analytical-execution-with-gemini

Creating Hypotheses

Let’s start with your ability to create hypotheses.

Imagine Meta is seeing a decline in user engagement on Instagram Reels over the past quarter. Engagement here is defined as a composite metric including likes, comments, shares, and average watch time.

Question 1: What are some initial hypotheses you would formulate to understand the potential causes of this decline in Instagram Reels engagement? Please walk me through your thought process.

(After you provide your answer, I will give you a benchmark answer for a senior data scientist.)

Some hypothesis i have would be in-app cannibalization, other app cannibalization, or decreasing content quality, decreasing recommendation. To pick one to go through the process - let’s look at in-app cannibalization. I would first look at user event logs to see if users are using another in app feature before reels or going to another feature in the middle of using reels. Compare that against the user event logs from before to see which other in-app feature is a Candidate. Then measure the time spent on this new feature and see if there is a statistical change in user usage rate with this feature before and after.

Show thinking

Okay, that’s a good start. Identifying potential areas like in-app cannibalization, external competition, content quality, and recommendation effectiveness are all relevant. Your approach to investigating in-app cannibalization by looking at user event logs and comparing usage patterns over time is logical.

Here’s what a benchmark answer from a senior data scientist might look like for this question:

Benchmark Answer (Senior Data Scientist - Creating Hypotheses):

“That’s an interesting and important problem. A decline in engagement on a core feature like Reels warrants a structured investigation. My initial approach would be to categorize potential hypotheses to ensure comprehensive coverage. I’d think about it in terms of:

  1. Product-Related Hypotheses:

    • H1: Recent UI/UX changes: Hypothesis: A recent app update or change to the Reels interface (e.g., new navigation, button placement, algorithm tweak display) has negatively impacted usability or discoverability. Initial Test Idea: Correlate engagement drop with rollout dates of specific features. Segment users by app version.
    • H2: Performance or Bug Issues: Hypothesis: Increased loading times, bugs in the Reels player, or issues with specific devices/OS versions are frustrating users. Initial Test Idea: Analyze crash logs, performance metrics (load times, playback failures), and user-reported bug data, segmenting by device/OS.
    • H3: In-app Cannibalization (as you mentioned): Hypothesis: A different feature within Instagram (e.g., Stories, a new video format, enhanced main feed discovery) is drawing users’ attention and time away from Reels. Initial Test Idea: Analyze user session flow data. Are users who previously spent X minutes on Reels now spending X+Y minutes on another feature? Look for inverse correlations in feature usage.
    • H4: Content Algorithm Changes: Hypothesis: Recent modifications to the Reels recommendation algorithm are surfacing less relevant or lower quality content to users, leading to decreased interaction. Initial Test Idea: Review A/B test results for recent algorithm changes. Analyze content diversity and relevance metrics for users with declining engagement.

  2. Content-Related Hypotheses:

    • H5: Decreasing Content Quality/Freshness: Hypothesis: The overall quality, originality, or trendiness of content on Reels is declining, making it less appealing. Initial Test Idea: This is harder to measure directly, but we could look at proxies like the ratio of original audio vs. trending sounds, creator activity (are top creators posting less?), or run content quality scoring models if available. Conduct qualitative user research.
    • H6: Content Saturation/Fatigue: Hypothesis: Users are experiencing fatigue from specific content types or trends that were previously popular but are now overused. Initial Test Idea: Analyze trends in content categories. Survey users about their content preferences and feelings of repetitiveness.

  3. User-Related Hypotheses:

    • H7: Shift in User Demographics or Preferences: Hypothesis: The active user base on Reels is changing, and their preferences are different (e.g., an influx of older users who engage differently). Or, existing users’ tastes are evolving. Initial Test Idea: Analyze demographic shifts in the Reels user base and their engagement patterns. Conduct user surveys.
    • H8: User Base Saturation/Novelty Wearing Off: Hypothesis: Reels has reached a saturation point with its current addressable audience, and the novelty factor has diminished, leading to a natural plateau or slight decline. Initial Test Idea: Look at new user acquisition rates for Reels. Compare engagement patterns of tenured vs. new users.

  4. External Factors:

    • H9: Increased Competition: Hypothesis: Competing platforms (e.g., TikTok, YouTube Shorts) have launched compelling new features or are more aggressively acquiring user attention. Initial Test Idea: Monitor market intelligence reports. Analyze cross-app usage data if available (though often limited). Look for correlations with major competitor campaigns or feature launches.
    • H10: Seasonality/External Events: Hypothesis: External factors like holidays, major news events, or changes in internet usage patterns (e.g., people spending more time outdoors in summer) are impacting engagement. Initial Test Idea: Compare current trends with historical data from previous years, controlling for other factors.

My thought process is to first diverge (brainstorm a wide range of possibilities) and then converge (identify which hypotheses are most plausible or impactful to test first based on available data, ease of testing, and potential business impact). I’d also prioritize hypotheses that the team has direct control over (e.g., product changes) while acknowledging external factors. For each hypothesis, I’d think about what data would be needed to support or refute it and what the simplest first analytical step would be.”

Quantitative Analysis

Let’s say we’ve prioritized the hypothesis that “Recent modifications to the Reels recommendation algorithm are surfacing less relevant or lower quality content to users, leading to decreased interaction.” You have access to A/B test results for several algorithm changes that were rolled out globally in the past quarter. You also have user-level data on their engagement (likes, comments, shares, watch time) and the characteristics of the Reels they were shown (e.g., content topic, creator ID, video length, audio type).

