LMNT: Strategic Planning Framework & Monthly Session Outline

Date Created: 2026-02-16 Purpose: Strategic framework connecting LMNT’s data & analytics investment to business value across all teams

Long-term goal: Build a data & insights engine for cross-functional decision-making

Every team at LMNT should be able to make faster, more confident decisions because the data function exists. The work is organized into four workstreams, each with different hypotheses, methods, and outputs. Together they form the engine.

#	WORKSTREAM	TYPE
1	Data Foundation & Governance	`Infrastructure`
2	Commercial Performance Insights	`Analytical`
3	Strategic Analysis & Recommendations	`Strategic`
×	BI & Self-Service Enablement	`Operational`
=	Data-driven decision-making engine across all teams

The feedback loop

The data function doesn’t just build tables. It creates a compounding cycle where insights improve decisions, decisions generate outcomes, and outcomes generate better data. This only works if every participant understands their role.

Data Foundation (Snowflake, dbt, Polytomic)
      │
      ▼  clean models, governed metrics
Insights Layer (analysis, dashboards, BI)
      │
      ▼  reports, answers, recommendations
Business Teams make decisions
      │
      ▼  actions in market
Outcomes (revenue, growth, efficiency)
      │
      ▼  new data + new questions
      └──→ feeds back to Data Foundation ↺

Everyone’s role in the loop

ROLE	WHO	RESPONSIBILITY
Build	BrainForge (Awaish, Robert)	Engineer the data foundation, model data correctly, surface insights, recommend analyses
Orchestrate	Shivani	Prioritize what gets built, translate between teams and data team, present value to leadership
Use & Validate	Team leads (Phil — retail, Laura — wholesale, Will — retail ops)	Consume insights, validate data against operational reality, surface new questions
Verify	Finance	Ensure data aligns with accounting treatment, provide reconciliation inputs
Evaluate	Leadership	Receive synthesized insights from Shivani, make resource allocation and investment decisions

Why this matters: The “Zoom Out” document captures the engineering sequencing well — what’s been ingested, what’s been modeled, what’s coming next. What it doesn’t yet capture is who does what with each layer once it’s live, and how the value compounds across teams over time. This framework fills that gap. The goal is not to replace the Zoom Out but to extend it: from “here’s what we’re building” to “here’s what it enables, for whom, and how we know it’s working.”

Recap: Current challenges

Engineering-first framing, not value-first
- Conversations focus on what’s being built (tables, models, ingestion), not what business questions those tables answer or which team benefits
- Stakeholders hear “wholesale mart is in QA” but don’t know what that unlocks for them
No structured feedback loop between data work and team outcomes
- Data is built, exposed, and then… unclear who’s using it, how, and what decisions resulted
- No mechanism to capture “this analysis helped us do X” — makes ROI justification difficult
Stakeholders don’t know what they can ask
- Shivani frequently asks questions that can’t be answered yet, leading to frustration
- No clear guide for “here’s what you can answer today, here’s what’s coming”
Revenue definitions not finalized
- Net revenue formula not signed off with finance or retail team
- Creates recurring friction when numbers don’t match across sources
- <10% variance is normal but hasn’t been formally communicated as acceptable
Uneven adoption across teams
- Wholesale team actively uses data; retail team is earlier; finance engagement is friction-heavy
- No structured plan to onboard each team at the right moment with the right deliverable
Missing the “so what” layer
- We have data and models, but we’re not consistently translating “here’s what the data shows” into “here’s what you should do about it”
- Shivani needs to prove value to stakeholders — she needs a stream of analyses and recommendations, not just clean tables

Near-term goal: Land the first strategic planning cadence

APPROACH	STATUS	RESOURCING (IF AVAILABLE)
Review existing “Zoom Out” doc and align on progress to date	Done
Complete wholesale mart QA and finalize for production	In Progress	Awaish finishing QA
Build retail comparison models (Walmart vs Target POS, two grains)	In Progress	Awaish finishing models; Phil’s template ready
Pilot Omni BI tool with wholesale + retail data	Upcoming	Omni demo scheduled
Finalize net revenue definition with Will and finance	Not Started	Schedule working session with Will + finance
Publish “What Can I Answer Right Now?” guide for stakeholders	Not Started
Run first monthly strategic planning session (this doc)	In Progress	Robert leading
Deliver first retail channel comparison analysis (Phil’s request)	Not Started	Dependent on retail models completing
Establish reconciliation report cadence with Shivani	Not Started
Build data quality scorecard (completeness, freshness, variance)	Not Started	Start building out with D&I team

