Amazon Seller Assistant RCO Playbook: Policy Packs for Profit, LTV, and Brand Safety

Why this playbook now

Amazon introduced agentic AI into Seller Assistant on September 17, 2025. Growth teams that encode goals, guardrails, and priorities directly into product, policy, and asset metadata can steer the agent toward profitable actions by default and reduce approval friction.

Objective

Guide growth teams to steer Seller Assistant planning toward profit, LTV, and brand safety by embedding numeric goals and explicit guardrails in catalog, policy, and asset metadata.

Control plane overview

• Goals layer: revenue, contribution margin, LTV, inventory health, retention.
• Guardrails layer: margin floors, TACOS caps, promo budgets, stock SLAs, returns and warranty policies, price floors and MAP, asset and C2PA usage rules.
• Priority layer: ranked SKUs, bundles, cross sell graphs, preferred promo sequences, channel preferences.

Data model blueprint

Define a Policy Pack that the assistant reads before proposing or executing actions. Keep scopes additive and inheritable.

• Account scope: GoalSpec, global Guardrails, default budgets.
• Brand scope: voice, compliance, asset constraints, MAP, regional rules.
• SKU scope: unit economics, inventory, eligibility, attach rate.
• Bundle scope: composition, price bounds, cannibalization rules.
• Campaign template scope: triggers, pacing, stop conditions.

Minimal schema keys

• goal_spec: version, time_window_days, objective_rank [profit, LTV, revenue].
• economics: margin_floor_pct, tacos_ceiling_pct, price_floor, price_ceiling, map_price.
• inventory: reorder_point, lead_time_days, stock_sla_days, oos_block_threshold.
• policy: returns_window_days, warranty_terms_id, escalation_paths.
• promo: monthly_budget, per_sku_budget_cap, cooldown_days, stacking_rules.
• priority: sku_weight, bundle_weight, cohort_weight, channel_allowlist.
• assets: c2pa_required, allowed_claims, disallowed_claims, expiry_utc, region_allowlist.

Example Policy Pack snippet

{
  "account_id": "BRAND-1234",
  "goal_spec": { "objective_rank": ["profit", "LTV", "revenue"], "time_window_days": 28 },
  "guardrails": {
    "economics": { "margin_floor_pct": 22, "tacos_ceiling_pct": 12 },
    "promo": { "monthly_budget": 150000, "per_sku_budget_cap": 8000 },
    "inventory": { "stock_sla_days": 2 }
  },
  "priority": {
    "high_ltv_segments": ["SubscribeSave", "B2B"],
    "channel_allowlist": ["SP", "SB", "Coupons"],
    "default_bundle_weight": 1.4
  },
  "sku_overrides": {
    "SKU-A": {
      "economics": { "margin_floor_pct": 30, "price_floor": 24.99 },
      "inventory": { "reorder_point": 500, "lead_time_days": 9 },
      "priority": { "sku_weight": 2.0 }
    },
    "SKU-BUNDLE-AX": {
      "bundle_of": ["SKU-A", "SKU-X"],
      "economics": { "margin_floor_pct": 28 },
      "priority": { "bundle_weight": 2.2 }
    }
  },
  "policies": { "returns_window_days": 30, "warranty_terms_id": "W-12M-STD" },
  "assets": { "c2pa_required": true, "disallowed_claims": ["cures"], "expiry_utc": "2026-01-31T23:59:59Z" }
}

Encode in Amazon surfaces

• Catalog metadata: store Policy Pack identifiers at account and brand level, and attach per SKU and bundle overrides in custom attributes or product type fields. Include price floors, MAP, eligibility, and stock SLAs. For image governance, use C2PA manifests and align with the guidance in default C2PA ranking lever.
• Offer and promotions: align coupon minimums, deal windows, ad daily caps, and placement allowlists to promo.monthly_budget and per_sku_budget_cap. Map promo templates to cohorts so the agent can pick the lowest CAC channel first.
• A+ Content and Brand Story: attach asset usage claims in captions and image alt text, and embed C2PA manifests in image files that define allowed channels, expiry, regional restrictions, and claim sets. Reference warranty and returns language variants by policy tokens, not free text. Close the loop from creative to outcomes with the approach in closed loop growth engine.
• Brand assets library: tag each asset with usage_intent, audience, claim level, region, and expiry. Require c2pa_required true for any hero or logo asset. Optimize imagery choices with insights from packaging and PDP images rank.

Reasoning Chain Optimization tactics

• Reward model for action selection: score(action) = ProfitPriority x Feasibility x PolicyScore.
  • ProfitPriority: weighted margin after ad spend and promo, LTV uplift, cannibalization penalty.
  • Feasibility: inventory coverage vs SLA, lead time risk, operational capacity.
  • PolicyScore: 1 if all guardrails pass, else 0.
• Planning traces hinting: provide the assistant a compact checklist to evaluate each step. Use binary pass or fail signals and numeric targets in metadata so the agent does not need to infer policy from prose.
• Default behaviors: if multiple actions tie, prefer bundles and high LTV segments, then lower CAC channels, then evergreen creatives.
• Stop conditions: block any action where predicted margin after ads falls below margin_floor_pct or TACOS would exceed tacos_ceiling_pct, or if stock coverage is below oos_block_threshold.

Priority layer construction

• Rank SKUs by 28 day contribution margin and LTV uplift. Tag top decile as must promote with sku_weight above 1.8.
• Build bundle graph from attach rates and returns risk. Assign bundle_weight when attach rate and margin combined are above threshold.
• Map preference templates: launch coupon then SP exact then SB video for replenishment SKUs, or SB then SP broad for new to brand. Store these as template ids with guardrails on pacing and bids.

Operational cadence

• Day 0 to 7: audit unit economics, stock, returns, and warranty policies. Produce v1 Policy Pack and attach to catalog.
• Day 8 to 21: run shadow simulations. Require the agent to output action scores and which guardrails were binding. Fix gaps in metadata.
• Day 22 to 35: soft launch recommend only on top 20 SKUs and 3 bundles. Enforce budget caps and stop conditions.
• Day 36 to 60: expand to 60 percent of revenue base. Enable auto apply for actions with PolicyScore equal to 1 and ProfitPriority above target.

Testing and monitoring

• Pre flight: simulate 200 planned actions across price, promo, ads, and content. Require zero guardrail violations.
• Online: track contribution margin after advertising, TACOS, LTV uplift for targeted cohorts, inventory turns, return rate, warranty claim rate, and share of actions that passed all guardrails.
• Post mortem: for any action with low realized margin, identify whether economics, inventory, or policy was the binding constraint and update Policy Pack weights or floors.

Governance and safety

• Dual key for irreversible actions like price changes or warranty text edits.
• Audit log with action, inputs, policy version, scores, and approver.
• Drift alarms when realized margin deviates from predicted by more than 15 percent or when asset usage approaches expiry.

Playbook checklist

• Goals written as numeric targets with time windows.
• Guardrails defined as floors, ceilings, and booleans.
• Priorities encoded with weights at SKU, bundle, and cohort.
• Assets tagged with C2PA and policy tokens.
• Templates for promo sequences with triggers and stop conditions.
• Simulation and monitoring live.

Outcome

With numeric goals, explicit guardrails, and prioritized objects in metadata, Seller Assistant picks profitable campaigns and actions by default and operates as a controllable growth PM, not a chatbot.