ML Interview Q Series: How would you decide which proposal best boosts DAU, and what data or metrics would guide you?

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Comprehensive Explanation

Daily active users can be interpreted as the total number of unique individuals who engage with the app in a single day. A simple mathematical representation for the number of daily active users on day d is shown below in large latex font:

where a_{u,d} is 1 if user u is active on day d, and 0 otherwise, while N is the total number of users in the system. In the context of TikTok, being “active” might mean opening the app, watching at least one video, uploading new content, commenting, or any other meaningful interaction that the product team decides qualifies as “active.”

To determine which executive’s proposal—improving recommendation algorithms, acquiring new users, or enhancing creator tools—will most effectively move the DAU metric, several considerations are helpful.

It is often valuable to look at both leading and lagging indicators related to user behavior, user growth, and user engagement. A leading indicator might be the number of new sign-ups or conversions from marketing campaigns if you are focusing on user acquisition. For content engagement, it could be watch time or the rate at which users interact with recommended videos. For creator tools, it could be the number of new videos posted and the retention of content creators. By carefully looking at these signals over time, you gain insight into which levers have the strongest relationship with DAU changes.

When deciding among these proposals, you might run controlled experiments to see how much each initiative influences engagement. For instance, one could measure how improving recommendation quality impacts per-user session length, how new user acquisition efforts affect the retention rate, and how creator tool improvements correlate with an increase in original content posted. Understanding the correlation and causal link between each project and the overall DAU metric helps you prioritize effectively.

You can also consider resource constraints and execution timelines. Even if a feature is likely to yield significant gains, if it is extremely resource-intensive with an extended development cycle, a quicker experiment on a more modest feature might still be preferable. Furthermore, A/B testing can isolate the effect of each feature enhancement, ensuring that the results are measurable and attributable to the specific changes you make.

Follow-up Questions and Detailed Answers

How would you structure an A/B test to determine if a better recommendation algorithm increases DAU?

You can randomly segment a portion of existing active users into two groups: a control group (using the old recommendation system) and a treatment group (exposed to the improved recommendation). The test would run for a sufficient duration to account for daily and weekly usage cycles, ensuring you capture variations in content consumption patterns. Key metrics to measure include average session length, the proportion of daily returning users, and the number of videos watched per session.

It is important to ensure that the users in both groups share similar characteristics (device type, region, usage frequency) so that you minimize confounding variables. By comparing how the control and treatment groups’ DAU differs, you can quantify the direct impact of the improved recommendation algorithm.

What metrics would you evaluate to see if acquiring new users is more effective at driving DAU?

Acquiring new users can indeed raise the overall number of active users, but you should also measure retention. If new users sign up and then never return, the DAU gain might be short-lived. Potential metrics include first-week retention, user growth rate, daily sign-ups, and churn rate. You might also use cohort analysis to track how new users behave over time. If your newly acquired users remain active at a lower rate than existing users, it could indicate that simply acquiring users isn’t as efficient for long-term DAU growth unless you fix retention issues.

How would you judge whether improving the creator tools drives DAU the most?

One way is to track how many new videos are generated by creators after rolling out better tools, as well as changes in user engagement with these newly posted videos. Improved creator tools might reduce friction for content creation, increasing the volume and variety of content. More engaging or diverse content can drive up session time and daily visits, which ultimately reflects in DAU. Evaluating average user session length, the rate of returning users, and the percentage of users who become creators are all valuable indicators. You can also measure how many new creators emerge after each product improvement and whether the new content they produce leads to a notable lift in platform-wide daily activity.

What are some potential pitfalls when interpreting DAU changes?

One pitfall is confounding seasonal or external factors, such as holidays or marketing campaigns, which might temporarily boost user activity independent of product changes. Another concern is incorrectly attributing causal relationships: a spike in user activity could coincide with a viral trend on the platform rather than the feature you launched. It is also possible to see short-lived gains in DAU due to novelty effects; users might try the feature once but not stick around. Proper experimentation with control groups, as well as analyzing changes in retention and repeated usage, helps mitigate these risks.

How would you use a hybrid approach to further validate the decision?

Sometimes you do not have to pick just one approach in a stark way. You could simultaneously test minor improvements in each area. By running small-scale experiments, you collect indicative data on which approach shows the greatest short-term lift in key metrics. You can then weigh both user experience impact and estimated engineering costs. If, for example, the improved recommendation algorithm shows a strong increase in daily engagement in a small test but requires a large engineering commitment, while user acquisition campaigns are cheaper to run but yield a smaller effect, you might still opt for the former if it has a greater potential to sustain long-term growth. Evaluating the long-tail impact is often as important as the immediate changes in DAU.

