w_L * r)
          P = Σ(w_P * r)   (this equals Paasche when weights are (p0*q1) shares)
    */
    SELECT
        t1.Channel,
        t1.Category_top,
        SUM(weights.w_L * t1.price_rel) AS idx_Laspeyres,
        SUM(weights.w_P * t1.price_rel) AS idx_Paasche
    FROM t1
    JOIN weights
      ON weights.Channel = t1.Channel
     AND weights.Category_top = t1.Category_top
     AND weights.ProdIdx = t1.ProdIdx
    GROUP BY
        t1.Channel, t1.Category_top
)
SELECT
    Channel,
    Category_top,

    idx_Laspeyres,
    idx_Paasche,

    /* Fisher = geometric mean of L and P */
    SQRT(idx_Laspeyres * idx_Paasche) AS idx_Fisher,

    /* Mix / structure-change influence:
       - ratio form: how much changing weights (structure) moves index
       - diff form: absolute difference (can be easier to read)
    */
    (idx_Paasche / idx_Laspeyres)      AS mix_effect_ratio,
    (idx_Paasche / idx_Laspeyres) - 1  AS mix_effect_pct,
    (idx_Paasche - idx_Laspeyres)      AS mix_effect_diff

FROM idx
ORDER BY Channel, Category_top;

WITH t1 AS (
    /* 1) Product-level aggregation (within Channel + Category)
          - unit prices p1, p0 as unit values (value/qty)
          - price relative r = p1/p0
          - base-valued current quantity: p0*q1 (needed for Paasche weights in Σ(w*r) form)
    */
    SELECT
        a.ProdIdx,
        ISNULL(a.ORDERBYCUST,0) AS Channel,
        p.Category_top,

        SUM(a.QUANTITY)  AS qty_akt,       -- q1
        SUM(b.QUANTITY)  AS qty_py,        -- q0

        SUM(a.LINEVALUE) AS sales_val_akt, -- p1*q1
        SUM(b.LINEVALUE) AS sales_val_py,  -- p0*q0

        SUM(a.LINEVALUE) / SUM(a.QUANTITY) AS price_akt, -- p1
        SUM(b.LINEVALUE) / SUM(b.QUANTITY) AS price_py,  -- p0

        (SUM(a.LINEVALUE) / SUM(a.QUANTITY))
        / (SUM(b.LINEVALUE) / SUM(b.QUANTITY)) AS price_rel, -- r = p1/p0

        -- p0*q1 : base price times current quantity (for Paasche weights in Σ(w*r) form)
        (SUM(b.LINEVALUE) / SUM(b.QUANTITY)) * SUM(a.QUANTITY) AS base_val_q1p0

    FROM DB_sales_stat a WITH (NOLOCK)
    JOIN DB_sales_stat b WITH (NOLOCK)
      ON  a.PRODIDX = b.PRODIDX
      AND ISNULL(a.ORDERBYCUST,0) = ISNULL(b.ORDERBYCUST,0)
      AND b.ORDERDATE = DATEADD(year,-1,a.ORDERDATE)
    JOIN Products p WITH (NOLOCK)
      ON p.ProdIdx = a.ProdIdx
    WHERE
        a.ORDERDATE >= CAST(DATEADD(day,-10,GETDATE()) AS date)
        AND (a.ORDERBYCUST IS NULL OR a.ORDERBYCUST = 1)  -- manual + ecom
        AND p.Category_top IS NOT NULL
        AND a.QUANTITY  > 0 AND b.QUANTITY  > 0
        AND a.LINEVALUE > 0 AND b.LINEVALUE > 0
    GROUP BY
        a.ProdIdx, ISNULL(a.ORDERBYCUST,0), p.Category_top
),
chcat AS (
    /* 2) Totals per Channel + Category
          - needed to compute shares/weights
    */
    SELECT
        Channel,
        Category_top,
        SUM(sales_val_py)  AS total_sales_py,   -- Σ(p0*q0)
        SUM(base_val_q1p0) AS total_p0q1        -- Σ(p0*q1)
    FROM t1
    GROUP BY Channel, Category_top
),
weights AS (
    /* 3) Weights (structures)
          w_L : Laspeyres weights = base period value shares (p0*q0 share)
          w_P : Paasche weights  = base-valued current shares (p0*q1 share)
          Both sets sum to 1 within Channel + Category
    */
    SELECT
        t1.Channel,
        t1.Category_top,
        t1.ProdIdx,

        t1.sales_val_py  / chcat.total_sales_py AS w_L,   -- w0 = (p0*q0)/Σ(p0*q0)
        t1.base_val_q1p0 / chcat.total_p0q1     AS w_P    -- wP = (p0*q1)/Σ(p0*q1)
    FROM t1
    JOIN chcat
      ON chcat.Channel = t1.Channel
     AND chcat.Category_top = t1.Category_top
),
idx AS (
    /* 4) Indices per Channel + Category
          L = Σ(w_L * r)
          P = Σ(w_P * r)   (this equals Paasche when weights are (p0*q1) shares)
    */
    SELECT
        t1.Channel,
        t1.Category_top,
        SUM(weights.w_L * t1.price_rel) AS idx_Laspeyres,
        SUM(weights.w_P * t1.price_rel) AS idx_Paasche
    FROM t1
    JOIN weights
      ON weights.Channel = t1.Channel
     AND weights.Category_top = t1.Category_top
     AND weights.ProdIdx = t1.ProdIdx
    GROUP BY
        t1.Channel, t1.Category_top
)
SELECT
    Channel,
    Category_top,

    idx_Laspeyres,
    idx_Paasche,

    /* Fisher = geometric mean of L and P */
    SQRT(idx_Laspeyres * idx_Paasche) AS idx_Fisher,

