Data Platform cho E-commerce: Từ Clickstream đến Revenue Attribution

Trong thế giới e-commerce cạnh tranh khốc liệt ngày nay, việc hiểu rõ hành trình khách hàng từ lần click đầu tiên đến giao dịch cuối cùng không còn là lựa chọn mà đã trở thành yêu cầu sống còn. Tuy nhiên, theo khảo sát của chúng tôi với 50+ e-commerce businesses tại Việt Nam, 73% doanh nghiệp vẫn đang "mù mờ" về hiệu quả thực sự của từng kênh marketing - họ biết doanh thu tổng thể nhưng không biết kênh nào đang "ăn tiền" và kênh nào đang "ném tiền qua cửa sổ".

Bài viết này sẽ giúp bạn hiểu cách xây dựng một Data Platform cho e-commerce, từ việc thu thập clickstream data, tích hợp 15-20 data sources khác nhau, đến xây dựng multi-touch attribution model để optimize marketing ROI. Kèm theo case study thực tế từ một fashion e-commerce tại Việt Nam đã tăng ROAS 40% chỉ sau 6 tuần triển khai.

TL;DR - Key Takeaways

• E-commerce cần tích hợp 15-20 data sources từ website, transactions, marketing, logistics đến customer service
• Attribution là game-changer: Multi-touch attribution giúp hiểu đúng customer journey, tránh over-invest vào last-click channels
• Architecture pattern: Real-time (Segment → Kafka → Warehouse) + Batch (Airbyte → BigQuery → dbt → Looker)
• Quick wins: RFM segmentation, cart abandonment recovery, channel ROI analysis có thể triển khai trong 2-4 tuần
• ROI điển hình: 30-50% improvement trong marketing efficiency, 15-25% increase trong repeat purchase rate

E-commerce Data Landscape: Mê Cung 15-20 Data Sources

Một e-commerce trung bình phải đối mặt với 15-20 data sources khác nhau. Hãy xem danh sách điển hình:

1. Website & App Analytics

Google Analytics 4 (GA4):

• User behavior: page views, sessions, bounce rate
• Traffic sources: organic, paid, direct, referral, social
• E-commerce events: view_item, add_to_cart, purchase
• Custom events: click_promotion, search, filter_products

Google Tag Manager (GTM):

• Event tracking: custom interactions
• Enhanced e-commerce tracking
• Custom dimensions & metrics

Heatmap tools (Hotjar, Microsoft Clarity):

• Click patterns, scroll depth
• Session recordings
• Form abandonment points

2. Transaction Systems

E-commerce platforms:

• Shopify: Orders, products, customers, inventory
• Magento/WooCommerce: Same + custom tables
• Custom backend: Thường là Node.js/PHP API + PostgreSQL/MySQL

Dữ liệu quan trọng:

• Order details: SKU, quantity, price, discounts, shipping
• Payment status: pending, paid, refunded
• Customer info: email, phone, address, segments

3. Marketing & Advertising

Paid channels (chiếm 60-80% e-commerce traffic):

• Facebook Ads: Impressions, clicks, CPC, conversions by campaign/adset/ad
• Google Ads: Search, Shopping, Display - same metrics
• TikTok Ads: Video views, engagement, conversions
• Shopee/Lazada Ads: Marketplace advertising data

Email marketing:

• Mailchimp/SendGrid: Sent, opens, clicks, unsubscribes
• Campaign performance, automation workflows

Organic channels:

• Google Search Console: Impressions, clicks, position, queries
• SEO tools (Ahrefs, Semrush): Rankings, backlinks

4. Customer Service & Engagement

• Zendesk/Freshdesk: Tickets, response time, CSAT scores
• Intercom: Live chat conversations, chatbot interactions
• Reviews platforms: Shopee/Lazada reviews, Google reviews

5. Logistics & Fulfillment

• Shipping partners (Giao Hàng Nhanh, Giao Hàng Tiết Kiệm, J&T):
  • Tracking status: picked, in-transit, delivered, returned
  • Delivery time, shipping cost
• Warehouse management: Inventory levels, SKU locations

Thách Thức: Data Silos & Inconsistency

Mỗi source có format riêng, update frequency khác nhau:

• GA4: Real-time nhưng chỉ có user behavior
• Shopify: Near real-time transactions nhưng không có marketing context
• Facebook Ads: Daily aggregation, không có customer-level
• Email: Campaign-level, thiếu individual interactions

Kết quả: Bạn có 20 dashboards riêng biệt nhưng không câu trả lời nào cho câu hỏi: "Khách hàng này tương tác với brand như thế nào trước khi mua?"

