DTC eyeglasses brand unlocks growth with Faraday's consumer data

Consumer brands today run on data. From personalization to retention, every marketing move depends on how well you know your audience. But here’s the catch: even with mountains of first-party data, not all of it is actually usable.

Some data is locked behind privacy regulations, some is incomplete, and much of it simply isn’t actionable. Meanwhile, CMOs are under pressure to reduce CAC, prove marketing’s contribution to revenue, and differentiate in markets where competitors often have access to the same third-party sources. You can’t grow on data alone—you need data you can act on.

Problem

And that was exactly where a leading DTC consumer eyewear brand we work with found themselves. They needed reliable customer intelligence to guide merchandising, lifecycle messaging, and geo activation. But much of their most informative first-party data—like prescriptions and exam info—qualifies as Protected Health Information (PHI) under HIPAA.

HIPAA tightly governs how covered entities and their business associates handle PHI. Using PHI to segment, target, or personalize marketing requires explicit patient authorization or de-identification that meets strict standards. In practice, marketing teams are blocked from using this data to understand demographics, build segments, or activate campaigns. Even analytics teams face barriers when joining or sharing it downstream. The result: blind spots that inflate CAC, weaken segmentation, and slow campaign velocity.

This eyewear brand’s CMO and team had major gaps: their customer base appeared to skew much older than reality, household and location context was incomplete, and they lacked confidence to prioritize creatives, offers, and store-adjacent campaigns.

Solution

But with Faraday, it doesn’t have to be this way. Even when first-party data is incomplete, restricted, or locked behind HIPAA, Faraday makes it actionable.

Faraday’s data pillars

Faraday provides three complementary types of data that work together as a privacy-safe growth toolkit:

1. Identity completion – Filling in the missing fields that make a record usable (e.g., appending verified address, phone, or email to partial identifiers).
2. Consumer data – Rich demographic, lifestyle, property, and geo datapoints that unlock segmentation and analytics without relying on PHI.
3. Custom predictive datapoints – On-demand models like Likelihood-to-Convert, Best-First-Product, or Credit Score Proxy, built on top of the Faraday Identity Graph.

Unlike generic datasets available to any competitor, Faraday’s Identity Graph is proprietary—built from verified, diverse sources and continuously maintained for completeness. Together, these pillars help CMOs complete their records, enrich them with context, and predict what comes next.

Why consumer data was the unlock

In this eyewear brand’s case, the consumer data appends pillar supplied the missing context—without touching PHI. Faraday’s U.S. identity graph linked public and commercially available consumer datapoints to their customer records, enriching them with:

• Demographics (age, income proxies, household composition)
• Lifestyle attributes (interests, shopping behaviors, property/home tenure)
• Location context (precise household-level geo, nearest store)

This enrichment enabled:

• A truer picture of the brand’s age mix and household dynamics.
• Weekly closest-store data powering one-to-one geo messaging (email, SMS, push).
• Append-driven cohorts aligned with survey-based attitudinal segments—without needing PHI.
• An identity layer that could be extended across martech partners via Faraday’s Universal ID.

With these foundations in place, the next step was to operationalize the data.

How we rolled it out

The rollout followed a clear progression. Matching is the foundation—without reliable matches, enrichment and predictions fall apart. Deterministic keys like email and name+address ensure that the right consumer datapoints attach to the right individual, building trust in the insights that follow. Strong matching also reduces wasted spend, prevents duplicate outreach, and gives teams confidence that when a segment is defined, it truly represents the audience they want to reach. To cement that foundation, the team validated coverage by cohort, compared age accuracy against survey and date-of-birth panels, and tuned household logic with clear match-rate expectations.

With this groundwork in place, Faraday’s consumer data began to deliver tangible value. Weekly closest-store feeds powered geo-personalized campaigns, while enriched cohorts aligned cleanly with the brand’s attitudinal segments. These quick wins proved that non-PHI consumer data could fill critical gaps and unlock action right away, giving marketing teams the confidence to activate without regulatory risk.

The natural next step was to extend beyond enrichment into prediction. Faraday’s custom predictive datapoints drove repeat-purchase and product-interest segments, and surfaced growth opportunities through a lite Market Opportunity Analysis. By layering prediction on top of trusted enrichment, the brand gained a clear path from raw data to measurable outcomes: age distribution rebalanced to align with survey ground truth, store-adjacent messaging became a high-engagement staple, and campaign testing velocity increased without waiting on PHI approvals.

Why CMOs choose Faraday consumer data

In a market crowded with data vendors making big promises, Faraday stands apart by focusing on what actually moves the needle for marketing leaders. Many providers chase inflated match rates or push generic attributes that look good in a deck but fail in the field. Faraday’s advantage lies in pairing a privacy-safe foundation with exclusive, activation-ready data tied to a proprietary identity graph.

• Privacy-safe by design: Non-PHI append workflow that respects HIPAA.
• Action-ready: Datapoints map cleanly to one-to-one channels, geo plays, and predictive models.
• Proven impact: Keys, QA, and repeatable playbooks turn enrichment into lower CAC, stronger pipeline contribution, and higher LTV—not dashboards.

Conclusion

Consumer brands don’t need more raw data—they need the right data they can actually use. Faraday delivers exactly that: privacy-safe enrichment, predictive insight, and proven rollout. If you’re ready to turn non-useful records into measurable growth, we should talk.