Data Cloud
Pricing Use Case
Empowering Financial Service Agents with a Unified Customer View and Proactive Automation
Data Cloud
Summary
Aplan Financial Services aims to revolutionize its customer service operations by using a unified view of customer interactions and financial product holdings. By consolidating data from their core banking system and existing Service Cloud instance Aplan Financial will use Data Cloud to create a comprehensive 360-degree customer profile. This unified data will power Calculated Insights to predict customer needs, identify new service opportunities, and improve customer satisfaction. Aplan’s 300 service agents will gain access to these insights directly within their Service Cloud console, enabling them to provide more personalized and efficient service, anticipate issues before they arise, and enhance customer satisfaction.
About
Aplan Financial Services is a well-established regional bank providing a wide range of financial products including checking and savings accounts, loans, and investment services. They are committed to providing exceptional customer service and are investing in technology to create more personalized and proactive customer experiences.
Industry
Salesforce products used
Consumption Credits Deep Dive
Step 1: Connecting and Transforming Data
Aplan Financial Services connects two key data sources into Data Cloud to build a comprehensive view:
- Salesforce Service Cloud: to ingest customer records, cases, and activity data
- Core Banking System: to bring in critical customer and account data, including product holdings and transaction history
To understand the credits and cost related to connecting and transforming this data we must first start by understanding the scope of profile and Engagement data they’d like to bring in from these destinations, which is outlined below:
- Profile Data: 10 million non-unique records across both systems. Based on this scope, we can determine the total amount of data to be processed in year one related to profile records:
- From Salesforce Service Cloud — 1/3rd of profile records: 3.3 million profiles ingested with 5% updated daily, totaling roughly 64 million rows annually
- From core banking system— 2/3rds of profile records: 6.7 million profiles ingested with 5% updated daily, totaling roughly 128 million rows annually
- Engagement Data: 30 million records annually. Based on this scope, we can determine the total amount of data to be processed in year one related to engagement records, assuming Aplan initially uploads one previous quarter’s worth of historical engagement data to start (represented by 25% of the total):
- From Salesforce Service Cloud— 1/3rd of engagement records: 2.5 million historical records ingested initially with another 10 million records expected throughout the rest of year one, totaling 12.5 million rows annually
- From the core banking system— 2/3rds of Engagement records. 5 million historical records ingested annually with another 20 million records expected through the rest of year one, totaling 25 million rows annually
- Data Transformations applied to 1% of all ingested data. In order to get the data into a proper format for harmonization, Aplan runs data transforms on a small portion of the records ingested daily, processing a total of 173 million rows annually
Summary Table for Credits and Cost
The "Credits Consumed" column is calculated using the following formula: (Rows Processed * Credit Multiplier) / Unit of Multiplier
| Pricing Meter | Rows Processed | Credit multiplier | Unit | Unit of Multiplier (e.g. per million rows processed) | Credits Consumed | Cost Per Credit | Total Cost (List Price) |
|---|---|---|---|---|---|---|---|
| Internal Data Pipeline- Batch Ingestion from Salesforce sources | 76,666,667 | 500 | Rows processed | 1,000,000 | 38,333 | $0.005 | $192 |
| External Data Pipeline- Batch ingestion from non Salesforce sources | 153,333,333 | 2,000 | Rows processed | 1,000,000 | 306,667 | $0.005 | $1,533 |
| Data Transforms- Batch | 173,375,000 | 400 | Rows processed | 1,000,000 | 69,350 | $0.005 | $347 |
| Total Annual Cost | 376,017 | $0.005 | $1,880 |
Step 2: Unifying Profiles
To create a single, actionable view of each customer, Aplan Financial Services unifies profiles from all connected data sources using Data Cloud's identity resolution capabilities. The number of rows processed for this action is calculated based on the number of source profiles processed by an identity resolution ruleset. After the first time a ruleset runs, only new or modified source profiles are counted. Here again, we've used the assumed rate of 5% of profiles updated daily, which results in an estimate of approximately 192 Million rows annually to be processed through the identity resolution engine annually. After Identity Resolution runs, Aplan ends up with 3.3 million unique unified profiles.
