Accenture interview questions & answers

Model answer

The flow

1. Identify the scenario: Recognize the presence of selection bias in your data.
2. Analyze the data: Examine the data collection process and the dataset.
3. Develop a strategy: Plan how to mitigate the bias.
4. Implement the solution: Apply the chosen methods to reduce or eliminate the bias.
5. Evaluate the results: Check the effectiveness of your solution.
6. Reflect and learn: Consider what worked and what could be improved.

The answer

Situation: I was working on a project to analyze customer churn for a subscription-based service. Our goal was to identify patterns and predictors of customer churn to improve retention strategies. However, I noticed that our dataset only included customers who had interacted with customer support, which introduced selection bias.

Task: My task was to ensure that our analysis was representative of the entire customer base, not just those who had contacted support, as this could skew our understanding of churn predictors.

Action:

• Recognized the Bias: I identified that the dataset was biased because it only included customers who had interacted with support, potentially missing out on those who churned silently.
• Analyzed the Data: I examined the data collection process and confirmed that the dataset was not representative of the entire customer base.
• Developed a Strategy: I proposed expanding the dataset to include a random sample of customers who had not interacted with support.
• Implemented the Solution: I collaborated with the data engineering team to extract a more comprehensive dataset that included a random sample of all customers, ensuring a more balanced representation.
• Evaluated the Results: After re-running the analysis with the new dataset, I found that the predictors of churn were more accurate and aligned with business expectations.

Result: By addressing the selection bias, we improved the accuracy of our churn model by 15%, which helped the company implement more effective retention strategies. This experience taught me the importance of critically evaluating datasets for biases and the impact they can have on analysis outcomes.

Why this works

• Testing Analytical Skills: The interviewer is assessing your ability to identify and address selection bias, a common issue in data science.
• Sanity Check: A strong answer includes a clear explanation of how the bias was identified and mitigated, demonstrating critical thinking.
• Common Pitfall: Weak answers often fail to clearly articulate the steps taken to address the bias or lack specific outcomes, which can undermine credibility.

Model answer

The flow

1. Understand the audience: Recognize the executive's background and priorities.
2. Simplify the concept: Use relatable analogies and avoid technical jargon.
3. Highlight business value: Focus on how data science can drive business outcomes.
4. Provide examples: Use specific, relevant examples to illustrate points.
5. Invite questions: Encourage dialogue to address concerns or curiosity.

The answer

Understand the audience: When explaining data science to a business executive, it's crucial to first understand their background and what aspects of the business they are most concerned with. This helps tailor the conversation to their interests and knowledge level.

Simplify the concept: Data science is essentially about extracting insights and knowledge from data to make informed decisions. Imagine it as a tool that turns raw data into actionable insights, much like how a chef turns raw ingredients into a delicious meal.

Highlight business value: Data science can significantly enhance decision-making processes by providing data-driven insights. For example, it can help in identifying customer trends, optimizing operations, and predicting future market behaviors, ultimately leading to increased efficiency and profitability.

Provide examples:

• Customer Segmentation: By analyzing customer data, we can identify distinct segments within our customer base, allowing for more targeted marketing strategies and personalized customer experiences.
• Predictive Maintenance: In manufacturing, data science can predict equipment failures before they occur, reducing downtime and maintenance costs.
• Fraud Detection: In finance, data science models can detect unusual patterns that may indicate fraudulent activities, protecting the company from potential losses.

Invite questions: I encourage you to ask any questions or share any specific areas of interest where you think data science could make an impact. This way, I can provide more targeted examples or explanations that align with your business goals.

Why this works

• Tailored Communication: The answer is tailored to the executive's level of understanding and interest, avoiding technical jargon that could confuse or disengage them.
• Business Focus: By focusing on the business value and providing relevant examples, the answer connects data science to tangible business outcomes, which is what executives care about.
• Engagement: Inviting questions fosters a two-way dialogue, allowing for clarification and deeper understanding, which is crucial in executive communication.
• Weakness Avoidance: A weak answer would either be too technical, losing the executive's interest, or too vague, failing to demonstrate the potential impact of data science on the business.

