Keep it Simple to Increase CRM Adoption and Improve Data Quality

Many organizations recognize the value of customer relationship management (CRM) software and understand, at a high-level, the benefits it can bring to an organization. Besides basic contact management functions, modern CRM systems like Microsoft Dynamics CRM have service and support modules, can deliver sophisticated marketing campaigns, and have powerful reporting capabilities. With all these features and functions comes a level of complexity that, if unmanaged, may overwhelm users, which often results in low CRM adoption.

For organizations grappling with a bloated CRM environment, complaints of “confusing,” “hard to navigate,” or “too many fields” may be familiar. For those considering implementing CRM, an overly complex environment risks hurting adoption, negatively impacting data quality, and limits the ability to leverage the analytic capabilities of the system.

Begin by understanding what data you really need today. Avoid getting bogged down in theoretical discussions about what data you will need to support future-oriented analysis, you can cross that bridge later. Define the criteria you will use to segment leads, contact and accounts. Consider how you want to categorize Opportunities, Cases or Products for reporting or management purposes.  If you are using phrases like “may want” or “could”, when defining data requirements, then you really don’t need the data; it’s just nice to have.

Once you’ve identified what data is needed, then it’s a relatively straightforward process to audit the system, and streamline CRM interfaces so that business-critical data is front and centre. Remove or hide fields that are not needed. Ensure that fields that are likely to be updated (address, phone, email, etc.) are noticeable to users when they open a record.

Be mindful of who will be creating and / or updating records, and how. An interface that has too many fields, or requires navigation, may make it challenging for users to quickly enter records, especially when using a mobile device. Avoid the temptation to apply restrictive business logic or validation rules, such as requiring that a company’s website address be entered before saving an account record. When users have the ability to save and continue, and complete data entry at a later time, better software adoption practices are reinforced.

Develop a common data language within your organization and share it with your end users.  This often takes the form of data standards or a data governance plan and may require embarking on a data cleansing initiative.

Think that’s overkill for your company? Not so. Imagine how frustrating it would be if you tried grouping contacts by province when users have been entering Alberta as: ‘Alta’, ‘AB’, ‘Alberta’…or forgetting it altogether! Data standards ensure that users create and manage data in a consistent fashion.  Identify ‘Data Stewards’ who will own aspects of your organization’s data and be champions of data quality.

Empower your Data Stewards to monitor the data they are responsible for, so they can correct erroneous data in a timely fashion. Ask them to track and measure the frequency and nature of the errors. Analyzing the logs will give you insights as to where users make errors or miss entering critical data.

Only then, based on behavioural evidence, should you develop business rules or add restrictive validation logic to prevent errors and enforce data standards.