Question 2: How would you quantitatively analyze whether the algorithm changes are indeed the cause of the engagement decline? Describe the statistical methods you might use and how you would interpret the results. What potential pitfalls or confounding variables would you be mindful of? (After you provide your answer, I will give you a benchmark answer for a senior data scientist.)

Using the results from the A/B test, I would compare user engagements rates between the two groups with a z test. The results would depend on the the minimum effect size, significance and power we set. We can be certain of a difference and if we want to quantify the actual difference we can do point estimates with confidence intervals.

Show thinking

That’s a good foundational approach. Using A/B test results is definitely the primary way to go, and a z-test for comparing rates or means is a valid statistical tool for assessing differences between the control and treatment groups. Mentioning significance, power, and confidence intervals for effect size is also important.

However, for a senior role, we’d expect a more detailed and nuanced plan.

Here’s what a benchmark answer from a senior data scientist might look like for this question:

Benchmark Answer (Senior Data Scientist - Quantitative Analysis):

“Okay, if we’re testing the hypothesis that recent algorithm changes led to decreased engagement due to less relevant/lower quality content, here’s how I’d approach the quantitative analysis:

  1. Deep Dive into A/B Test Results (Primary Approach):

    • Granular Metric Analysis: ‘Engagement’ is composite. I’d first break it down.

      • For each A/B test corresponding to an algorithm change, I’d analyze its impact on individual metrics: average likes per user, average comments per user, average shares per user, average watch time per user, and also metrics like session length on Reels, Reels viewed per session, and click-through rate on Reels.
      • I’d use t-tests (for continuous metrics like watch time) or chi-squared/z-tests (for proportions like liker rate) to compare treatment and control groups. I’d pay close attention to p-values for statistical significance and confidence intervals for the magnitude of the effect.

    • Segmentation: Aggregate results can be misleading. I would segment the A/B test results by key user dimensions:

      • New vs. Tenured Users: Do new users react differently to algorithm changes than established users?
      • Creator vs. Consumer: Are content creators experiencing different engagement on their content? Are consumers engaging differently?
      • High vs. Low Activity Users: Are highly engaged users more sensitive to these changes?
      • Demographics (Age, Region): Are there specific demographic groups disproportionately affected?
      • Device/OS: To rule out technical predispositions.
      • This can help identify if an algorithm change was broadly negative or only for specific user segments, which might point towards relevance issues for those segments.

    • Time-Series Analysis within A/B Tests: Look at how the treatment effect evolved over the duration of the A/B test. Was there a novelty effect? Did the negative impact worsen or stabilize over time? This helps understand user adaptation.

    • Multiple Algorithm Changes: If several changes were rolled out, the ideal scenario is that they were tested in isolated A/B tests. If not (e.g., layered changes or sequential rollouts without clean holdbacks), it becomes trickier.

      • If changes were sequential, I’d look at the incremental impact of each, but this is less clean.
      • If feasible, I might try to design a retrospective analysis using observational causal inference methods (see point 3), though this is more complex.

  2. Connecting to “Less Relevant/Lower Quality Content”:

    • This requires linking algorithm changes to content characteristics. For the A/B tests:
      • I would analyze if the distribution of content characteristics shown to users in the treatment group differed significantly from the control group. For example:
        • Were users in the treatment group shown more content from new creators vs. established ones?
        • Did the topical diversity of content change?
        • Was there a shift in the proportion of “high quality” vs. “low quality” content (if we have proxies for quality, like content from verified users, professionally produced content, or even user reports/hides on content)?
      • I’d then try to correlate these shifts in content characteristics with the observed changes in engagement metrics. For example, if an algorithm change led to users seeing 20% more content from a topic they historically don’t engage with, and their engagement dropped, that’s strong evidence. This might involve building a simple regression model within the A/B test data: Engagement_Change ~ Change_in_Content_Relevance_Metric + other_factors.

  3. Observational Causal Inference (If A/B tests are insufficient or for broader context):

    • If A/B tests weren’t perfectly executed, or if we want to analyze the impact of multiple overlapping changes or look at effects not captured in specific experiments, I might consider methods like:
      • Difference-in-Differences (DiD): If some user groups were less exposed to the algorithm changes initially, or if changes rolled out progressively by region, we could compare the change in engagement for exposed groups to the change for unexposed/less-exposed groups, before and after the changes. This requires careful selection of control groups and checking the parallel trends assumption.
      • Regression Discontinuity Design (RDD): If algorithm changes were triggered by users crossing a certain threshold (e.g., activity level), RDD could be applicable.
      • Propensity Score Matching or Inverse Propensity Score Weighting: To create pseudo-control groups for users affected by algorithm changes by matching them with similar users who weren’t (as much) affected, based on pre-change characteristics. This is complex and relies on the “unconfoundedness” assumption.

  4. Statistical Methods and Interpretation:

    • Beyond standard hypothesis tests, I’d use regression models (e.g., OLS, logistic regression) to control for multiple variables simultaneously and quantify effect sizes. For example, User_Engagement ~ Algo_Version + User_Tenure + Content_Type_Dummy_Variables + ....
    • I would be very clear about statistical significance (p-value < 0.05 usually) vs. practical significance (is the effect size meaningful for the business?). A tiny but statistically significant drop might not be the primary driver.
    • Confidence intervals around effect estimates are crucial to understand the range of uncertainty.

