CRM Software Data Quality Best Practices
- Bad data directly contributes to business process inefficiencies, inaccurate reporting, lower staff productivity, inferior customer engagement, missing insights and loss of user trust in the CRM system.
- When trust is eroded in CRM data, user adoption will fall until the CRM system becomes useless.
- According to Gartner report titled, Measuring the Business Value of Data Quality, "Data quality effects overall labor productivity by as much as a 20%."
The Customer Relationship Management Data Problem
Customer data can be a company's most valuable asset. However, customer data decays at 2 to 5 percent per month so without a CRM data maintenance program that asset can quickly become a liability.
A study by Sirius Decisions rendered what is now the well cited 1-10-100 rule which states that it takes $1 to verify a CRM record when it is initially entered, $10 to clean it later, and $100 if nothing is done (as ramifications of the mistakes are repeated multiple times.)
More than just an escalating cost, inaccurate CRM data contributes to misinformed decisions, invalidates reporting, creates manual interventions and leaves users frustrated. Once the CRM system is recognized for inaccurate, incomplete, obsolete, duplicate or out-of-date data users will stop using the application.
Any downstream business initiatives such precision marketing campaigns, artificial Intelligence, digital transformation or big data are doomed from the start without quality data. Bad data in. Bad data out.
CRM Data Quality Best Practices
To make data the fuel that accelerates business process effectiveness, customer insights, information reporting and data-driven decision making we pulled several CRM data best practices from our executive team and consulting staff.
Here's a few.
Start with a measurable business case. Executives must understand the true cost of poor data quality. Unless there is a measured problem that is deemed unacceptable management is not going to invest in a solution.
It takes a team. As with all technology projects executive sponsorship is required. Additional roles for CRM data projects include data owners, data stewards, data consumers and system administrators.
Data owners have the final say for how data is sourced, configured, used, enriched and archived. Data Stewards are responsible for data policy compliance and quality objectives. Data stewards ensure that data policy processes are understood and followed. Data consumers must understand why data quality policy and rules are needed and the negative impact if they don't do their part.
Users own the data they enter. CRM system administrators configure CRM software to enforce data entry rules, perform periodic data maintenance and make quality measures visible with reporting and dashboards. We often use RACI charts so everyone understands who is responsible for what.
Follow a proven process when assessing data quality. Reference the CRM Data Quality Program as an example.
Take a phased approach to data hygiene. Rather than assessing and improving every Lead, Customer and Contact record data field, meet with the users to identify which data are needed for specific business processes, and then start by assessing and improving just those high value fields. Below are some examples given to me by a Salesforce consultant.
Once you have identified the most essential data, consider further prioritization by first focusing only on your ideal customer profile (ICP) or high value customer segments. Most companies earn over 60 percent of margins from less than 20 percent of customers so prioritizing data quality for the 20 percent or less of high contribution customers makes financial sense.
Apply up to 8 dimensions when assessing data effectiveness:
- Accuracy: Data is without errors
- Completeness: Percentage of Lead, Account and Contact records with data values
- Value: Relative importance of each data element, normally designated by the data owner
- Utilization: Frequency of data reads and updates
- Consistency: Rate at which data is the same among shared systems
- Age: Time elapsed since date last modified
- Structure: Rate of proper data value standardization (i.e., formatting), naming conventions
- Duplication: Amount or rate of redundant Lead, Account and Contact records
All accounts should have a unique identifier. This may come from your master data management (MDM) system or you may use an industry recognized identifier such as DUNS number.
Create a data quality policy. Data is only as good as its entry to the system. A CRM data quality policy shows users how to standardize data so that they don't create the same record multiple times.
CRM system administrators can reduce poor data entry at the source with CRM software configuration. Administrators should replace text fields with dropdown list values where it makes sense, create data entry validation rules to enforce data policy standards and make essential data fields required.
CRM software security privileges and permissions can also aid data quality, but care must be taken not to impact staff productivity, collaboration or information sharing.
A data maintenance routine and cadence are needed to review, clean, dedupe, append and archive records. This type of program will include at least four steps.
- Standardize CRM data. In order to search, segment and report on Leads, Accounts and Contacts those records must have consistent naming conventions and uniform data. The most common variances occur due to slightly different spellings or formatting of data values. CRM software queries can be used to surface many of these errors and mass update tools can be used to fix them.
- Merge duplicates. Creating onscreen prompts or warning messages to flag duplicates at the point of entry reduces their occurrence. Periodic use of merge-purge tools in the CRM software resolves those that still get through.
- Enrich customer data with a third-party trusted source. Third party data providers validate your existing data, replace inaccurate data and append missing data. These services offer integration to the CRM system for real-time or periodic refreshes.
- Deprecate idle data. Unused data creates unnecessarily busy screens, clouds more relevant data and degrades the user experience. If you believe there's a potential for only occasional use of idle data, move it to a data lake. Otherwise, archive it.
CRM system administrators can make data quality measures visible to easily identify data quality progress and variances, prioritize data steward actions, and promote continuous data improvement.
Start by inserting a data quality score on each Lead, Account and Contact record so users easily see the data quality measure at the source. Then create a data management dashboard that shows quality scores for all records and allows drilldown to investigate further. Below is dashboard we created and routinely use in CRM implementations.