Product Analytics Portfolio

Volta Neobank

An end-to-end analytics narrative for a fictional neobank — from onboarding funnel to A/B test, cohort retention, a difference-in-differences causal test, segmentation, and a Market & Jobs layer. Every chart below is rendered from the repository's own committed CSVs. No install, no server, no JavaScript.

17
analytical projects
funnel → JTBD → causal
170+
tests · 98% coverage
ruff · mypy · CI green
+5.72pp
KYC activation lift
A/B, p<0.0001
+9.2pp
M3 retention lift
post-fix cohorts
+9.2pp
causal DiD ATT (M3)
diff-in-diff, parallel trends ✓
36.0% vs 4.8%
referral conversion
anchor vs 45+ gap

Onboarding funnel

Where do new users drop off between install and first transaction?

registration: 73.2%73.2%registrationkyc_start: 67.2%67.2%kyc_startkyc_complete: 56.6%56.6%kyc_completecard_ordered: 71.7%71.7%card_orderedfirst_tx: 63.7%63.7%first_tx

KYC completion is the critical step: the largest relative drop in the funnel. Registration loses the most users in absolute terms — two lenses, reported both.

KYC progress-bar A/B test

Does a progress bar in the KYC flow lift completion?

KYC completionKYC completion · control: 55.8%KYC completion · treatment: 61.5%controltreatment

Treatment lifts completion by +5.72pp (p<0.0001, no SRM) — ship. This is the randomized claim the causal layer later strengthens.

Cohort retention

Did the fix hold up in long-term retention?

pre-fix · M0: 100.0%pre-fix · M1: 51.8%pre-fix · M2: 38.1%pre-fix · M3: 30.7%pre-fix · M4: 25.3%pre-fix · M5: 22.5%pre-fix · M6: 19.9%pre-fix · M7: 19.1%pre-fix · M8: 17.4%pre-fix · M9: 17.4%pre-fix · M10: 16.8%pre-fix · M11: 15.0%pre-fixpost-fix · M0: 100.0%post-fix · M1: 63.6%post-fix · M2: 48.5%post-fix · M3: 39.9%post-fix · M4: 35.7%post-fix · M5: 32.3%post-fix · M6: 32.1%post-fix · M7: 29.9%post-fix · M8: 28.4%post-fix · M9: 27.6%post-fix · M10: 25.4%post-fix · M11: 23.9%post-fixM0M2M4M6M8M10M11

Post-fix cohorts retain better from M1 onward (+9.2pp at M3). The gap persists across the curve, not just at one month.

Causal layer — difference-in-differences

Was the retention lift caused by the fix, or just a shared time trend?

M3 retentionM3 retention · in-app: pre: 36.1%M3 retention · in-app: post: 47.4%M3 retention · partner: pre: 37.7%M3 retention · partner: post: 39.8%in-app: prein-app: postpartner: prepartner: post

DiD ATT = +9.2pp: the treated flow rises at the cutoff while the comparison flow does not. Pre-trends flat, placebo null, covariates balanced.

Customer segmentation

Who are the users and where does revenue concentrate?

Power Users: 40.5%40.5%Power UsersGrowth Users: 32.9%32.9%Growth UsersCasual Users: 18.8%18.8%Casual UsersDormant Users: 7.8%7.8%Dormant Users

The top segment is 12% of users but ~41% of revenue — a classic concentration story that drives the per-segment monetization strategy.

Market & Jobs — premium upsell

Does the premium upsell transfer to every job segment?

Premium Status: 41.2%41.2%Premium StatusYoung Professionals: 17.2%17.2%Young ProfessionalsTravelers: 11.9%11.9%TravelersFamily Budgeters: 4.7%4.7%Family BudgetersDigital Newcomers 45+: 1.8%1.8%Digital Newcomers 45+

The anchor segment converts at ~17% while Digital Newcomers 45+ sit near 2% — the value proposition does not transfer. Segment-specific offers, not one upsell.

Market & Jobs — referral conversion

Does referral scale to new segments?

Premium Status: 36.0%36.0%Premium StatusYoung Professionals: 29.6%29.6%Young ProfessionalsTravelers: 21.3%21.3%TravelersFamily Budgeters: 8.6%8.6%Family BudgetersDigital Newcomers 45+: 4.8%4.8%Digital Newcomers 45+

Referral converts best in the anchor and collapses for 45+ and family budgeters — don't scale referral spend before segment-specific incentives.

Market & Jobs — segment sizes

How big is each job segment in the simulated base?

Young Professionals: 12,00012,000Young ProfessionalsFamily Budgeters: 10,00010,000Family BudgetersDigital Newcomers 45+: 8,0008,000Digital Newcomers 45+Travelers: 6,0006,000TravelersPremium Status: 4,0004,000Premium Status

Five JTBD segments simulated at scale — the population the Market & Jobs layer reasons about.