  5. Potential Pitfalls & Confounding Variables:

    • Seasonality/External Events: The overall engagement decline might be partly due to external factors (e.g., holidays, competitor actions). A/B tests inherently control for contemporaneous events if they run for the same period, but if comparing multiple A/B tests run at different times, this needs consideration. The DiD approach would also aim to control for this.
    • Network Effects/Interference: Algorithm changes can have network effects. For instance, if an algorithm change causes some users to post less, this could reduce content availability for their friends, impacting their friends’ engagement even if their friends were in the control group. This is a common issue in social network A/B testing (often tackled with cluster-randomized A/B tests, but if not done, it’s a caveat).
    • Simpson’s Paradox: Aggregate data might show one trend, while segmented data shows the opposite. This is why segmentation is critical.
    • Selection Bias: If the algorithm changes weren’t uniformly rolled out or if A/B test participation wasn’t perfectly random.
    • Instrumentation/Data Quality Issues: Ensuring the engagement metrics and content characteristics are logged accurately. I’d start by validating the data.
    • Look-elsewhere effect/Multiple testing: If we test many metrics and many segments, we are bound to find some statistically significant results by chance. I would use methods like Bonferroni correction or False Discovery Rate (FDR) control if running many comparisons, or pre-specify primary metrics and segments.

My overall goal would be to establish a clear causal link, if one exists, between specific algorithm changes and the decline in specific engagement metrics, and to understand why those changes might be perceived as surfacing less relevant or lower-quality content.”

Question 1: Creating Hypotheses

Imagine you’re on the Facebook Reels team, and your stakeholders come to you with a concern: they’ve noticed a recent dip in user engagement on Reels, specifically a decrease in the number of unique users creating and sharing original Reels.

How would you approach this problem? What are some initial hypotheses you’d form to explain this drop in engagement, and how would you structure them to be testable?

“That’s an interesting and important problem. A decline in engagement on a core feature like Reels warrants a structured investigation. My initial approach would be to categorize potential hypotheses to ensure comprehensive coverage. I’d think about it in terms of:

  1. Product-Related Hypotheses:

    - H1: Recent UI/UX changes:

    - H2: Performance or Bug Issues:

    - **H3: In-app Cannibalization **

    - H4: Content Algorithm Changes:

  1. Content-Related Hypotheses:

    - H5: Decreasing Content Quality/Freshness:

    - H6: Content Saturation/Fatigue:

That’s a solid start, and you’ve hit on some critical areas. Categorizing your hypotheses is an excellent practice, as it helps organize your thinking and ensures you’re not missing major buckets of potential causes.

Let’s break down your initial thoughts and then I’ll offer some senior-level benchmarks.

Your categories:

  • Product-Related Hypotheses: Good call. These are often immediate suspects when engagement shifts.
  • Content-Related Hypotheses: Absolutely essential for a content-driven platform like Reels.

Now, let’s refine and expand on your specific hypotheses, focusing on making them more testable and actionable from a data perspective.

Benchmark Answer: Creating Hypotheses

A senior data scientist would not only list these categories but also articulate each hypothesis with a clear direction, a potential cause, and an implied method of testing. They’d also think about the order in which they might investigate these, prioritizing based on potential impact and ease of measurement.

Here’s how a senior data scientist might elaborate on your points and add a few more:

Overall Approach: “To tackle a dip in Reels engagement, my first step would be to align with stakeholders on the exact definition of ‘engagement’ in this context (e.g., unique creators, number of Reels created, watch time per creator, shares per Reel). Then, I’d define the timeframe of the dip. With that clear, I’d prioritize hypotheses based on potential impact and ease of data access.”

1. Product-Related Hypotheses:

  • H1: Recent UI/UX Changes Impact Creation Flow.
    • Specifics: “We hypothesize that a recent change to the Reels creation flow (e.g., button placement, editing tools, sharing options) has increased friction, leading to a decrease in the completion rate of new Reels.”
    • Testable: “We can test this by analyzing funnel metrics for Reels creation, comparing conversion rates at each step pre- and post-change, and potentially running A/B tests on specific UI elements if we identify a problematic step.”
  • H2: Performance or Bug Issues are Hindering Creation/Consumption.
    • Specifics: “We hypothesize that increased app crashes, upload failures, or lag during editing/viewing are discouraging users from creating or engaging with Reels.”
    • Testable: “We can monitor backend error logs, client-side performance metrics (e.g., load times, crash rates specific to Reels), and analyze user reports/feedback. A correlation between performance degradation and engagement drop would support this.”
  • H3: In-app Cannibalization from New/Promoted Features.
    • Specifics: “We hypothesize that a new feature or increased promotion of an existing feature within the Facebook app (e.g., Stories, other video formats) is drawing user attention and time away from Reels creation.”
    • Testable: “We can analyze time spent and activity patterns across different Facebook features for users who historically created Reels. Is their overall time on Facebook stable, but just redistributed? We’d look for inverse correlations in engagement between Reels and other features.”
  • H4: Content Algorithm Changes Reduced Reach/Motivation for Creators.
    • Specifics: “We hypothesize that recent changes to the Reels content recommendation algorithm have decreased the visibility or distribution of newly created Reels, thus reducing the motivation for users to create more.”
    • Testable: “We can analyze the average reach and impression counts for newly uploaded Reels, comparing them to historical benchmarks. We’d also look at creator retention metrics – are creators seeing fewer views on their content, leading them to stop creating?”

