Customer relationship management (CRM) is a crucial for managing your company’s relationships and interactions with existing and potential customers. 

CRM plays a crucial for Retail Industries. Also, many industries outsource this phase of data entry job to BPOs. 

BPOs have to do the entire operation of CRM Data entry and integration to other applications over CITRIX. RPA is best suited for such activities, as it uses Computer Vision technology to automate CITRIX.

We need detailed and accurate data in order to serve customers well. So CRM systems frequently need to integrate with other systems like ERP, PLM or any industry-specific systems for data. Data flows back and forth between the systems to keep different parts of the business connected.


While vast volumes of data entry can take thousands of person-hours, accuracy is still not guaranteed. Though these are routine tasks, they are complex and require a lot of time and high cost. In some organisations, data needs to be transferred to other systems for immediate processing. Sometimes missed data or late transfer may lead to problems when done manually. In some cases, data needs to be migrated based on conditions. If something were to go wrong during data migration, it would be hard to recover from the losses. There might be many migration tools in the market, but unfortunately, they work only with similar kinds of CRM’s.


Robotic Process Automation (RPA) is used as a data migration tool because highly structured, rules-based migration activities fit what robots do best. Robots can mimic fundamental extract, transform, load (ETL) data migration methodology.

Extract Design: well-defined requirements about how data will be extracted, held, and verified.

  • Transform: solution design rules guide data transformation for the targeted to-be data structure.
  • Load: The data is introduced into the new system using custom interfaces. Finally, it is ensured that migration has been successful and complete

RPA tools with API functionality can integrate applications that don’t have native integrations. Through stable backend automation, RPA can push and pull data from CRM and other systems. Critical and high-volume initiatives are top use cases for using RPA for CRM data entry. The right RPA tool can handle any system or application.

Use case example

An organisation needs data to be moved from CRM to PLM(Process Lifecycle Management). It uses Hubspot CRM for maintaining prospect leads. The leads are segregated based on the Hubspot score by defining the conditions. These leads are moved to PLM; if the leads are not present in the PLM system, a new contact is created, or if a lead already exists, then the contact is updated. This process has to trigger as and when a new lead arrives in Hubspot. The integration of CRM and PLM data helps manufacturers in an organisation to keep a fresh pipeline of new product ideas moving, growing, nurturing and transforming the pipeline into profit.

In this case, RPA Bots mimics human activities such as collecting/extracting data from CRM, migrating data to Enterprise systems PLM with pre-defined process rules. During the migration of data, it reads key information and loads into PLM systems and updates PLM objects data as per the pre-defined rules. If there are legacy scanned documents or drawings available at some physical or shared drive, these BOTS can help read and extract data from such documents. The processes can be scheduled either by automatic or interactive processing.


Benefits of Automated Data Migration Services

Benefits of Automated Data Migration Services

  • Increased Productivity to allow staff to focus on higher-value activities
  • Improved processes to stay ahead of competitors
  • Faster processing – robots can work 24/7, 365 days a year
  • Reduced Manual Processes – Save employee time on Data entry
  • Less administration time and interaction with customers
  • Robots work faster and provide a consistent rules-based approach


  • With efficient process modelling, automation architecture, and preparation, RPA has proven to minimize data migration time by 50% and costs by 40%