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EMERSON_DATA_SOURCE_DISCOVERY_MEMO

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Emerson LMNT — Data source discovery memo

Audience: LMNT Leadership, Analytics, Engineering
Source: Emerson/Walmart 3P Dataset (LMNT_425 schema in Snowflake; Emerson private share)
Date: December 2025 (§1–§7 and §3.1–§3.11); §3.12–§3.25 profiled April 2026

1. Executive Summary

What This Is

Emerson Healthcare serves as LMNT’s data provider for Walmart retail performance. They aggregate Walmart’s 3P (third-party seller) data and make it available through a Snowflake Private Share, giving LMNT access to:

  • 26 LMNT product SKUs in Walmart’s catalog
  • 4,577 stores with active POS sales
  • ~$56M in combined revenue (POS + Digital) from March–December 2025
  • 3.35M+ units sold across brick-and-mortar and omnichannel
  • Distribution center logistics, store traits, and merchandising attributes

This is one of LMNT’s most comprehensive retail datasets, offering store-level sales visibility and omnichannel performance tracking that isn’t typically available from retailers.

April 2026 extension (same share, LMNT_425): The memo now also profiles 14 additional Snowflake tables—commercial wholesale / Sage order-to-cash and AR (LMNT_EOD_OPENORDERS, RPT_LMNT_*), Geodis 3PL inventory and open-order / receipt files, and WALMART_SHILOH_ITEM_ATTRIBUTES (Omni / EG hierarchy alongside §3.1). Detail, row-level metrics, and interpretation are in §3.12–§3.25 (Snowflake snapshot 2026-04-10 where cited).

Why This Matters Now

  • Walmart is a Major Revenue Channel
    The Walmart dataset provided through Emerson gives LMNT daily, store-level, and SKU-level visibility into its business. This level of detail allows LMNT to measure velocity, distribution, and omnichannel performance across thousands of stores. The current dataset reflects approximately $56M in captured sales over the last nine months, based on the scope of data Emerson shares.

  • Expansion Opportunity
    LMNT is in 4,577 of Walmart’s 6,192 stores (74% penetration). That’s 1,600+ stores where LMNT could expand.

  • Emerson Relationship is Evolving
    Based on recent discussions, LMNT may be transitioning administrative responsibilities away from Emerson. Understanding what data we get, how we get it, and what alternatives exist is critical for continuity.

  • Store-Level Insights are Rare
    Most retailers provide aggregated reporting. This dataset enables:

    • Geographic performance analysis (which states/stores drive revenue)
    • Store segmentation (format, traits, demographics)
    • Inventory and distribution diagnostics
    • Digital vs. in-store channel analysis

Key Findings

  • Strong Distribution: LMNT is in 74% of Walmart stores nationwide
  • Balanced Channels: POS (26.6M) are nearly equal
  • Recent Launch: All items created Dec 2024–Jul 2025 (launch phase data)
  • 9 Non-Selling SKUs: 26 items in catalog, 17 actively selling
  • State Concentration: Top 5 states account for majority of revenue
  • Data Dependency: Currently reliant on Emerson’s Snowflake share
  • Wholesale / Sage pipeline (April 2026 snapshot): Open order lines on the order of ~27k rows covering ~5.9k distinct orders (LMNT_EOD_OPENORDERS / RPT_LMNT_OPENORDERS); invoiced lines ~33k across ~5.7k invoices (RPT_LMNT_INVOICEDORDERS); supporting tables for cuts, shipment tracking, cash allocations, and AR (ending / paid) — see §3.12–§3.19
  • Geodis 3PL (April 2026 snapshot): Current inventory ~896 snapshot rows; ~332k inventory history rows; open-order detail history ~5.0M rows (~6.6k distinct ship IDs / orders — high row count reflects repeated file snapshots, not unique open lines); ~3.8k receipt lines — see §3.20–§3.24
  • Walmart item hierarchy extension: WALMART_SHILOH_ITEM_ATTRIBUTES — 26 rows (aligned to catalog scale in §3.1), Omni / EG ladders and exclusion flags — see §3.25

What You’ll Find in This Memo

  • Section 2: Understanding the Emerson data relationship and ETL considerations
  • Section 3: Table-by-table profiling with business interpretation
  • Section 4: Cross-table insights (distribution, assortment, sales performance)
  • Section 5: Deep-dive business analysis (state performance, Target comparison)
  • Section 6: Recommendations and next steps
  • §3.12–§3.19: LMNT commercial / Sage — EOD and reporting open orders, invoiced orders, cuts, shipment tracking, cash allocations, ending AR, paid AR
  • §3.20–§3.24: Geodis — inventory (current and history), inventory aging, open-order detail history, receipts
  • §3.25: Walmart Shiloh / Omni / EG item attributes (26-SKU extension vs §3.1)

2. Understanding the Emerson Data Relationship

How LMNT Gets Walmart & Target Data Today

Emerson Healthcare acts as an intermediary between LMNT and Walmart:

  • Walmart provides 3P seller data through their Luminate platform
  • Target provides item catalog and store/DC metadata through their internal syndication and supplier systems.
  • Emerson ingests this data into their Snowflake environment
  • LMNT accesses the data via Snowflake Private Share (zero-ETL replication)

What is Snowflake Private Share?