2. Content-Related Hypotheses:

  • H5: Decreasing Content Quality/Freshness is Reducing Viewer Engagement, which in turn discourages creators.
    • Specifics: “We hypothesize that the overall quality (e.g., watch completion rates, likes per view) or freshness (e.g., novelty, trend participation) of Reels content has declined, leading to fewer viewers, which then reduces the incentive for creators to produce new content.”
    • Testable: “We can analyze aggregate metrics for content quality (e.g., average watch time per Reel, number of skips) and freshness (e.g., new trending audio usage, diversity of content themes). This is a feedback loop: lower quality viewing experience -> fewer viewers -> less incentive to create.”
  • H6: Content Saturation/Fatigue is leading to a perceived lack of novelty.
    • Specifics: “We hypothesize that users are experiencing ‘Reels fatigue’ due to an abundance of similar content, leading to less excitement to create or consume new material.”
    • Testable: “This is harder to quantify directly but could be hinted at by metrics like a plateau in watch time per user while content volume continues to grow, or increased negative feedback signals (e.g., ‘not interested’ clicks). Survey data could also be useful here.”

Additional Senior-Level Hypotheses:

  • H7: External Factors (e.g., Competitive Landscape, Macro Trends).
    • Specifics: “We hypothesize that external platforms (e.g., TikTok, YouTube Shorts) have introduced compelling new features or trends that are siphoning off our creator base and their content.”
    • Testable: “This would involve competitive analysis (monitoring competitor launches, trending content) and potentially looking at cross-platform user behavior data if available (though often challenging). It’s less directly testable with our internal data but crucial to consider.”
  • H8: Creator Monetization/Incentive Shifts.
    • Specifics: “We hypothesize that changes in creator monetization opportunities, promotional programs, or creator support have reduced the incentive for key creators to produce content on Reels.”
    • Testable: “We can analyze participation rates in creator programs, payouts, and engagement metrics for power creators. Are our top creators still active? Have they migrated platforms or reduced their output?”
  • H9: Policy/Moderation Changes Impacting Content Creation.
    • Specifics: “We hypothesize that recent changes in content policies or moderation enforcement (e.g., stricter guidelines, increased removal rates) have inadvertently discouraged certain types of content or creators.”
    • Testable: “We can look at the volume and types of content removals, strike rates for creators, and appeals. Is there a correlation between policy changes and a drop in specific content categories or creator activity?”

You’ve laid a good foundation. The next step, once hypotheses are formed, is to figure out how to test them.

Let’s move on to the next section.

Question 2: Quantitative Analysis

Following up on our Reels engagement problem: Let’s say your initial data exploration points towards H1: Recent UI/UX Changes Impact Creation Flow as a strong contender. You’ve identified a specific change made two weeks ago to the “Add Audio” step in the Reels creation flow, where the user now has to go through an extra click to browse audio options.

How would you quantitatively analyze the impact of this specific UI/UX change on Reels creation, and what statistical methods or tools would you employ to demonstrate its effect? Be specific about the data you’d look for and the analysis steps.

“Okay, if we’re testing the hypothesis that recent algorithm changes led to decreased engagement due to ui/ux changes impact creation flow, here’s how I’d approach the quantitative analysis:

  1. Deep Dive into A/B Test Results (Primary Approach):

    - Granular Metric Analysis: ‘Engagement’ is composite. I’d first break it down.

        - For each A/B test corresponding to an algorithm change, I’d analyze its impact on individual metrics: average likes per user, average comments per user, average shares per user, average watch time per user, and also metrics like session length on Reels, Reels viewed per session, and click-through rate on Reels.

        - I’d use t-tests (for continuous metrics like watch time) or chi-squared/z-tests (for proportions like liker rate) to compare treatment and control groups. I’d pay close attention to p-values for statistical significance and confidence intervals for the magnitude of the effect.

    - Segmentation: Aggregate results can be misleading. I would segment the A/B test results by key user dimensions:

You’re on the right track by immediately thinking about A/B test results and breaking down engagement into granular metrics. That’s a crucial step. However, I need to point out a slight misinterpretation of the hypothesis we’re focusing on for this question.

The specific hypothesis for this question is: H1: Recent UI/UX Changes Impact Creation Flow, specifically the “Add Audio” step requiring an extra click. You mentioned “algorithm changes” in your response, but for this specific follow-up, let’s keep the focus purely on the UI/UX change in the creation flow. While algorithm changes are a valid hypothesis for the overall engagement dip, they’re not what we’re testing with this particular question.

Let’s refine your answer with that in mind, and then I’ll provide the benchmark.

Your (Refined) Approach:

  1. Deep Dive into A/B Test Results (Primary Approach):
    • Context: If this “extra click” change to the “Add Audio” step was rolled out via an A/B test, this is indeed the most robust way to analyze its impact.
    • Granular Metric Analysis for Creation Flow: Since the hypothesis is about creation flow, the metrics I’d focus on are specific to that funnel:
      • Start Rate: Proportion of users who initiate the Reels creation flow after entering the app.
      • Completion Rate: Proportion of users who start the creation flow and successfully publish a Reel. This is the critical metric for this hypothesis.
      • Drop-off Rates at Each Step: Specifically, I’d look at the drop-off rate at the “Add Audio” step and subsequent steps.
      • Time to Create: Average time taken from initiating creation to publishing a Reel.
      • Number of Reels Created per Creator: If the change made it harder, creators might make fewer Reels.
    • Statistical Tests: I’d use:
      • Proportion Z-tests or Chi-squared tests: For comparing conversion rates (e.g., completion rate, drop-off rates) between the treatment (new UI) and control (old UI) groups.
      • T-tests or Mann-Whitney U tests: For continuous metrics like “Time to Create” or “Number of Reels Created per Creator.”
    • Statistical Significance and Effect Size: I’d evaluate p-values for statistical significance (e.g., $p < 0.05$) and also look at confidence intervals to understand the magnitude and direction of the observed effect.
  2. Segmentation:
    • I would segment the A/B test results by key user dimensions:
      • New vs. Existing Creators: The impact might differ for users familiar with the old flow vs. those experiencing the new flow for the first time.
      • Power Creators vs. Casual Creators: Power creators might be more sensitive to workflow changes.
      • Device Type/OS: Potential performance issues or UI rendering differences could affect specific device segments.
      • Geographic Region: Cultural differences or network performance might play a role.
    • This segmentation helps identify if the negative impact is universal or localized to certain user groups.

Benchmark Answer: Quantitative Analysis

“You’re absolutely right to hone in on A/B testing as the primary mechanism if the change was rolled out that way. That’s the gold standard for causal inference. If it wasn’t an A/B test, we’d need to consider quasi-experimental methods.

Let’s assume, for now, it was an A/B test for that specific ‘Add Audio’ UI change.

1. Confirming the A/B Test Setup & Data Acquisition: * Experiment Logs: First, I’d verify the A/B test configuration: start/end dates, traffic allocation (e.g., 50/50, 10/90), target audience (e.g., all users, specific geos), and ensure there was proper randomization. Any issues here would invalidate the results. * Data Sources: I’d identify the relevant logging tables for Reels creation funnel events (e.g., reels_creation_flow_start, reels_audio_selection_view, reels_publish_success), user attributes, and experiment assignment.

2. Defining Core Metrics & Success Criteria: * Primary Metric: The absolute top-line metric would be Reels Creation Completion Rate (Number of Reels Published / Number of Users who Start Creation Flow). This directly addresses the hypothesis. * Guardrail Metrics: We must ensure the change didn’t negatively impact other crucial aspects: * Overall app engagement (time spent on Facebook). * Retention of new creators. * Quality of created Reels (e.g., watch time per created Reel). * Funnel Metrics: Break down the creation funnel into key steps: * Reels_creation_initiated -> Audio_selection_entered -> Audio_selected -> Editing_started -> Reels_published. * I’d calculate conversion rates between each step for both control and treatment groups, paying special attention to the drop-off at the ‘Add Audio’ step.

3. Statistical Analysis Plan:

  • Pre-Analysis Check:
    • SRM (Sample Ratio Mismatch): Verify that the observed traffic split in the data matches the intended allocation. A significant mismatch ($p < 0.001$) suggests a logging or randomization issue.
    • Baseline Comparability: For key user attributes (e.g., age, geo, device), ensure that control and treatment groups were comparable before the experiment began.
  • Choosing the Right Test:
    • For Proportions (e.g., Completion Rate, Drop-off Rates): I’d use a Z-test for proportions or a Chi-squared test. A Z-test is often preferred for large sample sizes in A/B testing.
    • For Counts/Rates (e.g., Reels Created per User): If the distribution is skewed (which is common for counts), a Mann-Whitney U test or a bootstrapped t-test might be more robust than a simple t-test. If the data approaches normality with sufficient sample size, a t-test could be appropriate.
    • For Time-based Metrics (e.g., Time to Create): A t-test or Mann-Whitney U test, depending on distribution.
  • Statistical Significance and Power:
    • Set a significance level, e.g., $\alpha = 0.05$.
    • Ensure the experiment ran long enough to achieve sufficient statistical power to detect a meaningful change. If the observed change is small but the business impact is significant, we need to be sure we had enough power.
  • Confidence Intervals: Crucial for understanding the magnitude and precision of the effect. A negative impact on completion rate with a narrow confidence interval clearly indicates an issue.

4. Advanced Considerations (Senior Level):

  • Novelty vs. Primacy Effects: Has the experiment run long enough to move past any initial ‘novelty’ or ‘primacy’ effects where users are just reacting to something new? We want to measure the long-term impact.
  • Segmented Analysis: As you mentioned, deep dive into segments:
    • Creator Cohorts: Analyze by when users first became creators. New creators might react differently than seasoned ones.
    • Frequency of Creation: Are casual creators more affected than power creators?
    • Device/OS: Are specific devices experiencing more issues with the new UI?
  • Regression Analysis (if no A/B test): If the change was a full rollout without an A/B test (less ideal but happens), I’d consider interrupted time series analysis or difference-in-differences.
    • Interrupted Time Series: Plot the daily Reels creation completion rate over time, with a clear marker for the change date. Use regression to model the trend before and after the intervention, looking for changes in level and slope.
    • Difference-in-Differences: Find a comparable “control group” (e.g., users in a region where the change wasn’t rolled out, or a proxy metric that wasn’t impacted). Compare the change in Reels creation in the “treatment” group vs. the “control” group. This is harder to do rigorously for a global UI change.
  • Proxy Metrics for Qualitative Impact: If direct quantitative metrics don’t fully capture user frustration, look at proxy signals like increased customer support tickets related to creation, or a spike in uninstalls/negative app reviews.
  • Interpreting Results: Don’t just report p-values. Explain the business impact. “The new UI resulted in a 3.5% statistically significant decrease in Reels creation completion, translating to X fewer Reels published daily, which could lead to Y reduction in overall watch time.”