Model answer

The flow

1. Read the schema: Identify the tables and fields involved.
2. Pick the grain: Determine the level of detail required for the query.
3. Join/filter: Use WHERE to filter rows before aggregation.
4. Aggregate or window: Apply grouping and aggregation functions.
5. Filter with HAVING: Use HAVING to filter aggregated results.
6. Check edge cases: Consider NULLs and duplicates.

The answer

Step 1: Read the schema

• Identify the tables and fields involved in the query.

Step 2: Pick the grain

• Determine whether the query needs to operate on raw data or aggregated data.

Step 3: Join/filter

• Use WHERE to filter rows before any aggregation takes place. For example, if you have a table sales with columns product_id, quantity, and price, and you want to filter for sales of a specific product, you would use:

  SELECT product_id, quantity, price 
  FROM sales 
  WHERE product_id = 123;

Step 4: Aggregate or window

• Perform necessary aggregations using GROUP BY. For example, to calculate the total sales per product:

  SELECT product_id, SUM(quantity * price) AS total_sales
  FROM sales
  GROUP BY product_id;

Step 5: Filter with HAVING

• Use HAVING to filter aggregated results. For instance, to filter products with total sales greater than $1000:

  SELECT product_id, SUM(quantity * price) AS total_sales
  FROM sales
  GROUP BY product_id
  HAVING total_sales > 1000;

Step 6: Check edge cases

• Consider how NULLs and duplicates might affect the query results. Ensure that the logic correctly handles these cases.

Why this works

• Testing understanding: The interviewer is assessing your understanding of SQL query execution order and the purpose of WHERE and HAVING.
• Sanity check: A strong answer will clarify that WHERE filters rows before aggregation, while HAVING filters after aggregation.
• Common pitfalls: Weak answers may confuse the two clauses or fail to explain their roles in the query lifecycle.
• Edge cases: A strong candidate will mention handling NULLs and duplicates, which can affect the outcome of the query.

Model answer

The flow

1. Formulate Hypothesis & Metric: Define what constitutes a 0.5% lift in conversion.
2. Determine Unit of Randomization: Decide whether to randomize at the user or session level.
3. Calculate Power & Sample Size: Use statistical formulas to determine the required sample size.
4. Run Experiment & Guard Against Peeking: Execute the test while avoiding premature data analysis.
5. Analyze Results with Guardrails: Interpret results with confidence intervals and statistical significance.

The answer

Hypothesis & Metric

• Hypothesis: Implementing a new feature will result in a 0.5% increase in the conversion rate.
• Metric: Conversion rate, defined as the number of users who complete a purchase divided by the total number of users.

Unit of Randomization

• We will randomize at the user level to ensure independence between groups and to account for user-specific behavior.

Power & Sample Size

• Desired power: 80%
• Significance level (alpha): 0.05
• Baseline conversion rate: Assume 5% based on historical data.
• Minimum detectable effect (MDE): 0.5% increase, making the target conversion rate 5.5%.

To calculate the sample size, we use the formula for sample size in A/B testing:

$$ n = \frac{2 \times (Z_{1-\alpha/2} + Z_{1-\beta})^2 \times p_1 \times (1 - p_1)}{(p_2 - p_1)^2} $$

where:

• $Z_{1-\alpha/2}$ is the z-score for a 95% confidence level (~1.96).
• $Z_{1-\beta}$ is the z-score for 80% power (~0.84).
• $p_1$ is the baseline conversion rate (0.05).
• $p_2$ is the target conversion rate (0.055).

Plugging in the values, we get:

$$ n = \frac{2 \times (1.96 + 0.84)^2 \times 0.05 \times (1 - 0.05)}{(0.055 - 0.05)^2} \approx 156,000 $$

Run Experiment & Guard Against Peeking

• Run the experiment for a pre-determined duration, ensuring no interim analysis is conducted that could bias results.

