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MuleSoft Accelerator for Financial Services

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Use case 5 - Optimize customer experiences with Data Cloud

Deliver personalized and tailored experiences to customers by leveraging a comprehensive single source of truth


With the emergence of more digital-first customers, the amount of real-time data captured by social media and web browsers has exploded. This solution enables financial institutions to leverage the power of APIs to quickly ingest data from both internal and external sources to create a detailed view of the customer and enable moment-based engagement for personalized customer experiences across multiple channels.

Use case description

This use case simplifies the ingress and egress model of Data Cloud for quicker delivery and value realization. The use case leverages data warehousing platforms like Snowflake, Amazon S3, Databricks and Salesforce Financial Services Cloud as data inputs into Data Cloud. Once this data is ingested, Data Cloud analyzes the data to develop segments of customers. Once the segments are activated, Marketing Cloud and Salesforce Financial Services Cloud consume the output of the Data Cloud activations.


Data CloudThe Salesforce Data Cloud helps clients collect and unify all of their customer data.
CIMThe Cloud Information Model for MuleSoft Accelerators defines a set of standard data structures that can be used as canonical representations of common entities for integrating systems.
FINSAbbreviated term referring to the Financial and Insurance industries, consisting of the Banking, Insurance, and Wealth Management domains.
FSCThe Salesforce Financial Services Cloud suite of applications. This includes Retail Banking, basic Insurance capabilities, and Wealth & Asset Management.

Before you begin

bulb.png The Getting Started with MuleSoft Accelerators guide provides general information on getting started with the accelerator components. This includes instructions on setting up your local workstation for configuring and deploying the applications.

API-led diagram

API-led diagram for optimize customer experiences with Data Cloud

High-level architecture

The following diagram represents the portion of the overall solution that pertains to this use case.

Architecture diagram for optimize customer experiences with Data Cloud


The goal of this solution is to provide support for ingesting data from multiple sources into Data Cloud and then using the results of Data Cloud activations to optimize customer experiences by providing Customer Service Representatives (CSRs) with marketing insights.

  • Ingest customer profiles from Salesforce FSC into Data Cloud
  • Ingest financial account and transaction data from Snowflake into Data Cloud
  • Ingest web engagement data for individuals from Databricks into Data Cloud
  • Ingest external financial account data from Amazon S3 into Data Cloud
  • Publish marketing messages to Salesforce FSC chatter feeds from segmentation data produced by Data Cloud

Use case considerations

  • Customer profiles are ingested into Data Cloud from Salesforce Financial Services Cloud using native integration.
  • Data Cloud APIs support both bulk and streaming options to ingest data.
  • Data Cloud publishes segments every 12 or 24 hours based on the configuration.
  • The unified customer profile in Data Cloud is based on the global identifier of the customer.

The segments created for this solution are described below:

Cross-sell segment

The cross-sell segment is created based on the following criteria to target a client to open a money market account:

  • Length of time as a client (minimum of 1 year) based on when the first account was opened
  • Client owns a savings account
  • Average daily balance greater than or equal to $3000 across savings and checking account types at the bank
  • Client does not already have a money marketing account at the financial institution

Upsell segment

The upsell segment will enable banks to identify a set of existing clients that will likely need a mortgage in the near future.

  • Length of time as a client (minimum of 3 years) based on when the first account was opened
  • Client owns a money market account
  • Average daily balance is greater than or equal to $10,000 across checking, savings and money market account types for both internal and external accounts
  • Web behavior - channel visit is over 20 minutes and main subject area is real estate

Functional considerations

  • Snowflake will function as a customer's Enterprise Data Warehouse (EDW)
  • Databricks will function as a data store for web engagements data
  • Amazon S3 will function as a data store for validated external financial accounts

Technical considerations

  • Profiles from Salesforce FSC will be ingested directly via a Data Cloud data stream
  • Custom Data Cloud ingestion APIs will be created for ingesting other data

Solution overview

The following sections describe the ingesting of data into Data Cloud (ingress) and the consumption of segmentation data produced by Data Cloud (egress).

Ingest data into Data Cloud

This section describes the detailed steps for data ingestion into Data Cloud using data streams and ingestion APIs.

Ingest data into Data Cloud using a data stream

A Data Cloud data stream is configured to regularly ingest data from Salesforce FSC. The steps are as follows:

  • Data Cloud activates a data stream configured to pull data from Salesforce FSC
  • New and updated customer profiles are retrieved from FSC
  • The profile data is mapped into custom objects in FSC
  • Data Cloud performs identity resolution to produce the unified profiles

Ingest data into Data Cloud using ingestion APIs

The following activity diagram describes the orchestration of ingesting data into Data Cloud using ingestion APIs. Here is a brief overview of the processing steps:

  • A scheduled process for polling financial data is triggered
  • The source system is polled for updated records
  • The records are transformed into canonical format
  • The records are pushed to Data Cloud via the ingestion API
  • Data Cloud performs identity resolution to link the data to the unified profiles

The same steps are repeated for polling web engagements data as well as external financial accounts data.

Data Cloud ingress activity diagram

Create marketing insights from segmentation data

The segmentation data produced by Data Cloud activations is used to generate messages for a Customer Service Representative (CSR) to be used for marketing purposes.

Push segmentation results to Amazon S3

A Data Cloud activation pushes segmentation results to an Amazon S3 bucket

Data Cloud egress activity diagram to S3

Generate cross-sell insight messages

  • A Process API receives notification of the bucket event and retrieves the data
  • The data is used to generate cross-sell insight messages for individual customers
  • The Process API publishes the messages to the chatter feeds for those accounts
Data Cloud egress activity diagram to Mule

Downloadable assets

FINS System APIs

FINS Process APIs

Custom components

Shared APIs (can be used across any use case)

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Published by
MuleSoft Solutions
Published onMar 26, 2024
Contact nameMuleSoft Solutions
Asset overview

Asset versions for 1.11.x

Asset versions


Financial ServicesNo values left to add
Industry Vertical
Financial ServicesBankingNo values left to add