Assortment · Power BI · Head of E-Com

Assortment Planning + Power BI: Built for Head of E-Com

Most retailers find Assortment problems too late. Ward delivers automated insight cards. What changed, why, and what to do. While there's still time to act. Your E-Commerce team has the data. What they don't have is bandwidth to find what's buried in it.

Assortment Planning powered by Microsoft Power BI

Assortment Planning is the process of ward analyzes sell-through by store cluster to recommend which skus to add, drop, or reallocate.

When connected to Microsoft Power BI, Ward reads power bi rest api datasets, underlying sql/azure data, dataflow outputs and enriches them with contextual signals to generate assortment insight cards. Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.

How Ward delivers Assortment insight cards: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

Key capabilities

  • Store cluster segmentation
  • SKU rationalization recommendations
  • Whitespace opportunity detection
  • Planogram optimization inputs
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

Dashboards

Pinned views built from saved data-lake queries.

Revenue vs. forecast +4.2% WoW
Gross margin % −3.2pp
Fill rate, fresh 83%
Shrink, West region +0.8pp

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"
Live product demo, Ward analyzing retail data in real time.

How Ward connects to Microsoft Power BI

Ward sits alongside Power BI. Your dashboards visualize. Ward detects and explains what changed. No dashboard login needed for your morning brief.

Setup: Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched.

Data Ward reads from Power BI

Power BI REST API datasets
Underlying SQL/Azure data
Dataflow outputs

Impact metrics with Power BI

Time to Insight
Push, not pull
Insight cards delivered without waiting for someone to look.
Anomaly Detection
Between-refresh coverage
Issues surfaced before the next scheduled Power BI review.
Decision Velocity
Cause analysis included
No drill-down investigation; cards carry root cause context.
Report Efficiency
Ad-hoc requests reduced
Proactive cards answer questions before analysts get asked.

Data lake enrichment

Ward enriches Power BI data with: Power BI datasets, Underlying SQL/Azure data, Weather & events, Demographics, Custom feeds

Your online and offline data live in different worlds.

Pain points
  • ×Nobody can see one inventory position across every channel
  • ×Online promo performance is measured separately from in-store
  • ×Customer behavior data is siloed by channel
  • ×BOPIS/BORIS operational complexity is growing unchecked
  • ×Digital marketing attribution stops at the click
How Ward helps
  • Unified insight cards across online and in-store channels
  • Cross-channel promo effectiveness with true attribution
  • Customer journey tracking across digital and physical touchpoints
  • BOPIS fulfillment performance monitoring with exception cards
  • Full-funnel marketing attribution to in-store conversion

Retailers with unified omnichannel data see 30% higher lifetime value per customer. Source: Harvard Business Review

Frequently asked questions

Ward connects to the same data sources Power BI uses. Or reads Power BI datasets via REST API. Your reports stay untouched. Data points include: Power BI REST API datasets, Underlying SQL/Azure data, Dataflow outputs.

Your online and offline data live in different worlds. Ward solves this with automated insight cards: Unified insight cards across online and in-store channels. Cross-channel promo effectiveness with true attribution. Customer journey tracking across digital and physical touchpoints.

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 assortment 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