Analyze Results with Guardrails

• After the experiment, calculate the conversion rates for both groups.
• Use confidence intervals and p-values to determine if the observed lift is statistically significant.
• If the p-value is less than 0.05 and the confidence interval does not include zero, we can conclude the lift is significant.

Why this works

• Testing Statistical Understanding: The interviewer wants to see if you understand the statistical principles behind A/B testing, such as power and sample size calculations.
• Guarding Against Common Pitfalls: A strong answer addresses issues like peeking at data, which can lead to incorrect conclusions.
• Weak Answer Pitfalls: Failing to specify the unit of randomization or skipping the calculation of sample size can show a lack of thoroughness.
• Trade-offs: Recognizing the trade-off between sample size and power demonstrates an understanding of practical constraints in experimentation.

Model answer

The flow

1. Segment the population: Determine the total population and relevant segments.
2. Apply penetration rate: Estimate the percentage of households purchasing a new mattress annually.
3. Determine frequency: Estimate how often each household buys a mattress.
4. Calculate total demand: Multiply the number of households by penetration and frequency.
5. Estimate price: Determine the average price of a mattress.
6. Sanity check: Validate the final number with industry benchmarks or logical reasoning.

The answer

1. Segment the population

• The USA has approximately 330 million people.
• Assume an average household size of 3 people.
• Calculate the number of households: $330 \text{ million} / 3 = 110 \text{ million households}$.

2. Apply penetration rate

• Assume 10% of households purchase a new mattress each year.
• Calculate the number of households buying mattresses: $110 \text{ million} \times 10\% = 11 \text{ million households}$.

3. Determine frequency

• Assume each household buys a new mattress every 10 years.
• Therefore, the frequency of purchase is 0.1 mattresses per household per year.

4. Calculate total demand

• Total annual demand: $11 \text{ million households} \times 0.1 = 1.1 \text{ million mattresses}$.

5. Estimate price

• Assume an average price of $500 per mattress.
• Total market value: $1.1 \text{ million mattresses} \times $500 = $550 \text{ million}$.

6. Sanity check

• Cross-verify with industry reports. If the industry reports suggest a market size close to this estimate, the calculation is reasonable.
• Consider factors like economic conditions or trends that might affect mattress sales.

Why this works

• Testing segmentation skills: The interviewer assesses the candidate's ability to break down a large population into manageable segments.
• Logical estimation: The candidate must make reasonable assumptions about penetration and frequency, demonstrating analytical thinking.
• Sanity check importance: A strong answer includes a validation step to ensure the calculated figure is plausible.
• Common pitfalls: Weak answers may skip steps, use unrealistic assumptions, or fail to validate the final number against external data.

Model answer

The flow

1. Segment the population: Identify the relevant segments of the population involved in manufacturing mobile phones.
2. Apply penetration: Estimate the penetration rate of mobile phone manufacturing within these segments.
3. Frequency: Determine the frequency of manufacturing cycles.
4. Price: Consider the average cost of manufacturing a mobile phone for sanity check.
5. Sanity-check the order of magnitude: Validate the final number with industry benchmarks or logical reasoning.

The answer

1. Segment the population:

• Focus on the number of manufacturing plants in China that produce mobile phones. Assume there are about 500 major manufacturing plants dedicated to mobile phone production.

2. Apply penetration:

• Assume that out of these 500 plants, 80% are actively producing mobile phones at any given time. This gives us 400 active plants.

3. Frequency:

• Assume each plant produces an average of 10,000 phones per week. This is based on the assumption that a plant operates 5 days a week and produces 2,000 phones per day.

4. Price:

• Consider the average manufacturing cost of a mobile phone is around $200. This is used to sanity-check the economic feasibility of the production numbers.

