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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Visit Execution | 36% | - In-store process management
|
| Topic 2: Integrations | 6% | - System connectivity
|
| Topic 3: Analytics | 6% | - Reporting and KPIs
|
| Topic 4: Visit Planning | 6% | - Territory and route management
|
| Topic 5: Platform | 10% | - Core platform configuration
|
| Topic 6: Data Setup | 36% | - Consumer Goods Cloud data model
|
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NEW QUESTION # 35
How can a merchandiser perform a check-in to log the arrival time and geo coordinates for an inventory-check appointment?
Answer: B
Explanation:
A merchandiser can perform a check-in to log the arrival time and geo coordinates for an inventory-check appointment by using the Salesforce Mobile App. The Salesforce Mobile App is a mobile version of Salesforce that allows users to access and use various features and tools on their mobile devices. A check-in is a feature that allows users to record their arrival at a location, such as a retail store, by using their device's GPS. A check-in can be performed by tapping the Check In button on the visit record in the Salesforce Mobile App. Verified Reference: [Salesforce Consumer Goods Cloud Implementation Guide], page 34.
NEW QUESTION # 36
A Company by the name of Northern Trail Outfitters(NTO) leverages top industry professionals to manage direct store delivery. How should a consultant explain the power of Consumer Goods Cloud's Einstein for CG- Visit Recommendations feature to NTO?
Answer: B
Explanation:
Einstein for CG- Visit Recommendations is a feature that uses artificial intelligence to suggest optimal store visits for field reps based on predefined rules and criteria. By adopting this feature with the consumer goods cloud, professionals can focus on their work while having information served to them that will support them in being competitive. For example, they can prioritize the most profitable or strategic stores, avoid unnecessary or redundant visits, and optimize their routes and schedules. Verified Reference: [Salesforce Consumer Goods Cloud Implementation Guide], page 36.
NEW QUESTION # 37
Which of the following are supported KPI Type values for Retail Store KPI records?
Answer: B,C,E
Explanation:
The supported Retail Store KPI types include Price, Facing, and Revenue, along with Share of Shelf, Out of Stock, and Inventory. KPI Type identifies the business interpretation of the Assessment Indicator Definition associated with a Retail Store KPI and is particularly important for Retail Execution measurements and analytics.
Facing is used when the organization measures the number of product facings visible on a shelf or display. It commonly supports planogram and merchandising compliance. Price supports assessments where the field representative records and evaluates the displayed or observed product price. Revenue provides the business classification for revenue-oriented performance measures.
Salesforce ' s Retail Store KPI configuration explicitly provides these values in the KPI Type picklist: Facing, Share of Shelf, Out of Stock, Inventory, Price, and Revenue. The KPI Type can also contribute to calculated performance indicators such as average facing compliance, average out-of-stock compliance, and average price compliance.
Promotion is business context rather than a KPI Type; a Promotion can instead be associated with the Retail Store KPI. Units can function as a unit of measure or assessment concept but is not one of the supported KPI Type values identified for the Retail Store KPI.
Study Guide Topics:Data Setup - > Retail Store KPI - > KPI Type - > Facing - > Price - > Revenue
NEW QUESTION # 38
GreenTech, a Consumer Goods company wants to sell new products in a particular retail store. Which Tableau CRM dashboard should they use to find opportunities to sell new products in a retail store location?
Answer: C
Explanation:
The Whitespace Analysis dashboard is specifically designed to identify opportunities to introduce additional products or product categories into stores. It analyzes sales information across products, categories, and retail locations to expose areas where a store has unused sales potential.
Salesforce describes whitespace as an opportunity where a product or category that performs successfully elsewhere is not currently generating sales in a particular store. The dashboard allows sales managers to evaluate revenue and quantity patterns, identify top-performing products, compare performance across stores, and determine where introducing a product could increase revenue. This makes it directly applicable to GreenTech ' s requirement to determine which new products may be suitable for a particular retail location.
The dashboard can answer questions such as which stores have the potential to sell top-selling products and whether a product or category can be introduced into additional stores. It also provides product-performance trends so the sales manager can evaluate whether a candidate product has demonstrated sufficiently strong performance elsewhere before expanding distribution.
Therefore, Whitespace Analysis provides the cross-selling and assortment-expansion insight needed for this scenario.
Study Guide Topics:Analytics - > CRM Analytics - > Whitespace Analysis - > Product Opportunities - > Store Sales Potential
NEW QUESTION # 39
A Field Rep is having challenges measuring their share-of-shelf due to a recent change in packaging to a competitor ' s product, which makes their products look almost identical. When using Einstein, the competitor
' s product was marked incorrectly as the rep ' s product. What should the rep do to prevent this from happening in the future?
Answer: C
Explanation:
The representative should edit the product tags so that the detected object is associated with the correct product classification. Accurate tagging is fundamental to maintaining reliable object-recognition results, particularly when two products have visually similar packaging.
Object detection identifies objects within an image and maps those detections to labels or product classes. If a competitor changes packaging so that its product resembles another SKU, the resulting classification can incorrectly increase the measured facings or share-of-shelf for the wrong product. Correcting the tag provides accurate classification information and helps maintain the integrity of the shelf measurement.
Salesforce ' s Einstein Vision architecture supports a feedback process for incorrectly classified objects. When an object-detection model misclassifies an image, corrected examples and labels can be added back to the dataset and used in subsequent training or retraining. Salesforce describes this feedback mechanism specifically as a way to improve models after misclassification.
Creating a new planogram would only alter the expected shelf arrangement and would not correct product identity. Turning competitor products around manipulates the physical merchandising environment rather than correcting the data. Canceling the task similarly fails to improve future recognition.
Study Guide Topics:Platform - > Einstein Object Detection - > Product Tags - > Classification - > Share-of- Shelf Accuracy
NEW QUESTION # 40
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