For discussion: Commercial Performance Insights

What we want to accomplish	How we will accomplish it	What our output will be
Track revenue performance across wholesale, retail, and DTC — Understand how each channel is performing on a consistent set of metrics so Shivani can report a defensible number per channel	🟢 Complete wholesale mart QA and finalize — Validate revenue, partner counts, and segment breakdowns against Shopify and CRM sources	Retail Performance Dashboard — Walmart vs Target POS revenue, product/category performance, growth trends (weekly + monthly)
Compare channel performance on consistent definitions — Apples-to-apples comparison across Walmart, Target, wholesale, and eventually DTC so leadership can see the full picture	🟢 Build retail comparison models — Join `fact_sales` (POS) with `retail_fact_walmart_only_sales` for total Walmart; POS-only for apples-to-apples comparison. Two grains: by retailer and by category	Wholesale Partner Dashboard — Revenue by partner/segment, lifecycle metrics, RFM, churn flags
Identify growth drivers and underperformance — Surface which channels, products, geographies, or partners are driving results vs dragging, so teams can act	🟡 Layer DTC ecommerce modeling — Shopify DTC data is already ingested but not yet modeled. Once modeled, enables true omnichannel view	Cross-Channel Revenue Trend Report — Channel-level growth rates (WoW, MoM), trend lines, simple forecasts
Give each team lead a “home base” for their channel — Phil gets retail, Laura gets wholesale, eventually marketing and supply get theirs	🟡 Build cross-channel trending by product and geography — Compare product performance across channels. Geographic analysis if retail data permits	Quarterly Business Review Packet — Synthesized view for Shivani to present to leadership
	🔴 Add marketing cost data for true CAC/LTV by channel — Requires Meta, Google, trade spend ingestion (mid-year per Zoom Out)

Confidence key: 🟢 Near-term, high confidence · 🟡 Near-term, medium likelihood · 🔴 Valuable but further out · ⚪ Actively deprioritized

For discussion: Data Trust & Governance

What we want to accomplish	How we will accomplish it	What our output will be
Establish trusted, defensible revenue numbers — Every stakeholder should be able to reference one number per channel without debating definitions. Early on this may mean “Sales (pre-adjustments)” — precision improves over time, clarity comes first	🟢 Document known variances — Compare our models vs Shopify vs finance. Explain each discrepancy. Frame <10% variance as expected, not broken	Revenue Reconciliation Report — Side-by-side comparison with variance explanations, updated monthly
Finalize metric definitions — Net revenue, active partner, active customer, sales (POS vs order mgmt). Sign off with the people who own those numbers	🟢 Finalize net revenue definition — Working session with Will (retail) and finance. Define formula, document, get sign-off. Apply across all models	Data Dictionary — Signed-off definitions for all governed metrics. Living document, versioned
Build reconciliation process so data is validated before exposure — Each domain follows: ingest → model → document → structured QA → governed exposure	🟡 Build data quality scorecard — Completeness (% expected records), freshness (time to availability), variance (vs known sources). Weekly pulse	Data Quality Scorecard — Weekly metrics visible to Shivani and team
Set clear expectations on what the data can and cannot answer today — Reduce frustration from over-asking and under-delivering	🟡 Establish QA process for each new domain — Business owners review defined slices against known sources within a time-bound window. Approve before broad use	”What Can I Answer Right Now?” Guide — Stakeholder-facing one-pager: ✅ answerable, ⚠️ partial, ❌ blocked (with timeline)
	⚪ Automate reconciliation reporting — Deprioritized for now; manual cadence sufficient until domain count grows

For discussion: Strategic Analysis & Recommendations

What we want to accomplish	How we will accomplish it	What our output will be
Surface insights that help Shivani be a thought partner — Not just “here’s the data” but “here’s what we see, here’s what it means, here’s what you should consider doing”	🟡 Wholesale partner lifecycle & segmentation analysis — Which partners to focus on, where churn happens, what predicts retention. Feed directly to Laura	Monthly Analysis Recommendations — Prioritized pipeline of analyses with business question, effort, impact, timeline
Build a pipeline of analyses that connect data to decisions — Each analysis should answer a specific business question and enable a specific action	🟡 Retail channel deep dive — Product performance by retailer, geographic patterns (if data permits), velocity comparisons. Feed to Phil and Will	Stakeholder-Ready Insight Memos — Short, structured write-ups: what we found, what it means, what to do about it
Help teams prioritize using evidence, not intuition — When Shivani gets asked “should we expand into Target Northeast?” she should have data to reference	🟡 Revenue trend analysis with simple forecasting — Growth rates by channel, early seasonality signals, range-based forecasts for planning	Quarterly Strategic Planning Materials — Adoption stage mapping, analysis roadmap, value-delivered summary
Create the “so what” layer that’s currently missing — The bridge between clean tables and confident decisions	🔴 Customer segmentation and lifetime value modeling — Requires DTC + wholesale data stitched. Segment by behavior/value for targeted strategies	ROI & Impact Narrative — Quarterly summary for Shivani to present upward: value delivered, decisions enabled, what’s next
	🔴 Product portfolio optimization across channels — Which products to push in which channels. Requires product data across all three channels

For discussion: BI & Self-Service Enablement

What we want to accomplish	How we will accomplish it	What our output will be
Give stakeholders the ability to answer their own questions — Reduce the loop where every question goes through BrainForge. Build self-service capacity over time	🟢 Pilot Omni with wholesale + retail data — Use real business questions to validate: self-service exploration, metric consistency, cross-channel views in one place	Omni Pilot Results & Go/No-Go Decision — Does BI tool meet stakeholder needs? What works, what doesn’t, what’s next
Reduce ad-hoc requests through governed tools — If the dashboard answers the question, the team doesn’t need to file a ticket	🟡 Build governed dashboards with consistent metric definitions — Wholesale, retail, then cross-channel. Definitions match data dictionary exactly	Governed Dashboards — Wholesale performance, retail performance, cross-channel trending
Build a BI layer on top of clean, documented models — not raw tables — Dashboards on raw data create fast dashboards and slow trust. Governed logic first	🟡 Train Shivani and key stakeholders on self-service exploration — Show them how to answer their own questions within Omni	Stakeholder Training & Adoption Plan — Role-based onboarding for each team lead
	🔴 Expand BI coverage as new domains come online — DTC, marketing, supply chain added as models mature
	⚪ Evaluate Snowflake AI Analyst for natural-language querying — Interesting as interim self-service tool but not priority