Kiến Trúc Data Platform cho E-commerce

Để giải quyết mê cung data trên, e-commerce cần một kiến trúc kết hợp real-time và batch processing.

Real-time Pipeline: Clickstream & Events

User actions → Segment/RudderStack → Kafka → Stream processing → Data Warehouse
                                        ↓
                                  Real-time dashboards

Use cases:

• Live dashboards: Current users on site, today's revenue, top products
• Real-time personalization: Show recommended products based on current session
• Fraud detection: Suspicious transactions trigger alerts immediately

Tech stack:

• Segment hoặc RudderStack: Customer Data Platform (CDP) thu thập events
• Apache Kafka: Message queue for high-throughput
• BigQuery/Snowflake: Data Warehouse với streaming inserts
• Looker/Tableau: Real-time BI dashboards

Batch Pipeline: Data Integration & Transformation

Data sources → Airbyte/Fivetran → Data Warehouse → dbt transformations → BI layer
  (Shopify,      (ELT tool)        (BigQuery)      (metrics, models)    (Looker)
   FB Ads, etc)

Workflow hàng ngày:

1. 01:00 AM: Airbyte syncs data từ tất cả sources
   • Shopify: orders, products của ngày hôm qua
   • Facebook Ads: campaign performance
   • Google Ads: keyword performance
   • Email: campaign metrics
2. 02:00 AM: dbt chạy transformations
   • Clean & standardize data
   • Join customer journey: web sessions → marketing touches → orders
   • Calculate metrics: LTV, CAC, cohort retention
   • Build attribution models
3. 06:00 AM: Dashboards refresh cho team sáng ra xem

Data Warehouse Schema: E-commerce Specific

Staging layer (staging_*):

• staging_shopify_orders: Raw Shopify data
• staging_facebook_ads: Raw Facebook Ads data
• Minimal transformation, 1:1 với source

Core layer (core_*):

• core_customers: Customer master data
  • customer_id, email, first_order_date, ltv, segment (VIP, regular, churned)
• core_orders: Enriched orders
  • Order details + customer info + marketing attribution
• core_products: Product catalog + performance metrics
• core_sessions: Web sessions với UTM parameters

Metrics layer (metrics_*):

• metrics_customer_cohorts: Monthly cohorts, retention curves
• metrics_channel_attribution: Multi-touch attribution by channel
• metrics_product_performance: Sales, margin, inventory turnover by SKU

Key Metrics cho E-commerce: What to Track

Acquisition Metrics

CAC (Customer Acquisition Cost) by channel:

CAC = Total marketing spend / New customers acquired

Benchmark Việt Nam (2024 data từ Carptech clients):

• Facebook Ads: 150,000đ - 400,000đ per customer (fashion, beauty)
• Google Ads: 100,000đ - 350,000đ (search intent cao hơn)
• TikTok Ads: 80,000đ - 300,000đ (younger audience)
• Organic/SEO: 20,000đ - 50,000đ (long-term investment)

LTV/CAC ratio:

• < 1: Mất tiền mỗi customer (unsustainable)
• 1-3: Break-even hoặc marginally profitable
• > 3: Healthy (có thể scale marketing)
• > 5: Excellent (nên invest mạnh vào channel này)

Conversion Metrics

Funnel conversion rates:

1. Landing page → Add to cart: 5-15% (industry average)
2. Add to cart → Checkout initiated: 60-80%
3. Checkout initiated → Purchase: 50-70%
4. Overall: Landing → Purchase: 2-5%

Cart abandonment rate: 60-80% (global average)

• Reasons: High shipping cost (55%), just browsing (37%), complicated checkout (28%)
• Recovery tactics: Email reminders (15-20% recovery rate), retargeting ads (5-10%)

Mobile vs Desktop conversion:

• Mobile traffic: 70-80% of total
• Mobile conversion: Thấp hơn desktop 30-50% (smaller screen, distractions)
• Optimization: Mobile-first design, one-click checkout, Apple Pay/Google Pay

Retention Metrics

Repeat purchase rate:

Repeat rate = Customers with 2+ orders / Total customers

Benchmark theo ngành:

• Fashion/Beauty: 25-35% (seasonal, trend-driven)
• Food/Beverage: 40-60% (habitual)
• Electronics: 15-25% (low frequency)

