Data Clean Room: 3 Use-Cases

Let me get straight to the point.

If you want to read more about Data clean room, please read my previous articles here, here and here

Here are the 3 use-cases

1. Campaign Effectiveness Measurement: Are Your Ads Actually Working?

Stop guessing if your ads are hitting the mark. Data clean rooms let you match campaign data with customer segments, revealing exactly which ads drive conversions for specific groups.

Sample Query:

SELECT r.segment, c.campaign_id, 
       AVG(c.conversion_flag) AS conversion_rate
FROM brand_campaign_data c
JOIN retailer_customer_segments r 
ON c.pseudonymized_customer_id = r.pseudonymized_customer_id
GROUP BY r.segment, c.campaign_id;        

The Impact: Optimize ad spend by doubling down on what works and ditching what doesn't.

2. Audience Overlap Analysis: Find Your Perfect Marketing Partners

Struggling to find the right partners to reach new customers? Data clean rooms can reveal which businesses share your audience, opening doors to powerful co-marketing opportunities.

Sample Query:

SELECT COUNT(DISTINCT b.pseudonymized_customer_id) AS overlap_count,
       (COUNT(DISTINCT b.pseudonymized_customer_id) * 100.0 / COUNT(DISTINCT r.pseudonymized_customer_id)) AS overlap_percentage
FROM brand_loyal_customers b
JOIN retailer_customers r
ON b.pseudonymized_customer_id = r.pseudonymized_customer_id;        

The Impact: Forge strategic partnerships that expose your brand to new, relevant audiences.

3. Product Affinity Insights: The Art of the Upsell

Want to boost sales with personalized product recommendations? Data clean rooms analyze retailer transactions to reveal which items your customers commonly buy together, unlocking powerful cross-selling potential

Sample Query:

SELECT t2.product_id AS other_product_id, COUNT(*) AS purchase_count
FROM retailer_transactions t1
JOIN retailer_transactions t2 
ON t1.pseudonymized_customer_id = t2.pseudonymized_customer_id
   AND t1.purchase_date = t2.purchase_date
   AND t1.product_id = <your_product_id>
   AND t2.product_id <> <your_product_id>
GROUP BY t2.product_id
ORDER BY purchase_count DESC;        

The Impact: Increase average order value and create a better customer experience by offering personalized product recommendations.

Ready to transform your marketing with customer insights that respect privacy? Embrace the future of data-driven collaboration – explore data clean rooms today.