Two-Hour Strategic Planning Session Outline

Session Structure (2 hours)

Part 1: Current State Assessment (30 min)

Goal: Understand where we are and what’s working/not working

Agenda:

Quick Wins Review (10 min)
- What analyses/reports have been used by stakeholders?
- What questions have been answered successfully?
- What’s driving value right now?
Blockers & Gaps (10 min)
- What questions can’t be answered yet?
- What’s blocking stakeholder adoption?
- What’s missing from the data/reporting?
Stakeholder Pulse Check (10 min)
- Who’s actively using the data?
- Who’s asking questions we can’t answer?
- What are the top 3 stakeholder priorities right now?

Output: Current state snapshot, prioritized gaps

Part 2: Engineering → Business Value Mapping (30 min)

Goal: Connect what we’re building to what stakeholders need

Framework: Data Adoption Stages

Stage 1: Foundation (What We Have)

✅ Data ingestion working
✅ Basic models built
✅ Tables available in Snowflake

Stage 2: Understanding (What Stakeholders Need)

❓ Can answer basic questions
❓ Can compare sources
❓ Can validate data quality

Stage 3: Decision-Making (What Drives Value)

❓ Can identify opportunities
❓ Can track performance
❓ Can forecast trends

Stage 4: Strategic Planning (What Makes Her a Thought Partner)

❓ Can recommend actions
❓ Can justify priorities
❓ Can demonstrate ROI

Exercise:

Map current engineering work to adoption stages
Identify gaps: What stage are we at? What’s next?
Prioritize: What moves us to the next stage fastest?

Output: Engineering-to-value roadmap

Part 3: Analysis Recommendations & Prioritization (45 min)

Goal: Create prioritized list of analyses to execute

Framework: Analysis Prioritization Matrix

High Impact + Low Effort (Quick Wins)

Do these first
Build momentum
Prove value quickly

High Impact + High Effort (Strategic Investments)

Plan these carefully
Require stakeholder alignment
Drive long-term value

Low Impact + Low Effort (Fill Gaps)

Do when capacity allows
Complete the picture
Reduce friction

Low Impact + High Effort (Avoid)

Don’t do unless critical
Reassess if stakeholder asks

For Each Analysis, Define:

Business Question: What does this answer?
Stakeholder: Who needs this?
Impact: Why does this matter?
Effort: How hard is this?
Dependencies: What needs to exist first?
Timeline: When can we deliver?

Output: Prioritized analysis pipeline (next 4-8 weeks)

Part 4: Sequencing & Roadmap (15 min)

Goal: Create clear sequence and timeline

Considerations:

What analyses build on each other?
What needs to happen first (foundation)?
What can happen in parallel?
What’s blocked by engineering work?

Output: 4-8 week analysis roadmap with dependencies

🔄 Analysis Recommendations by Adoption Stage

Stage 1→2: Foundation → Understanding

Analysis 1: Retail Channel Performance Comparison (Walmart vs Target)

OBJECTIVE	ANALYSIS
Goal: Compare Walmart and Target retail channel performance to answer basic business questions, establish a trusted retail baseline, and guide channel allocation strategy.	Retail Data Structure Walmart sends 12 tables via Emerson external share (POS + order management system data). Target sends 3 tables via Emerson (POS + daily inventory).
Driving Questions:	Key Models:
- How is Target performing versus Walmart right now?	- `fact_sales`: POS data from both Walmart and Target (apples-to-apples comparison)
- What were sales across both stores yesterday?	- `retail_fact_walmart_only_sales`: Walmart order management data (NOT POS)
- What are the differences in how they define sales?	- Products table: joined for product/category breakdowns
- What can I actually glean from retail data right now?
Key Metrics: Revenue by retailer (POS), Growth trends (WoW, MoM), Product/category performance by retailer, Revenue recognition differences, Geographic performance (if available)	Critical Note: For total Walmart revenue, must join `fact_sales` + `retail_fact_walmart_only_sales`. For apples-to-apples Walmart vs Target, use `fact_sales` POS data only.

SAMPLE INSIGHTS	RECOMMENDATIONS
Walmart sends two data types (POS + order management) while Target sends only POS — for apples-to-apples comparison, use `fact_sales` only. For total Walmart picture, both tables must be joined.	Build Retail Channel Comparison Dashboard - Weekly/monthly POS revenue by retailer. Growth trend comparisons. Product/category performance breakdown. “What Can I Answer Right Now?” reference panel.
Revenue recognition timing may differ between retailers — Walmart may recognize revenue at different times than Target. Week ending dates may differ. This creates small but visible discrepancies that need explanation, not fixing.	Document Data Definitions & Differences - Create one-pager explaining Walmart vs Target data structures. Clarify when to use POS-only vs total Walmart revenue. Set expectations: small discrepancies between platforms and our model are normal.
Product performance varies significantly by retailer — some products over-index at Walmart vs Target. Category-level analysis reveals channel preferences that should inform product placement and marketing.	Retail Reporting (Two Grains) - Phil requested two grains: by retailer and by category. Awaish finishing models for this. Once ready, populate Phil’s template in Google Sheets. Migrate to Omni once BI tool is live.
Geographic data availability is uncertain — Shivani asked about “Target in the Northeast vs California.” Need to verify if geographic fields exist in raw tables before committing to this analysis.	Establish Retail Baseline Metrics - Define “normal” weekly/monthly ranges for each retailer. Set up alerts for significant deviations. Create comparison benchmarks stakeholders can reference independently.