This is a unique Snowflake feature where data is shared directly between Snowflake accounts without copying or ETL:

  • No data movement: Tables appear in LMNT’s Snowflake as if they’re native
  • No latency: Updates from Emerson are instant
  • No ETL cost: No rows are counted/charged for ingestion
  • Governance: Emerson controls access; LMNT has read-only permissions

Key Implication: This is why we recommended Snowflake as a warehouse option in the ETL assessment. Private Share is a significant advantage for this data source.

What Happens if LMNT Moves Away from Emerson?

Based on recent discussions, LMNT may transition administrative responsibilities. Here’s what we need to understand:

Questions to Answer with Phil/Team:

  • Will LMNT still have access to Walmart data through Emerson’s Snowflake share?
  • Does LMNT have direct access to Walmart Luminate, or is Emerson required?
  • If we lose Emerson access, what are the alternatives?
    • Direct Walmart Luminate API integration (via Fivetran/Polytomic)
    • Another 3P data aggregator
    • Build custom ingestion

Risk Assessment:

  • Low Risk: Emerson continues data sharing even if administrative relationship changes
  • Medium Risk: Need to build Walmart connector
  • High Risk: Lose historical data access (need to export/backup now)

Recommendation: We should export critical historical data to LMNT-owned storage as a backup, regardless of relationship continuity.

3. Table-Level Documentation & Profiling

Each section includes:

  • Metrics Summary Table
  • Business Description
  • Profiling Interpretation

All results reflect actual queries executed against the LMNT_425 schema.

Source: WALMART

3.1 WALMART_ITEM_ATTRIBUTES

Summary Metrics

MetricValue
Total Rows26
Distinct Items (WM_ITEM_NBR)26
Distinct UPCs (WM_UPC_NBR)16
Distinct Vendors (VENDOR_NBR)1
Item Creation Date Range2024-12-16 → 2025-07-11

Business Description

Master catalog of Walmart items sold by LMNT.

Contains detailed product attributes including vendor data, UPCs, packaging dimensions, logistics flags, merchandising text, and lifecycle dates.

Interpretation

  • Full catalog includes 26 LMNT items
  • Only 16 unique UPCs, indicating pack variations or cross-linked UPCs
  • Single vendor present (EMERSON HEALTHCARE LLC)
  • Items are newly launched between late 2024 and mid 2025

3.2 WALMART_STORESALES

Summary Metrics

MetricValue
Total Rows2,257,319
Distinct Stores4,577
Distinct Items Sold17
Total Units Sold3,352,022
Total Sales$29,435,812.51
Date Range2025-03-05 → 2025-12-01

Business Description

Store-level POS transactions capturing sales amount, units, item identifiers, store identifiers, and Walmart fiscal attributes.

Interpretation

  • LMNT sells in ~4,600 stores nationwide
  • 17 items actively selling in brick-and-mortar
  • Strong sales velocity with over 3.35M units and $29.4M in POS revenue
  • Dataset covers ~9 months of sales (aligned with program launch)

3.3 WALMART_OMNISALES

Summary Metrics

MetricValue
Total Rows8,251
Distinct Items17
Distinct Service Channels5
Distinct Order Channels2
Total GMV$26,625,400.03
Total Net Sales$26,625,400.03
Date Range2025-03-05 → 2025-12-01

Business Description

Daily ecommerce and omnichannel sales, including GMV, net sales, units, and service/order channel breakdowns.

Interpretation

  • Digital sales nearly match POS with $26.6M GMV
  • Same 17 items active online and in-store → unified assortment strategy
  • Coverage across 5 service channels indicates deep digital penetration
  • Time range matches POS, confirming consistent ingestion

3.4 WALMART_STOREITEM

Summary Metrics

MetricValue
Total Rows5,516,563
Distinct Stores4,584
Distinct Items16

Business Description

Store–item mapping table showing item availability, stocking flags, and merchandising attributes for each store.

Interpretation

  • LMNT items appear in ~4,584 stores, matching POS distribution
  • 16 unique items in availability mapping (vs. 17 selling)
  • Used for availability, distribution voids, and replenishment diagnostics

3.5 WALMART_STORETRAIT

Summary Metrics

MetricValue
Total Trait Records30,954,234
Distinct Stores4,956

Business Description

Store trait assignments describing operational, geographic, service, and format characteristics for Walmart stores.

