Customer · SAP

Customer Behavior, powered by SAP

Understand the person behind the basket. Ward runs it directly off your SAP feed, read-only, with no changes to your ERP.

How SAP feeds customer

Customer Behavior is a insight card type Ward runs continuously. Ward tracks basket composition shifts, daypart patterns, and customer segment migration.

How Ward delivers Customer findings: Ward analyzes transaction-level data to detect shifts in basket composition, shopping frequency, daypart preferences, and segment movement.

How the connection works. 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
app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83%, backroom replenishment lag at 2–4p
promo.liftBOGO crackers cannibalized Brand Y by 28%, net category +6%

Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling
Ask anything, Ward routes to the right agent. Cmd+K

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Sources

Connect external systems to the data lake.

NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"
Customer on SAP data, live product demo.

What Ward has eyes on.

Ward ingests SAP rather than replacing it. POS transactions, inventory positions, purchase orders come across on a read-only connection, get enriched with contextual data, and come back as insight cards. Your ERP is untouched.

The customer model runs on a daily cycle, not on a reporting calendar. It flags the pattern, attributes root cause, and attaches what to do about it before the number reaches a review deck.

Why this combination
is its own problem.

Most customer projects stall at data access. This one does not, because SAP already exposes POS transactions, inventory positions, purchase orders through an API Ward reads directly.

What the first 90 days
actually look like.

  1. 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.

  2. 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.

  3. 03

    Weeks 4 to 12: steady state

    Ward hands you cards each day, each with root cause and a recommended move. Volume settles at a level a single person can read over coffee. The measure of success is not how many insight cards arrive, it is how many get acted on.

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

POS transactions
Inventory positions
Purchase orders
Material master
Vendor master
Promotion calendar

Impact metrics with SAP

Replenishment Accuracy
Gaps flagged early
Inventory positions matched to POS velocity before shelf impact.
Shrinkage
Anomalies surfaced continuously
Transaction-level detection catches what periodic audits miss.
Forecast Accuracy
External signals layered
Weather, events, and competitor data sharpen SAP demand signals.
Promo ROI
True lift isolated
Sell-through data exposes cannibalization and halo effects.

Data lake enrichment

Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates

Frequently asked questions

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.

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.

See what customer problems Ward catches.

Root causes, not just alerts. See it on your data.

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

Find out what your data has been hiding.

Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info