Client Questions

What are the exact differences between Walmart POS data and Walmart order management data? How should we talk about these two sources to stakeholders?
How do Walmart and Target define “week ending” dates? Are they consistent? How does this affect weekly comparisons?
What geographic data is available in the raw retail tables? Can we answer Shivani’s “Target Northeast vs California” question, or is this not in the data?
What’s the acceptable variance between our retail data and retailer platforms? How do we explain differences to Phil and the retail team?
Has Will (retail team) been engaged on revenue definitions yet? We had one discovery call — what’s the follow-up plan?

Analysis 2: Revenue Reconciliation & Data Trust

OBJECTIVE	ANALYSIS
Goal: Establish trust in data by documenting data quality, reconciling differences with known sources (Shopify, finance/accounting), and setting clear expectations on acceptable variance.	Wholesale Revenue Reconciliation Compare our `wholesale_mart` revenue to: Shopify order totals (operational baseline). Finance/accounting reports from QuickBooks/NetSuite. Document variance and provide line-by-line explanations.
Driving Questions:	Retail Revenue Reconciliation
- Why doesn’t our wholesale revenue match finance?	Compare our `fact_sales` to retailer platforms (if accessible).
- Can we trust this data?	Document structural differences (POS vs order management).
- How do we define net revenue?	Explain variance between sources.
Key Metrics: Variance % (our data vs Shopify vs Finance), Data completeness (% of expected records), Data freshness (time to availability), Known limitations count	Net Revenue Definition Alignment Current status: NOT FINALIZED. Need to align with Will (retail) and finance on: Net revenue = Gross - Discounts - Refunds? Include/exclude shipping? Taxes? Reconcile with accounting treatment.
Comparison Sources: Shopify (operational data), QuickBooks/NetSuite (accounting), Retailer platforms (if accessible)	Data Freshness Tracking - Measure time from source to Snowflake availability. Identify delays or bottlenecks in pipeline.

SAMPLE INSIGHTS	RECOMMENDATIONS
<10% variance between our data and finance is normal and expected — accounting systems recognize revenue differently than operational data. Different cut-off dates, accrual vs cash basis, refund timing. This is standard across every client we work with.	Create Reconciliation Report - Side-by-side comparison: Our numbers vs Shopify vs Finance. Variance explanations for each discrepancy. Acceptable variance threshold: <10% for wholesale, TBD for retail. Update monthly as data matures.
Wholesale team has no issues with our reporting — finance is the outlier. The people actually using the data for operations are satisfied. Finance variance is a separate issue caused by different accounting treatment, not data quality.	Finalize Revenue Definitions - Schedule working session with Will (retail) and finance. Define net revenue formula. Document and get sign-off. Apply consistently across all models.
Revenue definition is NOT finalized — this causes recurring confusion. Awaish met with Will once (discovery). Built models with assumptions. But net revenue (gross - discounts - refunds?) has not been formally agreed. This will keep causing friction until it’s locked down.	Set Expectations with Shivani - “Accounting never matches operational data perfectly. Here’s why.” Frame <10% variance as a success, not a problem. Challenge the expectation of exact match. Redirect energy from reconciliation to analysis.
No access to finance’s accounting system — we don’t have access to QuickBooks/NetSuite. Reconciliation is done via sheets Shivani sends to the team, which creates a manual bottleneck.	Build Data Quality Scorecard - Weekly metrics: completeness, freshness, variance. Known limitations documented. Data pipeline health monitoring. Escalation process for issues.

Client Questions

What’s the acceptable variance between our data and finance? Is <10% acceptable? How do we formally communicate this threshold to Shivani and the finance team?
How should we define net revenue for LMNT? Revenue minus discounts minus refunds? Include shipping? Include taxes? Who needs to sign off?
Can we get direct access to QuickBooks/NetSuite reporting? Or will reconciliation always go through Shivani as intermediary?
What reconciliation cadence makes sense? Monthly? Quarterly? Or only when finance raises a discrepancy?
How do we handle the fact that revenue definitions haven’t been finalized? Should we push for a dedicated working session with Will and finance before building more models?

Analysis 3: “What Can I Answer Right Now?” Guide

OBJECTIVE	ANALYSIS
Goal: Set expectations by documenting what questions can/cannot be answered with current data and provide a roadmap for upcoming capabilities.	Available Analyses Inventory - Catalog all models, tables, and dashboards currently available. Map to stakeholder questions. Identify gaps with timelines.
Driving Questions:	Answerable vs Blocked Matrix
- What questions can I answer today?	✅ Questions we can answer now (with which model/table)
- What questions are blocked and by what?	⚠️ Questions partially answerable (what’s missing)
- When will I be able to answer more?	❌ Questions blocked (dependency + timeline)
Stakeholder Value: Reduces frustration, Shows progress, Guides realistic expectations, Provides roadmap for capabilities	Roadmap for Blocked Questions - Map each blocked question to engineering work in flight. Provide estimated timelines. Update monthly.

