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| Section | Objectives |
|---|---|
| Shopper and Retail Insights | - Behavioral and shopper analytics
|
| Category Management Foundations | - Category management principles and framework
|
| Execution and Business Planning | - Category execution and collaboration
|
| Category Strategy Development | - Strategic category planning
|
| Data and Analytics | - Retail and shopper data analysis
|
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NEW QUESTION # 69
Which of the following most accurately describes incremental contribution?
Answer: D
Explanation:
The correct answer is C .
In efficient assortment, incremental contribution is not simply the sales volume of the item being added. The key word is incremental . It means the extra volume the category gains after accounting for substitution, switching, and cannibalization from existing items. The CMA/CPCM standards for Efficient Assortment specifically include the requirement to "generate incremental item contribution by understanding cannibalization and source of volume." Option C is the best answer because it defines the net additional category volume created by adding a particular item. Option A is incomplete because expected item volume may include volume stolen from existing items. Option D is wrong because it focuses only on the added item's own volume, not the category- level increment. Option B is wrong because option C accurately describes the concept.
NEW QUESTION # 70
What is the Dollar Sales per $MMACV for the Product Group in Store 478?
Answer: C
Explanation:
The correct answer is D .
Dollar Sales per $MMACV measures sales productivity normalized by store or market selling power. CPG Data Insights defines Sales per $MM ACV as a velocity measure calculated by dividing sales by the market's All Commodity Volume expressed in millions, and explains that it helps compare productivity across markets, retailers, or products with different distribution levels.
For Store 478 :
Product Group Actual Dollar Sales = $10,000
Store 478 ACV $ Sales = $2,000,000
ACV expressed in millions = $2,000,000 ÷ $1,000,000 = 2
Calculation:
$10,000 ÷ 2 = $5,000
So the Dollar Sales per $MMACV for the Product Group in Store 478 is $5,000 .
Option C, $4,000, is the total-store benchmark calculation: $400,000 ÷ 100 = $4,000. The question asks specifically for Store 478 , not the total store benchmark.
NEW QUESTION # 71
What is Midtown Mart's share of wallet (SOW) for Category X?
Table shown:
Answer: D
Explanation:
The correct answer is A .
Share of Wallet measures the portion of a shopper group's total category spending that is captured by the retailer. CMKG describes share of wallet as the percentage of total category dollars spent on the brand or retailer being analyzed.
For Category X , the relevant figures are:
Midtown Mart Shoppers: Dollars - Market = $20,000
Midtown Mart Shoppers: Dollars - Midtown Mart = $15,000
So the calculation is:
$15,000 ÷ $20,000 = 75%
That means Midtown Mart captures 75% of the Category X spending made by Midtown Mart shoppers. The denominator is not all shoppers in the market. The denominator must be the total Category X market spend of Midtown Mart shoppers. That is why option B, C, and D are incorrect. Option C, 25% , incorrectly uses Category X as a share of all grocery market dollars. Option D, 60% , uses $15,000 ÷ $25,000, which compares Midtown Mart's Category X dollars to all shoppers' market dollars and is not the correct SOW denominator.
NEW QUESTION # 72
What is the primary purpose of Affinity Models in Category Management?
Answer: B
Explanation:
The correct answer is B .
The CPCM course places affinity-type work inside advanced predictive analytics. The official CPCM course material states that advanced category analytics includes "predictive analytics including collaborative filtering, clustering algorithms, regression models and time-to-event models." In category management, affinity modeling is used to identify relationships between items that are bought together. Oracle Retail describes market basket/affinity analysis as using data-mining techniques to search for sales patterns between products within transactions, such as rules connecting products purchased together.
Option B is therefore the best answer because chips and salsa is a classic co-purchase relationship. Option A describes clustering, not affinity modeling. Option C describes switching or substitution analysis. Option D describes sales forecasting, usually handled through regression, time-series, or other forecasting models.
NEW QUESTION # 73
Using the chart, what is the most complete insight for Mid-Mart regarding the Snack Category?
Answer: C
Explanation:
The correct answer is A .
The CPCM course emphasizes turning data into insights and then into action. It states that category managers must draw insights from data, understand tactical analysis, and identify category opportunities that can be acted on through category tactics. This question is testing exactly that: not just calculating the gap, but identifying the gap, the source of the gap, and the dollar opportunity.
From the chart:
Mid-Mart ACV share = 25.0%
Mid-Mart Snack share = 20.0%
So Mid-Mart is:
25.0% - 20.0% = 5.0 points behind Total Store
The total Snack market is $200,000 . If Mid-Mart achieved its fair share of Snacks based on its 25% Total Store ACV share, expected Snack sales would be:
$200,000 × 25% = $50,000
Actual Mid-Mart Snack sales are:
$40,000
So the opportunity is:
$50,000 - $40,000 = $10,000
The driver is the Club Channel , because Club has 25% ACV share but 30% Snack share. Club is 5 points over fair share, while AO Grocery is exactly aligned at 50% ACV share and 50% Snack share. Therefore, the most complete insight is that Mid-Mart is 5.0 points behind Total Store, the gap is driven by the Club Channel, and the opportunity is $10,000.
NEW QUESTION # 74
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