Interpretation

  • Coverage exceeds LMNT’s selling footprint (~4,580 stores)
  • Useful for segmentation, modeling, and identifying expansion opportunities
  • High record volume reflects many-to-many trait assignments

3.6 WALMART_STOREDIMENSIONS

Summary Metrics

MetricValue
Total Stores6,192
Distinct STORE_NBR6,192

Business Description

Walmart’s complete store master with location, format, region, and operational attributes.

Interpretation

  • LMNT distributes to ~4,580 of Walmart’s 6,192 stores
  • Remaining ~1,600 stores represent expansion potential
  • Essential for accurate store segmentation and geographic rollups

3.7 WALMART_DCITEMS

Summary Metrics

MetricValue
Total DC-Item Records219,253
Distinct WM Items26
Distinct DCs47

Business Description

Distribution center inventory dataset detailing vendor pack quantities, wholesale pack quantities, costs, shipping/receiving activity, and DC-level coverage.

Interpretation

  • All 26 LMNT catalog items appear in the DC network
  • 47 distribution centers → strong national supply chain coverage
  • Supports DC operations, replenishment modeling, and inventory analytics

3.8 WALMART_CALENDAR

Summary Metrics

MetricValue
Total Calendar Records21,553
Date Range1990-12-29 → 2049-12-31

Business Description

Walmart’s full fiscal, retail, and Gregorian calendar mapping, including LY/YOY comparison fields.

Interpretation

  • Covers 60 years of dates, enabling long-term YOY analysis
  • Required for aligning sales to Walmart fiscal periods
  • Supports forecasting models and future-year dashboards

Source: Target

3.9 TARGET_ITEMDATABASE

Summary Metrics

MetricValue
Total Rows23
Distinct Target Items (Child TCIN)22
Distinct Parent TCINs2
Distinct UPCs22
Distinct Vendors2
Item Launch Date RangeApr 6, 2025 → Sep 21, 2025

Business Description

This table represents the authoritative Target assortment master for LMNT.Master catalog for LMNT products authorized for sale at Target. Includes Target IDs (TCIN, DPCI), UPCs, vendor info, pricing, lifecycle dates, ecommerce flags, and merchandising metadata. This is the authoritative Target assortment master for LMNT.

Profiling Interpretation

  • Target assortment includes 22 sellable child TCINs grouped under 2 parent TCINs, indicating limited bundle or variant hierarchies
  • One-to-one mapping between TCINs and UPCs suggests minimal pack duplication
  • Items launched between April and September 2025, confirming Target as a newer retail channel compared to Walmart

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3.10 TARGET_DAILY_SALES_TCIN_LOC

Summary Metrics

MetricValue
Total Rows626.6K
Distinct Items Sold (TCIN)19
Distinct Selling Locations2.0K
Distinct Reporting Channels3
Distinct Fulfillment Types7
Total Units Sold934.8K
Total Sales$10.7M
Sales Date RangeOct 2, 2025 → Dec 14, 2025

Business Description

Daily Target sales fact table captures item-location-level sales across store and digital channels. It includes revenue and unit measures with detailed attribution for reporting channels, fulfillment methods (store, ShipT, drive-up, etc.), and promotional classifications.

Profiling Interpretation

  • LMNT is actively selling 19 of the 22 authorized Target items, indicating near-full assortment activation
  • Sales span ~2.0K Target locations, representing broad national distribution
  • Presence of 3 reporting channels and 7 fulfillment types confirms strong omnichannel engagement
  • $10.7M in sales and ~935K units over ~2.5 months indicates a high-velocity early retail ramp
  • Sales window aligns with post-launch period, making this dataset ideal for launch performance analysis

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3.11 TARGET_DAILY_INV_TCIN_LOC

Summary Metrics

MetricValue
Total Rows2.36M
Distinct Items23
Distinct Locations2.04K
Inventory Date RangeOct 1, 2025 → Dec 14, 2025
Average Instock Percentage73.8%
Average Out-of-Stock Percentage0.43%

Business Description

Daily data on inventory (on-hand, on-transfer, on-purchase) and stock status (in-stock/out-of-stock) per item and location. Used for inventory health analysis, replenishment optimization, and service-level evaluation.

