It often falls to software developers, engineers, customer support teams and even to customers to troubleshoot problems and ensure a smooth onboarding experience.This can lead to inefficiencies for customer success managers and implementation teams who may have to spend a lot of time and resources to data onboarding when they could be focusing on other core tasks. It can also be frustrating for customers because they may have to deal with vague error messages or spend time formatting their data to meet the requirements of the import process.

The consequences of a poor data onboarding experience can be significant. It can lead to reduced time-to-value for the customer, as they may have to spend more time and effort getting their data imported and may not be able to fully use the product or service until the process is complete. In addition, data onboarding challenges can damage the customer's trust in the company and can lead to an opportunity cost for customer support teams, as they may have to spend time addressing these issues instead of responding to other customer inquiries.

Results of a poor data onboarding experience:

Inefficiency for CSMs and implementation teams: Customer success managers and implementation teams may have to devote significant time and resources to data onboarding instead of focusing on more value-added tasks.

Reduced time-to-value for the customer: Customers may have to spend more time and effort getting their data imported, which can delay their ability to fully use the service and cause frustration.

Data onboarding job roles: Individuals with various titles, such as those in customer service or customer success roles, may be responsible for data onboarding. In a survey, 60% of respondents identified themselves as being in a "customer-facing" function.

Must-have features for data onboarding:

Parsing:

The process of parsing data involves taking a large amount of information and dividing it into smaller manageable pieces. This can be done through the use of tools that transform a file into an array of discrete data and make the process efficient for customers.

Structuring:

Proper data structuring is important for ensuring that data flows smoothly into an application database through an API. The data must be labeled appropriately and meet the expected format, or the API will not function properly.

Validation:

Data validation checks the data to ensure that it matches the required format or value. Implementing this feature can prevent issues from occurring later on and eliminate the need for customers to constantly fix and reupload data.

Transformation:

Data transformation involves making changes to data as it enters a system to ensure it meets the desired value. This process makes small, systematic adjustments to the data to make it usable, rather than returning it to the user with an error message.

Data Mapping:

Data mapping, also known as data matching, involves taking unknown starting data and matching it to a known target. It is essential that a data importer is able to do this effectively as imports may fail if data elements don’t match exactly.

Overall, it is important for banks and financial institutions to prioritize data onboarding and address the challenges it presents in order to create a positive onboarding experience for their customers and improve customer relationship.