The Consumer Packaged Goods (CPG) industry has been under enormous pressure to confront a litany of market challenges, from consumer fragmentation to stubbornly high inflation. In a market where consumers are more discerning than ever, retailers must pull out all the stops to connect with their target audience and get sales back on track.
A robust personalization strategy can help – and existing retail partners often have a trove of consumer data that brands can use to this end. But data sharing has proven complicated until now. Thanks to an emerging technology – the data clean room – retailers now have the controlled environment they need to securely exchange data.
This article explains how data clean rooms work, how they benefit retailers and how to implement them effectively.
Data Clean Rooms Help Secure Data Sharing
Retailers know the value of consumer data, but sourcing it is always a challenge. One of the biggest hurdles: showing potential data collaboration partners – like retailers – exactly how they can exchange data without sacrificing its proprietary value.
Data clean rooms can help brands overcome this challenge. That’s because they put security and privacy first, allowing for joint data analysis without exposing personally identifiable information. What’s more, both sides control the flow of information and can tighten or loosen the tap at any time.
What does this secure environment look like in practice? For one, sensitive data is typically anonymized and aggregated within the clean room. Advanced encryption protocols ensure that data remains unreadable outside the clean room environment, protecting it from unauthorized access. In fact, data never actually moves anywhere. Instead, partners set strict access controls to dictate exactly who can query data, what can be analyzed and how results are shared.
These tight controls can give retailers the peace of mind they need to collaborate via data clean rooms, and this technology holds enormous value for retailers.
Data Offers More Knowledge and Personalization
By expanding retailers’ access to consumer information, data clean rooms can create more avenues for deeper personalization. In particular, the technology can improve:
- Customer profile complexity. By combining retailer data with CPG data, brands can build more comprehensive consumer profiles. For example, a snack brand could merge loyalty program data from retailers with its own online purchase records to identify repeat buyers and learn which flavor varieties are most popular. This enriched profile data enables the brand to tailor marketing based on preferred flavors and purchasing frequency, driving more relevant product recommendations.
- Audience analysis. Clean rooms allow retailers to conduct detailed audience segmentation, helping them identify high-value consumer groups. For instance, a beverage company could analyze which demographics are purchasing its products through various regional retailers, revealing that younger audiences prefer sparkling water options in urban stores. This insight allows the company to craft more focused digital ads and in-store promotions that appeal to specific demographics based on location.
- Attribution modeling. Data clean rooms make it easier for retailers to accurately identify the campaigns and channels driving sales. For example, a skincare brand could track the impact of a recent Instagram campaign alongside in-store promotions, using clean room insights to see which channel led to higher conversions. This clarity helps the brand allocate marketing spend more efficiently, ensuring it reaches consumers where they’re most engaged.
- Demand forecasting. Combining sales data from multiple retailers helps anticipate demand more effectively. If a cereal brand that partners with national retailers needs access weekly sales data, data clean room insights can detect seasonal spikes in demand for its products to optimize production schedules and manage supply chain hiccups in high-demand regions.
By leveraging these insights, retailers can create the kind of tailored experiences that drive genuine brand loyalty.
How to Implement Data Clean Rooms
When it comes to data clean room software, there are a number of options available for retailers, including:
- Media clean room (like Google Ads Data Hub or Amazon Marketing Cloud) to establish a clean room within an advertising ecosystem you already use.
- Third-party clean room provider (like Snowflake or Databricks) that specializes in secure data sharing across multiple parties.
- Custom-built solution to create your own walled garden for secure data collaboration.
Each option offers unique advantages depending on the organization’s (and its retail partners’) data sharing needs. But no matter which software model you choose, it’s important to take a “crawl, walk, run” approach to implementation. This way, you can narrow the focus of your data clean room project and gradually scale their efforts at the right pace for your business.
What does this approach look like in practice? Start with one or two digitally mature retail partners and work with them to:
- Prioritize high-impact use cases. Identify a handful of use cases, such as audience analysis or demand forecasting, that can help you address existing data blind spots. By starting with specific goals, the data clean room will be more likely to yield measurable results.
- Define your collaborative strategy. Make sure everyone’s on the same page about the mutual benefits of data collaboration, the data both parties will share and any necessary privacy and usage guardrails. This step is crucial for aligning expectations and ensuring that everyone feels comfortable with the data-sharing arrangement.
- Test, measure and scale. Test out the data clean room in a limited capacity for a predefined trial period. We recommend keeping it simple in terms of technology: leverage out-of-the-box capabilities where possible and gradually add complexity as needed. Along the way, carefully measure the impact of this investment and assess whether it makes sense to partner with more retailers, share more data sets, or deploy in larger markets.
This phased approach makes it easier to validate the data clean room’s value, refine processes and strengthen retailer relationships before fully expanding the initiative. By following these steps, you can more effectively implement data clean rooms and extract meaningful value from your data collaboration.
A Culture of Data Collaboration Benefits Everyone
Despite their symbiotic relationship with CPGs, retail partners may still hesitate to share the data they have. At first glance, the benefits of a data clean room can appear skewed almost entirely in CPGs’ favor. And more cynical retailers might argue that such open collaboration could undermine their leverage in contract negotiations with CPGs.
But the truth is that data clean rooms are a win-win for everyone. Retailers get access to CPGs’ knowledge about everything from sales trends to consumer behavior patterns. This data empowers them to make better stocking, promotional and pricing decisions. Interested retailers can seize on this messaging to pitch their vision for a more collaborative future.
Ultimately, the brands that take advantage of data clean technology will be better positioned to reach consumers and gain a leg on the competition, no matter the shape of the market.
Daniel Vieira Viveiros is the SVP, Data and Analytics at CI&T.
