Pricing · Tableau

Tableau plus Ward: price optimization

Your Tableau data is already good enough for pricing. Ward reads straight from it and returns cards with root cause attached.

Price Optimization on Tableau data

Here is price optimization in plain terms. Ward monitors price elasticity shifts in real time and recommends adjustments that protect margin without sacrificing volume.

Under the hood. Ward continuously measures price elasticity by category, tracks competitive pricing signals, and models the margin-volume tradeoff.

How the connection works. Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.

What it does

  • Real-time elasticity measurement
  • Category-level price sensitivity
  • Competitive price monitoring
  • Margin-volume tradeoff modeling
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"
Pricing on Tableau data, live product demo.

What Ward has eyes on.

The pricing model runs each day, not on a reporting calendar. It flags the pattern, attributes what caused it, and attaches what to do about it before the number reaches a review deck.

Ward reads Tableau rather than replacing it. Tableau Hyper extracts, underlying database (direct), published data source metadata come across on a read-only connection, get enriched with contextual data, and come back as findings. Your BI is untouched.

Why this combination
is its own problem.

Price Optimization needs tableau Hyper extracts and underlying database (direct) at minimum. Tableau carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.

How Ward connects to Tableau

Ward does not replace Tableau. Ward adds the proactive layer Tableau lacks. When a metric moves, Ward explains why and recommends action.

Setup: Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context.

Data Ward reads from Tableau

Tableau Hyper extracts
Underlying database (direct)
Published data source metadata

Impact metrics with Tableau

Time to Insight
Cards before dashboards
Anomalies explained before anyone opens Tableau.
Anomaly Detection
Extract-gap coverage
Catches issues between Tableau extract refresh cycles.
Decision Velocity
Investigation eliminated
Root cause embedded in cards; no ad-hoc queries needed.
Analyst Productivity
Detection work offloaded
Analysts freed from triage to focus on strategic work.

Data lake enrichment

Ward enriches Tableau data with: Tableau data sources, Underlying database, Weather & events, Competitor pricing, Customer data

Frequently asked questions

Ward connects to the same databases Tableau uses. Or reads Tableau Server metadata via REST API for context. Data points include: Tableau Hyper extracts, Underlying database (direct), Published data source metadata.

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 pricing 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