    /* Mix / structure-change influence:
       - ratio form: how much changing weights (structure) moves index
       - diff form: absolute difference (can be easier to read)
    */
    (idx_Paasche / idx_Laspeyres)      AS mix_effect_ratio,
    (idx_Paasche / idx_Laspeyres) - 1  AS mix_effect_pct,
    (idx_Paasche - idx_Laspeyres)      AS mix_effect_diff

FROM idx
ORDER BY Channel, Category_top;

about CTE, you can read here: How Works Recursive CTE in SQL Server?

O co chodzi z „co widzimy na kasie” vs „czy to realna podwyżka cen”?

„Co widzimy na kasie” = unit_value_yoy_pct

To jest zmiana średniej ceny jednostkowej, liczona z Twoich danych jako:

• cena_akt_kat = SUMA sprzedaży / SUMA ilości
• cena_py_kat = SUMA sprzedaży rok temu / SUMA ilości rok temu
• różnica YoY = (cena_akt_kat / cena_py_kat − 1)

Czyli to odpowiada na pytanie:

„Ile średnio (ważone ilościami) płaci klient za sztukę w tej kategorii w tym miesiącu vs rok temu?”

To jest to, co „widać na kasie” / w raportach finansowych, bo bierze to, co faktycznie się sprzedało.

Problem: ta miara miesza dwa efekty:

1. zmianę cen produktów
2. zmianę miksu sprzedaży (czyli tego, które produkty sprzedawały się bardziej)

„Czy to realna podwyżka cen?” = laspeyres_yoy_pct (oraz fisher)

Laspeyres odpowiada na pytanie:

„Gdyby klienci kupowali w tym miesiącu dokładnie taki sam koszyk produktów jak rok temu (te same udziały), to o ile zmieniłyby się koszty tego koszyka z powodu cen?”

Czyli: izolujesz efekt cen, trzymając „miks” stały (taki jak w poprzednim roku).

Dlatego to jest częściej KPI dla „inflacji cen” w kategorii.

Mini-przykład, który pokazuje różnicę

Masz kategorię z 2 produktami:

• Produkt A: tani
• Produkt B: drogi

Rok temu (PY)

• A: cena 10, sprzedano 90 szt. → wartość 900
• B: cena 100, sprzedano 10 szt. → wartość 1000
  Razem: 1900 zł, 100 szt.
  Średnia cena = 1900 / 100 = 19 zł

Teraz (AKT) – ceny się nie zmieniły, ale miks tak

• A: cena 10, sprzedano 50 szt. → 500
• B: cena 100, sprzedano 50 szt. → 5000
  Razem: 5500 zł, 100 szt.
  Średnia cena = 5500 / 100 = 55 zł

Co pokaże Twoje unit_value_yoy_pct?

(55 / 19 − 1) = +189%

Czyli „na kasie” wygląda, jakby ceny w kategorii eksplodowały…
ale ceny A i B się nie zmieniły ani o grosz.

To wzrost jest tylko dlatego, że klienci kupili dużo więcej produktu droższego (B).
To jest właśnie efekt miksu.

Co pokaże Laspeyres w tym samym przykładzie?

Laspeyres trzyma wagi z poprzedniego roku (PY). Skoro ceny się nie zmieniły:

• relacja cen A: 10/10 = 1
• relacja cen B: 100/100 = 1

Zatem indeks Laspeyresa = 1 → 0% zmiany cen.

I to jest „realna podwyżka cen” (w sensie: zmiana cenników / stawek), a nie zmiana struktury sprzedaży. Price index

Price index

Co robić w praktyce?

Jeżeli raport jest „dla biznesu” (zarząd / sprzedaż / controlling), to zwykle najlepiej pokazywać:

1. unit_value_yoy_pct jako „co widać w średniej cenie sprzedaży”
2. laspeyres_yoy_pct jako „prawdziwy KPI podwyżek cen”

A obok można dopisać interpretację:

Różnica między unit_value a Laspeyres to efekt miksu
(np. więcej droższych produktów / więcej premium / mniej promek / inne pack size)

mix_effect

Możemy policzyć prostą miarę:

• Mix effect (w p.p.) = unit_value_yoy_pct − laspeyres_yoy_pct

To nie jest „idealna dekompozycja ekonomiczna”, ale w praktyce działa świetnie jako sygnał:

• dodatnie → miks poszedł w stronę droższych produktów
• ujemne → miks poszedł w stronę tańszych/promocyjnych

Onciązenia Price Index

Obciążenie wynikające z substytucji dóbr (commodity substitution bias). Ob‑ciążenie to wynika ze zmian relatywnych cen poszczególnych dóbr wchodzą‑cych w skład koszyka CPI. Efekt substytucji polega na tym, że konsumenci reagują na zmiany cen przez zamianę tych dóbr lub usług konsumpcyjnych, które są relatywnie droższe, na dobra relatywnie tańsze [Hałka i Leszczyń‑ska 2011].