Question 3: Determining Goals & Success Metrics

Staying with the Reels team and the overall goal of increasing original Reels creation and engagement:

If you were tasked with setting the key success metrics for a new initiative aimed at boosting original Reels creation, what would those metrics be? How would you define “success” for this initiative, considering both short-term impact and long-term product health? What potential pitfalls or unintended consequences would you consider, and how would your metrics account for them?

I would define some leading metrics for short term success such as reels created, creation success rate as well as long term metrics like DAU of creators.

That’s a good start by differentiating between leading/short-term and lagging/long-term metrics, and you’ve identified some core metrics like “Reels created” and “DAU of creators.” These are definitely in the right direction.

However, a senior data scientist would elaborate significantly more on why these metrics are chosen, how they’re defined precisely, what other metrics are crucial to prevent negative externalities, and how “success” is quantified beyond just an increase.

Let’s expand on your answer and then I’ll provide the benchmark.

Your (Refined) Approach:

For a new initiative aimed at boosting original Reels creation:

Short-term / Leading Metrics:

  • Reels Created:
    • Definition: The total number of new, original Reels published within a defined time frame (e.g., daily, weekly).
    • Rationale: Direct measure of the initiative’s primary goal.
  • Creation Success Rate:
    • Definition: The percentage of users who start the Reels creation process and successfully publish a Reel.
    • Rationale: Measures friction in the creation flow, which the initiative might aim to reduce.

Long-term / Lagging Metrics:

  • Daily Active Users (DAU) of Creators:
    • Definition: The number of unique users who publish at least one original Reel on a given day.
    • Rationale: Indicates the health of the creator base and sustained creation activity.

Now, let’s look at the benchmark answer, which adds crucial layers of detail, strategic thinking, and foresight into potential pitfalls.

Benchmark Answer: Determining Goals & Success Metrics

“This is a critical exercise, as poorly chosen metrics can lead to unintended consequences or a misdirection of resources. For an initiative focused on boosting original Reels creation, I’d propose a comprehensive set of metrics, carefully defining success and considering guardrails.

Overall Goal: Increase the volume and sustained engagement of original Reels creators and their content, ultimately driving overall Reels viewership and platform health.

Defining “Success”: Success wouldn’t just be an increase in numbers, but a healthy, sustainable increase that doesn’t compromise content quality or user experience. This means we’d define specific, measurable targets for each metric, e.g., “Increase daily Reels creation by 10% within 3 months, with no decrease in average watch time per Reel and a 5% improvement in new creator retention.”

Key Success Metrics:

A. Primary (Output & Outcome) Metrics:

  1. Original Reels Created (Volume):

    • Definition: Total number of new, original Reels published per day/week by unique users. (We need to distinguish from re-shared or duplicated content if that’s not the goal).
    • Rationale: This is the most direct measure of the initiative’s primary goal. It’s a leading indicator of content supply.
    • Target: Absolute increase, e.g., ‘X% increase in daily original Reels creation.’

  2. Unique Original Reels Creators (Breadth):

    • Definition: Number of unique users who publish at least one new, original Reel per day/week.
    • Rationale: Measures the size and growth of our creator base. It’s important to not just get more content from existing creators but to grow the number of people creating.
    • Target: Absolute increase, e.g., ‘Y% increase in weekly unique original Reels creators.’

B. Engagement & Health Metrics (Guardrails & Quality Indicators):

These are crucial to ensure we’re not just getting more content, but good content that keeps users engaged.

  1. Creator Retention (Lagging Indicator of Creator Health):

    • Definition:
      • N-Day Retention: % of users who created a Reel in week 0 and create another Reel in week 1, week 4, etc.
      • Creator DAU/WAU/MAU: Daily/Weekly/Monthly Active Creators.
    • Rationale: Ensures creators continue to find value and are not just creating once and churning. This is vital for long-term supply.
    • Target: Maintain or improve retention rates across different cohorts (new vs. existing creators).

  2. Content Quality Metrics (Proxy for Viewer Satisfaction):

    • Average Watch Time per Original Reel:
      • Definition: The total time users spend watching original Reels divided by the total number of original Reels viewed.
      • Rationale: A key signal that the content being created is engaging viewers. A drop here suggests the new content isn’t high quality.
    • Reel Completion Rate:
      • Definition: Percentage of times an original Reel is watched to 100% completion.
      • Rationale: Strong indicator of content quality and viewer interest.
    • Engagement Rate per Original Reel (Likes/Shares/Comments per View):
      • Definition: Sum of likes, shares, comments on original Reels divided by total views of original Reels.
      • Rationale: Measures active viewer engagement.
    • Target: Maintain or ideally improve these quality metrics.

  3. Creator Cohort Analysis (Long-term Impact):

    • Definition: Track the behavior (creation frequency, viewership of their content, retention) of users who started creating Reels after the initiative launched, compared to similar cohorts before.
    • Rationale: Helps understand the sustained impact on new creators brought in by the initiative.

C. Leading/Process Metrics (for specific initiative mechanics):

If the initiative involves specific product changes or campaigns, we’d add metrics related to those. E.g., if it’s about making creation easier:

  1. Reels Creation Funnel Conversion Rates:
    • Definition: Conversion rates at each step from “Reels entry point click” to “Reel published.”
    • Rationale: Directly measures the efficiency of the creation flow, indicating if UX improvements are working.

