CASE STUDY · AUTOMOTIVE DIGITAL PLATFORM

Data-Driven Feature Roadmap & Adoption Strategy

How outside-in data transformed digital feature adoption and drove measurable engagement uplift for a major automotive OEM's digital finance platform.

01 THE STRATEGIC CONTEXT

A Platform With Potential,
Underperforming Against It

Our client - a leading automotive OEM operating a major digital finance and ownership platform — had invested significantly in building a broad feature set for their connected vehicle and financial services ecosystem. Despite strong underlying product fundamentals, feature utilisation metrics told a more complicated story: users were navigating to features and leaving without completing flows, engagement was plateauing, and internal teams lacked a consolidated view of where the friction lived and why.

The challenge was not merely technical. It was diagnostic. The organisation possessed substantial internal data - behavioural analytics, session recordings, call centre transcripts - but these sources existed in silos, mapped to different team mandates, and were being interpreted through different lenses. There was no single source of visual truth that could align product, engineering, and experience teams around a common understanding of the user journey and its pain points.

"The question was never whether data existed - it was whether the organisation could synthesise it into a story coherent enough to drive decisions."

INFOVISION ANALYTICAL FRAMEWORK, 2025

Compounding this, the client's roadmap prioritisation process was largely governed by internal assumptions and velocity-driven delivery cycles, rather than by evidence anchored in competitive benchmarking and genuine user need. This created a strategic blind spot: product teams were building confidently in directions that may not have reflected where user expectations — shaped by adjacent digital experiences in BFSI, e-commerce, and fintech — were actually heading.

Multiple Data Streams - One Integrated View

InfoVision deployed a structured multi phase engagement model, designed to move from diagnostic understanding to directional recommendation without losing fidelity between layers of analysis. Each phase built on the last, ensuring that insights derived from one lens could be triangulated against evidence from others before reaching the product team.

Research & Insights

We conducted deep qualitative and quantitative research to understand the full digital experience landscape. This included industry benchmarking across the BFSI sector — drawing inspiration from fintech, banking, and insurance platforms — to establish a competitive baseline and identify the gap between current-state experience and the emerging standard users now expect. We mapped Voice of Customer (VOC) data to identify recurring friction patterns, frustration signals, and unmet expectations, layering these against existing journey maps to pinpoint where users were dropping off and why.

Digital Data Analysis

Working across Adobe Analytics, Medallia, Omniture, and AB testing instrumentation, we consolidated the client's existing data infrastructure into a coherent, unified structure. Rather than treating each tool as a separate reporting source, we built a cross-platform behavioural model that mapped user intent, flow completion rates, and exit patterns at a feature-by-feature level. This gave the product team an experience-quotient score per feature — a single, comparable metric that could anchor prioritisation conversations and replace anecdote-driven roadmap debate.

Feature POV Creation

Armed with both competitive intelligence and quantified behavioural data, we developed point-of-view documents for each major feature area — Payments & Billing, Account Management, Financing, and Connected Services. Each POV articulated what the data said about current performance, what users in analogous categories expected, what competitors were doing to close that gap, and what a directionally correct redesign or re-prioritisation would look like. These were not aspirational concept papers; they were evidence-backed investment cases grounded in measurable friction points.

Experience Insights

The final workstream synthesised all research, data, and POV outputs into an always-on consolidated dashboard — a single visual source of truth accessible to product, engineering, and CX leadership simultaneously. Experience scores were modelled using data from GA, Omniture, Medallia, and AB testing tools, mapped against CS and digital benchmarks. The result was a holistic, real-time view of where each digital asset stood — not as a retrospective report, but as a living intelligence layer that could inform decisions on a rolling basis.

KEY FINDINGS

What the Data
Revealed

Across the Payments & Billing feature set alone, the experience quotient analysis surfaced several structurally significant issues that had gone unmeasured — and therefore unaddressed — under the previous reporting model.

• PAYMENT SCHEDULING — HIGH EXIT SIGNALExit rates on the payment scheduling flow were materially elevated versus benchmark, suggesting that while users were arriving at the feature with intent, the interaction design was not meeting their expectation for simplicity and confirmation. The Visit vs Exit data confirmed a pattern of abandonment at the point of commitment rather than at discovery.

• STATEMENT & HISTORY — ENGAGEMENT SUPPRESSIONDespite being a high-frequency need among finance platform users, the Statement & History module showed elevated exit rates and below-average completion. Comparative data from BFSI platforms indicated that users in this category expect progressive disclosure, not dense data tables — a design gap that was quantifiably impacting retention.

• FINANCING NEGATIVITY PERCEPTIONVOC and competitive benchmarking revealed that confusion around financing rates — particularly in the dealer-mediated context — was a systemic trust barrier. GM Financial, Toyota Financial, and competitive OEM platforms were all surfacing the same signal: users expected transparency at the point of decision, not just at the point of contract.

• MULTI-CHANNEL ATTRIBUTION GAPA significant portion of digital experience degradation was being driven by mis-attribution between in-app, web, and dealer touchpoints. Users who originated in one channel and completed (or abandoned) in another were falling outside the measurement model, creating an artificially positive reading of digital-channel performance.

From Insight to Operational Advantage

The consolidated intelligence framework delivered immediate and durable value across three dimensions: roadmap clarity, team alignment, and product decision velocity.

Evidence-Backed Roadmap Prioritisation

For the first time, the product team could sequence feature investment decisions against a shared, measurable evidence base — rather than competing subjective priorities. Experience quotient scores created a common language between product, CX, and engineering, reducing the political overhead of roadmap negotiation and replacing it with data-led dialogue.

Unified View of the Digital Asset

The consolidated dashboard resolved the fragmentation problem at its root, giving leadership a real-time, comparable view of performance across all major feature areas. This eliminated the need to triangulate between separate tool reports and allowed the team to respond to emerging issues in near-real-time rather than in retrospective review cycles.

Competitive Intelligence Layer

The outside-in research component gave the client a structured view of where competitor OEMs and adjacent BFSI platforms were setting user expectations — providing an early-warning system for experience gaps before they became churn signals. This repositioned the team from reactive to anticipatory in their product strategy.

Feature Adoption Uplift Pathway

Each feature POV included a directional design and communication recommendation — giving implementation teams a clear starting point. By grounding redesign briefs in behavioural evidence rather than assumption, the organisation significantly reduced the iteration cycle required to achieve measurable adoption improvement on prioritised features.