Note: This estimate does not include Real-Time Identity Resolution. It assumes the use of standard Batch Identity Resolution processes. Banking customers, in particular, may require real-time identity resolution for new customer onboarding and immediate action scenarios, which would incur additional costs.
Summary Table for Credits and Cost
The "Credits Consumed" column is calculated using the following formula: (Rows Processed * Credit Multiplier) / Unit of Multiplier
| Pricing Meter | Rows Processed | Credit multiplier | Unit | Unit of Multiplier (e.g. per million rows processed) | Credits Consumed | Cost Per Credit | Total Cost (List Price) |
|---|---|---|---|---|---|---|---|
| Batch Profile Unification | 192,500,000 | 100,000 | Rows processed | 1,000,000 | 19,250,000 | $0.005 | $96,250 |
| Total Annual Cost | 19,250,000 | $0.005 | $96,250 |
Step 3: Activating the Data
With unified profiles, Aplan Financial Services creates Calculated Insights to derive actionable information about their customers. These insights, providing valuable intelligence, are then leveraged alongside the comprehensive data stored in Data Cloud to power a variety of business use cases through efficient data queries. The volume processed for these activities depends on the number of rows associated with all data model objects used when running the action. See below for a breakdown of each activation related component:
- Calculated Insights: They create 5 key insights such as a "Propensity to Churn Score," a daily "Product Holdings Summary," and an annual "Customer Lifetime Value”. Running these insights daily results in an estimated 16 trillion rows processed annually.
- Data Queries: Queries happen when data and insights from Data Cloud are needed inside Service Cloud. Queries typically operate on data at rest, processing data in batches or on demand when it is requested. Aplan Financial is focused on two use cases that consume Queries which are:
- Equipping service agents with unified profile data on contact records
- Displaying transactional data from the core banking system on the Service Console as a related list for their agents to see
Aplan’s service agents will process approximately 3,000 data queries daily (300 agents averaging 10 queries each) for a total of 3.6 trillion rows annually associated with viewing contact records and related transactions on the Service Console. The count of rows processed depends on the structure of a query as well as other related factors such as the volume of data being queried.
Summary Table for Credits and Cost
The "Credits Consumed" column is calculated using the following formula: (Rows Processed * Credit Multiplier) / Unit of Multiplier
| Pricing Meter | Rows Processed | Credit multiplier | Unit | Unit of Multiplier (e.g. per million rows processed) | Credits Consumed | Cost Per Credit | Total Cost (List Price) |
|---|---|---|---|---|---|---|---|
| Calculated Insights- Batch | 16,726,125,000 | 15 | Rows processed | 1,000,000 | 250,892 | $0.005 | $1,254 |
| Data Queries | 3,613,500,750,000 | 2 | Rows processed | 1,000,000 | 7,227,002 | $0.005 | $36,135 |
| Total Annual Cost | 7,477,893 | $0.005 | $37,389 |
Data Storage Deep Dive
Calculating Storage Needs
Based on the volume of data ingested and processed from various sources (Service Cloud, Core Banking, Online Activity) to support the unified 360-degree view and insights, Aplan Financial Services will require roughly 2 TB of storage.
The estimated storage need of 2 TB for the first year is based on the substantial volume of ingested raw data, particularly the high-frequency transaction data and streaming activity. This estimate also includes storage for the 500k unified profiles, the results of daily and real-time calculated insights, and the necessary system overhead to support the platform's operations. This is a conservative estimate for the first year's data volume.
Summary Table for storage needs and costs
| Storage Needed (in TB) | Storage Multiplier | Unit | Unit of Multiplier | Cost per unit (monthly) | Total Cost (12 months) |
|---|---|---|---|---|---|
| 2 | 1 | TB of Storage | 1 | $23.00 | $552 |
| Total Annual Cost (year one) | $552 |
Total Year One Investment : $136,072
Aplan Financial Services invested $136k in Data Cloud based on the Consumption Credits and Data Storage needs to implement their Service Cloud use case:
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