Consumer-Goods-Cloud-Accredited-Professional Practice Exam Questions with Answers Consumer Goods Cloud Accredited Professional (AP-204) Certification

Out of Stock, Share of Shelf, and SKU Facings are three KPIs that can be derived using Planogram detection. A Planogram is a visual representation of how products should be arranged on a shelf. Planogram detection is a feature that uses Einstein Vision to compare the actual shelf image with the expected planogram image and calculate metrics such as:

Out of Stock, which measures the percentage of products that are missing from the shelf.

Share of Shelf, which measures the percentage of space occupied by a product or brand on a shelf.

SKU Facings, which measures the number of times a product SKU is visible on a shelf. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 23-24.

https://help.salesforce.com/s/articleView?id=sf.retail_task_admin_create_assessment_task_record_page.htm &type=5

 Inventory Check is a Lightning component that can be added to a Consumer Goods assessment task record page by an admin. An assessment task is a type of action plan task that allows users to collect data and perform actions related to a retail store. An inventory check is a type of assessment task that allows users to verify the availability and quantity of products in a store. The Inventory Check component displays the products that are associated with the store and allows users to search, scan, and update the product information. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 40.

Creating a distinct set of KPIs for endcap locations allows NTO to specifically track and analyze the performance of these areas, providing targeted insights.

The Shipment object is specifically designed for managing direct store delivery processes, tracking the movement of goods from distribution to retail locations.

Store Products are integral to various tasks in Consumer Goods Cloud, including inventory and promotion checks, as well as search and scan functionalities.

White Space Analysis is a Tableau CRM dashboard that shows opportunities to sell new products in a retail store location. It analyzes the sales performance and product distribution across different stores and segments, and identifies gaps or white spaces where new products can be introduced or existing products can be expanded. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 37.

Quip Documents, Einstein Predictions, and Custom Object Task List are three components that can be added to a new page for the Consumer Goods Cloud mobile application. These components provide additional functionality and information for users in the field. They are:

Quip Documents, which allows users to access and collaborate on Quip documents within the app. Quip documents are cloud-based documents that can include text, images, spreadsheets, charts, and more.

Einstein Predictions, which allows users to see predictions and recommendations from Einstein AI based on their data and actions. For example, users can see predictions for sales revenue, product demand, or visit outcomes.

Custom Object Task List, which allows users to create and complete tasks related to custom objects within the app. Custom objects are objects that are defined and configured to suit specific business needs. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 40-41

Salesforce Maps integrated with Consumer Goods Cloud allows the creation of layers of Retail Stores, aiding in geographical and spatial analysis for field operations.

Retail Store KPIs are key performance indicators that measure how well a retail store is performing against a specific goal or target. For a promotional end cap audit, Retail Store KPIs should be defined at the energy bar product category level, as this would capture all the energy bars that are included in the BOGO promotion and also minimize the number of records created. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 30-31.

Action Plan Templates in Consumer Goods Cloud can be cloned, enabling users to replicate and modify templates for various purposes and scenarios.

The object where a Consumer Goods Cloud Admin needs to establish the expected value for the planogram metrics is Assessment Indicator Definition. A planogram is a visual representation of how products should be arranged on a shelf. A planogram metric is a measure of how well the actual shelf image matches the expected planogram image. An Assessment Indicator Definition is an object that defines an indicator or metric that can be used to assess the performance or compliance of a retail store or an action plan task. By creating an Assessment Indicator Definition for the planogram metric, such as out of stock, share of shelf, or SKU facings, an admin can establish the expected value for that metric and use it to calculate the actual value based on the shelf image. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 23-24.

Uploading an image of the planogram is essential for enabling field teams to perform planogram checks, providing a visual reference for in-store product placement and arrangement.

Promotion Product, Promotion Channel, Retail Store KPI, and Retail Visit KPI are four objects that have promotions associated with them using the Consumer Goods Cloud Data Model. These objects help users to define, execute, and measure promotions in retail stores. They are:

Promotion Product, which represents a product that is part of a promotion.

Promotion Channel, which represents a channel or segment where a promotion is applicable.

Retail Store KPI, which represents a key performance indicator that measures how well a retail store is performing against a specific promotion goal or target.

Retail Visit KPI, which represents a key performance indicator that measures how well a field rep executed a promotion during a store visit. Verified References: [Salesforce Consumer Goods Cloud Implementation Guide], page 30-31.

These steps are essential in setting up Einstein Vision for automatic product detection on shelves, involving enabling the feature, defining relevant tasks, and training the models with appropriate product images.