LMNT Data Analysis Roadmap
Created: February 16, 2026 Purpose: Strategic analysis roadmap connecting engineering work to business value and stakeholder adoption
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. |
| Driving Questions: | Includes: Shopify wholesale-tagged orders, manual freight orders, CRM-tracked accounts |
| - Which wholesale partners should we focus on? | |
| - What does the partner journey look like from first to repeat order? | Data needed: Partner ID, all order dates, order values, partner segment (health practitioner, gym, retailer), CRM tags, geographic region |
| - Where does partner churn happen, and what predicts it? | |
| Excludes: E-commerce DTC orders, retail (Walmart/Target) | |
| Key Metrics: | |
| - Active partner count | Partner Lifecycle Mapping (wholesale_dm_customer) |
| - Revenue by partner segment | Map partner journey by: |
| - Order frequency & recency | 1) Initial order type and channel |
| - Partner retention/churn rate | 2) Reorder behavior (e.g., 3 of 5 expected reorders completed) |
| - Partner lifetime value | 3) Segment progression (single-location → multi-location) |
| - nCAC per partner | 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 recommendations 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. | Geographic Expansion Analysis - Identify high-performing regions and replicate success. - Flag underserved markets with high partner density potential. - Align wholesale team territories with data insights. |
| CRM data (Google Sheets) + Shopify tags create visibility gaps - Partner tagging is inconsistent; some wholesale orders may be miscategorized. | 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 (health practitioners vs gyms)?
- Where do we currently capture reasons for partner churn? Is it support tickets, sales rep notes, or do we have no structured churn data?
- Do we track multi-location expansion explicitly, or infer it from shipping addresses? Can we identify when a partner goes from 1 to 3 to 5 locations?
- How are wholesale orders currently tagged in Shopify? Is the
tagsfield consistently applied? Are there orders missing the wholesale tag? - What does the CRM in Google Sheets actually contain? Which fields are reliable vs. stale? When was it last cleaned?
- Are there historical pricing/promo changes for wholesale (e.g., volume discount tiers, seasonal offers) we should isolate in the data?
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: | |
| - How is Target performing versus Walmart right now? | Key Models: |
| - What were sales across both stores yesterday? | - fact_sales: POS data from both Walmart and Target (apples-to-apples comparison) |
| - What are the differences in how they define sales? | - retail_fact_walmart_only_sales: Walmart order management data (NOT POS) |
| - What can I actually glean from retail data right now? | - Products table: joined for product/category breakdowns |
| Key Metrics: | 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. |
| - Revenue by retailer (POS) | |
| - Growth trends (WoW, MoM) | Geographic Analysis (if available) |
| - Product/category performance by retailer | - Check raw Target tables for region/store fields |
| - Revenue recognition differences | - Check Walmart tables for geographic dimensions |
| - Geographic performance (if available) | - Compare performance by market if data permits |
| 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. Document this clearly for stakeholders. | 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?
- How should we handle the Walmart-only sales table when reporting total retail revenue? Is this an additive join, or does it overlap with POS data?
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: | |
| - Why doesn’t our wholesale revenue match finance? | Retail Revenue Reconciliation |
| - Can we trust this data? | Compare our fact_sales to retailer platforms (if accessible). |
| - How do we define net revenue? | Document structural differences (POS vs order management). |
| Explain variance between sources. | |
| Key Metrics: | |
| - Variance % (our data vs Shopify vs Finance) | Net Revenue Definition Alignment |
| - Data completeness (% of expected records) | Current status: NOT FINALIZED. |
| - Data freshness (time to availability) | Need to align with Will (retail) and finance on: |
| - Known limitations count | - Net revenue = Gross - Discounts - Refunds? |
| - Include/exclude shipping? Taxes? | |
| Comparison Sources: | - Reconcile with accounting treatment. |
| - Shopify (operational data) | |
| - QuickBooks/NetSuite (accounting) | Data Freshness Tracking |
| - Retailer platforms (if accessible) | - 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?
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 key trends and anomalies. |
| Driving Questions: | |
| - Where is revenue heading? | Growth Rate Analysis |
| - Which channels are growing fastest? | - Calculate growth rates by channel (WoW, MoM, QoQ). |
| - What growth targets are realistic? | - Identify accelerating vs decelerating channels. |
| - Are there seasonal patterns? | - Analyze growth drivers. |
| Key Metrics: | Simple Forecasting |
| - Revenue by channel | - Create basic forecasts for next 30/60/90 days. |
| - Growth rates (WoW, MoM, YoY) | - Use trend analysis and growth rates. |
| - Trend analysis (seasonality, patterns) | - Account for seasonality (if enough data). |
| - Simple forecasts | - Provide range estimates, not point estimates. |
| Segmentation By: | Scenario Planning |
| - Channel (wholesale, retail Walmart, retail Target) | - Base case / growth case / downside case. |
| - Product/Category | - Tie to OKR targets. |
| - Time period (daily, weekly, monthly) | - 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 (holiday bumps, new year dips) 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 — blind spot - 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? What’s driving growth?
- Do we have enough historical data to identify seasonal patterns? How many months of data do we have per channel?
- What growth assumptions should we use for OKRs? What’s realistic by channel? How do we set growth targets?
- 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?
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: | |
| - Which products should we prioritize by channel? | Product Growth Trends |
| - What’s driving product growth or decline? | - Analyze product growth over time. |
| - How do we optimize product mix across channels? | - Identify growing vs declining products. |
| - Analyze growth drivers. | |
| Key Metrics: | |
| - Revenue by product/channel | Portfolio Analysis |
| - Units sold by product/channel | - Identify winners and losers. |
| - Product growth trends | - Analyze product mix and concentration. |
| - Product market share by retailer | - 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. This has 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 — which categories to invest in, which to maintain, which to reconsider. | 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?
- How do we use product insights with the wholesale team? Can we recommend products to specific partner segments?
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: | |
| - How should we think about our customers across channels? | Wholesale Partner Segmentation (from wholesale_dm_customer) |
| - Which segments are most valuable? | - Health practitioners, gyms, general retail, multi-location. |
| - What drives customer retention and churn? | - Order frequency, recency, lifetime value. |
| - CRM-enriched segments (from Google Sheets). | |
| Key Metrics: | |
| - Customer/partner count by segment | Retail Consumer Insights (if available) |
| - Segment lifetime value | - Product preferences by retailer. |
| - Segment retention/churn | - Purchase frequency patterns. |
| - Segment growth rate | - Geographic patterns (if data permits). |
| - Segment acquisition cost |
| 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? Should the wholesale team organize by segment?
- Can we combine Shopify DTC customer data with wholesale partner data? Are there customers who buy both DTC and wholesale?
- What behavioral signals predict partner churn? Can we build an early warning system?
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
This roadmap should be reviewed monthly and updated based on stakeholder needs, engineering progress, and analysis results.