Could you share a brief Python code snippet to illustrate how you might analyze DAU data?

import pandas as pd
import numpy as np

# Suppose we have a DataFrame 'user_activity' with columns:
# 'user_id', 'date', 'active' (boolean).

# Sample data creation for demonstration:
data = {
    'user_id': [1, 1, 2, 2, 3, 3, 3],
    'date': pd.to_datetime([
        '2025-03-20', '2025-03-21',
        '2025-03-20', '2025-03-21',
        '2025-03-20', '2025-03-21', '2025-03-22'
    ]),
    'active': [True, True, True, False, True, True, True]
}
user_activity = pd.DataFrame(data)

# Now let's calculate the DAU by date:
dau_by_date = user_activity[user_activity['active']].groupby('date')['user_id'].nunique()

print("DAU by date:")
print(dau_by_date)

# You might then plot or analyze trends over time to see how DAU changes:
dau_by_date.plot(title='Daily Active Users Over Time')

This code snippet demonstrates how you might aggregate user activity data in Python and compute DAU. In a real setting, you would have a larger, more complex dataset, potentially with logs spanning millions of rows, and you would track deeper engagement metrics to discern which product initiative contributes the most to sustained user activity.

By carefully analyzing the outcomes of each proposed initiative through experimentation, metrics tracking, and cohort retention analysis, you can gain the evidence necessary to decide which executive’s approach is most likely to produce the largest sustainable lift in DAU.

Below are additional follow-up questions

How can you measure the impact of improvements in the recommendation algorithm for specific user cohorts, such as new users versus returning users?

Different cohorts often respond uniquely to product enhancements. For example, new users might be immediately influenced by a high-quality content feed, whereas returning or power users may have different expectations formed by their previous usage patterns. You could analyze these cohorts by segmenting them in your experimental design:

1. Split new vs. returning users in both control and treatment groups.

2. Track metrics like average session length, watch time, retention after X days, and the number of content interactions (likes, comments, shares).

3. Compare how each cohort’s DAU changes in response to the updated recommendation engine.

Potential pitfalls include sample imbalance (perhaps there are far more returning users than new users) and confounding variables, such as a marketing campaign that boosts new user sign-ups during the same window. Mitigate these factors by using a well-structured cohort approach and ensuring that randomization or matching is performed carefully when splitting users.

How do you account for significant external events (e.g., holidays, competitor releases, influencer-driven traffic) that might temporarily inflate or deflate DAU?

External events can create sudden spikes or drops in user activity unrelated to your experiment. One tactic is to capture baseline activity patterns over a historical period to quantify typical daily or weekly fluctuations. When you detect an abnormal deviation, you can do the following:

• Compare the spike (or dip) against historical behavior during similar events (e.g., last year’s holiday season).

• Use time-series models or regression techniques to attribute changes in DAU to known external factors (marketing campaigns, external promotions) before isolating the effect of the product changes.

• Extend the experiment duration if feasible, so that the short-term anomaly has less impact on the total result.

A real-world edge case occurs when an unprecedented external event (like a viral global trend) skews the data so dramatically that historical patterns are no longer reliable. In such a scenario, you might need a “hold-out” region or user group where your changes are not introduced, serving as a baseline unaffected by your feature deployment (though still possibly impacted by the external event).

How would you maintain consistency in measuring DAU if the definition of an “active user” changes over time?

Sometimes, the product definition of “active” evolves—for instance, you might shift from considering “launching the app” to “watching at least one video” as the metric for daily activity. To maintain historical continuity:

• Preserve the legacy definition alongside the new one for a transitional period. Track both old_active and new_active to see how they compare.

• Gradually move your official reporting to the new definition, while documenting the rationale and magnitude of the change.

• Communicate clearly to stakeholders that DAU numbers under the new definition are not directly comparable to historical DAU if the activity threshold differs significantly.

Pitfalls include confusion among teams and stakeholders about whether a rise or fall in DAU is due to the new definition or genuine user behavior shifts. You can mitigate this by overlapping metrics and clarifying changes in official reporting.

How do you isolate the effect of multiple simultaneous changes—e.g., new user acquisition campaigns launched around the same time as recommendation improvements?

When multiple experiments or feature launches overlap, it becomes challenging to attribute DAU changes accurately to one specific initiative. Strategies to mitigate this:

• Factorial experiment design: If resources permit, you can assign users to control vs. treatment cells for each feature. For example, you could have a group with the new recommendation, a group with a user acquisition effort, both, or neither. You then measure differences between these cells.

• Staggered rollouts: Roll out each initiative in different time windows or regions. This helps tease apart which changes correlate with DAU lifts.

• Post-hoc regression analysis: Even if you cannot conduct a perfectly designed experiment, you could use statistical models to regress daily DAU on feature flags (which show who got which features) while controlling for user demographics, marketing spend, etc.

Potential edge cases include a synergy effect, where features combined might produce results beyond the sum of their individual impacts. Another edge case is negative interference, where the simultaneous introduction of multiple changes overloads users with new experiences, potentially harming the user journey.

How do you handle anomalies or outliers that artificially inflate DAU, such as bot traffic or a major influencer-driven spike?

Large, unexpected spikes can bias your experimental evaluation if not properly handled. Common approaches:

• Implement bot detection and filtering: If you can detect suspiciously high traffic from specific IP ranges or user agents, you should remove that data to avoid skewing DAU metrics.

