Promo Effectiveness for Pharmacy on Snowflake, built for supply chain
A pharmacy VP Supply Chain on Snowflake should not be hunting for promos problems. Ward pulls forward them each day.
Promo Effectiveness for Pharmacy on Snowflake, scoped to supply chain
You find out about stockouts after customers do. Ward writes the finding at the altitude a VP Supply Chain works at.
What promo effectiveness does: Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.
For Pharmacy & Health retailers, that means watching 20,000+ SKUs across pharmacies. Regulated inventory, seasonal demand spikes, and front-of-store optimization. Ward handles the complexity so your pharmacists focus on patients.
How Ward hands you Promos cards: Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
Setup: Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
Key capabilities
- Net lift measurement (not gross)
- Cannibalization quantification
- Pull-forward detection
- Promo ROI scorecards
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled cough and cold against the illness curve for the 38 Northeast pharmacies. Front of store is running two weeks behind the demand signal.
| Signal | Finding |
|---|---|
otc_sales_daily | Cough/cold units +31% WoW, on-shelf availability down to 88% |
replenishment | Vendor lead time 6 days, reorder points still on the summer baseline |
front_store.attach | Wellness attach on flu-season fills 19% vs. 34% chain best |
Recommend: raise cough/cold reorder points at all 38 stores now, pull the wellness endcap forward two weeks, and add the attach prompt at the counter.
otc_sales_daily…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
mckesson_wholesale_orders | import | 2m ago |
cardinal_lot_expiry | import | 2m ago |
sap_pos_transactions | import | 14m ago |
sap_inventory_snapshot | import | 1h ago |
retail_otc_sales_daily | import | 1h ago |
retail_front_store_margin | import | 1h ago |
retail_planogram_audit | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-front-store | permit | Model::"front_store_margin" |
ops-read-default | permit | Model::* |
phi-forbid-all | forbid | Model::"patient_*" |
supply-read-expiry | permit | Model::"lot_expiry" |
Why Promos matters for Pharmacy retail
Pharmacy has a unique promotional advantage: Rx refill cycles create a predictable visit cadence. The question isn't whether a promo lifts sales, it's whether it converts Rx-only visitors into front-of-store buyers or merely discounts for customers who would have purchased anyway. Ward measures effectiveness against the refill visit baseline.
Why this combination
is its own problem.
A VP Supply Chain in pharmacy retail owns numbers that move faster than the reporting cycle that covers them. Rx fill rate, OTC attach rate, expiry waste % shift store by store, daily. A monthly pack cannot represent that. You find out about stockouts after customers do.
- 01 Wait-time merchandising next to the pharmacy counter is the highest-incrementality promotional surface, but most promotional spend goes to circulars and front-of-store endcaps.
- 02 Promo ROI gets measured on category lift without separating Rx-attached incremental conversion from deal-seeker cherry-picking.
Benchmarks. Pharmacy front-of-store promo events typically show 25-60% gross lift but only 5-20% net incremental lift after deal-seeker cannibalization. Targeted Rx-customer offers usually achieve 2-4x the incremental conversion of blanket front-end promos.
What Ward has eyes on.
Ward pulls from Snowflake rather than replacing it. Any table or view in your Snowflake account, cross-database joins, historical data at any depth come across on a read-only connection, get enriched with contextual data, and come back as daily cards. Your Data Platform is untouched.
Know which promos actually work. Ward runs the model continuously rather than on a reporting cycle, which is why a finding lands the morning the pattern starts instead of at the end of the period.
Every one of your pharmacies gets its own baseline. Ward keeps a running read on Rx fill rate, OTC attach rate, expiry waste % against it and pulls forward only the deviations that hold up. The two that show up most in pharmacy retail are seasonal illness demand and rx-to-OTC conversion, and both are baseline problems before they are P&L problems.
At the metric level. Ward distinguishes between Rx-driven visit conversion, deal-seeker cannibalization, and category-specific promotional ROI. True incrementality is calculated against the predictable Rx visit cadence as a demand baseline.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Snowflake. Ward reads straight from any table or view in your Snowflake account, cross-database joins, historical data at any depth and starts building baselines. First daily cards land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: steady state
Ward returns insight cards every morning, each with root cause and the next step. Volume settles at a level a single person can read over coffee. The measure of success is not how many daily cards arrive, it is how many get acted on.
How Ward connects to Snowflake
Ward queries your Snowflake data warehouse directly. If your retail data lives in Snowflake, Ward reads it without moving or copying anything.
Setup: Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake.
Data Ward reads from Snowflake
Impact metrics with Snowflake
Data lake enrichment
Ward enriches Snowflake data with: Any Snowflake table, Weather & events, Demographics, Competitor data, Custom feeds
You find out about stockouts after customers do.
- ×Demand forecasts are off by 15-25% and nobody catches it until the shelf is empty
- ×Supplier fill rate problems announce themselves at the receiving dock
- ×Safety stock levels get set once a year and left alone
- ×No early warning system for supply chain disruptions
- ×Replenishment exceptions require manual triage every morning
- ✓Stockout prediction cards arrive 24-72 hours before empty shelves
- ✓Supplier fill rate tracking with automatic escalation
- ✓Dynamic safety stock recommendations based on current demand signals
- ✓Weather, event, and macro-driven demand adjustments
- ✓Replenishment exceptions auto-prioritized by revenue impact
Stockouts cost retailers $1.14 trillion in missed sales globally each year. Source: IHL Group
Pharmacy KPI impact
Frequently asked questions
Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading. For Pharmacy retail specifically, Ward monitors 20,000+ SKUs across your pharmacies and delivers automated insight cards with root cause analysis and recommended actions.
Ward tracks Rx fill rate, OTC attach rate, Expiry waste %, Script count, Front-store margin at the store-category level. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.
Ward connects via Snowflake SQL API with key-pair authentication. Read-only warehouse. Your data never leaves Snowflake. Data points include: Any table or view in your Snowflake account, Cross-database joins, Historical data at any depth.
Yes. Ward reads Snowflake data and combines it with contextual signals (weather, events, demographics) to generate Pharmacy-specific insight cards. No custom development required.
You find out about stockouts after customers do. Ward solves this with automated insight cards: Stockout prediction cards arrive 24-72 hours before empty shelves. Supplier fill rate tracking with automatic escalation. Dynamic safety stock recommendations based on current demand signals.
Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste %, tailored for Supply Chain decision-making. Each card includes what changed, why it matters, and what to do next.
Ward distinguishes between Rx-driven visit conversion, deal-seeker cannibalization, and category-specific promotional ROI. True incrementality is calculated against the predictable Rx visit cadence as a demand baseline.
A skincare promotion shows strong gross lift, but Ward reveals most of the uplift came from existing skincare buyers cherry-picking deals, not Rx customers making incremental front-of-store purchases. Ward recommends a different model: targeted checkout offers for Rx customers based on health profile. Pilot shows materially higher incremental conversion at lower promotional cost.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
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Root causes, not just alerts. See it on your data.
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