How Boll & Branch achieved a 30% conversion lift with customer context

At a glance

• The core challenge: Boll & Branch's customer segments had no meaningful correlation to products or marketing channels — and an untested assumption that their core audience skewed older was driving significant spend toward the wrong channels.
• The approach: Connected their BigQuery warehouse to the Faraday Identity Graph, built data-driven personas from enriched customer data, and assigned every contact to a segment — no survey required.
• The result: The data revealed their highest-value customers were wealthier, settled homeowners who were heavy social media users — not radio listeners. Rebuilding campaigns around that insight drove a 30% lift in email conversion rates.

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Boll & Branch knew their customers weren't all the same. The DTC linens brand had built a loyal following across a wide demographic range — but their segmentation couldn't reflect that. Their initial customer segments had no meaningful correlation to specific products or marketing channels, which made personalization guesswork rather than strategy.

The problem wasn't effort. It was context. First-party transaction data tells you what customers bought. It doesn't tell you who they are — their financial situation, life stage, household makeup, or the signals that actually predict how they'll respond to a given message. Without that context, even well-intentioned personalization can only go so deep.

The limits of traditional personas

Brands typically build personas one of two ways. The first is the qualitative route: surveys, focus groups, which produce rich profiles but can't scale. An additional problem here is that surveys often see single-digit response rates, which means the majority of your customer base might never be assigned to a segment at all.

The other option is pre-computed personas like PRIZM codes: these are universal, but generic, built on broad demographic clusters rather than patterns specific to your brand. They tend to produce segments with little variation in actual customer behavior.

Boll & Branch needed something different: a truly personalized retail persona framework built on their own customer data, enriched with enough depth to be universally assignable across their entire audience — not just the customers who filled out a survey.

From transaction history to full customer profiles

To start, Boll & Branch connected their BigQuery data warehouse to the Faraday Identity Graph (FIG).

Instead of relying solely on purchase history, they matched their first-party customer records — names, emails, physical addresses — against FIG's continuously maintained dataset covering 240M U.S. adults across 1,400+ verified consumer data points. That match transformed each customer from a list of past transactions into a rich household profile: financial signals, life-stage indicators, property data, lifestyle data points, and more.

How Faraday built Boll & Branch's personas

With that enriched foundation in place, Faraday's clustering algorithm built personas directly from Boll & Branch's own customer data — automatically identifying three distinct segments based on socio-demographic patterns in the enriched dataset.

Because the personas are grounded in FIG data covering nearly every U.S. adult, they could be assigned universally — to every customer in the database, not just those who had self-reported preferences. That universal assignability is what made the personas operationally useful. Faraday pushed the persona assignments back into Boll & Branch's BigQuery warehouse, giving their analytics team the metadata they needed to cross-reference segments against their attribution system, seeing which customers came from which channels, how different personas responded to discount codes, and what products each segment over-indexed on.

The insight that changed their strategy

Before the persona analysis, Boll & Branch assumed their core demographic skewed older — a belief that had driven significant investment in radio advertising.

The data told a different story. Their highest-value segment turned out to be wealthier, settled homeowners who preferred clean, modern aesthetics. And crucially, FIG data showed this cohort were heavy social media users — not radio listeners.

That single insight gave Boll & Branch the confidence to realign their marketing around this audience: from branding and product design to prospecting, engagement, and retention. Isolating the segment also enabled highly targeted focus groups, where they discovered these buyers were heavily influenced by interior design personalities — a finding that directly informed their successful partnership with designer Nate Berkus.

The results: 30% lift in conversion rates

With every contact assigned a persona, Boll & Branch redesigned their email campaigns around each segment. Creative aligned to the socio-demographic makeup of each persona. Product recommendations drawn from what each persona actually over-indexed on. Offers calibrated to what each segment responded to.

The result was clear : a 30% lift in email conversion rates.

And as their Chief Digital Officer, Katia Unlu, put it herself: "Faraday is in our DNA."

Ready to lift conversion rates?

If you want to talk through how customer context could work for your brand, talk to a Context Consultant. Or if you'd rather get started on your own, try it on buy.faraday.ai.