Customer Lifetime Value Optimizer

Segment ecommerce customers by repeat behavior, margin quality, membership depth, and churn or return risk, then turn rough order-history notes into a priori...

Security Scan

The skill's code, instructions, and requirements are consistent with its stated purpose: a heuristic, offline LTV action-plan generator that does not request credentials, install software, or call external services.

Purpose & Capability

Name and description match the implementation: the SKILL.md describes an offline planner and the handler parses user-provided segment notes into a markdown plan. No unrelated credentials, binaries, or config paths are requested.

Instruction Scope

SKILL.md limits the skill to using user-provided exports/notes and explicitly disallows connecting to live CRM/ESP/analytics systems or auto-sending messages. The handler code follows that scope (parsing inputs, applying heuristics, and rendering markdown) and does not read unrelated system files or environment variables.

Install Mechanism

There is no install spec (instruction-only), and the included Python files are local. Nothing is downloaded or extracted from external URLs.

Credentials

The skill requires no environment variables, keys, or config paths. The code reads only SKILL.md (its own documentation) and the user-provided input — proportional to the declared purpose.

Persistence & Privilege

Flags show always: false and normal model invocation. The skill does not request permanent presence or attempt to modify other skill/system configuration.

Assessment

This appears to be a self-contained, heuristic LTV planning skill. Before installing: (1) review the handler.py yourself if you can (it will run on the agent), (2) avoid feeding raw, sensitive PII (full customer records) into the skill—use aggregated or anonymized segment notes instead, (3) do not allow the agent to auto-apply recommendations to live CRM/ESP systems (the SKILL.md already warns against auto-sending), and (4) treat outputs as operator-facing suggestions, not finance-grade forecasts. If future versions introduce network calls or require credentials, re-evaluate before use.

Overview

Use this skill to convert customer-segment notes, order-history summaries, gross-margin signals, and retention context into a practical LTV action plan. It is built for operators who need fast prioritization across new-customer nurture, repeat purchase growth, margin protection, and winback strategy.

This MVP is heuristic. It does not connect to live CRM, CDP, ESP, loyalty, or analytics systems. It relies on the user's segment notes, exported summaries, and lifecycle context.

Trigger

Use this skill when the user wants to:

• identify which customer segments deserve the most retention investment
• design different lifecycle moves for high-value, price-sensitive, dormant, or return-risk customers
• rank LTV levers such as repeat rate, AOV, margin mix, or churn reduction
• turn rough order-history notes into a CRM or membership action brief
• separate revenue growth ideas from margin-quality and retention-quality risks

Example prompts

• "Which segments should we prioritize to improve LTV this quarter?"
• "Create a retention plan for VIP, new, and dormant customers"
• "How can we grow LTV without overusing discounts?"
• "Turn these order and membership notes into an LTV roadmap"

Workflow

1. Capture the customer segments, order behavior, and whether the main tension is repeat rate, AOV, churn, or margin quality.
2. Normalize the likely LTV signals: order history, repurchase cycle, segment mix, return behavior, and offer sensitivity.
3. Separate customer groups into different action lanes instead of giving one generic lifecycle answer.
4. Rank the highest-value LTV levers and attach practical plays, owners, and success metrics.
5. Return a markdown plan with segment diagnosis, lever ranking, and action packages.

Inputs

The user can provide any mix of:

• customer segments or membership tiers
• order history and repeat-cycle notes
• AOV, gross margin, bundle rate, or attach-rate context
• churn, dormancy, or lapsed-customer notes
• refund or return-risk observations
• lifecycle messaging constraints and incentive constraints

Outputs

Return a markdown plan with:

• a segment diagnosis table
• ranked LTV levers
• action packages by segment
• short, medium, and longer-horizon priorities
• measurement notes, assumptions, and limits

Safety

• Do not claim access to live CRM, ESP, loyalty, or analytics systems.
• Do not auto-send discounts, coupons, or lifecycle messages.
• Keep revenue lift and margin impact separate in the recommendations.
• Downgrade certainty when user-level order history is incomplete.
• Treat financial LTV models and operator-facing lifecycle plans as related but not identical.

Best-fit Scenarios

• CRM and membership planning for ecommerce teams
• repeat-purchase and lifecycle improvement reviews
• retention strategy design when data is partial but usable
• operator-led businesses that need an action plan before building a deeper model

Not Ideal For

• formal finance-grade LTV forecasting
• automatic customer scoring or trigger orchestration
• businesses with no segment or order-history visibility at all
• scenarios that require privacy-reviewed activation logic

Acceptance Criteria

• Return markdown text.
• Include segment diagnosis, lever ranking, action packages, and limits.
• Show at least one short-term, one medium-term, and one longer-term move.
• Keep the plan practical for CRM, lifecycle, and retention operators.