Obciążenie wynikające z substytucji rynku zbytu (outlet substitution bias).Obciążenie to wynika z migracji konsumentów w kierunku atrakcyjniejszych, często właśnie się pojawiających rynków dla zakupów. Takim nowym rynkiem może być np. hurtownia internetowa czy punkt sprzedaży wysyłkowej. Formuła Laspeyresa z wagami z okresu bazowego nie jest w stanie nadążać za tego typu zmianami preferencji konsumentów i nowymi kanałami dystrybucji.

Obciążenie wynikające z pojawiania się nowych dóbr (new goods bias). Źródłem tego rodzaju obciążenia są nowe dobra, z jakich w okresie objętym badaniem inflacji zaczęli korzystać konsumenci. Najczęściej są to produkty dotąd innowacyjne, powstałe na skutek wprowadzenia nowej technologii wyrobu, które weszły właśnie do powszechnego użycia. Z oczywistych przyczyn produkty te mogą w ogóle nie być uwzględnione w koszyku dóbr służących oszacowaniu CPI (przykładowo w Polsce opłaty za telefon ko‑mórkowy zaczęto uwzględniać dopiero w 2006 r.). Może też się zdarzyć, że spadek cen takich produktów znajduje odzwierciedlenie w CPI dużo później, niż faktycznie nastąpił. Co ciekawe, ocenia się, że ich liczba wy‑nosi od kilku do nawet kilkuset tysięcy w skali roku [Diewert 1996].

Obciążenie wynikające ze zmian jakości produktów (quality adjustment bias).Jest to ten rodzaj obciążenia szacunków CPI, który wynika ze zmieniającej się (np. wraz z rosnącymi oczekiwaniami klientów‑konsumentów) jakości oferowanych przez rynek dóbr. Abraham [1995] podaje tu przykład samochodów, których jakość, komfort jazdy, bezpieczeństwo, cena, a także chęć ich posiadania są dziś zupełnie inne niż tych z lat 70. Szacowanie więc inflacji dla długich odcinków czasu musi uwzględniać fakt, że udział tych dóbr w koszyku jest zupełnie inny na początku i na końcu badanego przedziału czasowego. Z definicji wskaźnik CPI powinien mierzyć zmiany cen towarów i usług przy założeniu, że ich cechy nie uległy zmianie w stosunku do okresu bazowego. W rzeczywistości jednak produkty z koszyka dóbr ulegają zmianom – są ulepszane, modyfikowane, a często po prostu wycofywane [Hałka i Leszczyńska 2011].

Obciążenie wynikające z metody kalkulacji (formula bias). Obciążenie to, nazywane także elementarnym obciążeniem indeksu (elementary index bias[White 1999]) może powstać jako efekt zastosowania danej metody obliczeń na najniższym poziomie agregacji.

W przypadku gdy do kalkulacji wskaźnika ceny danego produktu używana jest średnia arytmetyczna ze wszystkich wskaźników cen danego dobra w kolejnych punktach notowań, wskaźnik cen będzie przeszacowany [Hałka i Leszczyńska 2011]. Jeśli natomiast najpierw najpierw liczona jest średnia cena dobra dla danego okresu ze wszystkich punktów notowań, a następnie jest ona odnoszona do średniej ceny tego
dobra w poprzednim okresie, wskaźnik cen nie powinien wykazywać tego rodzaju obciążenia [Ducharme 2000].

https://gnpje.sgh.waw.pl/pdf-100882-33049?filename=Consumer-Price-Index-Meas.pdf

Przykład: Masz dwa sposoby liczenia „indeksu ceny” dla tego samego dobra obserwowanego w kilku punktach notowań (np. sklepy A, B, C):

Metoda 1 (która daje przeszacowanie): średnia arytmetyczna ze wskaźników cen

Czyli najpierw liczysz dla każdego sklepu „procentową zmianę ceny”, a potem uśredniasz:

$$I_{\text{Carli}}=\frac{1}{n}\sum_{j=1}^n \frac{p_{1,j}}{p_{0,j}}$$ICarli​=n1​j=1∑n​p0,j​p1,j​​

To jest klasyczny „Carli index” (arytmetyczna średnia relacji cen).