SAMPLE INSIGHTS	RECOMMENDATIONS
Shivani frequently asks questions we can’t answer yet, causing frustration — having a clear “can/can’t” reference reduces this friction and redirects energy to questions we can actually answer.	Publish & Share Guide - Create one-pager for Shivani to reference. Update after each sprint/model deployment. Include in weekly standups.
Stakeholders don’t know what data capabilities exist — they’re guessing what to ask, often overshooting. A capabilities guide helps them ask the right questions.	Tie to Engineering Roadmap - Each “blocked” question should have a clear unblock date. Use this to show Shivani the pipeline of value coming.

Client Questions

Which stakeholders should receive this guide? Just Shivani, or Phil, Laura, and others too?
How often should we update the “What Can I Answer” guide? After each engineering sprint?
Should we proactively share this guide, or wait for Shivani to bring questions?

Stage 2→3: Understanding → Decision-Making

Analysis 4: Wholesale Partner Lifecycle & Performance

OBJECTIVE	ANALYSIS
Goal: Fully understand the wholesale partner lifecycle end-to-end and guide the wholesale team’s resource allocation, retention strategy, and growth initiatives.	Active Partners Definition Define Active Partners as those who placed any wholesale order in the past 6 months. Includes: Shopify wholesale-tagged orders, manual freight orders, CRM-tracked accounts.
Driving Questions:	Data needed: Partner ID, all order dates, order values, partner segment (health practitioner, gym, retailer), CRM tags, geographic region.
- Which wholesale partners should we focus on?	Excludes: E-commerce DTC orders, retail (Walmart/Target).
- What does the partner journey look like from first to repeat order?
- Where does partner churn happen, and what predicts it?	Partner Lifecycle Mapping (wholesale_dm_customer)
Key Metrics: Active partner count, Revenue by partner segment, Order frequency & recency, Partner retention/churn rate, Partner lifetime value, nCAC per partner	Map partner journey by: 1) Initial order type and channel. 2) Reorder behavior. 3) Segment progression (single-location → multi-location). 4) Inactive periods and reactivations.

SAMPLE INSIGHTS	RECOMMENDATIONS
Top 20% of partners drive ~80% of wholesale revenue — concentration risk, but also a retention opportunity if we can identify what makes these partners sticky.	Build Wholesale Partner Dashboard (weekly + monthly) - Tiles: Active partner count & net growth; Revenue by segment; RFM mix shifts; Churn rate & at-risk flags; Geographic concentration.
Health practitioner segment shows highest growth and repeat rate — this segment reorders more consistently and has lower churn than gyms or general retailers.	Partner Lifecycle Playbook - Welcome: First-order → first-reorder acceleration (within 30 days). Order-#2 push: personalized product recs based on initial order. Order-#3 milestone: volume discount unlock or dedicated account rep. At-risk: 45/60/90-day inactivity triggers with offer ladder.
Partner churn peaks between month 3-6 — partners who don’t reorder within 60 days of their first order rarely come back. The first 60 days are critical.	Segment-Specific Strategies - Health practitioners: exclusivity, early product access, clinical data. Gyms: volume bundling, seasonal promotions. General retail: minimum order thresholds, merchandising support.
Multi-location partners significantly more valuable — partners operating 3+ locations have 4x LTV vs single-location. Identifying and nurturing multi-location expansion is high-leverage.	Data Hygiene Initiative - Reconcile CRM tags with Shopify wholesale tags. Standardize partner segmentation definitions. Flag any partner segment with <50% reorder rate for review.

Client Questions

How should we define an “inactive” wholesale partner? Missing a reorder window by X days? Should the threshold vary by segment?
Where do we currently capture reasons for partner churn? Support tickets, sales rep notes, or no structured churn data?
Do we track multi-location expansion explicitly, or infer it from shipping addresses?
How are wholesale orders currently tagged in Shopify? Is the tags field consistently applied?
What does the CRM in Google Sheets actually contain? Which fields are reliable vs. stale?
Are there historical pricing/promo changes for wholesale we should isolate in the data?

Analysis 5: Revenue Trend Analysis & Forecasting

OBJECTIVE	ANALYSIS
Goal: Understand revenue trends across channels (wholesale, retail, e-commerce), identify growth patterns, and provide simple forecasts to support planning and OKR-setting.	Channel Revenue Trends - Analyze revenue trends by channel (wholesale, retail Walmart, retail Target). Identify growth patterns and seasonality. Compare channel performance over time. Highlight trends and anomalies.
Driving Questions:	Growth Rate Analysis
- Where is revenue heading?	- Calculate growth rates by channel (WoW, MoM, QoQ).
- Which channels are growing fastest?	- Identify accelerating vs decelerating channels.
- What growth targets are realistic?
Key Metrics: Revenue by channel, Growth rates (WoW, MoM, YoY), Trend analysis (seasonality, patterns), Simple forecasts	Simple Forecasting - Create basic forecasts for next 30/60/90 days. Use trend analysis and growth rates. Account for seasonality (if enough data). Provide range estimates, not point estimates.
Segmentation By: Channel, Product/Category, Time period (daily, weekly, monthly)	Scenario Planning - Base case / growth case / downside case. Tie to OKR targets. Support resource allocation decisions.