Cohort retention curves:

Churn prediction:

• Features: Days since last order, order frequency, email engagement, support tickets
• ML models: Logistic regression, Random Forest, XGBoost
• Action: Automated win-back campaigns cho at-risk customers

Operational Metrics

OTIF (On-Time In-Full):

• Industry benchmark: 85-95%
• Việt Nam logistics challenges: 75-85% typical
• Impact: 1% improvement = 0.5-1% increase in repeat rate

Fulfillment time:

• Order to shipment: Target < 24 hours
• Shipment to delivery (Hà Nội/HCM): 1-2 days
• Provinces: 2-4 days

Advanced Analytics: Game-Changing Use Cases

1. Multi-Touch Attribution: Hiểu Đúng Customer Journey

Problem với Last-Click Attribution: Ví dụ customer journey điển hình:

1. Day 1: Xem Facebook Ad về sản phẩm mới → Click → Browse → Leave
2. Day 3: Google search "[brand name] review" → Read blog → Leave
3. Day 5: Nhận email with discount code → Click → Add to cart → Leave
4. Day 7: Google search "[product name] mua ở đâu" → Click Google Ad → Purchase

Last-click attribution: 100% credit cho Google Ad Reality: Facebook Ad (awareness), SEO (consideration), Email (nurturing) đều quan trọng

Multi-touch attribution models:

Implementation với SQL + dbt:

-- Customer journey construction
WITH customer_touchpoints AS (
  SELECT
    customer_id,
    touchpoint_date,
    channel,
    ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY touchpoint_date) as touch_position,
    COUNT(*) OVER (PARTITION BY customer_id) as total_touches
  FROM core_sessions
  WHERE customer_id IN (SELECT customer_id FROM core_orders)
),

attribution_weights AS (
  SELECT
    *,
    CASE
      -- Linear: equal weight
      WHEN '{{ var("attribution_model") }}' = 'linear'
        THEN 1.0 / total_touches
      -- Time-decay: exponential weight
      WHEN '{{ var("attribution_model") }}' = 'time_decay'
        THEN POWER(2, touch_position - 1) / SUM(POWER(2, touch_position - 1)) OVER (PARTITION BY customer_id)
      -- Position-based: 40% first, 40% last, 20% middle
      WHEN '{{ var("attribution_model") }}' = 'position_based'
        THEN CASE
          WHEN touch_position = 1 THEN 0.4
          WHEN touch_position = total_touches THEN 0.4
          ELSE 0.2 / (total_touches - 2)
        END
    END as attribution_weight
  FROM customer_touchpoints
)

SELECT
  channel,
  SUM(revenue * attribution_weight) as attributed_revenue,
  SUM(marketing_cost) as marketing_cost,
  SUM(revenue * attribution_weight) / NULLIF(SUM(marketing_cost), 0) as roas
FROM attribution_weights
JOIN core_orders USING (customer_id)
GROUP BY channel

Kết quả thay đổi decisions:

• Một client của Carptech phát hiện Facebook Ads có ROAS 1.5x với last-click, nhưng 3.2x với position-based attribution
• Decision: Tăng Facebook budget 40%, scale awareness campaigns

2. Customer Segmentation: RFM Analysis

RFM = Recency, Frequency, Monetary:

• Recency: Bao lâu rồi không mua (days since last order)
• Frequency: Mua bao nhiêu lần (number of orders)
• Monetary: Chi bao nhiêu (total revenue)

Segmentation:

Implementation:

WITH rfm_scores AS (
  SELECT
    customer_id,
    DATE_DIFF(CURRENT_DATE(), MAX(order_date), DAY) as recency,
    COUNT(order_id) as frequency,
    SUM(total_amount) as monetary,
    -- Quintile scores (1-5)
    NTILE(5) OVER (ORDER BY DATE_DIFF(CURRENT_DATE(), MAX(order_date), DAY) DESC) as r_score,
    NTILE(5) OVER (ORDER BY COUNT(order_id)) as f_score,
    NTILE(5) OVER (ORDER BY SUM(total_amount)) as m_score
  FROM core_orders
  GROUP BY customer_id
)

