Metric-Drop Case: Why Wealthsimple Landing-Page Visitors Stopped Converting as Often

Quick Overview

A product analytics interview question about Wealthsimple, whose landing page now converts visitors about 25% worse than before. It tests whether a candidate can validate a metric drop, separate traffic-mix shifts from funnel problems, segment the data, and rank hypotheses for a fintech sign-up flow.

Metric-Drop Case: Why Wealthsimple Landing-Page Visitors Stopped Converting as Often

Company: Wealthsimple

Role: Product Manager

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Other

Wealthsimple's landing page is converting visitors at a rate about 25% lower than before. How would you work out why, and what would you do about it? ```hint Which half of the ratio moved Before blaming the page, find out whether fewer visitors are converting or whether a different kind of visitor is arriving, and whether the data itself can be trusted. ``` ### Clarifying Questions - What counts as a conversion here: starting sign-up, creating an account, passing identity verification, or funding the first account? - Is this the main homepage, or a landing page tied to one product or campaign, such as investing, crypto or tax filing? - Did the rate fall suddenly on a particular day or slide over several weeks, and is the 25% a relative change in the rate? - Have the number of visits and the absolute number of conversions moved too, or only the ratio between them? ### What a Strong Answer Covers - Confirming the drop is real: metric definition, tracking and consent changes, bot traffic, reporting delays, and a cross-check against backend account counts - Separating a change in who arrives (traffic mix, campaigns, seasonality) from a change in how well the same visitors convert - Segmenting by source, device, browser, region and product intent, and locating the funnel step where visitors now drop off - Ordering hypotheses by fit with the timing and the evidence, covering internal launches, experiments, vendor outages and external market or seasonal effects - A concrete response once the cause is known: the fix or rollback, the metric that confirms recovery, and alerting that would catch the next drop sooner ### Follow-up Questions - If absolute sign-ups are flat while the rate fell 25%, what does that tell you, and is it still a problem worth fixing? - If the drop is concentrated on one mobile browser and lines up with a redesign that had won its A/B test, how would you reconcile the two? - How would you set up monitoring so that a drop of this size is caught within a day?

Overview: A product analytics interview question about Wealthsimple, whose landing page now converts visitors about 25% worse than before. It tests whether a candidate can validate a metric drop, separate traffic-mix shifts from funnel problems, segment the data, and rank hypotheses for a fintech sign-up flow.

Wealthsimple logo
Wealthsimple
Dec 9, 2024
mediumProduct ManagerOtherAnalytics & Experimentation
0
0

Wealthsimple's landing page is converting visitors at a rate about 25% lower than before. How would you work out why, and what would you do about it?

Clarifying Questions Guidance

  • What counts as a conversion here: starting sign-up, creating an account, passing identity verification, or funding the first account?
  • Is this the main homepage, or a landing page tied to one product or campaign, such as investing, crypto or tax filing?
  • Did the rate fall suddenly on a particular day or slide over several weeks, and is the 25% a relative change in the rate?
  • Have the number of visits and the absolute number of conversions moved too, or only the ratio between them?

What a Strong Answer Covers Guidance

  • Confirming the drop is real: metric definition, tracking and consent changes, bot traffic, reporting delays, and a cross-check against backend account counts
  • Separating a change in who arrives (traffic mix, campaigns, seasonality) from a change in how well the same visitors convert
  • Segmenting by source, device, browser, region and product intent, and locating the funnel step where visitors now drop off
  • Ordering hypotheses by fit with the timing and the evidence, covering internal launches, experiments, vendor outages and external market or seasonal effects
  • A concrete response once the cause is known: the fix or rollback, the metric that confirms recovery, and alerting that would catch the next drop sooner

Follow-up Questions Guidance

  • If absolute sign-ups are flat while the rate fell 25%, what does that tell you, and is it still a problem worth fixing?
  • If the drop is concentrated on one mobile browser and lines up with a redesign that had won its A/B test, how would you reconcile the two?
  • How would you set up monitoring so that a drop of this size is caught within a day?