Metoda 2 (która nie ma tego obciążenia w tym sensie): najpierw średnia cena, potem relacja

Czyli najpierw uśredniasz ceny w okresie 0 i 1, a potem robisz iloraz:

$$I_{\text{Dutot}}=\frac{\frac{1}{n}\sum_{j=1}^n p_{1,j}}{\frac{1}{n}\sum_{j=1}^n p_{0,j}}$$IDutot​=n1​∑j=1n​p0,j​n1​∑j=1n​p1,j​​

To jest „Dutot index” (relacja średnich cen).

Klucz: Carli uśrednia procenty, a Dutot uśrednia poziomy cen.

Przykład 1 (najbardziej obrazowy): “tani sklep drożeje mocno, drogi tanieje trochę”

Mamy 2 sklepy sprzedające ten sam produkt.

Okres 0 (rok temu)

• Sklep A: 10 zł
• Sklep B: 100 zł

Średnia cena w okresie 0:

$$\bar p_0=\frac{10+100}{2}=55$$pˉ​0​=210+100​=55

Okres 1 (teraz)

• Sklep A: 20 zł (wzrost o 100%)
• Sklep B: 90 zł (spadek o 10%)

Średnia cena w okresie 1:

$$\bar p_1=\frac{20+90}{2}=55$$pˉ​1​=220+90​=55

✅ Metoda 2: „średnia cena → relacja” (Dutot)

$$I_{\text{Dutot}}=\frac{55}{55}=1.00 \Rightarrow 0\%$$IDutot​=5555​=1.00⇒0%

Wniosek: średnia cena produktu się nie zmieniła.

❌ Metoda 1: „średnia relacji cen” (Carli)

Najpierw relacje cen w sklepach:

• A: $20/10 = 2.00$20/10=2.00
• B: $90/100 = 0.90$90/100=0.90

Średnia relacji:

$$I_{\text{Carli}}=\frac{2.00+0.90}{2}=1.45 \Rightarrow +45\%$$ICarli​=22.00+0.90​=1.45⇒+45%

Wniosek: Carli mówi „+45%”, mimo że średnia cena w złotych się nie zmieniła.

➡️ To jest dokładnie ten “formula bias / przeszacowanie”: duży wzrost procentowy w tanim punkcie dostaje taką samą wagę jak mała zmiana w drogim punkcie, mimo że drogi punkt „ciąży” dużo mocniej w poziomie cen.

Dlaczego to jest „przeszacowanie” intuicyjnie?

Carli daje każdemu punktowi notowań taką samą wagę w %, niezależnie czy sklep miał cenę 10 czy 100.

• +100% na 10 zł to +10 zł
• -10% na 100 zł to -10 zł

W złotych te zmiany się znoszą, dlatego Dutot wychodzi 0%.
Carli patrzy na procenty i „widzi” 100% i -10%, więc wychodzi mu dodatnio.

Przykład 2 (mniej ekstremalny, ale nadal pokazuje mechanizm)

Okres 0

• A: 10 zł
• B: 20 zł
  $\bar p_0 = 15$pˉ​0​=15

Okres 1

• A: 11 zł (+10%)
• B: 18 zł (-10%)
  $\bar p_1 = 14.5$pˉ​1​=14.5

Dutot:

$$I_{\text{Dutot}}=\frac{14.5}{15}=0.9667 \Rightarrow -3.33\%$$IDutot​=1514.5​=0.9667⇒−3.33%

Carli: Relacje: 11/10=1.10 i 18/20=0.90

$$I_{\text{Carli}}=\frac{1.10+0.90}{2}=1.00 \Rightarrow 0\%$$ICarli​=21.10+0.90​=1.00⇒0%

➡️ Znowu: Carli jest „wyżej” niż Dutot (tu zawyża o 3.33 p.p. względem tej interpretacji).

Jak to się ma do Twoich danych (sprzedaż, ilości, wartości)?

W Twoim świecie „punkty notowań” mogą być np.:

• różne kanały (manual vs ecom),
• różne sklepy/oddziały,
• różne warunki transakcji.

Jeśli liczysz indeks na najniższym poziomie jako:

• średnią % zmian (Carli) → ryzyko przeszacowania,
• relację średnich cen (Dutot) → mniejsze ryzyko tego konkretnego obciążenia.

A w transakcjach sprzedażowych często jeszcze lepsze jest ważenie (np. ilościami lub wartościami), ale to już kolejny poziom metodologii.

p0 = [10, 100]
p1 = [20, 90]

carli = sum([p1[i]/p0[i] for i in range(len(p0))]) / len(p0)
dutot = (sum(p1)/len(p1)) / (sum(p0)/len(p0))

print("Carli index:", carli, "=>", (carli-1)*100, "%")
print("Dutot index:", dutot, "=>", (dutot-1)*100, "%")

Mini‑podsumowanie „w jednym zdaniu”

• Carli (średnia relacji) = średnia procentów → potrafi zawyżać, gdy poziomy cen w punktach są różne.
• Dutot (relacja średnich) = relacja średnich cen → nie ma tego typu zawyżenia w opisanym sensie.

The post Price Index appeared first on Customer Experience Management.

Dlaczego hiperpersonalizacja jest kluczowa?