SELECT
  customer_id,
  CASE
    WHEN r_score >= 4 AND f_score >= 4 AND m_score >= 4 THEN 'Champions'
    WHEN r_score >= 3 AND f_score >= 3 THEN 'Loyal'
    WHEN r_score <= 2 AND f_score >= 3 AND m_score >= 3 THEN 'At risk'
    WHEN r_score = 3 AND f_score <= 2 THEN 'Need attention'
    WHEN r_score >= 4 AND f_score = 1 THEN 'New'
    ELSE 'Churned'
  END as segment
FROM rfm_scores

ROI:

• Targeted email campaigns có open rate 2-3x cao hơn mass emails
• Win-back campaigns cho "At risk" segment: 15-25% recovery rate
• Champions referral program: 30-40% participation, 20% conversion on referrals

3. Inventory Forecasting: Reduce Overstock & Stockouts

Time-series forecasting models:

• Input features:
  • Historical sales (last 90 days)
  • Seasonality (day of week, month, holidays)
  • Marketing campaigns (scheduled promotions)
  • Trends (product lifecycle stage)
  • External factors (weather for fashion, payday for electronics)

Models:

• ARIMA: Traditional time-series (good baseline)
• Prophet (Facebook): Handles seasonality well, easy to use
• LSTM (Deep Learning): Chính xác hơn với large datasets

Demand forecast by SKU:

from prophet import Prophet
import pandas as pd

# Prepare data
df = pd.DataFrame({
    'ds': sales_dates,  # Date
    'y': sales_quantity  # Quantity sold
})

# Add regressors (promotions, etc)
df['promotion'] = promotion_indicator

# Fit model
model = Prophet(seasonality_mode='multiplicative')
model.add_regressor('promotion')
model.fit(df)

# Forecast next 30 days
future = model.make_future_dataframe(periods=30)
future['promotion'] = future_promotions
forecast = model.predict(future)

# Optimal stock level = Forecast + Safety stock
safety_stock = forecast['yhat'].std() * 1.65  # 95% service level
optimal_stock = forecast['yhat'] + safety_stock

Impact:

• Reduce overstock: 20-30% (free up capital, reduce markdowns)
• Reduce stockouts: 40-60% (capture more sales, better CX)
• ROI example: Fashion e-com with 1000 SKUs, revenue 30B VND/year
  • Overstock reduction: 2B VND freed up capital
  • Stockout reduction: 500M VND additional revenue
  • Total impact: 2.5B VND (~8% of revenue)

4. Personalization: Product Recommendations

Types of recommendations:

1. Collaborative filtering: "Customers who bought X also bought Y"
   • Implementation: Matrix factorization (ALS algorithm)
   • Works well: High traffic, many SKUs
2. Content-based: "Similar products based on attributes"
   • Features: Category, brand, price range, tags
   • Works well: New products, niche categories
3. Hybrid: Combine both approaches

Simple implementation với BigQuery ML:

-- Train collaborative filtering model
CREATE OR REPLACE MODEL `project.dataset.product_recommendations`
OPTIONS(model_type='matrix_factorization',
        user_col='customer_id',
        item_col='product_id',
        rating_col='implicit_rating') AS
SELECT
  customer_id,
  product_id,
  -- Implicit rating: views + 2*add_to_cart + 5*purchase
  SUM(views + 2*add_to_cart + 5*purchase) as implicit_rating
FROM user_product_interactions
GROUP BY customer_id, product_id;

-- Get recommendations
SELECT * FROM ML.RECOMMEND(MODEL `project.dataset.product_recommendations`,
  (SELECT 'customer_12345' AS customer_id))
ORDER BY predicted_rating DESC
LIMIT 10;

Performance:

• Click-through rate: 3-8% (vs 1-2% for generic recommendations)
• Conversion rate: 2-5% (vs 0.5-1%)
• Revenue impact: 10-20% of total revenue from recommended products

5. Price Optimization: Dynamic Pricing

Factors influencing optimal price:

• Demand elasticity: How sensitive customers are to price changes
• Competition: Competitors' prices for same/similar products
• Inventory level: Higher price if low stock, lower if overstock
• Customer segment: VIP vs price-sensitive customers
• Time: Peak hours, weekends, holidays

Simple rule-based approach:

def calculate_optimal_price(base_price, inventory_level, competitor_price, customer_segment):
    # Start with base price
    price = base_price

    # Inventory adjustment
    if inventory_level < 10:  # Low stock
        price *= 1.05  # +5%
    elif inventory_level > 100:  # Overstock
        price *= 0.90  # -10%

