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| Section | Objectives |
|---|---|
| Category Management Foundations | - Category management principles and framework
|
| Category Strategy Development | - Strategic category planning
|
| Shopper and Retail Insights | - Behavioral and shopper analytics
|
| Execution and Business Planning | - Category execution and collaboration
|
| Data and Analytics | - Retail and shopper data analysis
|
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NEW QUESTION # 22
What is the primary goal of SKU rationalization in supply chain management?
Answer: A
Explanation:
The correct answer is D .
SKU rationalization is about improving the product mix by removing or consolidating items that create unnecessary complexity without contributing enough value. The CPCM course includes Efficient Assortment and Retailer Economics and the Product Supply Chain , which means assortment decisions are not only shopper-facing; they also affect inventory, operations, cost, and execution. The CMKG supply-chain material states that product supply chain affects "inventory, forecasting, availability, cash flow, service levels, and ultimately the shopper experience." Option D is the only answer that reflects the real supply-chain objective: reduce operational complexity by removing slow-moving, duplicated, or redundant SKUs. Option A is the opposite; adding more products can increase complexity. Option B is too aggressive because high-cost products may still be profitable or strategically important. Option C is too narrow because SKU rationalization is not only about seasonal demand.
NEW QUESTION # 23
Which action would BEST reduce shrink in a perishable category?
Answer: C
Explanation:
The correct answer is D .
Perishable shrink is mainly controlled by matching supply to expected demand and rotating product before it expires. CMKG's Retailer Economics and Product Supply Chain material emphasizes that category managers need to understand how their decisions affect the retailer income statement and cost of goods sold, while category management and supply chain must be better aligned for store-level execution.
Improved forecasting reduces over-ordering and excess inventory. Shelf-life rotation ensures older or earlier- expiring product is sold first. FIFO/FEFO rotation is a standard perishable inventory control method because it helps reduce waste and spoilage by moving product before expiration.
Option A may help clear inventory in some cases, but increasing promotion frequency is not the best root- cause control for shrink. Option B is dangerous in perishables because larger orders can increase spoilage if demand is overestimated. Option C changes assortment composition but does not directly control spoilage, dating, or inventory loss. The strongest operational answer is improve forecasting and shelf-life rotation .
NEW QUESTION # 24
What does household penetration measure?
Answer: B
Explanation:
The correct answer is A .
Household penetration measures how many households purchased a product, brand, category, or retailer within a defined time period. CMKG explains that sales can be analyzed through Total Number of Buying Households , also called Penetration , multiplied by spend per household. CMKG further clarifies that penetration relates to the number of households purchasing the product.
Option A is therefore the exact definition. It measures the percentage of households that bought during the specified period.
Option B is wrong because new product introductions measure innovation or assortment activity, not household penetration. Option C describes purchase frequency, which measures how often buyers purchase.
Option D describes revenue or dollar sales, not penetration. Household penetration is a buyer-base measure, not a sales-value measure.
NEW QUESTION # 25
Which phase of analytics uses past data and models to estimate what's likely to happen next?
Answer: D
Explanation:
The correct answer is A .
Predictive analytics is the analytics phase that uses historical data and models to estimate future outcomes.
The CPCM course explicitly includes predictive analytics as part of advanced category analytics, including regression models, clustering algorithms, collaborative filtering, and time-to-event models. IBM defines predictive analytics as a branch of advanced analytics that makes predictions about future outcomes using historical data, statistical modeling, data mining, and machine learning.
Option C, descriptive analytics, explains what happened in the past. Option D, prescriptive analytics, recommends what action should be taken. Option B, generative, refers to creating new content or outputs and is not the correct analytics phase here. The phrase "what's likely to happen next" is the giveaway: that is predictive analytics.
NEW QUESTION # 26
The best Predictive Analytic tools use which of the following? Select the best answer.
Answer: A
Explanation:
The correct answer is A .
The CPCM course states that moving into advanced category analytics includes predictive analytics, specifically naming collaborative filtering, clustering algorithms, regression models, and time-to-event models. Those methods require historical data, statistical modeling, and machine-learning-style pattern recognition. IBM defines predictive analytics as predicting future outcomes by using historical data combined with statistical modeling, data mining techniques, and machine learning.
Option A is the most complete answer because predictive analytics needs all three: historical data to learn from, statistical models to quantify relationships, and machine learning to detect patterns and improve prediction. Option B omits machine learning. Option C omits statistical models. Option D omits historical data, which is the base input for predictive analytics.
NEW QUESTION # 27
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