Detaliści coraz częściej wykorzystują zaawansowane technologie, aby przewidywać potrzeby klientów — zanim ci uświadomią je sobie sami. Dzięki analizie zachowań zakupowych, preferencji żywieniowych czy historii transakcji możliwe staje się:

• zwiększenie ruchu w sklepach stacjonarnych i online poprzez kierowanie odpowiednich ofert do właściwych odbiorców,
• tworzenie trafnych, indywidualnych rekomendacji, które redukują ryzyko porzucenia koszyka i wzmacniają decyzje zakupowe,
• przeciwdziałanie utracie klientów w momentach zmian i przejść, gdy marka musi szczególnie zadbać o lojalność,
• budowanie długoterminowej relacji, bo klienci bardziej angażują się w komunikację, która jest dla nich naprawdę przydatna i adekwatna.

W efekcie personalizacja nie jest już dodatkiem — staje się kluczowym narzędziem do zwiększania sprzedaży i marż.

Korzyści dla klientów

Dla konsumentów hiperpersonalizacja oznacza po prostu… łatwiejsze życie. Zakupy stają się:

• szybsze, dzięki gotowym listom produktów i sprytnym podpowiedziom,
• bardziej intuicyjne, bo system „rozumie” preferencje użytkownika,
• inspirujące, gdy algorytmy podpowiadają pomysły na posiłki czy produkty komplementarne.

Według raportu Grocery Doppio Annual Digital Maturity Benchmark, aż 89% kupujących chętniej wraca do sklepu, jeśli doświadczenie zakupowe było pozytywne. A właśnie technologie personalizacyjne czynią te doświadczenia płynnymi, wygodnymi i przewidywanymi.

W branży spożywczej działa coraz więcej firm, które budują przewagę konkurencyjną dzięki innowacjom. Oto przykłady firm, które redefiniują doświadczenia zakupowe.

Halla – AI, która rozumie smak

Halla to platforma rekomendacji żywieniowych oparta na analizie smaku. Jej opatentowana technologia łączy dane konsumenckie, psychografię i unikalne profile smakowe, aby tworzyć „inteligentne” rekomendacje. Firma korzysta z danych setek miliardów interakcji zakupowych i współpracuje m.in. z Farmstead, oferując supertrafne podpowiedzi w czasie rzeczywistym. (Halla )

Birdzi – lojalność oparta na danych

Birdzi jest platformą analityczną zorientowaną na klienta. Za pomocą autorskiego oprogramowania VISPER dostarcza spersonalizowane promocje i komunikację dopasowaną do zachowań klienta. Retailerzy, którzy korzystają z Birdzi, mogą nie tylko zwiększyć wartość koszyka, lecz także wzmacniać lojalność dzięki bardziej inteligentnym interakcjom (Birdzi ).

Grocery Shopii – planowanie posiłków + e-commerce

Grocery Shopii, założona przez Katie H. Hotze, integruje planowanie posiłków z zakupami online. Dzięki uczeniu maszynowemu klient otrzymuje:

• propozycje przepisów,
• automatycznie generowane listy zakupów,
• podpowiedzi produktów cross‑sellingowych.

Celem firmy jest ograniczenie porzuceń koszyków oraz skrócenie czasu zakupów do minimum. (Grocery Shopii )

OjaExpress – technologia spotyka kulturę

OjaExpress to platforma e-grocery łącząca technologię i tradycję, wyspecjalizowana w produktach z różnych kuchni świata. Firma współpracuje z lokalnymi sklepami etnicznymi, zapewnia dostawę nawet tego samego dnia i pozwala klientom wygodnie kupować produkty afrykańskie, karaibskie, azjatyckie czy latynoskie. To świetny przykład, jak technologia może wspierać różnorodność kulturową i łatwy dostęp do produktów niszowych. (OjaExpress )

Inne przykłady przełomowych technologii w retailu

Aby jeszcze szerzej pokazać skalę przemian, poniżej kolejne obszary, w których technologie wywierają ogromny wpływ:

1. Vision AI i analityka obrazu

• automatyczne wykrywanie pustych półek,
• analiza ruchu klientów,
• personalizowane ekrany digital signage reagujące na odbiorcę.

2. Dynamiczne ceny i inteligentne etykiety

• elektroniczne etykiety cenowe (ESL),
• algorytmy reagujące w czasie rzeczywistym na popyt, pogodę czy stan zapasów,
• dynamiczne promocje oparte na predykcji.

3. Retail Media Networks

• sklepy stają się platformami reklamowymi,
• reklamy są targetowane na podstawie danych zakupowych,
• marki mogą kupować kampanie oparte na zachowaniach UV w czasie rzeczywistym.

4. Generatywna AI w obsłudze klienta

• automatyczne tworzenie opisów produktów,
• chaty zakupowe oparte na GenAI,
• personalizowane newslettery i oferty w 100% generowane przez AI.

