Promos · RELEX · Director Store Ops

Promo Effectiveness on RELEX, for store operations

Managing 800 stores from a spreadsheet is insane. Ward detects promos movement in your RELEX data and traces what caused it.

How a Director Store Ops runs promos off RELEX Solutions

Promo Effectiveness. Ward measures true promotional lift net of cannibalization, pull-forward, and pantry loading.

Managing 800 stores from a spreadsheet is insane. Ward filters to what a Director Store Ops can act on and drops the rest.

Under the hood. Ward isolates incremental volume from baseline, measures cross-SKU cannibalization, estimates pull-forward effects, and calculates true ROI.

Getting the data in. Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.

Capabilities

  • Net lift measurement (not gross)
  • Cannibalization quantification
  • Pull-forward detection
  • Promo ROI scorecards
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"
Promos on RELEX data, live product demo.

What Ward has eyes on.

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.

Ward pulls from demand forecasts, space plans, workforce forecasts from RELEX Solutions on a read-only connection. Nothing is written back, and your Supply Chain configuration does not change. RELEX stays the system of record.

Why this combination
is its own problem.

Promo Effectiveness produces a lot of output that is technically correct and operationally useless to store operations. Ward filters on whether the finding changes a decision a Director Store Ops can actually make.

What the first 90 days
actually look like.

  1. 01

    Week 1: connect

    Read-only credentials to RELEX Solutions. Ward reads straight from demand forecasts, space plans, workforce forecasts 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.

  2. 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 daily cards are directionally right and the thresholds are still moving.

  3. 03

    Weeks 4 to 12: steady state

    Ward sends daily cards each day, each with what caused it 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 RELEX Solutions

Ward integrates with RELEX for unified retail planning. Ward monitors RELEX forecasts against actuals and surfaces planning exceptions as insight cards.

Setup: Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound.

Data Ward reads from RELEX

Demand forecasts
Space plans
Workforce forecasts
Promotion plans
Replenishment orders

Impact metrics with RELEX

Forecast Accuracy
Drift flagged per store
Accuracy decay caught before it compounds across locations.
Space Productivity
Planogram gaps exposed
Space plans checked against actual sell-through per fixture.
Replenishment Efficiency
Manual triage reduced
Exceptions ranked by revenue impact and urgency.
Fresh Waste
Perishable ordering tightened
Hyperlocal weather and events sharpen fresh item forecasts.

Data lake enrichment

Ward enriches RELEX data with: RELEX forecasts, POS actuals, Weather & events, Demographic data, Supplier performance

Managing 800 stores from a spreadsheet is insane.

Pain points
  • ×Morning check-ins rely on phone calls and email chains
  • ×No single view of which stores need attention today
  • ×Labor scheduling is disconnected from demand signals
  • ×Planogram compliance is checked manually, quarterly
  • ×Exception management is reactive and inconsistent
How Ward helps
  • Morning brief delivered at 06:47 with prioritized action list
  • Estate-wide heat map of store performance, updated hourly
  • Staffing recommendations correlated with predicted traffic
  • Planogram compliance anomalies detected and flagged
  • Consistent exception handling with recommended actions

Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. Source: RSR Research

Frequently asked questions

Ward reads RELEX data via API. Monitors forecast accuracy and flags exceptions before they compound. Data points include: Demand forecasts, Space plans, Workforce forecasts, Promotion plans, Replenishment orders.

Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.

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 promos 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