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Universal Containers intends to use a custom Salesforce big object in its org and visualize the data using CRM Analytics. As the number of rows to be synced is quite large, the CRM Analytics consultant is looking to set up an incremental sync with additional filters added as part of the data sync to improve performance.
What should the consultant keep in mind while implementing this?
Universal Containers asks a CRM Analytics consultant to review the performance of its local data sync.
After removing unused objects and fields from connected data, what else should the consultant do to improve performance of the data sync?
To improve the performance of local data sync in Universal Containers, evaluating the connection mode for each connected object is a practical approach. Here's the rationale:
Optimization of Resources: Different connection modes (e.g., Full Sync, Incremental Sync) use different amounts of resources. Choosing the right mode for each object based on how frequently its data changes can optimize the sync process and reduce load times.
Efficient Data Handling: By tailoring the connection mode to the needs of specific data objects, the overall efficiency of the data sync process is improved, leading to faster refresh rates and more timely data availability.
Cost and Performance Balance: Evaluating and selecting the appropriate connection mode can also help balance performance needs with cost constraints, as some modes may consume more compute resources than others.
Universal Containers (UC) builds three Einstein Discovery models in Salesforce to predict and maximize its revenue per customer. The models are for every region UC has a business: EMEA, AMER, and APAC.
How should a consultant help UC deploy the three Einstein models to Salesforce?
In deploying Einstein Discovery models that are tailored to different regions (EMEA, AMER, and APAC), the best approach is to segment the account data by region and apply the specific model designed for each segment. This method ensures the following:
Relevance and Accuracy: Each model can be specialized to understand and predict based on regional dynamics, which may differ significantly across geographies in terms of market behavior, customer preferences, and economic conditions.
Efficiency: Deploying region-specific models avoids the dilution of predictive power that might occur if a single model were used across all regions, which could lead to less accurate predictions.
Scalability: This approach is scalable as UC can further refine each model as more regional data becomes available or as regional market conditions evolve.
The CRM Analytics consultant at Cloud Kicks is asked to a dashboard displaying Opportunities data on the account's record page. The dashboard should display only opportunity data related to the current account viewed.
How should the consultant accomplish this?
The administrator at Cloud Kicks has been asked to sync data from an external object created in Salesforce into CRM Analytics.
What should the administrator keep in mind?
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