5. Autonomiczne sklepy i inteligentne koszyki

• sklepy bez kas,
• koszyki rozpoznające produkty i podsumowujące zakupy w czasie rzeczywistym,
• błyskawiczne płatności i brak kolejek.
• FMCG analytics – management tools

Podsumowanie

Personalizacja w sprzedaży detalicznej nie jest już wizją przyszłości — to realny standard, który powstaje na przecięciu technologii, danych i ludzkich emocji. Klienci chcą czuć się zrozumiani, a detaliści chcą oferować bardziej intuicyjne i płynne zakupy. Firmy działające na styku AI, analityki i e-commerce pokazują, że odpowiednie wykorzystanie danych może tworzyć doświadczenia, które jednocześnie ułatwiają życie i budują długoterminową lojalność.

The post Przełomowe technologie w sprzedaży detalicznej 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.

Author: Oleg Doroftei, student of Business Analytics & Data Science

Definition and Basic Principles of Blockchain

Blockchain is a shared, immutable digital ledger, enabling the recording of transactions and the tracking of assets within a business network and providing a single source of truth. Blockchain uses the three principles of cryptography, decentralization, and consensus to create a highly secure underlying software system that is nearly impossible to tamper with. There is no single point of failure, and a single user cannot change the transaction records. Blockchain Sustainability.

The Role of Blockchain in Sustainability

Blockchain technology and sustainability have many commonalities, one of which is the need to make environmental programs as transparent and efficient as possible. Leveraging blockchain enables businesses to monitor and validate sustainable activities, therefore reducing fraud and improving corporate social responsibility. Some of blockchain’s main functions in sustainability are:

1.    Supply Chain Transparency

Blockchain guarantees a totally visible operation of supply chains. By using an immutable ledger, businesses can document every phase of a product’s cycle, enabling consumers and stakeholders to check ethical sourcing, fair labor standards, and environmentally friendly manufacturing procedures.

2.    Carbon Credit and Emission Tracking

Blockchain tracks emissions in an auditable, tamper-proof manner, which allows precise carbon credit accounting. This guarantees adherence to international environmental criteria and enables companies and governments to track their carbon offset initiatives. Blockchain Sustainability.

3.    Waste Management and Recycling

By offering digital tokens in return for getting rid of trash, smart contracts on blockchain platforms can encourage recycling programs. These automated solutions lower landfill overflow, track waste better, and support circular economy projects.

4.    Decentralized Energy Trading

With blockchain technology, businesses and people may bypass middlemen and sell excess renewable energy directly. This helps to spread green energy effectively and pushes the acceptance of sustainable power sources. Blockchain Sustainability.

Real-World Applications of Blockchain for Sustainability

1.    Agriculture

Blockchain systems track agricultural supply chains, guarantee fair trade, help lower food waste, and support sustainable farming methods. They also help companies like IBM Food Trust to increase food industry traceability. Blockchain Sustainability.

2.    Energy Sector

Companies in the energy sector are using blockchain technology to create decentralized energy networks. These networks allow for transparent renewable energy certifications and peer-to-peer energy trade. Platforms such as Power Ledger and We Power make blockchain-based energy trading possible. Tesla’s Virtual Power Plant.

3.    Fashion and Retail

Blockchain is used by luxury businesses and fashion stores to confirm ethical procurement and cut fake goods. Consumers can scan QR codes to check products’ sustainability credentials and authenticity.

4.    Healthcare and Pharmaceuticals

Blockchain tracks the source of raw materials, lowers waste, and guarantees responsible disposal of outdated medications, therefore guaranteeing that medical supply chains follow ethical and environmental rules. Blockchain Sustainability.

Challenges and Limitations

Blockchain technology still faces important sustainability limitations, especially due to the high energy use of certain models like Proof of Work, which require large amounts of electricity and generate significant environmental impact. It also struggles with scalability, as many blockchains cannot process high transaction volumes quickly or efficiently enough to support real-time applications such as detailed supply-chain tracking. In addition, governance challenges in decentralized networks make it difficult to ensure fair decision-making, ethical practices, and equal participation among users. These issues show that while blockchain has strong potential, it still needs improvements in energy efficiency, scaling methods, and governance structures to be truly sustainable.

Future of Blockchain in Sustainability

Although its future is uncertain, blockchain shows great promise for sustainability. As technology develops, blockchain systems are expected to become more scalable, energy-efficient, and widely adopted in sustainability projects. Key trends include the shift toward green blockchain protocols, integration with IoT and AI for better monitoring of sustainability metrics, increased corporate responsibility to meet ESG goals, and stronger collaboration between governments and businesses to create supportive regulations.

Conclusion

Blockchain is transforming sustainable business with improved transparency across different areas. This includes supply chains, energy, and finance. It tracks products from source to consumer, verifies carbon credits, and supports renewable energy trading.

All in all, it reduces fraud, lowers emissions, and promotes ethical practices. So, if you haven’t started using these advances, now is the time to adopt blockchain for sustainability. Act today and build a greener, more transparent future with blockchain.