Profiling Interpretation

  • Inventory coverage slightly exceeds selling footprint, suggesting early-stage network stocking ahead of full activation
  • ~74% average instock rate indicates reasonable availability during launch, with clear room for optimization
  • Strong alignment between inventory and sales date ranges enables joint sales–availability analysis

Source: LMNT ERP / commercial (wholesale / Sage reporting)

3.12 LMNT_EOD_OPENORDERS

Summary Metrics

MetricValue
Total rows (Snowflake INFORMATION_SCHEMA.TABLES, queried 2026-04-10)26,891
Distinct orders (ORDERNUMBER)5,871
Distinct order lines (approx., ORDERNUMBER + ITEMNUMBER)~26,845
Primary sourceSage (often SOURCE = SAGE)

Business Description

End-of-day open order extract from the commercial ERP. Grain is order line (order × item × ship-to with product status): dollars, cases, bill-to/ship-to, PO numbers, and product status (for example AV, SD, CH, DH). Multi-retailer: ship-tos include Target DCs, Walmart warehouses, and other customers — not a single-retailer table.

Interpretation

  • Answers “what is open in Sage?” for wholesale pipeline and open dollars.
  • Open follows ERP close rules; lines can still carry SHIPDATE while classified open.
  • For invoiced/shipped outcomes, use RPT_LMNT_INVOICEDORDERS and RPT_LMNT_SHIPMENTTRACKING — not this table alone.
  • Pairs with RPT_LMNT_OPENORDERS (same universe; reporting filters differ).

Questions

  • What is the open pipeline by customer, ship-to, and product status?
  • How do open dollars and cases trend by week for supply planning?
  • Which ship-tos account for the largest open exposure?
  • How does EOD reconcile to the reporting open-order table (RPT_LMNT_OPENORDERS)?

3.13 RPT_LMNT_OPENORDERS

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)26,891
Relationship to LMNT_EOD_OPENORDERSSame row count — aligned commercial universe
Extra reporting fieldse.g. FILTERDATE, FILTEROPENED

Business Description

Reporting open orders on the same grain as the EOD extract, with reporting filters and dates for standard cuts (e.g. as-of filter date, orders opened in window).

Interpretation

  • Use for report-ready open order views; use EOD for the raw Sage-aligned extract.
  • Counts should reconcile to EOD; differences are usually timing or filter logic.

Questions

  • What does the official “open order report” show for a given filter date vs raw EOD?
  • Which orders entered the open pipeline in a specific reporting window?
  • How do filtered open dollars compare to finance’s published open-order numbers?

3.14 RPT_LMNT_INVOICEDORDERS

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)33,265
Distinct invoices (INVOICENUMBER)5,693
GrainInvoice line

Business Description

Invoiced wholesale lines: invoice number, lot, allowances, net and gross dollars — billed demand. Primary object for recognized invoice revenue and invoice history alongside open pipeline.

Interpretation

  • Not retail POS; this is B2B invoicing.
  • Fewer distinct invoices than rows → multi-line invoices are normal.
  • Join outward to shipment tracking for logistics proof points.

Questions

  • What was invoiced by month, customer, and SKU?
  • How do net vs gross dollars move after allowances?
  • Which invoices tie to which open order lines or lots?
  • How does invoiced volume compare to open pipeline and shipment tracking?

3.15 RPT_LMNT_CUTS

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)486
GrainOrder line (shorts / backorders)

Business Description

Cuts and shorts where ordered quantity was not fully fulfilled — cases shorted, eaches shorted, order line references vs original order.

Interpretation

  • Small, exception-heavy table by design.
  • Use for fill rate and service analytics, not for total demand.

Questions

  • Where are we shorting cases or units by customer or SKU?
  • How do cuts trend vs total order volume?
  • Which lines had partial fulfillment, and does that correlate with inventory or Geodis status?

3.16 RPT_LMNT_SHIPMENTTRACKING

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)5,914
GrainShipment / invoice line (carrier + logistics)

Business Description

Shipment execution: carrier, BOL, tracking numbers, invoice linkage, ship dates, cases, dollars — bridges billing to logistics.

Interpretation

  • Use with invoiced orders for how and when product moved.
  • Supports operations and POD-style questions; not a substitute for retailer POS.

Questions

  • What carrier and tracking ID apply to an invoice or order?
  • How do ship dates align across ERP, invoicing, and 3PL files?
  • What is shipment volume by lane or customer?

3.17 RPT_LMNT_ALLOCATIONS

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)15,736
GrainCash application / allocation line

Business Description

Payment allocations: checks, remittances, deductions, defective allowances, MCS-style linkage to invoices.

Interpretation

  • Row-level cash application — explains partial payments and deductions.
  • Pairs with RPT_LMNT_ENDINGAR and RPT_LMNT_PAIDAR for full AR lifecycle.

Questions

  • What payments and deductions hit which invoices?
  • How large are trade or defective deductions by customer or period?
  • Which invoices show partial allocation or unresolved cash?

