ML Interview Q Series: How would you determine if the upsell carousel should show national brands instead of store brands?

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Comprehensive Explanation

One common way to evaluate whether the up-sell carousel should prioritize national brands instead of the store’s private-label products is to conduct an experiment and measure key metrics under different conditions. The main objectives usually revolve around understanding how the carousel selection (store brand vs. national brand) impacts user behavior, profitability, brand perception, and other critical factors for the business. Below is a detailed breakdown of how you might approach this challenge.

Designing an Experimental Framework

A typical approach is to run a carefully structured A/B test (or potentially a multi-variant test):

• Control Group: Users who see the carousel containing store-brand items only.

• Treatment Group: Users who see national-brand items, or some mixture of national- and store-brand items.

In practice, you might further refine this approach by implementing multiple test arms (for instance, different proportions of store-brand and national-brand products in the carousel) if you suspect that a combined presentation might work best. However, the simplest starting point is a direct A/B comparison.

Metrics to Track

1. Conversion Rate (CR): The fraction of carousel impressions that lead to an add-to-cart event. You can also examine overall cart conversion: out of all users who see the carousel, how many ultimately make a purchase of at least one of those items?

2. Average Order Value (AOV): The average amount spent per checkout. This is critical for understanding if national-brand items—which may have higher prices—encourage or discourage overall spending.

3. Gross Margin: Evaluating profit margin is often more insightful than just revenue. Store-brand items might have better margins. Even if national brands drive higher sales volumes, the net profit could be lower if margins are lower.

4. Customer Satisfaction or Feedback: This can come in the form of direct user ratings, post-purchase feedback surveys, or inferred signals like how frequently an item is returned.

5. Long-Term Retention and Brand Loyalty: Sometimes store-brand products are used to build and maintain customer loyalty. There might be an interplay between short-term revenue and long-term loyalty. You may wish to track repeat purchases or membership renewal rates (if relevant) over time.

Statistical Significance and Sample Size

When running an A/B test, you aim to ensure that any observed difference is not just random noise. Typically, you will:

• Randomly assign users to control or treatment to reduce bias.

• Gather data until you reach a sufficient sample size for robust conclusions.

You may want to measure whether a difference in conversion rate is statistically significant. For instance, if p1 is the conversion rate in the control group and p2 is the conversion rate in the treatment group, the difference in these rates can be analyzed with a standard difference-in-proportions test. One might look at the z-statistic formula for the difference in proportions:

Where:

• hat{p}_1 is the observed conversion rate in the control,

• hat{p}_2 is the observed conversion rate in the treatment,

• n_1 is the sample size of the control,

• n_2 is the sample size of the treatment,

• z is the test statistic from the standard normal distribution.

You would similarly compute or derive a p-value from z to see if the result is statistically significant at a chosen confidence level (for example 95%).

Potential Challenges and Considerations

• Seasonality and User Segmentation: Grocery habits may vary by season, day of the week, user demographics, or user segments (e.g., brand-loyal shoppers vs. price-sensitive shoppers). Be sure your experimental design accounts for these variations.

• Brand Cannibalization: Offering national brands could increase revenue from these items but might reduce sales of store-brand products that have higher profit margins. Careful analysis of margin impact is critical to see the net effect on profitability.

• UI/UX Factors: The carousel design, the number of displayed items, and how the user navigates them could influence whether brand type or user experience drives the results.

• Logistical Constraints: Stock availability and fulfillment times might differ between store brand and national brand. If national-brand stock runs out more frequently, user experience could be impacted.

Practical Data Pipeline Example

Below is a simplified code snippet in Python to illustrate how you might automate data collection and compute some A/B test metrics. This code is for demonstration and does not represent a full production pipeline:

import numpy as np
import pandas as pd
from scipy import stats

# Suppose we have a DataFrame with columns:
# 'user_id', 'variant' (Control or Treatment),
# 'carousel_shown' (True/False),
# 'item_purchased' (True/False),
# 'purchase_value' (float)