Sources

• https://www.avvale.com/newsroom/blockchain-and-sustainability
• https://www.debutinfotech.com/blog/blockchain-for-sustainability
• https://sustainabilitydirectory.medium.com/what-are-the-limitations-of-blockchain-in-sustainability-9685e2739f4c
• https://vivasoft.com.np/blockchain-for-sustainability/

The post Blockchain Sustainability 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.

The “Marketplace Deduplication” Problem

Context: Large marketplaces (Amazon, eBay, Alibaba, or a niche aggregator) allow thousands of third-party sellers to upload their own product feeds. Product Grouping for E-commerce Datasets indeed.

The Problem:

• Seller A uploads: “Apple iPhone 13, 128GB, Midnight”
• Seller B uploads: “iPhone 13 128 GB Black Unlocked”
• Seller C uploads: “Smartfon Apple iPhone 13 128GB (MLPF3PM/A)”

The “Omnichannel Customer Stitching” Problem (Single Customer View)

Context: A retailer sells through a Website, a Mobile App, and Physical Stores. They want to know if the person browsing the app is the same person buying in the store.

The Problem:

• Record A (Online): email: j.smith@gmail.com, cookie_id: xyz123, behavior: viewed running shoes
• Record B (In-Store POS): card_hash: ****-1234, loyalty_id: 998877, name: John Smith
• Record C (Customer Support): phone: +48 500..., name: Johnny Smith, complaint: "Shoes size 42 too small"

Why it’s hard:

The “Competitor Price Monitoring” Problem

Context: An e-commerce store wants to automatically adjust their prices to be $1 cheaper than their biggest competitor.

The Problem:

• Your Product: “Samsung Galaxy S20 FE 5G Cloud Navy”
• Competitor Site: “Samsung S20 Fan Edition (Navy) – 5G Compatible”

Why it’s hard:

Abstract

• Problem: E-commerce catalogs suffer from redundancy (same product, different sizes/variants).
• Gap: Manual grouping is impossible; Deep Learning is overkill/imprecise for strict SKU grouping.
• Method: We compare Jaccard (Set) vs. TF-IDF (Vector) and propose a normalization pipeline.
• Result: Our method achieved X% accuracy with Y% reduction in computational time.

Set-Theoretic Approaches for Product Grouping (Jaccard)

• Concept: Treats text as a “Bag of Words” (BoW) without weights.
• Key Papers/Concepts:
  • Cohen et al. (2003) often discuss string metrics for entity matching.
  • Shingling / MinHash: In large datasets, calculating Jaccard for all pairs is $O(N^2)$. Literature focuses on Locality Sensitive Hashing (LSH) (MinHash) to approximate Jaccard similarity efficiently.
  • Pros in Literature: High interpretability, excellent for “near-duplicate” detection.
  • Cons: Fails when synonyms are used (e.g., “pants” vs “trousers”) or when word importance varies.

Vector Space Models (TF-IDF)

• Concept: Maps text to a high-dimensional Euclidean space.
• Key Papers/Concepts:
  • Salton et al. (1975) (The foundational VSM paper).
  • Character n-grams: Papers often cite that for noisy user-generated content (UGC), character n-grams outperform word tokens because they handle misspellings morphologically.
  • Pros: Handles “rare words” (like model numbers) better due to IDF (Inverse Document Frequency) weighting.

The State-of-the-Art (Deep Learning / Embeddings)

• If you publish, reviewers will ask: “Why not BERT?”
• SBERT (Sentence-BERT): Current SOTA uses transformer models to generate dense vector embeddings.
• Your Counter-Argument: Deep learning is computationally expensive and “black-box”. For industrial product grouping where exact feature matching (like “Samsung” + “Galaxy”) is critical, classical methods (TF-IDF/Jaccard) often offer better precision and control than semantic embeddings which might group “iPhone 12” with “Samsung S20” because they are both “phones”.

II. Related Work

• Mention LSH (Locality Sensitive Hashing) for Jaccard.
• Mention DBSCAN and Agglomerative Clustering as standard algorithms.
• Cite limitations of BERT in high-precision SKU matching.
• Product Grouping for E-commerce

III. Methodology (The Core)

1. Preprocessing – The Normalization Filter for Product Grouping for E-commerce:
   • Define your Regex rules mathematically.
   • $T_{clean} = f(T_{raw})$ where $f$ removes tokens $t \in \{Dimensions, Colors, Stopwords\}$.
2. Representation:
   • Approach A (Jaccard): $J(A,B) = \frac{|A \cap B|}{|A \cup B|}$
   • Approach B (TF-IDF): Cosine Similarity $\cos(\theta) = \frac{A \cdot B}{||A|| ||B||}$
3. Clustering Algorithm:
   • Explain why you chose Connected Components (Graph theory) for Jaccard or Hierarchical Clustering for TF-IDF.

IV. Experiments (Product Grouping for E-commerce)

• Dataset: Your dataset of “several thousand products”.
• Metrics: You must measure quality.
  • Precision: Are elements in the cluster actually the same product?
  • Recall: Did we find all sizes of that product?
  • F1-Score: Harmonic mean of the two.