    # Competition adjustment
    if competitor_price < price * 0.95:
        price = competitor_price * 1.02  # Beat competitor by 2%

    # Customer segment adjustment
    if customer_segment == 'VIP':
        price *= 0.95  # 5% loyalty discount

    return round(price, -3)  # Round to thousands

ML-based approach:

• Train regression model: price ~ demand + features
• Optimize: Find price that maximizes price × predicted_demand - cost

Caution: Aggressive dynamic pricing có thể harm brand trust. Best practices:

• Transparent pricing policies
• Limit price fluctuations (±10-15%)
• Personalized discounts rather than base price changes

Case Study: Fashion E-commerce Tăng ROAS 40% Trong 6 Tuần

Background:

• Company: Thời trang nữ online, Hà Nội
• Revenue: ~500M VND/month
• Marketing spend: 150M VND/month (30% of revenue)
• Channels: Facebook Ads (60%), Google Ads (30%), Email (10%)
• Problem: ROAS đang giảm, không biết kênh nào hiệu quả thực sự

Pain points:

• Shopify có dữ liệu orders, nhưng không biết customer từ đâu
• Facebook Ads Manager show conversions, nhưng khác số liệu Shopify
• Google Analytics có traffic, nhưng không match với revenue
• Quyết định budget allocation based on "gut feeling"

Solution: Data Platform trong 6 tuần

Week 1-2: Setup data pipelines

• Airbyte connectors:
  • Shopify → BigQuery (orders, customers, products)
  • Facebook Ads → BigQuery (campaigns, adsets, ads performance)
  • Google Ads → BigQuery
  • Mailchimp → BigQuery
• Segment implementation:
  • JavaScript SDK trên website
  • Track events: page_viewed, product_viewed, add_to_cart, purchase
  • Include UTM parameters trong tất cả events

Week 3-4: Data modeling với dbt

• Customer journey table:

  -- Kết nối sessions với orders
  SELECT
    s.session_id,
    s.customer_id,
    s.session_date,
    s.utm_source,
    s.utm_medium,
    s.utm_campaign,
    o.order_id,
    o.order_date,
    o.total_amount
  FROM sessions s
  LEFT JOIN orders o
    ON s.customer_id = o.customer_id
    AND o.order_date BETWEEN s.session_date AND DATE_ADD(s.session_date, INTERVAL 30 DAY)
  

• Multi-touch attribution model: Position-based (40% first, 40% last, 20% middle)
• RFM segmentation

Week 5-6: Analysis & optimization

Phát hiện #1: Facebook Ads thực tế hiệu quả hơn Google Ads

• Last-click attribution:
  • Facebook ROAS: 1.8x
  • Google ROAS: 3.5x
  • → Conclusion: Nên tăng Google, giảm Facebook
• Multi-touch attribution:
  • Facebook ROAS: 3.2x (vai trò awareness + nurturing)
  • Google ROAS: 2.8x (mostly last-click)
  • → Conclusion: Facebook đang under-valued!

Phát hiện #2: Email remarketing có ROI cực cao

• Cart abandonment emails: 18% recovery rate
• Browse abandonment: 8% conversion
• ROI: 42x (spend 3M VND → revenue 126M VND/month)
• Action: Tăng email automation workflows

Phát hiện #3: 60% revenue từ 12% customers (Champions + Loyal)

• Champions (5%): AOV 2.5M VND, mua 6+ lần/year
• Loyal (7%): AOV 1.8M VND, mua 3-4 lần/year
• Action: VIP program với early access, exclusive discounts

Actions taken:

1. Reallocate budget:
   • Facebook: 90M → 110M (+22%)
   • Google: 45M → 35M (-22%)
   • Email: 15M → 20M (+33%)
2. Optimize campaigns:
   • Facebook: Shift từ conversion campaigns sang awareness + retargeting
   • Google: Focus vào branded keywords (higher intent)
3. Launch automated flows:
   • Cart abandonment (send after 2 hours, 24 hours, 3 days)
   • Browse abandonment (send next day)
   • Post-purchase (thank you + product care tips)
4. VIP program: Free shipping, 10% off, early access cho Champions

Results after 6 tuần:

Key learnings:

• Attribution models change decisions drastically
• Email/automation are criminally under-utilized
• Customer retention >>> acquisition (cheaper, higher LTV)

Implementation Roadmap: 90 Days to Working Data Platform

Phase 1: Foundation (Weeks 1-4)

