First-Party Data is More Valuable than Ever

For five years, enterprise data strategy ran on a deadline that no longer exists.

In 2020, Google announced plans to phase out third-party cookies in Chrome within two years.^[1] That announcement helped accelerate significant investment in first-party data infrastructure, customer data platforms (CDPs), loyalty programs, and identity strategies.

Then the deadline kept moving. In 2024, Google reversed course. By 2025, it confirmed Chrome would keep third-party cookies with no new consent prompt, and later retired Privacy Sandbox altogether.^[2]

The deadline was cancelled; the degradation was not.

Safari and Firefox have restricted third-party cookies for years, while Apple’s tracking controls and state privacy laws continue to limit cross-site and mobile signals.^[3] For retailers who have already put three to five years and significant capital into this effort, the mandate has shifted.

That investment now needs to produce measurable commercial value.

From Data Defense to Revenue Growth

First-party data now needs to be treated as a profit-and-loss (P&L) asset, not simply an IT investment.

That does not mean collecting more data. It means making the existing foundation more useful.

Logged-in sessions, loyalty interactions, and known transactions give retailers something increasingly difficult to replicate with third-party signals: a direct connection between a customer and their behavior.

Those authenticated moments are the assets.

Retailers can strengthen that picture with zero-party data: preferences, interests, and purchase intentions that customers intentionally share. Fit finders, preference centers, style builders, and similar tools can capture that information while providing immediate utility.^[4]

The strategic point is simple: collection is a design problem, not just a persuasion problem.

The final step is connection. Transaction history, digital behavior, service interactions, returns, and stated preferences cannot remain isolated. Those gaps limit not only what retailers know about their customers, but also what the AI and personalization systems built on top of that data can do.

Before AI Can Innovate, Data Must Unify
52% of organizations say their current data unification and structure are limiting their AI initiatives.^[5]

Once those pieces work together, the commercial opportunity becomes much clearer.

Think of the opportunity in two stages: stage one builds and connects the customer data asset. Stage two puts that asset to work.

One Asset, Three Returns

1. Monetize It Through Retail Media

Retail media provides the most direct path from first-party data to new revenue. U.S. advertisers are expected to spend $71.09 billion on retail media in 2026.^[6] Purchase data can help vendor brands reach relevant shoppers while connecting media exposure to actual transactions.

The recommendation engine itself may be one of the most valuable surfaces retailers already own. Search results, replenishment prompts, complementary product recommendations, and modules such as “complete the look” can drive basket growth while also creating sponsored inventory.

The economics can be attractive. BCG has estimated margins of 70% to 90% for media sold through retailers’ owned channels.^[7]

But two guardrails matter:

• The auction has to be relevance-weighted, not revenue-weighted. A large bid should not turn a bad recommendation into a good one.
• Disclosure is a design decision, not a legal checkbox. Sponsored influence should be clear enough that monetization does not undermine the trust that makes recommendations valuable.

2. Raise the Ceiling on AI Personalization

Retailers have heard the personalization promise before. Many have pilots that never moved far beyond a homepage recommendation strip.

The problem may be upstream.

Partial inputs produce partial recommendations. Fragmented transactions, stale preferences, and disconnected identities limit what even sophisticated AI can do.

A stronger customer profile can inform search ranking, merchandising, offer selection, lifecycle messaging, and next-best actions. That relevance matters: 64% of shoppers say brand messages are too generic, while 80% are more likely to ignore brands that send irrelevant messages.^[8]

The goal is not more personalization. It is better relevance.

3. Make Customer Research More Useful

First-party data can also move research beyond the fictional “average customer.”

• Recruit from behavioral reality. Study actual cart abandoners, dormant high-value customers, buy-online, pick-up-in-store (BOPIS) users, or frequent returners.
• Learn continuously. Quizzes, preference centers, fit finders, and onboarding flows can reveal how customers describe their needs and improve search, taxonomy, content, and experience design.
• Close the loop between what customers say and what they do. Interviews explain motivation. Behavioral data shows whether that explanation holds more broadly.

A preference expressed in a quiz that never shows up in purchase behavior is a signal about the quiz, not th customer.

Measure What Moved

Stage two raises the standard for success. The value of a unified customer profile is not how complete it looks in a dashboard. It is what that profile allows the business to do differently.

Retail media should prove incremental revenue. Personalization should improve the decisions that shape merchandising, search, lifecycle timing, and offers. Research should turn customer behavior and feedback into decisions the business can act on.

The deadline that helped drive the investment is gone. The investment itself is not. Now the data foundation built for defense has to produce a return.

You already paid for the data. Now have it pay you back.

Key Takeaways

• Finish Connecting the Data Foundation: Bring point-of-sale, digital behavior, service history, loyalty, and zero-party data together so the same customer profile can support activation, analytics, and identity resolution across systems.
• Put the Asset to Work Where It Can Generate Return: Start with high-value commercial use cases, particularly retail media, personalization, and customer research. For retail media, begin with owned inventory, prove incrementality against transaction data, and protect relevance and disclosure as the program scales.
• Tie Each Use Case to a Measurable Business Outcome: Evaluate the data investment by what it changes: incremental revenue, stronger merchandising and personalization decisions, and research findings grounded in real customer behavior rather than assumptions.