Potential Pitfalls & Unintended Consequences (and how metrics account for them):

  1. Quantity over Quality:

    • Pitfall: Boosting creation volume might lead to a flood of low-quality, spammy, or unengaging content.
    • Mitigation through Metrics: The “Content Quality Metrics” (Watch Time, Completion Rate, Engagement Rate per Reel) are critical guardrails. If creation volume goes up but quality metrics drop, it’s a red flag. We might even introduce a “quality score” for Reels.

  2. Creator Burnout/Churn:

    • Pitfall: Aggressively pushing users to create more might lead to burnout and long-term churn.
    • Mitigation through Metrics: “Creator Retention” and “Creator DAU/WAU/MAU” are essential. If we see a short-term spike in creation but a dip in retention, the initiative is unsustainable. We’d also monitor the frequency of creation per creator – if it becomes too high for a sustained period, it’s a risk.

  3. Cannibalization:

    • Pitfall: New Reels creation might come at the expense of other valuable content creation on Facebook (e.g., Stories, traditional video posts).
    • Mitigation through Metrics: Monitor engagement and creation metrics for other content formats across the platform to ensure we’re not just shifting activity around without growing the pie. Overall time spent on Facebook/Instagram would also be a key guardrail.

  4. Audience Fatigue:

    • Pitfall: Too much content, even if some is good, can overwhelm viewers and lead to fatigue.
    • Mitigation through Metrics: Monitor overall “Reels Watch Time per User” and “Reels viewed per session.” If these plateau or decline despite increased content, it suggests fatigue. “Skip rates” on Reels could also indicate this.

  5. Policy Violations/Harmful Content:

    • Pitfall: Increased creation volume might lead to an increase in policy-violating or harmful content.
    • Mitigation through Metrics: Work closely with policy and safety teams to monitor metrics like “reported content volume for Reels,” “actioned content volume,” and “prevalence of harmful content” related to newly created Reels.

By setting clear targets across these intertwined metrics and constantly monitoring the guardrails, we can drive the desired outcome while ensuring the long-term health of the Reels ecosystem.”

That was a comprehensive breakdown! You can see how a senior role requires not just identifying metrics, but also articulating their ‘why,’ defining them precisely, and anticipating the potential negative impacts.

Let’s move to the final focus area.

Question 4: Demonstrating Agility

Imagine you’ve launched the new initiative to boost Reels creation, and you’ve been monitoring your metrics closely. After two weeks, you observe the following:

  • Primary Metric (Original Reels Created): A modest but statistically significant increase of 5%. Good news!
  • Guardrail Metric (Average Watch Time per Original Reel): A statistically significant decrease of 3%. Not so good.
  • Other Guardrail (Creator Retention): No statistically significant change observed yet.

This presents a dilemma: you’re achieving the volume goal, but at the potential cost of content quality.

How would you respond to this situation? What immediate actions would you take, what further analysis would you perform, and how would you communicate this to your stakeholders? This is where your agility and ability to adapt come into play.

The primary metric increased and that’s good, however with a decrease in guardrail we should proceed with caution. First we should dig deeper to understand if the decrease is evenly spread across all segments or particular segments. Once we have done that we should ensure there is causaility between the guardrail metric decrease and the new initiative. If all those are correct then we should ensure the overall metric with total watch time with more original reels but lower average watch time is still positive, and roll out the initiative while trying to correct the average watch time

You’ve identified the core tension and correctly emphasized the need for deeper investigation and caution. That’s a strong start. Your points about segmentation and establishing causality are spot on.

However, a senior data scientist would also articulate a more immediate action plan, a more structured approach to proving causality (especially in an ongoing initiative), and a nuanced communication strategy that doesn’t just present the dilemma but also proposes a path forward. They’d also explicitly discuss the “trade-off” decision.

Let’s refine your answer, and then I’ll provide the benchmark.

Your (Refined) Approach:

“You’re right, the increase in creation is positive, but the decrease in watch time is a critical flag. This requires immediate attention and a cautious approach.

Immediate Actions & Further Analysis:

  1. Stop/Pause Consideration: My very first thought is to assess if we need to immediately pause or roll back the initiative to prevent further degradation of content quality, especially if the watch time drop is significant or accelerating. This depends on the magnitude and business impact.
  2. Segmented Analysis of Watch Time Drop: I’d immediately dive into the data to understand who is causing this drop in watch time.
    • Creator Segments: Are new creators creating lower-quality content? Or are existing creators whose content used to perform well now creating poorer quality content due to the initiative?
    • Content Type/Category: Are specific types of Reels (e.g., certain themes, lengths, use of new features) associated with the watch time drop?
    • Audience Segments: Are certain viewer demographics or engagement levels reacting negatively?
    • Device/Geo: Is the issue localized?
  3. Causality Investigation:
    • A/B Test Data: If the initiative was A/B tested, I’d review the results of that test with these metrics in mind. The A/B test is the strongest evidence of causality.
    • Time Series Analysis: Even with an A/B test, I’d look at the daily trends of “Original Reels Created” and “Average Watch Time per Original Reel” since the launch. Is the watch time drop directly coinciding with the increase in creation?
    • Correlation vs. Causation: Ensure that the increase in creation is directly leading to the decrease in watch time. For example, if the initiative made it easier to create very short, low-effort Reels, this could explain the drop.
  4. Overall Metric (Total Watch Time on Original Reels):
    • You are correct to consider this. I’d calculate Total Watch Time on Original Reels = (Original Reels Created) * (Average Watch Time per Original Reel).
    • If total watch time is still positive, it means the increased volume is (for now) offsetting the quality decline. This gives us some breathing room but doesn’t negate the underlying quality issue.

