To succeed in the Consultant interview process at Decision Point, you must perform exceptionally well across several core evaluation areas. Your interviewers will assess your capabilities through live case studies, technical questions, and behavioral discussions.
Structured Case Solving
This area evaluates your ability to dissect ambiguous business challenges and formulate structured, data-backed solutions. Interviewers want to see how you think, organize your thoughts, and validate your hypotheses.
Be ready to go over:
- Hypothesis-driven frameworks – Structuring your approach using issue trees and logical frameworks to isolate business problems.
- Market sizing and estimation – Breaking down complex estimation questions into logical mathematical steps using reasonable assumptions.
- Root-cause analysis – Systematically identifying why a business metric (such as profitability or market share) is underperforming.
Advanced concepts (less common):
- Designing custom frameworks for multi-channel retail distribution challenges.
- Evaluating the impact of supply chain disruptions on retail shelf availability.
Example questions or scenarios:
- "A major beverage manufacturer is seeing a decline in profitability despite stable sales volume. How would you investigate the cause?"
- "Estimate the total volume of laundry detergent consumed in India annually."
CPG & Commercial Analytics
Since Decision Point is a specialized analytics partner for the CPG and retail sectors, demonstrating domain-specific knowledge is a major differentiator. You should understand how consumer brands drive growth and how data supports their commercial strategies.
Be ready to go over:
- Revenue Growth Management (RGM) – Pricing strategies, brand portfolio management, and trade promotion optimization.
- Market Mix Modeling (MMM) – Understanding how marketing spend across different channels drives overall sales volume.
- Assortment and space optimization – Determining the ideal product mix for retail shelves to maximize category growth.
Advanced concepts (less common):
- Understanding the mechanics of price elasticity modeling and its limitations.
- Analyzing the digital shelf and e-commerce channel performance metrics.
Example questions or scenarios:
- "How would you determine if a buy-one-get-one-free promotion is driving incremental sales or simply cannibalizing full-price purchases?"
- "What data sources would you look at to optimize the product assortment of a snack brand in high-density urban convenience stores?"
Technical Data Interpretation
As a Consultant, you will act as the translator between data science teams and business stakeholders. You must prove that you can interpret quantitative outputs and ensure data integrity.
Be ready to go over:
- SQL and data manipulation – Writing logical queries, joining tables, and aggregating data to extract business insights.
- Excel modeling – Building structured, error-free models to analyze business scenarios and perform sensitivity analyses.
- Data visualization principles – Designing clear, impactful charts and dashboards that communicate insights instantly.
Advanced concepts (less common):
- Understanding the basic inputs, outputs, and evaluation metrics of machine learning models (e.g., precision, recall, R-squared).
Example questions or scenarios:
- "Walk me through how you would structure a SQL query to identify customers who spent more than the average customer in the last 30 days."
- "If a predictive model has a high accuracy rate but very low recall, what does that tell you about its performance in predicting rare events?"
