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| Section | Weight | Objectives |
|---|---|---|
| Tactical Planning & Execution | 25% | - Space Management & Store Clustering - Pricing Strategy & Analysis - Promotional Strategy & Evaluation - Efficient Assortment Development |
| Data Competency & Analysis | 25% | - POS Data Analytics - Panel Data & Advanced Analytics - Shopper Data & Geo-demographic Analysis |
| Business & Supply Chain Knowledge | 15% | - Retailer Economics & Profitability - Supply Chain Principles |
| Professional Standards & Communication | 10% | - Ethics & Legal Implications - Fact-Based Storytelling & Presentation |
| Category Assessment & Strategy | 25% | - Category Definition, Segmentation & Role - Root Cause Analysis & Insight Generation - Category Health Measurement |
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64. Frage
What is the primary focus of the 'What' section in storytelling?
Antwort: A
Begründung:
The correct answer is B .
In fact-based category storytelling, the "What" section establishes the business situation, opportunity, issue, or insight supported by relevant data. It is not the place to dump every chart or every possible observation.
CMKG explains that fact-based presentations should focus on growth opportunities for the retailer and translate those opportunities into strategies tied to action. It also states that fact-based presentations should use relevant facts that support the presentation purpose, and irrelevant facts or insights should not be included.
Option A is wrong because detailed appendices may support the story, but they are not the primary focus of the "What" section. Option C is wrong because exploratory analysis happens before the story is built; the story presents the selected insight, not every possible analysis path. Option D is exactly the bad practice CMKG warns against: data that distracts from key ideas and opportunities weakens the presentation.
65. Frage
Which of the following purchase behaviors best explains the category performance?
Dollars: +5%
Number of Households: +2%
Trips per Household: -2%
Units per Trip: +3%
Dollars per Unit: +2%
Antwort: B
Begründung:
The correct answer is C .
The category dollars increased by +5% . To identify what best explains that performance, compare the listed purchase-behavior drivers. The strongest positive driver shown is Units per Trip at +3% . Number of Households is also positive at +2%, and Dollars per Unit is positive at +2%, but neither is as strong as Units per Trip. Trips per Household is negative at -2% , so it cannot be the best explanation for growth.
CMKG's shopper analytics explanation supports this type of driver analysis. It explains that sales are driven by household purchasing behavior and spending, and gives the formula: Total Number of Buying Households × Spend per Buying Household = Dollar Sales . CMKG further breaks spending into purchase occasions and spend per trip, which is exactly the kind of logic tested in this question.
Option A is wrong because total baskets are not clearly increasing; the household gain is offset by the decline in trips per household. Option B is partially correct but not the strongest driver. Option D is also positive, but
+2% is lower than the +3% gain in units per trip.
66. Frage
Which of the following is the first step in the multivariate clustering process?
Antwort: D
Begründung:
The correct answer is A .
The multivariate store clustering process starts by identifying the Product Demographic Affinity Profile , because the analyst first needs to understand which demographic groups have the strongest relationship or affinity with the product/category being studied. ARC's category-specific store clustering guidance identifies
"Identify the Product Demographic Affinity Profile (PDAP)" as a core step and then moves into calculating product demand potential.
This sequence matters. You cannot calculate demand potential correctly until you understand the demographic profile that is most relevant to the product or category. Once the product's demographic affinity is known, the analyst can compare that profile to store-level demographic profiles and then create meaningful clusters based on demand and opportunity.
Option B is later in the process because clusters are created after the relevant product and store-level measures are understood. Option C is important, but it follows the product affinity logic. Option D also comes after identifying the demographic affinity profile.
67. Frage
What is the primary purpose of Affinity Models in Category Management?
Antwort: A
Begründung:
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.
68. Frage
What is the primary purpose of sub-segment fair share analysis?
Antwort: C
Begründung:
The correct answer is B .
Fair share analysis is a relative-performance analysis. CMKG explains that indices compare a result against another relevant reference point or benchmark, and specifically describes Fair Share Index as a way to compare a tactic such as share of shelf, items, promotions, or displays against category share. In category management, the same logic applies to sub-segments: the analyst compares a sub-segment's share against a relevant benchmark to decide whether it is overdeveloped, underdeveloped, or performing at a reasonable level.
Option A is wrong because profitability analysis focuses on margin, profit dollars, or financial return, not fair- share comparison. Option C is wrong because category management does not automatically allocate resources equally; resources should follow shopper demand, strategy, opportunity, and performance. Option D is wrong because identifying the best-selling product is a ranking or sales-volume analysis, not a fair-share analysis.
69. Frage
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