Casestudy
Data engineering solution in Salesforce for a SaaS product giant
Single Source of Truth (SSOT) for Entitlement Process
Business Problem
A global SaaS giant sells a variety of software products in Human Capital Management area, to both medium and enterprise customers. The company uses Salesforce for customer relationship management (CRM). The CRM contains multiple sources of information about customer entitlements, including contracts, invoices, and support agreements.
With time, as the company grew, they have acquired other smaller companies in the business space they are operating. These acquisitions brought in different CRMs as well.
Moreover, before enterprise-wide Salesforce CRM adoption, there were some bespoke and discrete systems used to manage customer entitlements.
Another challenge is that the entitlements are created and updated by RevOps team which does not typically talk to the Customer experience team. At times both the team updates Entitlements.
This resulted in confusion and inconsistency when trying to determine what products and services a customer was entitled to. It also requires additional resources and processes to keep Entitlement, Agreement etc in-sync between the two teams. The company wanted to establish a single source of truth for customer entitlements to improve efficiency, accuracy and reduce manual operational cost.
Solution
To address this issue, the company leveraged the Data Engineering reference solution Architecture developed by Lirik and we implemented a new Entitlement Single Source of Truth (SSOT) system for Entitlement with a centralized database for storing and managing customer entitlements.
The database includes all relevant information about customer entitlements, including contract details, product licenses, and support agreements.
The SSOT system includes 4 layers viz.
- Input Tier to create Data Pipeline from various Source Systems
- Business Process Tier to Validate the Source Data, create Entitlement SSOT by combining relevant data objects like product, account, Line item etc. There is certification and generate exception data.
- Database Tier to store SSOT, Exception as well as Validation rules set by business teams.
- Data Services Tier to serve the Entitlement SSOT to different Data Consumer systems for RevOps, Customer experience, Marketing and Data Science areas
It also has user interface that allows customer service representatives to easily access and update customer entitlement information as needed. And it gives flexibility to RevOps team to correct the exception records.
Technology and Architecture
The core architecture revolves around MongoDB Atlas which was used not only as Database tier but we utilised its services and Node.JS engine to develop Business Tier functions for Data Certification, Exception Engine and Data services.
For Data Pipeline Kafka was used to interface with various source systems and create input stream for MongoDB Atlas. An Interface was built using React.JS for Business Users to view certified SSOT records and correction of data. For developing Data Services GraphQL was used.
Results:
The implementation of the new Entitlement SSOT system has resulted in
- Cost Saving : Manual efforts is reduced to keep Entitlement, Agreement etc in-sync between multiple teams in RevOPS and Customer Experience.
- Improved efficiency and accuracy in managing customer entitlements. The centralized database allows customer service representatives to quickly and easily access complete and up-to-date information about a customer's entitlements, reducing the time spent on manual research and data entry. The user interface has also made it easier for customer service representatives to update and maintain customer entitlement information, improving the accuracy of the data.
Overall, the company has seen a 15% reduction in the time spent on entitlement-related tasks and a 10% improvement in customer satisfaction due to the increased accuracy and efficiency of the process.
The global SaaS company faced inconsistency in customer entitlements due to multiple CRMs, acquisitions, and Experience teams. They sought a single source of truth to enhance efficiency, accuracy, and reduce costs.
reduced manual efforts, increased efficiency and accuracy
decrease in entitlement related tasks
boost in customer satisfaction.
Conclusion:
The investment in Single Source of Truth (SSOT) systems with a Data Engineering solution helps in improving the efficiency and accuracy of Company's customer relationship management process, where multiple set of users update and use the data simultaneously.
The centralized database and user interface for a single source of truth reduces the time spent on manual research and data entry and improving the accuracy of the data. This results in improved customer satisfaction and cost savings for the company.