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| Section | Objectives |
|---|---|
| Topic 1: Retail Economics and Supply Chain | - Business Operations
|
| Topic 2: Pricing and Promotion Analytics | - Commercial Strategy
|
| Topic 3: Shopper and POS Data Analytics | - Building Data Competency
|
| Topic 4: Category Health and Assessment | - Category Performance Evaluation
|
| Topic 5: Fact-Based Selling and Storytelling | - Communication and Insights
|
| Topic 6: Assortment and Space Management | - Retail Optimization
|
>> Category-Manager日本語版問題解説 <<
現実はしばしば残酷です。私たちは他の人と競争するために何をしますか？ CMA証明書など、より便利な証明書ですか？おそらく、手元にあるいくつかの資格が最大の資産であり、Category-Manager試験準備はCategory-Manager試験に迅速に合格し、すぐに認定を取得することでその資金を提供することです。それについて疑ってはいけません。より有用な認定は、より多くの方法を意味します。 Category-Manager試験に合格すると、Category-Manager試験の急流に関連するビジネスを持つすべての企業に歓迎されます。
質問 # 56
Which of the following is an effective technique for creating compelling stories using data and analytics in category management?
正解：A
解説：
The correct answer is B .
A compelling fact-based category story is concise, relevant, and persuasive. CMKG's guidance is direct: fact- based presentations should use only relevant facts that support the presentation purpose, and each slide should have a clear purpose, be easy to understand, and include only insights that are compelling for the audience.
Option A is wrong because adding more data does not make a story stronger; it often creates noise. Option C is wrong because technical detail alone does not persuade a buyer or decision-maker. Option D is wrong because visuals without context do not create a story. Category storytelling requires the analyst to connect the facts to the business opportunity, explain why it matters, and identify the action. CMKG describes fact-based skills as going beyond analytics; they are about selling the action and opportunity to internal or external buyers.
質問 # 57
What does a high Sales per Point of Weighted Distribution (SPWD) indicate about a product's performance?
正解：A
解説：
The correct answer is D .
SPWD is a velocity/productivity measure. A high SPWD means the product is generating strong sales for each point of weighted distribution it has. In other words, the product is performing well where it is available
, even if it does not yet have broad distribution.
NielsenIQ explains that sales per distribution point accounts for distribution and ranks products on sales productivity based on distribution levels. It also gives the key interpretation: a product with higher total sales is not necessarily more productive if it has much higher distribution. That is exactly why option D is correct.
Option A is the opposite of the correct interpretation. A high SPWD does not suggest underperformance; it suggests strong velocity. Option B is wrong because total revenue alone does not account for distribution.
Option C is wrong because broad availability is measured by distribution or ACV weighted distribution, not by SPWD. A product can have low distribution and still have high SPWD if it sells strongly in the outlets where it is carried.
質問 # 58
Which of the following KPIs is most critical for resolving on-shelf availability issues in the retail supply chain?
正解：A
解説：
The correct answer is B .
On-shelf availability problems are supply-chain execution problems: the product must be available when the shopper wants to buy it. CMKG explains that supply chain affects inventory, forecasting, availability, cash flow, service levels, and shopper experience. Fill Rate is the most direct KPI among the options because it measures the ability to fulfill demand from available stock without lost sales or backorders. A weak fill rate leads directly to out-of-stocks and poor shelf availability.
Option A, Inventory Turnover, measures how quickly inventory sells through, but high turnover does not guarantee shelf availability. Option C, Gross Margin, is a financial metric, not an availability KPI. Option D, Order Cycle Time, measures replenishment speed, but it does not directly show whether customer or store demand is being fulfilled. Fill Rate is the best answer.
質問 # 59
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%
正解：B
解説：
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.
質問 # 60
What is the primary purpose of regression analysis?
正解：D
解説：
The correct answer is B .
Regression analysis is used to understand how a dependent variable changes in relation to one or more independent variables. In pricing analytics, that usually means analyzing how sales, units, profit, or demand respond to price or other business drivers. The CPCM pricing material identifies correlation and price regression analysis as methods used to evaluate historical pricing and project future sales and profit at specific price points. CMKG also lists advanced pricing analytics as including breakeven point, correlation, price regression, ABC, and slope.
Option A is wrong because calculating an average is descriptive statistics, not regression. Option C is too strong because regression can show relationships or associations, but it does not automatically prove causation. NIST's regression explanation specifically warns that cause-and-effect cannot necessarily be inferred from regression alone. Option D is wrong because classification belongs to classification models or supervised learning classification tasks, not standard regression analysis.
質問 # 61
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