# Example synthetic data creation
np.random.seed(42)
size = 10000
user_id = range(size)
variant = np.random.choice(['Control', 'Treatment'], size=size)
carousel_shown = np.random.choice([True, False], size=size, p=[0.9, 0.1])
item_purchased = np.random.choice([True, False], size=size, p=[0.2, 0.8])
purchase_value = np.random.exponential(scale=50, size=size) * item_purchased

df = pd.DataFrame({
    'user_id': user_id,
    'variant': variant,
    'carousel_shown': carousel_shown,
    'item_purchased': item_purchased,
    'purchase_value': purchase_value
})

# Filter only users who actually see the carousel
df_carousel = df[df['carousel_shown'] == True]

# Separate into control and treatment
df_control = df_carousel[df_carousel['variant'] == 'Control']
df_treatment = df_carousel[df_carousel['variant'] == 'Treatment']

# Compute basic metrics
cr_control = df_control['item_purchased'].mean()
cr_treatment = df_treatment['item_purchased'].mean()
aov_control = df_control['purchase_value'].mean()
aov_treatment = df_treatment['purchase_value'].mean()

print("Control Conversion Rate:", cr_control)
print("Treatment Conversion Rate:", cr_treatment)
print("Control Average Order Value:", aov_control)
print("Treatment Average Order Value:", aov_treatment)

# Perform a two-proportion z-test
n1 = len(df_control)
n2 = len(df_treatment)
p1 = cr_control
p2 = cr_treatment
p_pool = (p1*n1 + p2*n2) / (n1 + n2)
z_num = p1 - p2
z_den = np.sqrt(p_pool * (1 - p_pool) * ((1/n1) + (1/n2)))
z_value = z_num / z_den

p_value = 1 - stats.norm.cdf(abs(z_value))  # two-sided test
print("Z-statistic:", z_value)
print("p-value:", p_value * 2)

This demonstrates how you might programmatically generate synthetic data, compute core metrics (conversion rates, average order value), and do a basic two-proportion z-test to gauge if the difference is statistically significant.

Follow-up Questions

How would you ensure that your experimental design accounts for differences between user segments, like price-sensitive vs. brand-loyal shoppers?

You can stratify the experiment by user segments (for example, those who frequently buy national brands vs. those who prefer store brands). In practice, you might implement a stratified random assignment, where each relevant segment has separate control and treatment users. This way, you can analyze the effect within each segment and verify if the impact of switching to national brands is uniform or segment-specific. It is also common to use matched pairs or a block design if you have strong prior evidence that one subgroup’s behavior is significantly different from others, ensuring fair comparisons within each group.

What if switching to national brands increases revenue but decreases overall profit margins?

In this scenario, you should look at your margin-based metrics to confirm the net impact. If the store brand yields a higher profit margin but national brands significantly drive up conversion or average order value, you must weigh the added revenue against lower margins per unit sold. Often, you might find a trade-off where a slight drop in margin is compensated by a sufficiently large volume boost, making it worthwhile. On the other hand, a big margin difference could completely negate higher sales volumes, making the switch unprofitable. It is crucial to quantify both the short-term and long-term effect on profit.

Could you use a multi-armed bandit algorithm rather than a simple A/B test?

Yes. A multi-armed bandit approach allows you to adaptively allocate users to the best-performing strategy over time. Instead of splitting traffic randomly at a constant ratio, the algorithm shifts more users toward the winning variant as it gains confidence. This can reduce the opportunity cost compared to a fixed A/B test, especially if the difference between the strategies is large. However, multi-armed bandit methods can be more complex to implement and require careful monitoring, particularly regarding ensuring adequate exploration of all options before converging too quickly.

How would you address the confounding effect of promotional events or discounts during the test?

Promotional events or discounts can skew user behavior and overshadow the effect you are trying to measure. One practice is to pause or at least document major promotions so you can either run the test in a relatively stable period or ensure that both groups are equally exposed to the promotion. Another approach is to include promotional variables in your analysis model so you can control for them statistically. For example, you might incorporate a variable that indicates whether a purchase was influenced by a promotion, discount, or coupon code. By accounting for it in your regression or other statistical models, you can isolate the real effect of carousel brand choice from that of promotions.

Are there long-term user-behavior changes that might not be captured by a short test?

