Arianna Alonso.

Problem

A Fortune 100 sports streaming platform needed to redesign its end-of-season recap feature but lacked reliable behavioral data to guide product decisions.

Insight

• The recap feature was a high-visibility annual moment with measurable retention impact.
• Product needed clearer insight into how users engaged with the existing recap to inform improvements for the following season.

Action

• Instrumented behavioral analytics across the recap feature: impressions, video replays, completion rates, social shares.
• Delivered insights to product and design teams on engagement entry and drop-off points within the user journey.

Impact

• +24% video replays and +54% social shares on the redesigned recap feature the following season.
• Product team adopted behavioral analytics as the standard input for feature prioritization.

Problem

Users flowing into the subscription funnel from high-intent moments were dropping off at a critical conversion step. Despite strong intent signals, completion rates were suppressed by friction in the subscription and payment experience, resulting in lost recurring revenue.

Insight

Drop-off was heavily concentrated at the subscription selection and payment entry stages, where users encountered unclear value differentiation between plans and friction in mobile checkout completion.

Action

• Analyzed subscription funnel behavior end-to-end through Adobe Analytics.
• Identified high-friction steps in plan selection and payment entry, segmented by device type and user profile.
• Partnered with product and UX teams to simplify subscription tiers, improve mobile checkout flow, and reinforce value messaging at point of conversion.

Impact

• +5% lift in subscription conversion from the high-intent acquisition funnel.
• +$6.5M estimated incremental annual subscription revenue.

Problem

The airline relied solely on internal data, creating blind spots in understanding both CX and employee experience. Operational and experiential signals were fragmented across sources, and leadership had no unified view to connect real-time disruptions, customer sentiment, and competitive performance.

Insight

External data told a story internal surveys could not. Over 177,000 TripAdvisor and 6,000 Glassdoor records surfaced satisfaction drivers and competitive gaps that internal reporting was missing entirely. Combining those signals with operational KPIs in one executive view would give leadership what they needed to act, not just review.

Action

• Ran an outside-in social analysis using 100,000+ free-text comments processed with AI and ML, across TripAdvisor and Glassdoor, including a competitive benchmark vs. peer airlines.
• Mapped CX and employee experience interdependencies to show how employee enablement drove downstream customer outcomes.
• Designed and prototyped a blueprint CCO dashboard in Figma combining operational disruption data, experiential pulse metrics, and an AI-powered text analysis layer into one prioritized view.

Projected Impact

• Blueprint projected to deliver +25% faster executive decision-making by replacing quarterly retros with a live, unified signal layer.
• Competitive benchmark designed to feed directly into roadmap prioritization and investment decisions.
• AI chatbot layer designed to enable on-demand deeper analysis, surfacing insights beyond the dashboard at the point of decision.
• Prototype used to secure stakeholder buy-in and scope the implementation phase.

Problem

4,000+ open-ended onboarding responses were being coded by hand. Every refresh of the report cost weeks, and leadership could not act on themes in real time.

Insight

The bottleneck was not volume. It was the absence of a stakeholder-aligned taxonomy. Standardize the categories and the analysis can be automated end-to-end.

Action

• Led a team of 2 analysts on Voice of Customer analysis across the employee onboarding journey.
• Built custom models using Natural Language Processing (NLP) mapped to the categories executives actually used in planning.
• Combined statistical testing with qualitative synthesis to compare experiences across tenure cohorts.
• Replaced manual Excel-based heat maps with a dynamic Qualtrics heat map segmentable by geography, department, and other employee categories, giving leadership a self-serve view that required no analyst rebuild.
• Redesigned the executive dashboard into a single, unified onboarding journey view.

Impact

• 100% of manual coding eliminated, with time-to-insight collapsed to hours.
• Pinpointed the 3 onboarding key moments driving early-employee sentiment.
• Scaled the same model to subsequent cohorts with no additional analyst lift.

Problem

Executive reporting on live video performance was unreliable due to year-over-year inconsistencies that went unresolved for over 12 months. This created low confidence in reporting and limited the ability to make data-driven product and business decisions.

Insight

• The inconsistency was caused by a hidden implementation bug in live video tracking that had persisted for over a year.
• Tagging and data pipelines were not aligned to a consistent measurement framework, creating gaps across platforms and features.
• Without fixing the foundation, reporting would continue to degrade as new features launched.

Action

• Traced end-to-end data flow and validated implementation against the tagging architecture.
• Identified and resolved the root cause of a year-long live video tracking defect.
• Led a cross-functional fix across engineering, analytics, and QA.
• Owned analytics strategy and instrumentation across core product features, including purchase flows, subscription cancellation, and community chat.
• Built and standardized the measurement framework across web and mobile experiences.

Impact

• +13% reporting accuracy, closing the 12-month measurement defect.
• Established a reliable measurement program for a 50M+ user sports streaming platform.
• Enabled consistent, scalable tracking across product experiences.
• Increased confidence in product, marketing, and ad sales decision-making.

Problem

The product team wanted to optimize article placement and engagement but lacked a reliable experimentation framework to test UX variants and measure impact.

Insight

• Article engagement was measured inconsistently across placements, with no single source of truth.
• A/B tests were running without pre-defined success metrics, making post-test analysis unreliable.
• The team needed a repeatable measurement strategy that could scale to future experiments.

Action

• Defined measurement strategy and success metrics for article placement experimentation.
• Established tracking for impressions, clicks, and conversions across test variants.
• Built executive dashboards showing test performance in real time.
• Enabled reliable, statistically significant test readouts.

Impact

• +6% article open rate from optimized placement strategy.
• +9% click-through rate from UX variant testing.
• Established a scalable A/B testing measurement framework adopted across the product org.