CRM-to-CMP Customer Data Strategy for Zara
CRM analysis and CMP proposal to turn customer data into personalisation, loyalty, and omnichannel actions.
Type
Individual academic project
Area
CRM Analytics · Customer Data · Omnichannel Retail
Tools
Excel · CRM dataset · K-means logic
Techniques
CRM analysis · K-means clustering · Customer journey · Newsletter analysis · Loyalty hypotheses · CMP strategy
Output
CRM diagnosis + CMP strategy
Value
Individual project where I analysed a simulated 50-customer Zara CRM dataset through segmentation, channels, ticket, satisfaction, and comments to propose a customer-data capture and activation strategy towards CMP.
customers in simulated CRM dataset
global average ticket
average satisfaction
customers subscribed to newsletter
exploratory clusters
average ticket of highest-value cluster
01 / Fast scan
Case in 60 seconds
A quick scan of the case: what was happening, what needed to be solved, what I did, and what value is demonstrated.
Situation
SRetail brands need to reduce dependence on third-party data and build proprietary customer knowledge for personalisation, loyalty, pricing, promotions, and omnichannel experience.
Task
TIndividual project where I analysed a simulated 50-customer Zara CRM dataset through segmentation, channels, ticket, satisfaction, and comments to propose a customer-data capture and activation strategy towards CMP.
Action
A- 01Explore sales by category, channel, city, and payment method.
- 02Analyse satisfaction, newsletter, card, and negative comments.
- 03Build clusters and translate them into activation hypotheses.
- 04Design zero/first-party data capture mechanisms.
Result
R- Global average ticket was €55.69 and average items per order 1.38.
- Average satisfaction was 3.48/5; negative comments concentrated on sizing/availability, shipping, and price.
- 54% were subscribed to the newsletter and showed slightly higher purchase frequency and spend.
- The CMP strategy should capture actionable preferences, not only demographics.
- Clusters enable differentiated loyalty, recovery, and value journeys.
- 50 · customers in simulated CRM dataset
02 / Context
Problem
This section explains the business or analytical challenge before going into technical detail.
02.1
Executive summary
CRM analytics case where the objective was moving from basic transactional data to activation logic: what we know, what we do not know, what to capture, and how to turn it into personalised journeys.
02.2
My role
Individual academic project. I analysed the customer dataset, interpreted patterns, defined clusters, and proposed data capture/activation actions.
03 / Method
Approach
Methods, tools, and workflow. This shows how I structured the analysis.
03.1
Data & methods
- Simulated 50-customer CRM dataset with channel, city, category, spend, satisfaction, newsletter, card, and comments.
- Channel analysis: App 16 orders, physical store 17, and web 17; average ticket €59.10 web, €52.86 app, and €54.94 physical store.
- K-means with three clusters: 22 male app/web customers with €62.1 ticket, 22 female web customers with €40.9 ticket, and 6 older male customers with €86.8 ticket and 2.5 satisfaction.
- CMP proposal: Style DNA, Style & Tell, smart fitting rooms, in-app preferences, interactive newsletter, and cluster-based journeys.
03.2
Process
- 01Explore sales by category, channel, city, and payment method.
- 02Analyse satisfaction, newsletter, card, and negative comments.
- 03Build clusters and translate them into activation hypotheses.
- 04Design zero/first-party data capture mechanisms.
- 05Define actions and improvement KPIs.
04 / Decision
Evidence and impact
Outputs, findings, and implications translated into decisions or professional value.
04.1
Key findings
- Global average ticket was €55.69 and average items per order 1.38.
- Average satisfaction was 3.48/5; negative comments concentrated on sizing/availability, shipping, and price.
- 54% were subscribed to the newsletter and showed slightly higher purchase frequency and spend.
- Zara cardholders showed higher satisfaction but lower average spend than non-cardholders.
- Cluster 2 was small but critical: higher ticket, older age, and lower satisfaction.
04.2
Business implications
- The CMP strategy should capture actionable preferences, not only demographics.
- Clusters enable differentiated loyalty, recovery, and value journeys.
- Useful for CRM analyst, BI, customer insights, and marketing automation.
05 / Close
Professional close
Limitations, next steps, and available assets. This keeps the case honest and actionable.
05.1
Limitations
- Individual academic project with a simulated 50-customer dataset.
- Does not represent actual Zara data.
- Clustering is exploratory and requires validation with a larger sample.
05.2
What I would do next
- Validate clusters with a real, larger dataset.
- Measure uplift from personalised journeys versus control.
- Connect CMP with CRM, ecommerce, and physical store.
05.3
Assets
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