🛒 Что реально мониторить в FMCG данных

🛒 Что реально мониторить в FMCG данных

Практический guide для тех, кто работает в FMCG.

Consumer data:

1. Recency-Frequency-Monetary (RFM)
🔸 Recency — last purchase date
🔸 Frequency — purchases per period
🔸 Monetary — average purchase value
🔸 Use for segmentation, targeting

2. Cohort analysis
🔸 Groups по acquisition date
🔸 Track retention over time
🔸 Compare cohorts
🔸 Identify successful campaigns

3. Customer Lifetime Value (LTV)
🔸 Predict future revenue от acquired customer
🔸 Guide CAC decisions
🔸 Target > $150 для FMCG D2C

4. Net Promoter Score (NPS)
🔸 Loyalty measure
🔸 Predictor future purchases
🔸 Target > 50 для established brand

Marketing data:

1. CAC by channel
🔸 Instagram, TikTok, Google, retail promotion
🔸 Compare efficiency
🔸 Optimize spend allocation

2. Attribution modeling
🔸 First-touch vs last-touch vs multi-touch
🔸 Data-driven attribution best (Google, Amazon models)
🔸 Guide budget decisions

3. Marketing mix modeling
🔸 Cross-channel attribution
🔸 Aggregate level (not customer)
🔸 Traditional media + digital

Sales data:

1. Category performance
🔸 Your product vs competitor within category
🔸 Growth rate vs market growth
🔸 Share of shelf metrics

2. Price elasticity
🔸 Sales response к price changes
🔸 Category-specific
🔸 Promotional effectiveness

3. Retailer scorecards
🔸 Performance per retailer
🔸 Category share per retailer
🔸 Store-level analysis (top vs bottom)

Operations data:

1. Inventory turnover
🔸 Speed of stock movement
🔸 Working capital efficiency
🔸 Target 6-12x annually for FMCG

2. Order fulfillment metrics
🔸 On-time delivery
🔸 Order accuracy
🔸 Customer service impact

3. Supply chain analytics
🔸 Supplier performance
🔸 Cost trends
🔸 Risk management

Ключевые dashboards:

Weekly:
🔸 Revenue by product/channel
🔸 CAC and LTV trending
🔸 Retail sell-through
🔸 Marketing spend efficiency

Monthly:
🔸 Category performance
🔸 P&L review
🔸 NPS tracking
🔸 Inventory position

Quarterly:
🔸 Cohort analysis update
🔸 LTV predictions
🔸 Attribution models refresh
🔸 Category strategy review

Tools:
🔸 Собственная data platform (Snowflake, Databricks для large)
🔸 Google Analytics для web/marketing
🔸 Shopify для DTC (built-in analytics)
🔸 Wildberries/Ozon seller dashboards
🔸 Custom Power BI/Tableau dashboards

Reality check: most FMCG-companies underinvest в data. Right investment 3-5% revenue on data platforms и team. ROI typically 10-15x.

Мой consulting focus часто — data infrastructure setup. Помогаю build modern data stack. Typical project 4-6 месяцев, $150-400k.
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Антон Хоменок про ритейл и FMCG 🛒