V. Results & Discussion

• Hypothesis: Jaccard works better for clean data; TF-IDF works better for noisy data (typos).
• Observation: Jaccard is faster but TF-IDF + N-grams is more robust.

Novel Algorithm:

1. Stage 1 (Blocking): Use Jaccard on tokens to quickly find “Candidate Pairs” (fast, rough filter).
2. Stage 2 (Refinement): Use TF-IDF with Character N-grams on the candidates to calculate a precise similarity score (handles typos).
3. Stage 3 (Decision): Hard threshold (e.g., >0.85).

Relevant Search Terms & Papers

Entity Resolution (ER) is the problem of identifying which records in a database refer to the same real-world entity. An exhaustive ER process involves computing the similarities between pairs of records, which can be very expensive for large datasets. Various blocking techniques can be used to enhance the performance of ER by dividing the records into blocks in multiple ways and only comparing records within the same block.

However, most blocking techniques process blocks separately and do not exploit the results of other blocks. In this paper (https://dl.acm.org/doi/pdf/10.1145/1559845.1559870), authors propose an iterative blocking framework where the ER results of blocks are reflected to subsequently processed blocks. Blocks are now iteratively processed until no block contains any more matching records.

Compared to simple blocking, iterative blocking may achieve higher accuracy because reflecting the ER results of blocks to other blocks may generate additional record matches. Iterative blocking may also be more efficient because processing a block now saves the processing time for other blocks.

• “Short Text Clustering for E-commerce”
• “Product Entity Resolution with Noise”
• “Comparison of Jaccard and Cosine Similarity in Text Mining”
• “Blocking techniques for Entity Resolution” (This is crucial for scaling to thousands/millions of products). Product Grouping for E-commerce.

Specific types of papers to look for:

1. Koporec et al.: Papers on combining Jaccard with other metrics. https://www.sciencedirect.com/science/article/pii/S1364815225002981
2. Ganesan et al.: Research on “abstractive summarization” or short-text clustering in retail.

FAQ: Product Grouping in Noisy E-Commerce Datasets

The post Product Grouping in Noisy E-commerce Datasets appeared first on Customer Experience Management.

Blitzscaling Core Principles

• Speed Over Efficiency: Sacrifices conventional business wisdom (like perfect unit economics or efficiency) for rapid expansion.
• Uncertainty is Key: Operates in high-uncertainty environments where the market, business model, or competitive landscape isn’t fully defined.
• Market Capture: The primary goal is to achieve a dominant market share so quickly that competitors can’t catch up, often following a “first is last” mentality.
• Aggressive Investment: Involves massive spending on marketing, infrastructure, and headcount to fuel rapid growth. 

Blitzscaling process

The process moves through five stages, from founder-led chaos to complex, large-scale management 

1. Family: Founder controls everything.
2. Tribe: Founder manages people, not just levers.
3. Village: Formal processes emerge.
4. City: Complex city-like structure, requires sophisticated management.
5. Nation: Global scale, deep institutionalization. 

More you can read in the book

Blitzscaling process – business strategy building blocks

Family
At the family stage, the founder is directly involved in nearly every decision and activity. Authority is centralized, communication is informal, and progress depends heavily on the founder’s personal energy and judgment.

Tribe
As the organization grows, the founder shifts from doing everything to leading people. Trust, shared values, and close relationships become critical, and coordination relies more on influence and alignment than on formal systems.

Village
In the village stage, structure begins to replace intuition. Roles, processes, and basic management systems emerge to ensure consistency, efficiency, and accountability as the organization outgrows purely personal leadership.

City
The organization now resembles a complex city with multiple teams, layers, and interdependencies. Success requires sophisticated management, clear governance, and strong leaders who can operate autonomously while staying aligned with the broader strategy.

Nation
At the nation stage, the organization operates at global scale with deeply embedded institutions, culture, and systems. Leadership focuses on long-term vision, stewardship, and adaptability, as the company must balance consistency with local variation across markets.

Network Effects

Network effects are one of the most powerful forces for long-term growth. The internet is the amplifier. The network effects that each new user (seller or buyer) increases the value of the network for all, sellers and buyers, creating a self-reinforcing cycle that drives exceptional growth and value creation.

Direct and Indirect Network Effects

These effects can be direct, where greater adoption immediately makes the product more valuable to users, as seen with platforms like Facebook or WhatsApp. They can also be indirect, where increased usage encourages the development or consumption of complementary products that enhance the core offering—such as iOS attracting third-party app developers whose apps strengthen the platform itself. Marketplaces like eBay represent two-sided networks, where growth on one side of the market increases value for participants on the other.

Because of this dynamic, network-driven businesses can’t rely on slow, incremental growth. The benefits only emerge once the product achieves broad adoption within a specific market—critical mass is essential for the network effect to activate.

A more you can read here: What is digital age?, Service-Dominant Logic as Theorem of Customer Experience Management, Metody akwizycji i utrzymania klientów w e-commerce

Please leave a comment how do you like it.

The post Blitzscaling as business strategy appeared first on Customer Experience Management.