delta-and-beta/analyst-ecommerce

★ Insight ───────────────────────────────────── Skill files for Claude Code plugins serve as routing guides — Claude reads the description and body to decide when to load it. The lens section is especially valuable because it tells Claude how to interpret the topics, not just what they are. ─────────────────────────────────────────────────

Here is the generated skill file content:

---

E-commerce Analytics & Workflow

Overview

This skill covers analytics integrations and workflow optimization for e-commerce performance, grounded in Braze's partner ecosystem. It synthesizes how behavioral data flows from specialized analytics partners into actionable insights — covering identity resolution, omnichannel attribution, customer advocacy, and AI-driven decision intelligence.

Core lens: Customer behavior analysis, conversion optimization, and workflow performance metrics.

Use this skill when analyzing customer journeys, evaluating partner data pipelines, optimizing conversion workflows, or interpreting behavioral signals from Braze's e-commerce analytics integrations.

When to Use

• Investigating how an analytics partner enriches or extends Braze customer profiles
• Mapping behavioral signals (site activity, purchase history, advocacy scores) to campaign decisions
• Evaluating conversion funnel performance using partner-sourced data
• Optimizing omnichannel workflows that span physical and digital touchpoints
• Answering questions about identity resolution, decision intelligence, or customer lifetime value in an e-commerce context

When NOT to use: For core Braze campaign mechanics, message delivery, or non-analytics partner integrations, use a more targeted skill.

Topics Synthesized

Wunderkind (Signals)

Identity resolution platform that recognizes anonymous website visitors and resolves them into known customer profiles. Key use cases: behavioral signal capture, anonymous-to-known resolution, and performance attribution for e-commerce traffic. Feeds Braze with enriched identity data to power triggered and personalized messaging.

Tangerine Store360

Omnichannel platform connecting physical retail stores with online commerce. Relevant when analyzing unified customer behavior across in-store and digital channels — particularly for attribution, inventory-aware personalization, and cross-channel workflow optimization.

Personalize Analytics Partner

Source documentation is minimal. Treat as a behavioral personalization integration. When encountered, focus on how personalization signals are passed to Braze and how they influence segmentation or content decisions.

Peak

Decision intelligence platform applying AI to commercial decisions. Relevant for questions about AI-assisted forecasting, demand prediction, customer scoring, or automating business decisions based on behavioral and transactional data.

Okendo

Customer marketing platform focused on reviews, referrals, and loyalty — mobilizing customers as advocates to drive word-of-mouth and lifetime value. Use when analyzing review-driven conversion signals, UGC integration, or post-purchase advocacy workflows in Braze.

Lexer

Source documentation is minimal. Treat as a customer data platform (CDP) or segmentation-enrichment partner. Focus on how Lexer-sourced audience data surfaces in Braze for targeting or suppression.

Analytical Lens

Apply this skill with the following perspective:

Customer behavior analysis — How are customers moving through funnels, and what signals indicate intent, churn risk, or advocacy potential?

Conversion optimization — Where do partner integrations create leverage points for improving conversion rates — through better identity resolution, smarter personalization, or timely triggered messages?

Workflow performance metrics — How do data pipelines between partners and Braze perform operationally? What latency, match rates, or data quality issues affect downstream campaign effectiveness?

Quick Reference

| Partner | Primary Value | Data Type | |---|---|---| | Wunderkind | Anonymous visitor identity resolution | Behavioral / Identity | | Tangerine Store360 | Omnichannel physical + digital unification | Transactional / Location | | Personalize | Behavioral personalization signals | Behavioral | | Peak | AI-driven decision intelligence | Predictive / Scoring | | Okendo | Customer advocacy and review signals | Social / UGC | | Lexer | Audience enrichment and segmentation | Audience / CDP |

Common Mistakes

• Treating minimal-doc partners (Personalize, Lexer) as unsupported — these are live integrations; infer behavior from partner category and data flow patterns
• Conflating identity resolution (Wunderkind) with segmentation enrichment (Lexer) — different data layers with different latency and match-rate considerations
• Ignoring omnichannel attribution gaps — Tangerine's Store360 data often has in-store lag; account for this in workflow timing analysis
• Overlooking advocacy signals as conversion inputs — Okendo review data can directly influence purchase likelihood scoring in downstream Braze segments

★ Insight ───────────────────────────────────── Notice the Quick Reference table — this is the highest-value section for a Claude agent because it allows pattern matching at a glance before diving into topic detail. The Common Mistakes section is equally important: it encodes known failure modes so Claude avoids repeating them. ─────────────────────────────────────────────────