Week 1-2: Setup infrastructure

• Provision Data Warehouse (BigQuery recommended for startups)
• Setup git repo cho dbt project
• Install Airbyte (cloud or self-hosted)
• Setup Segment (free tier OK for start)

Week 3-4: Connect top 5 data sources Priority order:

1. E-commerce platform (Shopify/Magento) - orders, customers
2. Google Analytics 4 - web traffic
3. Top ad platform (Facebook or Google Ads)
4. Email marketing (Mailchimp)
5. Customer service (Zendesk) - optional

Deliverable: Raw data flowing into warehouse daily

Phase 2: Data Modeling (Weeks 5-8)

Week 5-6: Core models

• core_customers: Customer master table
• core_orders: Order facts với customer join
• core_sessions: Web sessions với UTM parameters

Week 7-8: Metrics models

• metrics_daily_revenue: Daily revenue by channel
• metrics_customer_cohorts: Monthly cohorts, retention
• metrics_rfm_segments: Customer segmentation

Deliverable: Clean, modeled data ready for analysis

Phase 3: Analytics & Dashboards (Weeks 9-12)

Week 9-10: BI dashboards Setup Looker/Metabase với dashboards:

1. Executive dashboard: Revenue, orders, AOV, trending
2. Marketing dashboard: CAC, ROAS, channel breakdown
3. Product dashboard: Top products, inventory alerts
4. Customer dashboard: Cohorts, segments, LTV

Week 11-12: Advanced analytics

• Multi-touch attribution model
• Churn prediction model (simple logistic regression)
• Product recommendations (collaborative filtering)

Deliverable: Self-service analytics cho team

Phase 4: Automation & Optimization (Ongoing)

• Automated alerts (revenue drop, inventory stockout)
• Weekly email reports cho leadership
• Monthly deep-dives on specific topics
• A/B testing framework
• Iterate based on insights

20-Item Implementation Checklist

Data infrastructure:

• Data Warehouse provisioned (BigQuery/Snowflake)
• dbt project initialized, version controlled
• Airbyte or Fivetran connectors setup for top sources
• Segment or RudderStack for clickstream tracking

Data modeling:

• Customer dimension table (SCD Type 2 if needed)
• Order fact table với foreign keys
• Session tracking với UTM attribution
• Product catalog với performance metrics

Key metrics calculated:

• CAC by channel
• LTV by cohort
• ROAS by campaign
• Conversion funnel (landing → purchase)
• RFM segments updated daily

Dashboards:

• Executive: Revenue, orders, AOV, new vs repeat
• Marketing: CAC, ROAS, attribution
• Product: Sales by SKU, inventory levels
• Customer: Cohort retention, segment distribution

Advanced analytics:

• Multi-touch attribution (at minimum linear model)
• Churn prediction (basic model)
• Product recommendations
• Inventory forecasting (top 20% SKUs)

Automation:

• Daily data pipelines running reliably
• Automated alerts for anomalies
• Weekly/monthly email reports

Kết Luận: Data Platform = Competitive Advantage

Trong thị trường e-commerce Việt Nam cạnh tranh khốc liệt, Data Platform không còn là "nice to have" mà là "must have". Các con số không nói dối:

• 40%+ improvement trong marketing efficiency khi có attribution đúng
• 15-25% increase trong repeat purchase rate với customer segmentation
• 20-30% reduction trong inventory costs với demand forecasting
• 10-20% revenue từ personalized recommendations

Nhưng quan trọng hơn con số, Data Platform giúp bạn:

• Đưa quyết định dựa trên data, không phải "gut feeling"
• Respond nhanh hơn với market changes
• Hiểu sâu hơn customers của mình
• Scale hiệu quả hơn khi business lớn

Next steps:

• Review lại data sources bạn đang có
• Đánh giá gaps trong data infrastructure
• Bắt đầu với quick wins: RFM segmentation, cart abandonment recovery
• Tiếp cận Carptech nếu cần support hands-on (carptech.vn/contact)

Tài liệu tham khảo:

• Google Analytics 4 E-commerce Implementation
• Segment E-commerce Spec
• dbt E-commerce Metrics
• Attribution Models Explained - Google

Bài viết này là phần của series "Data Platform for Industries" từ Carptech. Đọc thêm về Data Platform cho Fintech, Retail, và Manufacturing.

Carptech - Data Platform Solutions for Vietnamese Enterprises. Liên hệ tư vấn miễn phí.