3.18 RPT_LMNT_ENDINGAR

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)610
GrainInvoice × customer AR snapshot

Business Description

Open AR aging by invoice: not due, late by 7 / 14 / 21 / 30 days, net due date — snapshot of what is still owed.

Interpretation

  • Fewer rows than allocations because it is one snapshot row per invoice position, not every payment line.
  • Use for DSO / past-due monitoring.

Questions

  • What is outstanding AR by aging bucket and customer?
  • Which invoices are past due and by how much?
  • How does ending AR tie to open invoices and paid lines?

3.19 RPT_LMNT_PAIDAR

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)4,717
GrainPayment line (paid / remitted)

Business Description

Paid invoice activity: remittance, check references, amounts, allocation dates — collections side of AR.

Interpretation

  • Completes the story with invoiced orders (billing) and allocations (application).
  • Use for cash collection timing and reconciliation.

Questions

  • What cash was collected in a period by customer?
  • How do paid lines tie to specific invoices and checks?
  • What is the lag from invoice to allocation date?

Source: Geodis 3PL

3.20 LMNT_GEODIS_INVENTORY

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)896
GrainFacility × vendor × item × lot × UOM (as of file load)

Business Description

Current 3PL inventory snapshot: on-hand buckets (available, holds, damaged, etc.), LOADDATETIME, FILENAME — warehouse view of stock.

Interpretation

  • Align load datetime / file when defining “current.”
  • Reconcile to ERP and to receipts / open orders on timing, not necessarily 1:1 same instant.

Questions

  • What is on hand at Geodis by item and lot?
  • How does 3PL on-hand compare to open orders and receipts?
  • Which file represents the latest snapshot?

3.21 LMNT_GEODIS_INVENTORY_HISTORY

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)331,772
GrainInventory snapshot history (time series)

Business Description

Same dimensions as current inventory plus status and repeated loads — history of snapshots for trends and audits.

Interpretation

  • Expect many rows per logical position across loads; aggregate with care.
  • Filter by date or file for windows; avoid double-counting overlapping snapshots.

Questions

  • How did on-hand and hold buckets change week over week?
  • What did inventory look like as of a historical file date?
  • Where are sustained holds or damaged inventory concentrations?

3.22 LMNT_GEODIS_INVENTORY_AGING_OVERTIME

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)3,130
GrainVendor × item × file date × case type

Business Description

Aging-style cases by CASE_TYPE over FILEDATE — slow-moving and age-bucket style metrics on 3PL inventory.

Interpretation

  • Complements dollar AR tables (different domain — warehouse cases, not customer receivables).
  • Align FILEDATE with other Geodis files for consistent reporting weeks.

Questions

  • Which SKUs show elevated aging cases at the 3PL?
  • How does case-type mix shift over file dates?
  • Where should liquidation or transfer actions focus?

3.23 LMNT_GEODIS_OPEN_ORDERS_DETAIL_HISTORY

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)4,965,825
Distinct ORD_SHIP_ID6,577
Distinct orders (REFERENCE)6,561
Distinct source files (FILENAME)9,589
GrainOpen order line × file snapshot (stacked history)

Business Description

Geodis open order line file with TMS-style fields (ORD_SHIP_ID, REFERENCE, qty, TMS_STATUS, SHIP_TO_NAME, file metadata). Multi-retailer ship-tos. Row volume is high because each file load repeats lines — this is history of what Geodis reported, not unique open lines in the millions.

Interpretation

  • For a current DC view, dedupe to latest LOADDATE / FILEDATE / FILENAME per ORD_SHIP_ID (or equivalent).
  • Contrast LMNT_EOD_OPENORDERS: EOD ≈ one row per open Sage line; Geodis = 3PL operational status with repeated snapshots.

Questions

  • What is the latest TMS status for each warehouse line?
  • How did Geodis-reported lines move across file loads?
  • How does the 3PL open picture compare to Sage EOD for the same reference order?

3.24 LMNT_GEODIS_RECEIPT_DETAIL

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)3,791
GrainReceipt line (PO / receipt)

Business Description

Inbound receipts at the 3PL: PO, receipt date, item, lot, qty received vs ordered.

Interpretation

  • Connects purchase orders to inventory increases; not outbound retail shipment.
  • Use with inventory snapshots to validate inbound timing.

Questions

  • What was received by week and SKU?
  • Where are receipt shortfalls vs PO?
  • How do receipts align with inventory step-changes?

