# Playbooks

### Digital Analytics

1. **Acquisition Analysis & Campaign Optimization** — Uses ROAS/CAC, CTR, CVR, and First-Order % to find underperforming channels/campaigns, then reallocate budget and fix creative/targeting with Explore follow-ups.
2. **Conversion Funnel Optimization** — Diagnoses where CVR drops (Traffic → PDP → Cart → Checkout), segments by device/source/geo, and prescribes PDP, UX, and checkout fixes with before/after monitoring.
3. **Retention & LTV Growth** — Tracks cohorts, repurchase, and RFM segments to raise LTV; pairs UNS engagement with UOS outcomes to trigger win-backs, subscriptions, and VIP programs.
4. **Digital Channel Efficiency (Blended ROAS/CAC)** — Evaluates the whole mix (paid + owned), compares attribution models, balances brand vs performance spend, and tunes budgets to hit sustainable LTV:CAC.

### Grow, Expand & Defend Retail Distribution

1. **Distribution & Velocity Overview** — Explains how sales decompose into distribution (ACV/TDP) and velocity ($/TDP, units/store/week), then shows how to diagnose growth/decline by separating “more doors” vs “selling faster.”
2. **Expanding into a New Retailer** — A launch playbook to verify execution (doors live), measure early velocity, size phase-2 upside, and run quick lift tactics (e.g., TPR/demo) to accelerate ramp.
3. **Defending At-Risk Distribution** — Finds SKUs or accounts at risk (high distribution, low velocity; falling ACV), benchmarks against category, and outlines promo/placement fixes—or when to prepare a rationalized exit.
4. **SKU Rationalization (Swap Out)** — Ranks SKUs by velocity and contribution, flags low-productivity items, and shows how to model a swap to higher-performing SKUs while managing cannibalization.
5. **Assortment Expansion in Existing Retailers** — Identifies white-space SKUs per account, projects incremental sales using proven velocities, and packages a data-backed pitch for expanded facings.
6. **Best Practices: Data Storytelling for Retail Decks** — A narrative framework (context → drivers → proof → ask) with clear visuals (share/growth, ACV vs velocity) to persuade buyers and execs.

### Promotional Analysis & Trade Spend ROI

1. Single-Product Promo Tuning — Pinpoints the optimal **discount depth** and **frequency** for one SKU in one account by comparing lift vs. baseline erosion, incrementality, and ROI; guides when to go deeper, go shorter, or pull back to protect margin while keeping velocity up.
2. Cross-Market Promo Benchmark — Compares like-for-like promotions across **retailers/regions** to reveal what truly drives results (depth, duration, feature/display, timing, execution); distills repeatable **best practices** and retailer-specific plays to raise lift and ROI everywhere.
3. Promo Calendar ROI Planning — Builds a seasonal **promotion roadmap** that balances goals, budget, and capacity; forecasts event-level **lift & incrementals**, spaces cadence to avoid over-promotion, and runs **plan vs. actual** loops to prune low-yield tactics and scale winners.
4. Wholesale Impact & Forward-Buy — Aligns **sell-in vs. sell-through** during promos to detect **forward-buying**, quantify true incremental consumption, and adjust deal mechanics (scan-downs, caps, duration) so shipments match demand and post-promo orders don’t crater.
5. DTC→Retail Launch Story — Uses early **velocity**, promo-driven **trial lift**, and **post-promo baseline** to prove retail traction; ties in category/share context and reorder signals to pitch **store expansion**, backed by a repeatable launch-promo playbook.

### Pricing Analysis Optimization & Promotional ROI in Retail

1. **Everyday Pricing Analysis** — Reviews base vs average selling price, price position vs competitors, and ties price moves to velocity, with guidance on EDLP vs promo cadence and margin tradeoffs.
2. **Price Elasticity & Competitor Benchmarks** — Estimates elasticity from historical price/volume and promo lift, compares rival strategies, and runs “what-if” pricing scenarios to forecast revenue and profit.