SAMPLE INSIGHTS	RECOMMENDATIONS
Retail channel shows strong growth trajectory — Walmart and Target both growing. Retail becoming a larger share of total revenue. This shifts where attention should go.	Build Revenue Trend Dashboard - Weekly/monthly revenue trends by channel. Growth rate comparisons. Forecast vs actual tracking. Variance analysis.
Wholesale growth is steady but not accelerating — consistent growth driven by high-value partners. Opportunity to expand partner base, but growth won’t come organically.	Develop Forecasting Process - Create simple forecasting model (trend-based). Update forecasts monthly. Track forecast accuracy. Improve model over time as more data accumulates.
Need 6-12 months of data to confirm seasonal patterns — some early signals of seasonality in retail but not enough history to be confident. Flag this for future analysis.	Support OKR Goal-Setting - Use trend data to inform realistic growth targets. Provide scenario analysis for planning. Help Shivani articulate “why” behind targets to leadership.
E-commerce data not yet modeled — we have wholesale and retail in Broadmarts, but Shopify e-commerce (DTC) is not in product marts yet. This is a significant gap for total revenue picture.	Prioritize E-Commerce Modeling - Flag as engineering priority. Once modeled, include in revenue trends. Complete the total revenue picture.

Client Questions

What’s the growth trajectory by channel? Which channels are growing fastest?
Do we have enough historical data to identify seasonal patterns?
What growth assumptions should we use for OKRs? What’s realistic by channel?
When will e-commerce data be modeled? What’s the timeline for including DTC Shopify data in Broadmarts?
How should forecasts inform resource allocation? If retail is growing faster, should we shift engineering resources there?

Stage 3→4: Decision-Making → Strategic Planning

Analysis 6: Product Portfolio Performance

OBJECTIVE	ANALYSIS
Goal: Analyze product performance across channels (wholesale, retail, e-commerce) to guide product strategy, inventory decisions, and portfolio optimization.	Product Performance by Channel - Analyze which products perform best in which channels. Identify channel-specific product preferences. Compare product performance across Walmart, Target, and wholesale. Guide channel-specific product placement.
Driving Questions:	Product Growth Trends
- Which products should we prioritize by channel?	- Analyze product growth over time.
- What’s driving product growth or decline?	- Identify growing vs declining products.
- How do we optimize product mix across channels?	- Analyze growth drivers.
Key Metrics: Revenue by product/channel, Units sold by product/channel, Product growth trends, Product market share by retailer	Portfolio Analysis - Identify winners and losers. Analyze product mix and concentration. Identify portfolio gaps and opportunities.

SAMPLE INSIGHTS	RECOMMENDATIONS
Certain products over-index at specific retailers — some products perform disproportionately well at Walmart vs Target, or in wholesale vs retail. These insights should guide product placement and marketing spend by channel.	Build Product Performance Dashboard - Product performance by channel. Growth trends and forecasts. Portfolio analysis (concentration, gaps). Cross-channel comparison.
Product mix is shifting over time — as retail grows, product mix shifts toward retail-friendly SKUs. Implications for manufacturing, inventory, and marketing.	Develop Channel-Specific Product Strategies - Identify top products per channel. Recommend product placement changes. Guide new product launch strategy. Optimize marketing spend by product/channel.
Category-level analysis reveals strategic insights — analyzing at the category level (vs individual SKU) reveals patterns that guide portfolio strategy.	Optimize Product Mix - Balance portfolio across channels. Focus resources on high-performers. Address underperformers. Guide new product development.

Client Questions

What product/category breakdowns are available in the data? Can we analyze by product line, or only by SKU?
Do we have COGS data to analyze product profitability? Or is this only revenue-based for now?
How should product insights inform retail strategy? Should we be recommending product placement changes to Walmart/Target?
Are there products that only exist in certain channels? Or is the full portfolio available everywhere?

Analysis 7: Customer Segmentation & Lifecycle

OBJECTIVE	ANALYSIS
Goal: Segment customers by behavior and value across channels to enable targeted marketing, product decisions, and growth strategies.	Customer Segmentation - Define segments by behavior/value (wholesale partners, retail consumers, DTC e-commerce). Analyze segment characteristics and performance. Identify growth trends by segment.
Driving Questions:	Wholesale Partner Segmentation (from wholesale_dm_customer)
- How should we think about our customers across channels?	- Health practitioners, gyms, general retail, multi-location.
- Which segments are most valuable?	- Order frequency, recency, lifetime value.
- What drives customer retention and churn?	- CRM-enriched segments (from Google Sheets).
Key Metrics: Customer/partner count by segment, Segment lifetime value, Segment retention/churn, Segment growth rate, Segment acquisition cost	Retail Consumer Insights (if available) - Product preferences by retailer. Purchase frequency patterns. Geographic patterns (if data permits).

SAMPLE INSIGHTS	RECOMMENDATIONS
Wholesale and retail are fundamentally different customer types — wholesale partners are B2B relationships with lifecycle management needs. Retail consumers are end-users buying through Walmart/Target. Different strategies needed for each.	Build Customer Segmentation Dashboard - Segment performance metrics. Lifecycle analysis. Growth trends by segment. Acquisition and retention insights.
Wholesale partner segments have dramatically different economics — health practitioners have higher LTV and lower churn than gyms. Multi-location partners are the most valuable segment by far.	Develop Segment-Specific Playbooks - Create strategies for each wholesale partner segment. Develop retention programs for high-value segments. Create acquisition strategies for growth segments.
Retail consumer data is limited by what retailers share — we see POS data (units, revenue) but limited customer-level data from Walmart/Target. Consumer segmentation is primarily a wholesale and DTC exercise.	Implement Lifecycle Management - Track partner journey stages (new, active, at-risk, churned). Identify intervention points. Automate retention programs where possible.

