Pharmacy · Assortment · Tableau · VP Merchandising

Assortment Planning + Tableau + Pharmacy Retail: Built for VP Merchandising

Pharmacy operators find Assortment problems in post-mortems and quarterly reviews. Ward catches them daily — with root causes and recommended actions. Your Merchandising team has the data. What they don't have is bandwidth to find what's buried in it.

What is Assortment Planning for Pharmacy & Health?

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

For Pharmacy & Health retailers specifically, this means monitoring 20,000+ SKUs across pharmacies. Regulated inventory, seasonal demand spikes, and front-of-store optimization. Ward handles the complexity so your pharmacists focus on patients.

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
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Live product demo — Ward analyzing retail data in real time.

Why Assortment matters for Pharmacy retail

The most valuable assortment decisions aren't about trendy wellness products — they're about ensuring OTC companions for high-volume prescriptions are available and positioned correctly. Ward maps Rx-to-OTC companion patterns and recommends front-of-store assortment based on each store's actual prescription mix, not national averages.

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

Your category managers are drowning in spreadsheets.

Pain points
  • ×Promo planning relies on last year's playbook, not this week's data
  • ×Assortment reviews happen quarterly when they should happen daily
  • ×Price changes are reactive, not predictive
  • ×No visibility into true cannibalization across categories
  • ×Vendor negotiations lack real-time sell-through evidence
How Ward helps
  • Insight cards flag promo cannibalization the day it happens
  • Assortment gaps and whitespace opportunities surface automatically
  • Price elasticity shifts detected before margin erosion compounds
  • Category-level performance cards replace manual spreadsheet reviews
  • Vendor scorecards generated from actual fill rate and quality data

Retailers lose an estimated $300B+ annually to suboptimal assortment and promotional decisions. — McKinsey & Company

Rx-OTC companion analysis, diabetes category

Stores with the highest metformin prescription volume are significantly under-assorted in diabetes management OTC — glucose monitors, test strips, diabetic-friendly snacks, foot care. They share the same planogram as stores with half the Rx volume. Ward recommends expanding diabetes OTC in high-volume stores by reallocating space from underperforming seasonal items.

What a Ward insight card looks like

Ward · Pharmacy · Assortment06:47 AM

Cluster B stores (urban, high-traffic) underperforming on premium snacks vs Cluster A by 34%. Assortment gap: 12 SKUs missing.

✓ Action recommendedPharmacy context appliedTableau data

Pharmacy KPI impact

Expiry Waste
Flagged before close
Shelf-life velocity tracked per store.
Front-of-Store Margin
Highest-margin area
OTC adjacency and illness prep cards for the front end.
OTC Attach Rate
Rx-to-OTC conversion
Seasonal wellness bundling patterns identified.
Fill Rate
48–72hr lead time
Illness demand modeled before seasonal spikes hit.

Frequently asked questions

Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. 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 clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

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.

Yes. Ward reads Tableau data and combines it with contextual signals (weather, events, demographics) to generate Pharmacy-specific insight cards. No custom development required.

Your category managers are drowning in spreadsheets. Ward solves this with automated insight cards: Insight cards flag promo cannibalization the day it happens. Assortment gaps and whitespace opportunities surface automatically. Price elasticity shifts detected before margin erosion compounds.

Ward delivers daily insight cards covering Rx fill rate, OTC attach rate, Expiry waste % — tailored for Merchandising decision-making. Each card includes what changed, why it matters, and what to do next.

Ward tracks Rx-to-OTC companion purchase rates, category productivity per square foot, health condition clustering by store, and new-item triage — whether a wellness SKU can earn its space against a proven companion item.

Stores with the highest metformin prescription volume are significantly under-assorted in diabetes management OTC — glucose monitors, test strips, diabetic-friendly snacks, foot care. They share the same planogram as stores with half the Rx volume. Ward recommends expanding diabetes OTC in high-volume stores by reallocating space from underperforming seasonal items.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust 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.

Ward
Insight
Dispatch
Feedback
Evaluate
Learn
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
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See what Pharmacy assortment problems Ward catches.

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

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