Source: Walmart (hierarchy extension)

3.25 WALMART_SHILOH_ITEM_ATTRIBUTES

Summary Metrics

MetricValue
Total rows (Snowflake estimate, queried 2026-04-10)26
RoleAlternate Omni / EG hierarchy and mapping vs §3.1 item attributes
Key column groupsWM_Item_Nbr, UPC/GTIN, Omni_ dept → fineline, EG_ brand ladder, ADJ_Omni_*, Exclude / EG_Exclude, syndicated brand fields

Business Description

Extended item master for Walmart: Omni hierarchy (department through fineline), EG (Emmie / internal) brand and category ladder, adjusted Omni fields, and exclusion flags — supports reporting that needs Shiloh / EG mapping beyond the wide attribute set in WALMART_ITEM_ATTRIBUTES (§3.1).

Interpretation

  • Same catalog scale as §3.1 (~26 rows); join on WM_Item_Nbr / UPC to sales and inventory facts.
  • Respect Exclude / EG_Exclude when building cuts for syndicated or internal hierarchies.

Questions

  • How is each SKU mapped into Omni and EG hierarchies?
  • Which items are excluded from specific hierarchy or syndicated views?
  • How do Shiloh attributes compare to §3.1 for the same item?

4. Cross-Table Insights & Strategic Observations

This section synthesizes findings across all tables to answer key business questions.

4.1 Distribution Footprint Analysis

TableStoresCoverage
Total Walmart Fleet (STOREDIMENSIONS)6,192100%
Stores with Traits (STORETRAIT)4,95680%
Stores with Store-Item Mapping4,58474%
Stores with POS Sales4,57774%

What This Tells Us

  • LMNT has achieved strong national distribution – present in nearly 3 out of 4 Walmart stores within 9 months of launch.

  • Distribution and sales align tightly – 4,584 stores have LMNT on shelves, and 4,577 are actively selling. This suggests minimal “distribution voids” (stores with product but zero sales).

  • Expansion opportunity: ~1,600 stores – LMNT could potentially expand into the remaining 26% of Walmart’s footprint. These stores likely fall into:

    • Smaller format stores (Neighborhood Markets)
    • Geographic markets with lower CPG velocity
    • Stores not yet approved for LMNT’s category

Recommendation: Analyze store traits of the 1,600 non-selling stores to identify which are viable expansion targets vs. poor-fit formats.

4.2 Assortment & SKU Performance

MetricValue
Total Catalog Items26
Items with Store Sales17
Items with Digital Sales17
Items in DC Supply Chain26

What This Tells Us

  • Unified omnichannel assortment – The same 17 SKUs sell in-store and online, indicating a consistent customer experience.

  • Full supply chain readiness – All 26 catalog items are set up in Walmart’s DC network, even if only 17 are actively selling.

  • 9 SKUs are “zombie inventory” – These items exist in the catalog and DC system but have zero sales (see detailed table in Section 5.1). Reasons could include:

    • Items marked DELETE status (6 of 9 were deleted in May 2025)
    • Never launched to stores
    • Failed product tests
    • Seasonal/promotional items not yet activated

Recommendation: Review the 9 non-selling SKUs with merchandising team. If not part of a future launch plan, remove from catalog to clean up reporting.

4.3 Channel Mix: POS vs. Digital (Total Sales)

ChannelRevenue% of Total
POS (In-Store)$29.4M53%
Digital (Omni)$26.6M47%
Combined~$56M100%

What This Tells Us

  • Walmart is a major revenue driver – 56M in 9 months extrapolates to \~75M annualized from a single retailer.

  • Digital is nearly as important as in-store – Most CPG brands see 10-20% digital mix; LMNT’s 47% is exceptional and likely driven by:

    • Health-conscious customers shopping online
    • Subscription/repeat purchase behavior
    • Walmart+ membership penetration

  • Omnichannel strategy is working – Customers can discover LMNT in-store and reorder online (or vice versa).

Implication for analytics: Any Walmart reporting must include both POS and digital to accurately measure total performance. Analyzing only one channel would miss nearly half the picture.

5. Deep-Dive Business Analysis

This section provides actionable insights based on profiling results.

5.1 Non-Selling SKUs Analysis

We identified 9 items in the catalog for which no sales transactions exist:

Item NameStatusUPCObsolete DateObservations
LMNT ZS 4 GRF S 16OZDELETE810183490382025-05-13Grapefruit 4-pack can deleted May 2025
LMNT ZS ORNG ST 6CTDELETE810183490272025-05-13Orange stick 6-pack deleted May 2025
LMNT ZS RASBY ST 6CTDELETE810183490282025-05-13Raspberry stick 6-pack deleted May 2025
LMNT ZS 1 CTR S 16OZDELETE850055993102025-05-13Citrus single can deleted May 2025
LMNT ZS 4 BC LM 16OZDELETE810183490352025-05-13Black cherry lime 4-pack deleted May 2025
LMNT ZS 4 CTR S 16OZDELETE810183490362025-05-13Citrus 4-pack can deleted May 2025
LMNT ZS 4 WTR S 16OZACTIVE810183490372500-01-01Watermelon 4-pack — ONLY ACTIVE NON-SELLER
LMNT ZS CTRS ST 6CTDELETE810183490252025-05-13Citrus stick 6-pack deleted May 2025
LMNT ZS GRPFT ST 6CTDELETE810183490292025-05-13Grapefruit stick 6-pack deleted May 2025

Key Observations

  1. 8 of 9 were deleted in May 2025 – These appear to be a product rationalization or test that didn’t perform.