Client Questions

How do we define customer segments across channels? Are wholesale partners, retail consumers, and DTC customers fundamentally different analyses?
What customer-level data is available from Walmart/Target? Or is retail data aggregated only?
How should wholesale partner segmentation inform resource allocation?
Can we combine Shopify DTC customer data with wholesale partner data?
What behavioral signals predict partner churn? Can we build an early warning system?

Analysis 8: ROI & Impact Analysis

OBJECTIVE	ANALYSIS
Goal: Quantify value delivered by the data platform to justify investment, build executive support, and guide future priorities.	Value Delivered Quantification - Time saved on manual reporting. Decisions enabled by data. Revenue impact from data-driven actions. Adoption metrics (who’s using what).
Driving Questions:	Adoption Tracking
- What’s the ROI of our data investments?	- Which stakeholders are using which dashboards/reports?
- How do we justify continued investment?	- Which analyses have driven decisions?
- Where should we invest next?	- What’s the adoption trend?
Key Metrics: Time savings (hours/week), Decision impact, Revenue influence, Adoption rate by stakeholder, Cost avoidance	Executive Communication Package - Quarterly summary for Shivani to present upward. ROI narrative. Value milestones. Next-phase justification.

SAMPLE INSIGHTS	RECOMMENDATIONS
Value is being delivered but not measured — Shivani knows data is helpful, but she can’t quantify “how helpful” to her leadership. This makes budget conversations hard.	Build Quarterly ROI Report - Value delivered this quarter. Time savings. Decisions enabled. Adoption metrics. Next quarter roadmap.
Adoption is uneven across stakeholders — wholesale team uses data regularly; retail team is earlier in adoption; finance engagement is friction-heavy around reconciliation.	Create Adoption Scorecard - Track usage by stakeholder. Identify adoption blockers. Create interventions for low-adoption areas.
The strongest ROI narrative combines time savings + decision quality — “We replaced 10 hours/week of manual reporting AND enabled 3 strategic decisions this quarter” is more compelling than either alone.	Help Shivani Build the Narrative - Provide language for stakeholder communication. Draft quarterly executive summary. Support budget/renewal conversations.

Client Questions

How does Shivani currently justify data investment to her leadership? What format does her leadership expect?
Can we track dashboard/report usage? Do we have analytics on who’s accessing what?
What decisions have been influenced by data so far? Can we document specific examples?
What’s the budget cycle? When does Shivani need to justify continued investment?

📊 Analysis Prioritization & Sequencing

Quick Wins (Weeks 1-2)

Retail Channel Performance Comparison — Answers Phil’s Walmart vs Target question, builds confidence
Revenue Reconciliation & Data Trust — Addresses finance mismatch, builds trust, finalizes definitions
“What Can I Answer Right Now?” Guide — Sets expectations, reduces frustration

Strategic Investments (Weeks 2-4)

Wholesale Partner Lifecycle & Performance — Guides resource allocation, supports Laura and wholesale team
Revenue Trend Analysis & Forecasting — Supports planning, enables OKR-setting

Fill Gaps & Strategic (Weeks 4-8)

Product Portfolio Performance — Guides product strategy, optimizes channel mix
Customer Segmentation & Lifecycle — Enables targeted strategies, drives growth
ROI & Impact Analysis — Justifies investment, builds executive support

🎯 How to Present Recommendations

Structure for Each Analysis:

The Business Question
- What stakeholder question does this answer?
- Why does this matter?
The Analysis
- What we’ll analyze
- What insights we’ll deliver
- What format (dashboard/report/presentation)
The Value
- How this helps stakeholders
- What decisions this enables
- What problems this solves
The Effort
- How long this takes
- What’s required
- What dependencies exist
The Timeline
- When we can deliver
- What needs to happen first
- What’s blocked

Example Pitch:

Analysis: Walmart vs Target Comparison

The Business Question: “Phil asked: ‘How is Target performing versus Walmart?’ Right now, we can’t answer this. This analysis answers that question and sets up future comparisons.”

The Analysis: “We’ll compare POS data between retailers, showing:

Revenue by retailer
Growth trends
Product performance
Key differences in how they report data”

The Value: “This helps Phil understand retail channel performance and make resource allocation decisions. It also builds confidence that we can answer basic business questions.”

The Effort: “1-2 days of analysis. We have the data in fact_sales table. Main work is structuring the comparison and explaining differences.”

The Timeline: “Can deliver this week. No blockers. Sets up foundation for deeper retail analysis.”

🔄 Monthly Session Workflow

Before the Session:

Review Current State
- What analyses were delivered?
- What’s been used?
- What questions came up?
Prepare Recommendations
- Identify 3-5 new analysis ideas
- Prioritize by impact/effort
- Map to adoption stages
Check Engineering Status
- What’s been built?
- What’s blocked?
- What’s coming next?
Gather Stakeholder Input
- What questions are they asking?
- What’s their priority?
- What’s blocking them?

