Published On: 28 Jun 2023
Customer data and identity have never been more crucial, with consumers demanding privacy and personalization in equal measure. With upcoming changes in the tracking and identifier landscape, identity resolution and data enrichment in data clean rooms will be the way forward. How prepared are you to offer brilliant CX without compromising privacy and regulatory compliance?
Your customers are more privacy-aware while also demanding personalized experiences. Regulators have their ears to the ground. Browsers and big tech are cracking down on third-party cookies and other tracking measures. An example of this is Apple's explicit consent-based app tracking transparency (ATT) framework. Experts believe that less than half of consumers will agree to be tracked. This number could be lower in more privacy-aware communities and geographies. Apple and Google are also uncertain about creating and supporting alternatives to these tracking roadblocks.
In such times, advertisers, publishers and their tech stack partners are going back to the basics with first-party customer data, but bolstering it for better outcomes. Identity-driven data clean rooms that ingest your customer data and make it more holistic and actionable with data enrichment and identity resolution are the way forward. It will allow a privacy-sensitive way to analyze, understand, act on, and activate customer data.
Before we understand how the holy trinity of identity resolution, data enrichment, and data clean rooms works, let's look at what this word soup means.
A data clean room is a shielded setting where encrypted customer data is stored and matched with alternative data attributes or external datasets. Only authorized parties have access to data.
Two or more parties can collaborate on anonymized datasets while adhering to customer interests beyond just ticking off the compliance check box. This makes a data clean room far more effective, customer-centric, and privacy aware than traditional data collaboration tools.
Data enrichment fortifies first-party customer data by adding missing or new attributes. It allows marketers to get to know their customers beyond just interactions with their brand.
Data enrichment combines proprietary zero- and first-party data with second and third-party data sets to deliver deeper and more holistic customer insight. Marketers typically use data enrichment to fill in the gaps in customer intelligence, uncover behavioral patterns and activate personalized customer segments.
Identity resolution securely connects the moving parts of customer data across multiple data sources, channels, and devices. It unifies every consumer's data, attributes, and events across multiple data sources, breaking silos and giving marketers a single source of truth. An identity graph is at the center of identity resolution to determine if a consumer in one data set is the same as another, even if the two sets are based on different identifiers.
Identity resolution delivers a holistic master identity for each consumer to power personalization, targeting and measurement efforts.
Data clean rooms offer a watertight digital environment where brands match customer data with other second and third-party data sources and enrich their existing customer data with new attributes.
Though cookie-based targeting is typically perceived as the best solution for personalization, data clean rooms offer far better and more sustainable alternatives with data enrichment and identity resolution.
For example, identifiers like email, names, addresses and phone numbers are opt-in and not about to go out of fashion, especially in a digital-first shopping environment.
Data clean rooms offer brands a way to use these traditional identifiers for modern personalization. They also provide a robust way to manage and scale consumer consent across data and technology partners and vendors.
Many marketers are just starting to use data clean rooms for either identity resolution or customer data enrichment. But a compelling alternative to cookie-based or privacy-insensitive targeting is to combine data enrichment and identity resolution for actionable customer intelligence and high ROI personalization and targeting.
Identity resolution based on foundational and traditional identifiers like emails or MAID is one of the most crucial ways to maximize your data clean room investment. But not all clean rooms are made equal. For example, matching can lead to discrepancies if your brand uses home address as an identifier, but the same customer uses several delivery addresses. Or if one data collaborator uses only names as identifiers and the other uses phone numbers or emails, connecting customer data sets becomes complex. But there are solutions for this - one of which is to set up multiple identifiers and priorities to ensure exact matches. OR But there are solutions for this - Lifesight engineers have solved for this complexity.
A robust and effective data clean room aligns relevant identifiers, standardizes data, and addresses data interoperability challenges. It also aggregates first-party customer data with additional physical and digital behaviors and attributes to power compelling use cases in a digital-first advertising world.
Ultimately, marketers achieve a unique single view of each customer by considering their behaviors and preferences outside the brand's own universe. And in the long run, this process will only be possible with identity resolution and data enrichment inside the guardrails of data clean rooms.
A bank had generations worth of address-based identifiers for its customers. To bolster its marketing reach and ROI, it needed to understand its customers and their evolving needs over the years.
With a data clean room, the bank could:
The bank developed unique audience segments for its marketing efforts using Lifesight's Onboarding. It combined disparate digital and physical world attributes from third-party sources with the bank's address-based identifiers (data enrichment). It also passed its customer data through the identity resolution process.
Ultimately, an ML-based lookalike model helped the bank create high-intent audience segments for higher ROI on ,marketing touchpoints.
Essentially, data clean room based customer data enrichment and identity resolution gave the bank insights into:
The answer, of course, is absolutely not. Cookie deprecation will give rise to several adtech companies adopting or building data clean rooms for survival. So the process of choosing a good one is about to get highly complex.
While a lot of factors go into the decision, like your budgets and customer data collection maturity, some long-term considerations include data clean room partners that:
Lifesight, for example, offers a near-real-time identity graph. It doesn't just expand a brand's audience and identity taxonomies but also keeps it relevant with regular cleansing and updation mechanisms.
Lifesight had a privacy-first, holistic approach to customer data and advertising use cases much before it became a hot topic.
Onboarding, our new data clean room solution, combines that approach with our legacy product and data, identity resolution and data enrichment superpowers. It helps brands:
Onboarding powers well-rounded customer intelligence, hyper-personalized targeting, robust measurement, and countless other advertising, marketing, product, and strategy use cases.
And all of it better than cookie-based strategies ever could.
Fill the cookie-shaped hole in your heart withLifesight Onboarding.
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