Communication to Stakeholders:

I would immediately communicate the mixed results, presenting both the positive (volume increase) and the concerning (quality decrease). I’d highlight the need for deeper investigation before deciding on the next steps. My communication would be data-driven but also acknowledge the strategic implications.

Next Steps (Beyond Initial Analysis):

  • If causality is established, explore iterations of the initiative to focus on quality creation rather than just volume.
  • Consider A/B testing new variations of the initiative that might incentivize higher quality.
  • Work with product teams to identify if simplifying creation led to less effort from creators.

Now, let’s review the benchmark answer, which provides a more structured and comprehensive senior-level response, especially concerning communication and decision-making under uncertainty.

Benchmark Answer: Demonstrating Agility

“This is a classic scenario in product analytics, and it demands immediate, agile action. The situation presents a clear trade-off: increased quantity but decreased quality. My response would be multi-faceted:

1. Immediate Assessment & Triage (Act Fast):

  • Verify the Stats: Double-check the statistical significance and confidence intervals for both metrics. Are the effects truly robust, or are they borderline? What’s the practical significance of a 3% drop in watch time? While statistically significant, is it material enough to warrant a complete pause?
  • Segment Analysis (as you mentioned): This is paramount.
    • Creator Impact: Is the quality drop concentrated among new creators acquired by the initiative (e.g., they create lower-quality content)? Or are existing creators (who are usually high quality) now creating lower-quality content or being displaced?
    • Content Type: Are certain types of Reels (e.g., short, unedited, templated) driving the volume increase but pulling down average watch time?
    • Audience Impact: Are specific viewer segments (e.g., power users, casual users) showing lower engagement with the new content?
  • Granularity of the Drop: Is the watch time dropping because users are watching fewer individual Reels, or because the Reels they do watch are shorter/less engaging? (e.g., (Total Watch Time / Total Reels Viewed) vs. (Total Watch Time / Total Users))

2. Hypothesis Generation for the Quality Drop (Why is this happening?):

Based on the initiative, I’d form hypotheses about why quality might be suffering:

  • H1: Lower Barrier to Entry = Lower Effort: The initiative made creation too easy, attracting users who produce low-effort, less engaging content.
  • H2: Focus on Volume Over Quality: The initiative implicitly or explicitly incentivized more creation, not better creation.
  • H3: Algorithm Saturation: The increased volume is overwhelming the recommendation algorithm, leading to lower distribution for quality content or more exposure to lower-quality content.
  • H4: Cannibalization of High-Quality Creators’ Time: High-quality creators are now spending time on the initiative’s “easy” content instead of their usual high-effort Reels.
  • H5: Viewer Fatigue: An influx of any new content, even if some is good, might lead to overall viewer fatigue if the new content isn’t diverse or compelling enough.

3. Communication Strategy (Transparent & Action-Oriented):

  • Immediate Alert: I would immediately inform key stakeholders (Product Manager, Engineering Lead, Content Strategy Lead) of the mixed results.
  • Structured Update: Present the data clearly:
    • “Good news: Initiative A has successfully increased original Reels creation by 5%.”
    • “Concern: However, we’ve observed a 3% statistically significant decrease in the average watch time per original Reel. This suggests a potential trade-off between quantity and quality.”
  • Propose Next Steps: Don’t just present the problem. Propose the action plan:
    • “My immediate focus is on deeper segmentation to understand who is creating this lower-quality content and why.”
    • “We need to evaluate if the total watch time on original Reels (volume * quality) is still positive. If so, we have some breathing room to iterate.”
    • “Based on findings, we will propose either: A) Adjusting the initiative to incentivize quality, B) A/B testing variations that focus on quality, or C) A partial/full rollback if the quality degradation is severe and unsustainable.”
  • Highlight the Trade-off: Explain that sometimes there’s a natural tension between volume and quality. The discussion needs to be around where the acceptable balance lies.

4. Agile Iteration & Decision Making (Adapt & Iterate):

  • Quantitative Deep Dive (as discussed): Use SQL, Python (Pandas, NumPy, SciPy), and visualization tools (Matplotlib, Seaborn, Tableau) to perform the segmentation and trend analysis. Look at the distribution of watch times, not just the average.
  • Qualitative Insights: Partner with User Research to understand user sentiment. Are creators expressing frustration? Are viewers complaining about content quality? Look at App Store reviews or social media feedback.
  • Decision Framework:
    • If Total Watch Time (volume * quality) is still significantly up: This suggests the increased quantity is currently offsetting the quality drop. We might decide to continue the initiative but immediately iterate on it with changes designed to boost quality (e.g., better creation templates, clearer guidance, quality-based ranking in the algorithm). We’d A/B test these iterations.
    • If Total Watch Time is Flat or Down: This is a much bigger red flag. It indicates the initiative is actively harming the product. In this scenario, I would strongly recommend a partial or full rollback of the initiative while we redesign it to address the quality issue.
  • Monitor Closely: Regardless of the decision, implement enhanced monitoring dashboards for these key metrics and the newly identified problematic segments.

This situation calls for agility in analysis, clear communication of complex trade-offs, and a readiness to pivot the strategy based on data-driven insights to protect the long-term health of the product.”