During the Session:

Quick State Check (15 min)
- What’s working?
- What’s not?
- What questions came up?
Present Recommendations (30 min)
- Show prioritized list
- Explain each analysis
- Discuss trade-offs
Prioritize Together (30 min)
- Which analyses to do?
- What’s the sequence?
- What’s the timeline?
Align on Next Steps (15 min)
- What’s the plan?
- Who’s responsible?
- When do we check in?

After the Session:

Document Decisions
- What analyses are prioritized?
- What’s the sequence?
- What’s the timeline?
Create Action Items
- What needs to happen?
- Who’s responsible?
- When’s it due?
Update Roadmap
- Engineering work
- Analysis pipeline
- Stakeholder needs
Communicate to Team
- What’s prioritized?
- What’s the plan?
- What support is needed?

💡 Key Principles

1. Connect Engineering to Business Value

Always explain: “This engineering work enables this analysis, which answers this business question”
Map technical progress to stakeholder value
Show adoption stages clearly

2. Prioritize by Impact, Not Just Effort

Quick wins are great, but strategic investments matter more
Balance immediate value with long-term value
Consider stakeholder priorities

3. Make It Actionable

Each analysis should answer a specific question
Each analysis should enable a decision
Each analysis should drive value

4. Set Expectations

Be clear about what’s possible now
Be clear about what’s blocked
Be clear about timelines

5. Build Momentum

Start with quick wins
Build on successes
Show progress regularly

6. Help Her Articulate Value

Provide language for stakeholder communication
Help her explain “what” and “why”
Support her thought partnership

📝 Session Template

Monthly Strategic Planning Session - [Date]

Attendees: Shivani, Robert

Agenda:

Current State (30 min)
- Quick wins review
- Blockers & gaps
- Stakeholder pulse check
Engineering → Value Mapping (30 min)
- Where are we in adoption stages?
- What’s next?
- What’s blocked?
Analysis Recommendations (45 min)
- Present prioritized list
- Discuss each analysis
- Prioritize together
Sequencing & Roadmap (15 min)
- Create timeline
- Identify dependencies
- Set next steps

Decisions Made:

Analysis priorities
Sequence
Timeline
Owners

Action Items:

[Action] - [Owner] - [Due Date]

Next Session: [Date]

🎯 Success Metrics

For Shivani:

Can answer stakeholder questions confidently
Has analyses to support decisions
Can articulate value clearly
Feels like a thought partner

For Stakeholders:

Get answers to their questions
See value from data investments
Trust the data
Use data for decisions

For Us:

Reduced ad-hoc requests
Clear priorities
Better alignment
More efficient work

LMNT_Account_Transition_Prep.md - Overall context
LMNT_Uttam_Shivani_Thought_Partner_Analysis.md - How to guide strategically
LMNT_QA_Investigation_Framework.md - How to answer questions
LMNT_Snowflake_Tables_and_Shivani_Questions.md - Technical reference

This framework should be updated after each strategic planning session to reflect learnings and adjust approach.

Brainforge Knowledge

Explorer

LMNT_Strategic_Planning_Framework

LMNT: Strategic Planning Framework & Monthly Session Outline

Long-term goal: Build a data & insights engine for cross-functional decision-making

The feedback loop

Everyone’s role in the loop

Recap: Current challenges

Near-term goal: Land the first strategic planning cadence

For discussion: Commercial Performance Insights

For discussion: Data Trust & Governance

For discussion: Strategic Analysis & Recommendations

For discussion: BI & Self-Service Enablement

Two-Hour Strategic Planning Session Outline

Session Structure (2 hours)

Part 1: Current State Assessment (30 min)

Part 2: Engineering → Business Value Mapping (30 min)

Part 3: Analysis Recommendations & Prioritization (45 min)

Part 4: Sequencing & Roadmap (15 min)

🔄 Analysis Recommendations by Adoption Stage

Stage 1→2: Foundation → Understanding

Analysis 1: Retail Channel Performance Comparison (Walmart vs Target)

Analysis 2: Revenue Reconciliation & Data Trust

Analysis 3: “What Can I Answer Right Now?” Guide

Stage 2→3: Understanding → Decision-Making

Analysis 4: Wholesale Partner Lifecycle & Performance

Analysis 5: Revenue Trend Analysis & Forecasting

Stage 3→4: Decision-Making → Strategic Planning

Analysis 6: Product Portfolio Performance

Analysis 7: Customer Segmentation & Lifecycle

Analysis 8: ROI & Impact Analysis

📊 Analysis Prioritization & Sequencing

Quick Wins (Weeks 1-2)

Strategic Investments (Weeks 2-4)

Fill Gaps & Strategic (Weeks 4-8)

🎯 How to Present Recommendations

Structure for Each Analysis:

Example Pitch:

🔄 Monthly Session Workflow

Before the Session:

During the Session:

After the Session:

💡 Key Principles

1. Connect Engineering to Business Value

2. Prioritize by Impact, Not Just Effort

3. Make It Actionable

4. Set Expectations

5. Build Momentum

6. Help Her Articulate Value

📝 Session Template

Monthly Strategic Planning Session - [Date]

🎯 Success Metrics

For Shivani:

For Stakeholders:

For Us:

🔗 Related Documents

Graph View

Table of Contents