• Conduct influencer tracking: If a major influencer joins or promotes TikTok on a specific day, you might see a short-term surge in daily actives. Flag that timeframe for further analysis or exclude it from your primary experiment window if it cannot be controlled.

• Use robust statistics: Median or trimmed averages can sometimes provide a clearer signal when means are heavily influenced by extreme outliers.

An edge case arises if the spike is not purely an anomaly but triggers changes in user retention (e.g., new users who joined during an influencer spike end up staying long-term). In this scenario, you should carefully track those users post-event to see if their behavior stabilizes or if they churn quickly.

How do DAU and MAU (monthly active users) differ, and why might you focus on one over the other for this particular decision?

DAU measures short-term engagement—how many unique users come back each day—whereas MAU captures a broader window. If your strategic priority is to create habit-forming behavior and frequent usage, DAU is more sensitive and can help you detect changes quickly. MAU might mask daily fluctuations because a user who was active only once in a month still counts toward monthly active totals.

A risk in focusing solely on DAU is over-optimizing for short bursts of activity at the expense of long-term retention. On the other hand, relying exclusively on MAU can hide deeper engagement issues because you will not notice a drop in daily habit until users stop visiting entirely for an entire month. A balanced approach is to track both and see how daily usage patterns translate into monthly aggregates.

What are user privacy or compliance concerns with collecting detailed user engagement data to measure DAU?

Measuring DAU often requires logging user sessions, watch times, clicks, and more. This can raise privacy or data compliance questions, such as GDPR or CCPA requirements. Important considerations:

• Data minimization: Collect only what is strictly necessary. Storing excessive user-level data can raise legal and ethical concerns.

• Anonymization: Use techniques that strip personal identifiers and store aggregated usage statistics. This reduces risks if the database is compromised.

• Consent: Users may need clear, affirmative consent to have their engagement events tracked. Always align data collection with the user agreement and privacy policies.

A subtle pitfall emerges if certain regions have stricter data regulations. In such cases, it is possible that partial data must be excluded from experiments, potentially biasing your results if those user segments behave differently than the rest of the population.

How can you incorporate time-of-day or day-of-week patterns into DAU analysis to avoid misinterpretation?

User activity might peak during evenings, weekends, or specific cultural holidays. Ignoring these cyclical patterns can lead to false conclusions. Steps to address this:

• Normalize data by comparing the same weekdays or using seasonality models in time-series analysis. For instance, compare Friday to Friday rather than Friday to Monday.

• Segment your metrics by local time zones to capture when usage is highest in each region.

• Use rolling averages or smoothing techniques (e.g., 7-day moving average) to highlight long-term trends rather than daily volatility.

An edge case occurs when you launch a new feature mid-week but the usage pattern for that day historically differs from other days. Without accounting for such patterns, you risk attributing typical mid-week changes to your feature. A thorough baseline or a hold-out group helps mitigate this risk.

If improving the recommendation algorithm is resource-heavy with a long lead time while user acquisition campaigns yield immediate though smaller gains, how do you weigh short-term versus long-term DAU strategy?

Evaluating the trade-off involves:

• Return on investment estimation: Forecast the potential DAU lift from each approach. A small but immediate bump from acquisition might have limited retention impact, whereas a longer-term improvement to the recommendation engine could significantly lift user satisfaction and engagement.

• Opportunity cost: Resources committed to a large recommendation overhaul might delay other strategic initiatives. You need to balance immediate business needs (e.g., hitting short-term targets) against long-term product health and user loyalty.

• Risk tolerance: A highly complex recommendation system update carries higher technical risk. If done incorrectly, it may degrade user experience. Meanwhile, marketing-driven acquisition is more straightforward but might not lead to robust engagement if the product’s retention mechanics are weak.

A nuanced pitfall is that short-term pressure to grow numbers quickly can overshadow a better long-term move. Stakeholder alignment is crucial. Clear communication of the potential gains and risks in each scenario helps you determine which path makes sense given your company’s strategic horizon.

How do you diagnose the root causes behind user churn when assessing the best approach to raise DAU?

Churn is the opposite side of the DAU coin. To understand whether improvements in recommendations, acquisition, or creator tools will reduce churn, you need:

• Cohort and funnel analysis: Observe where in the user journey people drop off. If the majority churn after day 2, you might suspect suboptimal onboarding. If churn accelerates after month 3, content fatigue could be a factor.

• Qualitative feedback: Surveys and user interviews can reveal dissatisfaction with recommendations, lack of compelling new content, or a feeling that creator tools are insufficient.

• Segmentation by engagement patterns: Compare highly engaged users (several sessions per day) to casual users (once per week). The reason for churn often differs by engagement level.

A tricky edge case arises if user churn is driven by external factors (e.g., a competing platform emerges). You might mistakenly blame your own product’s shortcomings. This underscores the importance of gathering comprehensive data—from direct user feedback to competitive intelligence—to ensure you accurately diagnose the root causes.