Personalization strategy for a platform serving 1,300 financial institutions and 30 million end users, and the system that made it buildable rather than aspirational.
Huge Inc. · 2025–2026Candescent, the largest non-core digital banking provider in the US. B2B2C, white-label.
Discovery & Vision at Huge Inc. Fourteen weeks, extended as the scope grew.
Product strategy lead for the strategy layer: user strategy, personalization, prioritization, measurement, within a co-delivered experience vision.
1,300 financial institutions. 30 million end users. Five product surfaces.
A platform grown through acquisition into a disconnected ecosystem, failing its FI clients and, through them, millions of consumers. One-size-fits-all in a market where the leaders had gone adaptive.
FI clients described feeling at the mercy of the vendor, with 6–12 month implementation timelines. Internally, three separate mobile teams shipped against three separate roadmaps. But underneath the surface symptoms sat a harder structural problem: every product decision, from what a dashboard shows to how onboarding flows, was made without live intelligence about what the person on the other side of the screen actually needed in that moment.
And the standard fix, a handful of personas, could not work here. Candescent doesn't serve one bank's customers; it serves 1,300 institutions whose users range from a first-time checking customer at a rural credit union to the CFO of a growing SMB. A single "right" experience does not exist.
The platform knew an enormous amount about its users. None of that knowledge reached the experience. It needed a system for producing a million right experiences.
How do you build user intelligence into the product itself, so that what each person sees is composed from what they need right now, and it works for every institution without 1,300 bespoke builds?
A multi-stream discovery: a survey of banking professionals across C-level, VP, and IT roles; in-depth interviews with consumers, SMBs, and FI stakeholders; a competitive audit spanning 20+ players from Chase to Mercury; and an AI-assisted synthesis of 84,000+ consumer conversations. I owned the desk research and led the synthesis into the Discovery Report that became the reference document for everything downstream. The operating principle: learn before launching. Every hypothesis was stress-tested while decisions could still change.
Deep segmentation research surfaced a load-bearing bifurcation: Pragmatists who want simplicity and reliability versus Power Users who want control and data. It grew into a full B2B2C segmentation covering consumers, businesses, bankers, and administrators. Each segment carries a distinct product opportunity. This became the platform's user strategy.
Instead of asking "who is this user," the system asks "what is this user trying to do right now." Eight adaptive modes were designed, shared across consumers and SMBs and weighted differently by segment, each named as an action pair and derived from a jobs-to-be-done inventory. Each mode specifies the jobs it serves, the signal that detects it, and the product response: hero content, tone, and what to de-emphasize.
A framework is only as good as its operability. Every mode is paired with an explicit detection chain: four signal categories, weighted by context, that trigger the mode and drive the response. This turned personalization from an aspiration into an engineering specification.
To make the system buildable at platform scale, modes were mapped down to the component level. Every module on every page is typed as Fixed, Flex, Adaptive, or Optional, so the same design system serves every institution while each user's page composes itself around their current mode.
To pressure-test the logic before anything was built, I created a Persona → Mode Calculator: a working tool that scores modes from persona inputs and shows how any given user profile would experience the composed page. It let the team and the client interrogate the system with evidence, not intuition, while decisions could still change.
Co-created with our AI strategist: intelligence as architecture woven through the experience: personalized insights, tips, and recommendations fed by the same signal chain, governed by confidence thresholds and reasoning transparency, invisible by default. The research showed a real tension: an executive AI mandate on one side, users who hadn't asked for AI on the other. The Modes system resolved it. AI became the engine serving the user's current job, not a chatbot bolted onto a legacy app.
I built the prioritization methodology and the wave plan, from V1 Functional and V1 Sticky & Magnetic through V2 and Vision, with a documented rationale for every deferral, plus the measurement framework to track the transformation against outcomes. The roadmap treats personalization as runtime infrastructure: the intelligence ships with the product, so the experience keeps adapting after launch.
Two decisions defined the engagement more than any deliverable.
FI clients were excited about AI; end users were wary. We designed for the skeptics: intelligence woven through the experience rather than a feature demanding attention. Then we put the concepts back in front of those same users. The skeptics became enthusiasts once recommendations felt smart and never intrusive. That's what learning before launching buys: an AI story that was bold, evidence-backed, and already validated with the hardest audience.
Most personalization strategies die as principles on a slide. Forcing every mode down to a detection signal, a page composition, and a computable score is what made this one buildable.
The vision was adopted as one of Candescent's top three board-level strategic priorities. Intelligent Banking has since been publicly announced, with V1 set to launch in Q4 2026.
"The articulated strategy and early design thinking is really on point."
Chief Design Officer, CandescentThe work opened the door to follow-on engagements, where I helped define the MLP scope for the new app.
The hardest problems in financial services share a shape: the organization knows more about its customers than almost any industry, and almost none of that intelligence reaches the moment of decision. This engagement closed that gap twice. Before launch, by learning first. And after launch, by building the intelligence into the runtime itself, so the experience keeps adapting for every user, at every institution, at once.