For years, marketers in the consumer-packaged goods (CPG) sector have counted on third-party cookies to define and segment their audiences, reach prospects and clients, target their offerings and communication, and measure their success. 

But in a trend, we describe as ‘data depreciation’, the utility and pervasiveness of third-party cookies is diminishing. 

Forces driving this change in the market include consumers seeking more control over their data, new data privacy laws coming into effect worldwide, leading browsers restricting how advertisers may use third-party cookies, and the ways in which platforms like Google, Apple and Amazon are creating walled gardens for their customer data.

CPG is likely to be one of the industries that are most profoundly affected by this sea-change. 

Brands in this industry usually do not collect much first-party customer data because their products are sold through retailers, and they usually don’t have direct sales relationships with the consumer.

CPG brands will need to evaluate what tactics they can use instead of cookie-based behavioral advertising, specifically segment level targeting and re-targeting. 

We foresee that the shift from third-party cookies will lead to the decline of data management platforms (DMPs) focused on third-party data and the ascent of customer data platforms (CDPs).

In line with these changes in the landscape, forward-thinking CPG brands will be looking at how they can fast-track the development of a rich set of proprietary consumer data models through the collection of high quality first-party data. 

For those that succeed, a first-party proprietary data asset will be a critical competitive advantage in identifying and reaching consumers effectively.

To decrease their reliance on third-party data, marketers will need to:
Unify their data, focusing on the integrated storage of first-, second-, and third-party via a CDP and move away from a reliance on DMPs that focus on third-party data.
Shift their focus to acquiring and sharing data via a second-party data partnership or network.
Rethink the identity resolution concept as it is known today and prepare for a cookieless future.
Invest in data science and capabilities to address cross-channel measurement and attribution.
Focus on first-party data assets and build up consumer profiles over time. 

A proactive approach to future-readiness: harnessing first party data
In the months to come, CPG brands should focus on improving their data capabilities through the integration of existing data assets, along with the addition of new data assets to add context and connection to the assets they already have. 

They can grow this level of connectivity over time, integrating datasets across the organization’s capabilities, aggregating consumer IDs across engagements and channels, and building consumer data assets based on insight. 

They will also need to develop a measurement framework to benchmark progress over time. Such a framework enables a shift from descriptive reporting to diagnostic and prescriptive analysis. 

Rather than focusing on what is happening, the brand would have the capabilities in place to understand why it’s happening as well as to predict what’s next.

We recommend a roadmap with five stages to support the transition of capabilities towards a digital-first, future-forward ecosystem.

1. Prepare
The first stage is about developing cross-capability operations as well as defining the frameworks and criteria for transformation. During this stage, a brand will create integrated functional silos, a governance-driven culture, and alignment across change management principles. 

Integrated data capabilities, data use cases and data management practice should also be defined. Finally, the brand will identify incremental insights from social, programmatic, and other digital datasets for planning. 

2. Update
During this phase, the brand brings existing digital capabilities up to scratch, with a particular emphasis on strengthening skills, frameworks and methodologies related to first-party data. In this stage, the company will also implement updated tagging strategies. 

It will also prepare for the next stage by assessing and acquiring a CDP, data lake, and business intelligence tools.

3. Build
It’s in this phase where the brand begins to build its new data and capabilities for first-party data and prepares to scale an omnichannel solution that uses new and existing capabilities. 

Here, the brand will focus on driving insights and learning from the first two stages, while also building enhanced first-party data capabilities for full transformation. 

The outcomes should include optimized digital results, new marketing analytics skillsets, and optimization strategies based on the implementation of newly tagged signal data. First-party data structures are also built in this stage.

4. Combine
Stage four focuses on combining experience, technology, and data. This phase consists of onboarding new data sources across capabilities to integrate data and platforms for analysis, segmentation, and modeling purposes. 

This stage will see the brand continue to build experience-optimizing anonymous data assets, onboarded data for integration across the consumer data system architecture, updated models and segmentation, and segmentation signals for dynamic content optimization (DCO).

5. Scale
The final phase focuses on removing reliance on third-party data and completing the shift to a focus on first- and second-party data. The brand harnesses its new integrated capabilities to scale and accelerate personalized marketing. 

It orchestrates and activates the new models it updated in the fourth stage across all marketing channels and platforms, all while adding second-party data sources for further insight and modeling purposes. 

The fifth stage should result in enhanced and optimized models, improved multi-touch attribution (MTA) modeling capabilities, and deeper integrations across the consumer engagement platforms.

Anticipated transformation results
Upon completion of stage five, the brand should have updated, built, combined, and scaled its data capabilities. 

It will have integrated its siloed datasets, established, and aggregated consumer identity across engagements, and transformed proprietary consumer attributes into a consumer data asset based on insights collected from engagement data. 

These actions will position a brand with the long-term capability it needs to improve customer segmentation, increase the relevance of the content it serves to consumers, and improve the orchestration of media mix – and all of that while decreasing reliance on third-party data.

By Tom Corey VP, Consulting North America at Wunderman Thompson Data and Erica Clerf, Business Consultant, North America at Wunderman Thompson Data