  2. 1 ACTIVE item with zero sales – LMNT ZS 4 WTR S 16OZ (Watermelon 4-pack) is still marked ACTIVE but has never sold. This could mean:

    • Item was just added to the catalog (late 2024) but not yet distributed
    • Test SKU waiting for launch
    • Data lag or missing transactions

  3. Mix of sticks and cans – Both format types appear in non-sellers, suggesting the issue isn’t format-specific.

Action Items:

  • Confirm with merchandising: Is Watermelon 4-pack (674256756) planned for launch?
  • If not, mark as DELETE to clean up reporting
  • Remove deleted SKUs from future catalog loads to reduce noise in analytics

5.2 Example State-Level Sales Performance

Revenue & Units by Month (Last 3 Months, Pivoted):

  • m0 = current month-to-date (December 2025, partial)
  • m1 = last full month (November 2025)
  • m2 = month before last (October 2025)
STATE_NAMEREVENUE_M0UNITS_M0REVENUE_M1UNITS_M1REVENUE_M2UNITS_M2
TEXAS$108,49314,067$382,85350,627$469,63061,645
FLORIDA$68,6297,697$225,42725,365$260,48229,189
CALIFORNIA$43,0554,832$138,69715,272$158,65217,222
NORTH CAROLINA$35,0463,946$117,24613,084$134,97115,133
GEORGIA$34,0313,810$114,70212,860$131,35514,701
OKLAHOMA$33,6723,759$116,98713,069$144,29316,223
TENNESSEE$33,0903,697$112,59712,564$130,90614,657
MISSOURI$30,7243,428$106,46911,911$122,69813,805
ARIZONA$30,6883,447$106,58711,914$121,37813,650
ARKANSAS$30,3463,384$103,03811,496$125,22914,004
(remaining 40 states omitted for brevity)………………

Top 10 States Account for $1.9M / Month

From the data above (using m1/November as baseline):

  • Top 5 states: TX, FL, CA, NC, GA → $1.13M (46% of November total)
  • Top 10 states: + OK, TN, MO, AZ, AR → $1.87M (76% of November total)

Geographic Insights

  • State Concentration

    • Top 5 states (TX, FL, CA, NC, GA) account for nearly half of total Walmart revenue
    • Smaller states show consistent but low velocity, suggesting distribution exists but velocity per store is low

  • Under-Penetrated Opportunity States
    States with decent size but lower relative revenue:

    • New York: $60K/month (population rank #4 nationally)
    • Washington: $65K/month (population rank #13)
    • Massachusetts: $31K/month (population rank #16)
    • New Jersey: $27K/month (population rank #11)

    These states may have:

    • Lower Walmart density (stronger regional competitors like Wegmans, H-E-B)
    • Demographic mismatch with LMNT’s target customer
    • Distribution gaps (not in enough stores yet)

5.3 Comparative Analysis: Walmart vs. Target

We also profiled Target’s data (separate data source). Here’s how Walmart compares:

Target Monthly Sales (2025)

MonthRevenueUnits
Oct 2025$4.58M397K
Nov 2025$4.07M355K
Dec 2025 (MTD)$1.16M101K

Walmart Monthly Sales (Est. from 9-month total)

MetricWalmartTarget
Avg Monthly Revenue~$6.2M~$4.3M
Avg Monthly Units~650K~380K

Key Takeaway: Walmart is outperforming Target by approximately 45% in revenue and 70% in unit volume.

Why the Difference?

  • Store count: Walmart has more stores where LMNT is distributed
  • Customer base: Walmart’s broader demographic may align better with LMNT
  • Digital mix: Walmart’s 47% digital vs. Target’s 20% digital (approximate)

Target Channel Split

October 2025:

  • Store (STR): $3.67M | 323K units (~80% of revenue)
  • Online / Fulfilled (FF): $0.88M | 72K units (~19%)
  • FC: ~$28K | ~2K units (<1%)

November 2025:

  • Store (STR): $3.27M | 291K units (~80%)
  • FF: $0.77M | 61K units (~19%)
  • FC: ~$28K | ~2K units (<1%)

Observation: Target’s digital mix (~20%) is less than half of Walmart’s (~47%). This suggests:

  • Walmart+ is driving more digital adoption
  • LMNT may benefit from Walmart’s stronger e-commerce platform
  • Target customers prefer in-store discovery

6. Recommendations & Next Steps

6.1 Immediate Actions (This Week)

1. Clarify Emerson Relationship

Owner: Phil / LMNT Leadership
Action: Confirm future state of Emerson data access

  • Will Snowflake share continue?
  • Do we need to prepare for alternative ingestion?