5. Sanity-check the order of magnitude:

• Calculate the total weekly production: 400 plants * 10,000 phones = 4,000,000 phones per week.
• This number seems reasonable given China's large manufacturing capacity and its role as a global leader in electronics production.

Recommendation: China manufactures approximately 4 million mobile phones weekly.

Why this works

• Testing estimation skills: The interviewer assesses your ability to make logical assumptions and perform quick calculations.
• Sanity check: A strong answer includes a sanity check to ensure the numbers align with industry norms.
• Weak answers: Candidates may fail by not segmenting the population accurately or by neglecting to validate their assumptions with a sanity check.
• Logical reasoning: The approach demonstrates structured thinking and logical reasoning, which are crucial in consulting.

Model answer

The flow

1. Segment the population: Determine the height of Big Ben.
2. Apply penetration and frequency: Estimate the height of a single pretzel.
3. Calculate total units: Divide the height of Big Ben by the height of one pretzel to find the number of pretzels needed.
4. Sanity-check the order of magnitude: Validate the calculation with a rough comparison to known objects or structures.

The answer

Segment the population:

• Big Ben is approximately 96 meters tall.

Apply penetration and frequency:

• Assume a standard pretzel has a height of about 10 cm (0.1 meters).

Calculate total units:

• To find out how many pretzels are needed to build a tower as tall as Big Ben, divide the height of Big Ben by the height of one pretzel: $$\text{Number of pretzels} = \frac{96}{0.1} = 960$$

Sanity-check the order of magnitude:

• A pretzel tower of 960 pretzels, each 10 cm tall, should logically reach 96 meters, aligning with the height of Big Ben. This calculation seems reasonable given the dimensions.

Why this works

• Testing estimation skills: The interviewer is assessing the candidate's ability to make reasonable assumptions and perform quick calculations.
• Logical reasoning: A strong answer shows clear logical steps and justifies assumptions with real-world dimensions.
• Sanity check: A good candidate will validate their results by comparing them to known quantities, ensuring the final number is plausible.
• Common pitfalls: Weak answers might skip steps, use unrealistic assumptions, or fail to verify the feasibility of their calculations.

Model answer

The flow

1. Frame the problem → Identify the business objective (reduce churn) and define churn.
2. Features & baseline → Determine relevant features and establish a baseline model.
3. Model choice and why → Select a model suitable for classification.
4. Metric that matches the cost of errors → Choose metrics that reflect the business impact of false positives/negatives.
5. Overfitting checks → Validate model performance and ensure generalization.

The answer

1. Frame the problem:

• The objective is to predict customer churn to implement retention strategies.
• Define churn as a customer who has stopped using the service for a specified period.

2. Features & baseline:

• Features may include customer demographics, usage patterns, and interaction history.
• A baseline model could be a simple logistic regression using historical average churn rate.

3. Model choice and why:

• Choose models like Random Forest or Gradient Boosting for their ability to handle non-linear relationships and feature interactions.

4. Metric that matches the cost of errors:

• Precision: Important if the cost of false positives is high (e.g., offering discounts to non-churning customers).
• Recall: Critical if the cost of false negatives is high (e.g., missing out on retaining actual churners).
• F1 Score: Balances precision and recall, useful when both false positives and negatives are costly.
• AUC-ROC: Measures the model's ability to distinguish between classes, useful for comparing models.

5. Overfitting checks:

• Use cross-validation to assess model performance on unseen data.
• Monitor validation vs. training error to detect overfitting.
• Implement regularization techniques if necessary.

Why this works

• Testing understanding of business impact: The interviewer wants to see if you can align model evaluation with business goals.
• Sanity check: A strong answer identifies the trade-off between false positives and negatives, which is crucial for churn prediction.
• Weak answers fail: Candidates who only mention accuracy miss the nuanced cost of errors in business terms.
• Specificity: The answer should reflect an understanding of the specific business context and the implications of churn.