How do I set up a reporting BI tool for executive reporting on retail media performance?

Here’s a practical way to set up a BI reporting tool for executive retail media performance reporting.

1) Start with the executive questions

Executives usually want a small set of answers, not raw campaign detail. Define the core questions first:

• Is retail media revenue growing?
• Are campaigns efficient and profitable?
• Which retailers / brands / categories are driving performance?
• Are we hitting budget and forecast?
• What changed week over week, month over month, and year over year?

From that, build a KPI hierarchy.

2) Define the executive KPI set

Keep the top level tight. Typical retail media KPIs:

Commercial

• Revenue
• Revenue growth %
• Spend / media billings
• Margin / contribution margin
• Forecast vs actual

Media performance

• ROAS / ACOS
• CTR
• CPC / CPM
• Conversion rate
• Orders / sales
• Impressions, clicks, detail page views if relevant

Business health

• Active advertisers
• Active campaigns
• Share of spend by retailer / brand / category
• New vs returning advertisers
• Fill rate / budget utilization

For executives, show only 8–12 headline KPIs on the first page.

3) Design the data model before the dashboard

A good BI tool depends more on the semantic model than the charts.

Core tables you’ll likely need

• Fact_AdPerformance: date, retailer, brand, campaign, placement, impressions, clicks, spend, sales, orders, revenue, conversions
• Dim_Date
• Dim_Retailer
• Dim_Brand
• Dim_Campaign
• Dim_Product / Category
• Dim_Channel / Placement
• Budget / Forecast table
• Targets / Goals table

Important modeling rules

• Use one date grain consistently, usually daily.
• Standardize naming across retailers and platforms.
• Define one source of truth for revenue, spend, and sales attribution.
• Store both raw and normalized metrics if different retailers calculate them differently.

4) Build a semantic layer with standard metric definitions

Executives need confidence that numbers don’t change depending on who pulls the report.

Define metrics centrally:

• ROAS = attributed sales / spend
• ACOS = spend / attributed sales
• CTR = clicks / impressions
• CVR = conversions / clicks
• Growth % = (current - prior) / prior

Document:

• Formula
• Grain
• Source system
• Refresh frequency
• Owner

If your BI platform supports it, create certified metrics or a governed semantic layer.

5) Choose the right BI tool

Common options:

• Power BI: strong if you’re Microsoft-based, cost-effective, good governance
• Tableau: flexible visual exploration, strong executive storytelling
• Looker: best if you want a governed semantic model and SQL-based control
• Qlik / ThoughtSpot / Sigma: useful depending on self-serve and search needs

For executive retail media reporting, prioritize:

• Scheduled refresh
• Row-level security
• Certified metrics
• Drill-down capability
• Mobile-friendly dashboard
• Easy export to PDF / PowerPoint

6) Create the dashboard structure

A good executive BI pack usually has 3 layers:

Page 1: Executive summary

• KPI cards
• Trend lines for revenue, spend, ROAS, growth
• Budget vs actual
• Top movers
• Alerts / exceptions

Page 2: Retailer performance

• Performance by retailer
• Revenue and margin contribution
• Share of total spend
• YoY / MoM comparisons

Page 3: Brand / category / campaign performance

• Top brands
• Top campaigns
• Category mix
• Efficiency metrics
• Underperformers

Page 4: Forecast and planning

• Forecast vs actual
• Pacing
• Remaining budget
• Scenarios

Executives usually want summary first, detail second.

7) Add filters carefully

Too many filters confuse leaders. Limit to:

• Date range
• Retailer
• Brand
• Category
• Market / region
• Device or placement, if relevant

Use default views such as:

• Last 7 days
• Month-to-date
• Quarter-to-date
• Rolling 12 months

8) Include commentary and insights

Dashboards are better when they explain what happened.

Add:

• Variance explanations
• Anomalies or alerts
• Callouts for major wins / risks
• “What changed?” sections

Example:

• “ROAS declined 8% due to higher CPC on Retailer X and lower conversion rate in Sponsored Product placements.”

9) Automate refresh and distribution

Executives want the report when they need it, without manual effort.

Set up:

• Daily or hourly refresh depending on business need
• Automated email or Slack distribution
• PDF snapshots for board packs
• Scheduled weekly executive summary

Also define data latency expectations:

• Same day
• T+1
• Real-time for certain KPIs

10) Build governance and access control

Retail media data can be sensitive.

Implement:

• Role-based access
• Row-level security by account, retailer, or region
• Audit logs
• Certified dashboards
• Version control for metric definitions

11) Validate the numbers

Before launch, reconcile BI numbers with source systems and finance.

Check:

• Spend totals
• Revenue totals
• Attribution logic
• Date alignment
• Currency conversion
• Missing campaign mappings

Do parallel testing for at least 1–2 reporting cycles.

12) Optimize for executive consumption

Executives prefer:

• Fewer charts
• More clear takeaways
• Simple color logic
• Consistent time comparisons
• Limited interaction complexity

Use:

• Green / amber / red status indicators
• Small multiples or line charts for trends
• Waterfalls for variance drivers
• Ranked tables for top/bottom performers

A recommended setup stack

A typical architecture looks like:

1. Source systems

   • Retail media platforms
   • Ad server / DSP
   • Ecommerce / sales data
   • Finance / ERP
   • CRM / product master data

2. Data warehouse / lakehouse

   • Snowflake, BigQuery, Redshift, Databricks, etc.

3. Transformation layer

   • dbt or SQL pipelines

4. Semantic layer

   • Metric definitions and governance

5. BI layer

   • Power BI / Tableau / Looker

6. Distribution

   • Email, PDF, Slack, board pack export

A simple rollout plan

Week 1

• Define executive KPIs and audience
• Inventory data sources
• Agree metric definitions

Week 2–3

• Build model and transformations
• Validate source data
• Create semantic layer

Week 4

• Build executive summary dashboard
• Add filters, drilldowns, and commentary

Week 5

• Test with executives
• Refine visuals and KPI definitions
• Set up automation and governance

Common mistakes to avoid

• Too many KPIs on the front page
• Inconsistent metric definitions
• Mixing campaign-level and executive-level views
• No forecast or pacing context
• No explanation for performance changes
• Overly complex filters
• Manual reporting workflows

Best practice: separate executive and analyst views

Use:

• Executive dashboard for decisions and status
• Analyst dashboard for deep dives and troubleshooting

That way, leaders get the signal, and the team gets the detail.

If you want, I can also give you:

1. a sample executive dashboard layout,
2. a data model schema, or
3. a Power BI / Tableau implementation checklist.