Why it matters: Losing access mid-project would derail analytics roadmap.

2. Export Historical Data as Backup

Owner: Brainforge Data Team
Action: Export all 9 months of Walmart data to LMNT-owned storage (S3/Snowflake)

Why it matters: Even if Emerson continues sharing, we should own our historical data.

3. Clean Up Non-Selling SKUs

Owner: LMNT Merchandising + Brainforge
Action:

  • Confirm Watermelon 4-pack (674256756) launch plan
  • Remove DELETE-status SKUs from future reporting models

Why it matters: Reduces noise in dashboards and prevents confusion about assortment.

6.2 Short-Term Initiatives (Next 2 Weeks)

4. Build Store-Level Performance Dashboard

Owner: Brainforge Data Team
Deliverables:

  • State-level revenue trends (m/m, YoY)
  • Top 100 stores by revenue
  • Distribution void analysis (stores with product but zero sales)

Why it matters: Enables merchandising and sales teams to identify underperforming markets.

5. Analyze Expansion Opportunity

Owner: Brainforge + LMNT Merchandising
Action:

  • Profile the 1,600 stores where LMNT is not present
  • Use WALMART_STORETRAIT to segment by format, region, demographics
  • Identify top 200 “best fit” stores for expansion

Why it matters: Provides a data-driven roadmap for growing Walmart distribution from 74% to 85%+.

6. Digital vs. In-Store Attribution Analysis

Owner: Brainforge Data Team
Action:

  • Break down OMNISALES by service channel (5 channels identified)
  • Identify which digital channels drive most revenue
  • Analyze customer behavior (first purchase in-store, reorder online?)

Why it matters: LMNT’s 47% digital mix is exceptional—understanding what’s driving it can inform strategy at other retailers.

6.3 Medium-Term Initiatives (Next 30 Days)

7. Compare Walmart vs. Target Performance

Owner: Brainforge Data Team
Deliverables:

  • Unified dashboard showing both retailers side-by-side
  • SKU-level comparison (which products sell better where?)
  • Channel mix comparison (digital vs. in-store)

Why it matters: Informs which retailer to prioritize for future launches and promotional investment.

8. State-Level Velocity Analysis

Owner: Brainforge Data Team
Action:

  • Calculate revenue per store by state
  • Identify “high velocity” states (e.g., Texas) vs. “low velocity” states (e.g., New York)
  • Recommend either:
    • Marketing investment (to boost velocity in existing stores)
    • Distribution expansion (to add more stores in high-velocity states)

Why it matters: Provides clear priorities for growth strategy.

6.4 Data Quality & Governance

9. Define Data Refresh Cadence

Owner: Brainforge + Emerson
Action:

  • Document how often Emerson updates the Snowflake share
  • Set expectations for data freshness (daily, weekly?)
  • Create alerting for data delays

Why it matters: Stakeholders need to know when data is “current” vs. “stale.”

7. Appendix: Technical Details

7.1 Snowflake Private Share Details

  • Share Name: LMNT_425 (Emerson-provided schema)
  • Access Method: Snowflake Private Share (zero-ETL)
  • Refresh Cadence: TBD (needs confirmation with Emerson)
  • Data Retention: Full history since March 2025 launch

7.2 Table Relationships

WALMART_ITEM_ATTRIBUTES (26 items)
    └─> WALMART_STORESALES (4,577 stores, 17 items)
    └─> WALMART_OMNISALES (8,251 daily records, 17 items)
    └─> WALMART_STOREITEM (5.5M store-item records)
    └─> WALMART_DCITEMS (219K DC-item records)

WALMART_STOREDIMENSIONS (6,192 stores)
    └─> WALMART_STORETRAIT (31M trait assignments)
    └─> WALMART_STORESALES (join on STORE_NBR)

WALMART_CALENDAR (21K date records)
    └─> WALMART_STORESALES (join on fiscal dates)

7.3 Data Volume Estimates

TableRowsApprox SizeMonthly Growth
WALMART_STORESALES2.3M~500 MB+250K rows/month
WALMART_OMNISALES8K~2 MB+800 rows/month
WALMART_STOREITEM5.5M~1 GBRelatively static
WALMART_STORETRAIT31M~2 GBStatic (reference)
WALMART_STOREDIMENSIONS6K<1 MBStatic (reference)

Total Dataset: ~3.5 GB (including all reference tables)

Graph View