Ward runs customer on your SAP pharmacy data
Pharmacy retailers on SAP run customer through Ward. 20,000+ SKUs across your pharmacies, scans continuously around the clock.
How Ward turns SAP data into pharmacy customer
What customer behavior does: Ward tracks basket composition shifts, daypart patterns, and customer segment migration.
Pharmacy & Health changes the scale of the problem: 20,000+ SKUs, every one of your pharmacies. Regulated inventory, seasonal demand spikes, and front-of-store optimization. Ward handles the complexity so your pharmacists focus on patients.
How it runs. Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
The connection itself. Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
What you get
- Cross-sell opportunity detection
- Basket composition trends
- Daypart behavior modeling
- Customer segment migration
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 Customer matters for Pharmacy retail
Rx refill cycles give pharmacy a built-in behavioral rhythm no other vertical has. What customers do during each visit, whether they browse front-of-store and which categories they engage, determines whether the business is high-margin retail or just a dispensary with overhead. Ward tracks engagement patterns during Rx visits to surface conversion opportunities.
What Ward has eyes on.
Coverage is store by store, category by category. Ward keeps a running read on Rx fill rate, OTC attach rate, expiry waste % across 20,000+ SKUs and compares each store against its own baseline, not against the chain. That is the difference between knowing the estate is fine and knowing which seven pharmacies are not.
Ward ingests POS transactions, inventory positions, purchase orders from SAP Retail on a read-only connection. Nothing is written back, and your ERP configuration does not change. SAP stays the system of record.
The customer model runs every morning, not on a reporting calendar. It catches the pattern, traces root cause, and attaches the next step before the number reaches a review deck.
At the metric level. Ward tracks Rx visit-to-front-of-store conversion rate, wait-time-correlated browsing patterns, refill cycle purchase cadence, and health-condition-to-OTC correlations. First-time wellness purchases during Rx visits are flagged as high-value engagement signals.
Why this combination
is its own problem.
SAP Retail is the system of record for most pharmacy operators of your size, which means the indicators Ward needs are already there. The gap is not data collection. It is that nobody has time to read POS transactions and inventory positions every morning across every store.
- 01 Wait-time effect on front-of-store conversion is non-linear; chain-wide wait-time targets miss the per-store sweet spot that actually drives the highest attach.
- 02 Customer analytics focus on loyalty card patterns while most pharmacy visits are Rx-driven and don't involve loyalty engagement; the visit-cadence signal is the higher-value insight.
Benchmarks. Pharmacy Rx-customer front-end attach: 25-45% chain average, with top performers above 60%. The wait-time conversion sweet spot is typically 8-15 minutes; under 5 minutes and over 25 minutes both cut attach by 40-60%.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to SAP Retail. Ward pulls from POS transactions, inventory positions, purchase orders and starts building baselines. First findings land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: baselines
Ward needs roughly two weeks of history per store to separate a real deviation from normal variance. During this window the findings are directionally right and the thresholds are still moving.
-
03
Weeks 4 to 12: operating rhythm
Findings arrive daily and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
How Ward connects to SAP Retail
Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.
Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.
Data Ward reads from SAP
Impact metrics with SAP
Data lake enrichment
Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates
Pharmacy KPI impact
Frequently asked questions
Ward tracks basket composition shifts, daypart patterns, and customer segment migration. 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 analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.
Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.
Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Pharmacy-specific insight cards. No custom development required.
Ward tracks Rx visit-to-front-of-store conversion rate, wait-time-correlated browsing patterns, refill cycle purchase cadence, and health-condition-to-OTC correlations. First-time wellness purchases during Rx visits are flagged as high-value engagement signals.
Ward reveals that Rx wait time is the strongest predictor of front-of-store conversion, with a clear sweet spot: too short and customers skip browsing, too long and frustration overrides spending. Ward identifies the optimal window and recommends repositioning high-margin impulse items along the path between the pharmacy counter and the rest of the store.
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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