Brainforge LMNT Omnichannel Revenue Plan

LMNT Omnichannel Revenue Data Foundation, Documentation-First Plan

Prepared by: Brainforge (Uttam Kumaran, Robert Tseng)
Date: 11/15/2025
Audience: LMNT, Shivani Amar, Philip McKeating, Jason Wu, Dan O’Keefe

Executive Summary

We will build an enterprise-ready revenue data foundation by moving channel by channel, at LMNT’s pace. For each channel we will explore, define and map, ingest and backfill, then deliver value. No build begins until definitions, lineage, and owners are signed. We start with the commercial stack, Shopify, Amazon, Wholesale, and Retail feeds such as Emerson and Spins. In parallel we prepare for NetSuite by aligning revenue recognition fields and the order date taxonomy. This creates a durable platform for omnichannel reporting now and for inventory and forecasting next year.

Engagement Terms

• Duration, 3 months
• Commercials, $15,000 dollars per month. Tooling billed by vendors
• Flexibility, 14-day termination for convenience
• Cadence, align to LMNT three-week sprints
• Weekly working session and a monthly review

Operating Principles

• Documentation first. Definitions and ownership are locked before build.
• Channel by channel with overlapping waves. Pace matches access and backfill realities.
• Keep what works. Leave existing Looker acquisition reporting in place while we select a cross-org BI front end.
• Security and auditability. Least-privilege roles, change control through pull requests, visible decision log.

Deliverables

1. Instagantt master plan

   • Rows, Shopify, Amazon, Wholesale, Retail feeds, Cross-cutting.
   • Columns, Explore, Define and map, Ingest and backfill, Deliver value.
   • Stage gates and named owners on every row. Milestones, Definitions signed, First successful load, Model ready, Dashboard live.

2. Data Source Ownership RACI

   • For each dataset, owner, access path, grain, history depth, refresh SLA, PII flags, API throughput notes, backfill feasibility.

3. Data Dictionary and Metrics Naming Conventions

   • Revenue vs sales, refund and return handling, contribution margin placeholders.
   • Order date taxonomy, order date, process date, ship date, delivery date, rev-rec date, return processed.
   • Change control through PR with reviewer list

4. Lineage and Architecture Decision Records

   • End-to-end lineage diagram per channel.
   • Short ADRs for connector choices, history depth, refund policy, BI selection, security rules.

5. Backfill Strategy and Runbook

   • Shopify and Amazon realities documented, pagination, parallelization, incremental keys, error handling.
   • Staged history targets, first six months visible by a set date, full history runner continues in the background until complete.

6. dbt Project and CI

   • Layers, stg_, int_, mrt_. Tests, not null, unique, relationships, freshness.
   • CI runs dbt build on dev schemas, linting, and data diff on key marts. Tagged releases promote to prod.

7. Unified Transactions Model

   • Dimensions, channel, geo, SKU, partner, fulfillment.
   • Refund and returns logic harmonized across Shopify and Amazon.
   • Mapping section for future NetSuite tables and revenue recognition.

8. Wholesale 360

   • Account canon with parent and child groups.
   • 12-month SKU history, reorder cadence, RFM churn watchlist as the first value artifact.

9. NetSuite Readiness Packet

• Object and field mapping, revenue recognition alignment, order date taxonomy

10. AI Enablement Note, optional pilot

• Chat interface on curated marts and retrieval across metric docs. Guardrails, table allow-lists, row-level security passthrough, prompt logging.

Success Metrics

• Documentation completeness, 95 percent of priority metrics have signed definitions and owners.
• Lineage coverage, 100 percent of shipped models have current lineage diagrams and ADRs.
• Data coverage, within 10% Δ for accurate revenue reporting in the unified model with daily freshness.
• Adoption, at least 80% users onboarded onto reports across Finance and RevOps.
• Close time, month-end prep reduced to 50 percent of baseline or better.

Risks and Mitigations

Risks	Mitigations
High-volume Shopify and Amazon backfills; API limits extend full history	Parallelize backfills, use staged targets, run via background workers, and define clear cutover windows to avoid partial histories
Access or vendor delays (e.g., waiting on credentials, API keys, third-party approvals)	Stand up secure CSV ingestion pipelines as a fallback; maintain parallel connector paths to ensure continuity
Definition drift as the org scales	Restrict changes to PR-only edits, run a weekly Definitions Council, and maintain a transparent decision log visible to all stakeholders
ERP timing shifts (e.g., NetSuite posting delays, async journal updates)	Capture detailed NetSuite mapping early, and pre-document tie-out plan before data starts flowing to prevent reconciliation gaps
Inconsistent property naming or structure across modules	Ship a standardized naming convention + schema template and train internal teams on safe extension patterns

Meetings and Workshops

• Kickoff, access checklist and roles.
• Weekly Definitions Council, led by Phil and Shivani, lock glossary entries and policies.
• Shopify deep dive, then Amazon, then Wholesale, then Retail feeds.
• Weekly discovery review, doc-first, glossary and lineage line by line.
• Monthly executive review, stage gates cleared, risks, next rows to start.