Absolutely. Sometimes switching from store brand to national brand can alter user perceptions, brand affinity, and trust over a longer period. For instance, if customers consistently see national brands, they might attribute higher quality or prestige to your app’s offerings. Conversely, they might start to feel the in-house brand is being neglected. Observing changes in repeat purchase rates, user retention, and subscription renewal (if relevant) over an extended period is important. You might supplement the short-term experiment with user surveys, brand-perception interviews, or a longer rolling test that measures longer-term patterns in purchasing behavior.

Below are additional follow-up questions

What if the inventory or supply chain for national-brand items is less reliable, potentially leading to stockouts or delivery delays?

Supply chain volatility can significantly affect customer satisfaction and revenue. If national-brand products are prone to frequent stockouts, you risk frustrating users who click on the upsell, only to be informed that the item is unavailable. This can lead to cart abandonment or a negative perception of the app’s reliability.

A deeper approach is to integrate inventory-awareness into your A/B testing framework. For instance, you might track the “in-stock rate” for both store-brand and national-brand items. When inventory dips below a certain threshold, the carousel could automatically revert to displaying items more likely to be available. You would carefully measure whether stockouts correlate with decreased user satisfaction and see if certain thresholds of unavailability start damaging conversion.

One subtle issue is that in scenarios where the supply chain is unpredictable, you might not see consistent results across different test periods. Ideally, you would tag each order’s inventory status during the experiment (e.g., “item in stock,” “item delayed,” “item backordered”) to properly measure how significant these availability issues are.

Could adding national-brand items in the carousel have a halo effect on overall brand perception, even if users do not directly buy them?

Yes, there can be a halo effect: seeing well-known labels can make customers view the overall platform more positively, potentially increasing the credibility and perceived variety of the store. Even if a user does not immediately purchase a national brand, the mere presence might subtly influence user sentiment and encourage a repeat visit.

Measuring halo effects is challenging because traditional A/B metrics such as immediate conversion rate or direct margin do not necessarily capture long-term brand perception. You might supplement standard metrics with surveys or Net Promoter Score (NPS) measurements. For instance, you can show a brief questionnaire after purchase asking about user satisfaction, selection variety, or perceived brand alignment. Over time, you compare the satisfaction levels between control and treatment to see if any intangible brand-lift effect emerges.

A pitfall is conflating brand-lift with selection quantity. Maybe it is not the brand label that matters but simply the presence of more items in the carousel. Carefully isolating brand recognition from the quantity of options in the user interface is important.

How do you address correlated purchase behavior when multiple different items are shown in the same carousel?

Within a carousel, you typically offer several items. Even if a user is shown national-brand items in place of store-brand items, they might still buy a store-brand item that appears further in the list or vice versa. This can create correlated outcomes: user decisions are not independent across each suggested item.

One way to approach this is to set up your experiment so that each user sees a specific “carousel treatment” as a bundle rather than analyzing each item independently. You can then evaluate the overall performance of that bundle in terms of total conversions, total revenue, or other relevant metrics.

Additionally, advanced modeling can help: for instance, you might use a multinomial logit approach where each item in the carousel is a possible choice. You then measure how brand labels affect the user’s probability of choosing one item over another. This approach can capture cross-item correlations and substitution effects. However, these models can be complex to implement and require sufficient data to ensure stable parameter estimates.

How do you handle new users who have never checked out before versus returning users who might have different brand preferences?

New users often have minimal purchase history, making it difficult to predict whether they gravitate toward national or store brands. Returning users might have established preferences, either for store brands due to loyalty or for national brands due to perceived quality.

A recommended tactic is to segment the experiment by user type: new vs. returning. New users could be randomly assigned to store-brand vs. national-brand carousels. Returning users might be stratified based on past purchase patterns (e.g., “historically purchased store brand 70% of the time”). This segmentation lets you see if the effect of showing national brands is substantially different among these groups.

A hidden pitfall is that new users typically have higher churn rates or less trust in the platform, so they might behave erratically. That means your test could be skewed by an influx of new users who have not fully settled into a typical buying pattern. Carefully controlling for these differences, or at least analyzing them separately, is crucial for accurate conclusions.

Could national-brand promotions or marketing campaigns outside the app influence your experimental results?