Case Studies

• DTC Brand: Implemented real-time, full-funnel visibility, with 100% accurate benchmarks for LTV/CAC.
• DTC Brand: Eliminated 800+ redundant dashboards, saving analysts 40+ hours/month and cutting reporting overhead costs.
• CPG Brand: Centralized fragmented customer feedback into one dashboard in under 30 days, improving trend detection by 75% and enabling faster action on underperformers.
• CPG Brand: Replaced manual checks with real-time monitoring for 900+ Target stores, with >90% coverage and accuracy.
• Home Services Provider: Implemented end-to-end AI observability, routing negative feedback instantly and reducing issue resolution time to under 60 minutes.

Team & Pricing

Brainforge typically staffs a 3-role pilot team:

• Strategist: Main client POC responsible for setting and executing against KPIs, aligns output operator objectives, builds roadmap for new impact areas.

• Engineer: Primary technologist to design and consolidate systems

• Technical PM: Drives project timeline, negotiates with vendors, focuses on client adoption and enablement

Note: Open to fixed-cost structure or milestone based structure

Hourly Rates

Labor Category	Level	Hourly Rate
Managing Data Lead	Executive	$250/hour
Senior Data Engineer/Analyst	Senior	$200/hour
Technical Project Manager	Mid-Level	$150/hour

Fixed/Retainer Models

Service	Fixed Fee / Monthly
Omnichannel Revenue Data Accelerator	$15k/mo for 3mo
40hr/mo Minimum Retainer	$6k+/mo,extendedat$250/hr

Billing & Payment Terms

• Minimum Billing Unit: 1 hour, billed in 0.25-hour increments thereafter
• Email/Phone Response (15 mins or less): Not Billed
• Invoicing: Bi-weekly or Monthly (Net 15 or Net 30 terms)
• Retainers: Available for ongoing work, discounted based on volume
• Currency: All rates are in USD

Appendix

The Brainforge Approach

Today’s senior operators and growth leaders at $50M+ ARR companies face relentless pressure to scale faster, optimize resources, and navigate increasingly complex markets. While many organizations invest heavily in data analytics and AI tooling, holistic implementations fall short. Static dashboards remain underutilized, insights stagnate without clear pathways to action, and data-driven decisions continue to lag behind business urgency.

At Brainforge, we recognize that the enterprise analytics approach involving building dynamic dashboards and scheduling automated reports doesn’t meet the needs of how today’s leaders make decisions. Our unique approach bridges the gap between data and decision-making, embedding AI directly into the workflows that drive growth and profitability. Instead of adding another tool to your already complicated tech stack, we transform existing systems into intelligent copilots and decision architects that proactively surface insights and recommendations exactly when and where you need them.

Our approach outlines a clear, structured pathway from basic analytics assistance to sophisticated decision architectures. It emphasizes human-in-the-loop deployment opportunities, ensuring operators retain full control and trust, while leveraging AI to drive faster, smarter, and more profitable decisions.

The ultimate goal: Move beyond data visibility towards proactive signals and recommendations in the workflows of senior operators.

Vendor Cost Ranges

Category	Example Vendors	Monthly Range	Notes
Business Intelligence / Reporting	Looker, Tableau, Mode, Metabase	$1K–$3K	Pricing depends on seats and hosting.
Attribution / Marketing Mix Modeling	Rockerbox, Measured, Recast	$3K–$5K	Often scales with ad spend.
AI Observability / Anomaly Detection	Metaplane, Montecarlo	$0.5K–$2K	Can start lightweight; alerts via Slack/Teams.
Segmentation / CDP-lite	Segment, RudderStack, Hightouch	$2K–$4K	Based on MTUs/events processed.
Experimentation / Lifecycle Tools	Optimizely, Braze, Iterable	$2K–$5K	Optional; depends on pilot activation scope.

Stack may include

• Data Warehouse (Comparisons)

• Data Modeling, ETL, and Orchestration (Comparisons)

• Business Intelligence (Comparisons)

• Integrations between tools & GTM tools (Comparisons)

• LLM/Chatbot/MCP Server (Comparisons)