External advertising or marketing from national brands might influence user behavior independently of your carousel changes. If a big campaign or brand-wide discount is running, users might be more inclined to choose that brand, confounding the effect of your A/B test.

To mitigate this, you should track and record major external campaigns or price promotions that might coincide with your test period. If, for instance, a national brand is running a significant TV advertisement at the same time as your test, you might see a spike in purchases for that brand across both control and treatment groups. Though this might increase conversion in the treatment arm, it could be partly or wholly attributable to external marketing.

A robust approach is to run the experiment over a time span that includes periods with and without brand promotions. If you detect a strong effect only during promotional periods, you can refine your business decisions: maybe national-brand items in the carousel are most beneficial during promotion windows.

What if adding more national-brand options causes decision fatigue for users, leading to lower overall conversion?

Decision fatigue is the phenomenon where too many choices overwhelm a user, causing them to abandon or postpone a purchase. If national-brand items are displayed in addition to, rather than instead of, store-brand items, you could inadvertently enlarge the number of items in the carousel, potentially reducing user engagement.

One strategy is to ensure the total number of displayed items remains consistent in both the control and treatment. For instance, if the control has five store-brand items, the treatment might have five national-brand items or a mix that still sums up to five. This helps you isolate the effect of brand choice from simply having more or fewer options.

You should also track user interactions such as how far they scroll through the carousel or if they exit the checkout flow after seeing too many choices. If you notice that a complicated carousel drives users away, you might want to prune the list to a smaller, more targeted set of recommendations.

How do you prevent potential ethical or regulatory issues when using user data to power the new recommendations?

Even if you are only swapping store-brand items for national brands, you may be using user purchase history or demographics in your recommendation algorithms. Depending on the region, there could be regulations that restrict how personal data can be used for targeted advertising. You might need to ensure compliance with data privacy laws such as GDPR or CCPA.

You also need to be transparent in your privacy policy that user purchase behaviors, preferences, or demographics may inform the items displayed in the carousel. While this is often standard for e-commerce platforms, it becomes more critical as you expand personalization algorithms.

An edge case arises if your app is used by children or other protected groups who might have special data protection requirements. In such scenarios, you might implement additional filters that reduce personalization to comply with child privacy regulations.

If the store’s strategic goal is to promote its private label, should we still prioritize revenue when comparing store-brand and national-brand performance?

Sometimes profitability or brand-building is not the only objective. Strategic emphasis on private-label growth might override a purely revenue-based approach, especially if the store envisions long-term customer loyalty tied to their label. This can lead to conflict when an A/B test shows higher revenue for national brands but corporate strategy insists on store-brand promotion.

In these cases, you might run a multi-objective analysis that considers revenue, short-term margin, and brand share of total purchases. For example, you can track the share of cart items that are store-brand vs. national-brand. If showing national brands in the carousel still yields a majority share for your private label in the overall basket, you might consider that acceptable. But if it starts eating away at private-label dominance and jeopardizes brand strategy, you might keep showing private-label items regardless.

A pitfall is ignoring intangible benefits of store brands, like better negotiating power with suppliers or brand differentiation. Merely basing decisions on immediate numbers risks undermining broader strategic positioning.

How would you handle partial checkout completions where users add an item from the carousel but later remove it before final checkout?

It is not uncommon for customers to remove items from their carts before ultimately paying. If users frequently remove national-brand items (perhaps because they realize the price is higher) and go on to buy store-brand equivalents, the test might misleadingly show a high initial add-to-cart rate but a low final purchase rate.

A solution is to instrument the entire funnel: measure not just add-to-cart but also cart-retention (how often an item stays in the cart until checkout). You might define a multi-step metric:

• Item added to cart.

• Item remains in cart after re-check or re-visit.

• Final purchase occurs.

This can clarify whether initial interest translates to actual revenue. Tracking these funnel stages can help you see if national-brand items simply attract clicks but fail to convert at checkout, or if store-brand items achieve fewer clicks but a higher final conversion rate.

A subtle edge case arises when users leave items in their carts across sessions. Some might come back later to complete the purchase, or an upcoming promotion might change their mind. Your data pipeline needs to handle multi-session cart states carefully to avoid inaccurate or double counting.