Most companies would acknowledge that quality customer data is important.
In most cases, however, customer data quality is not good enough.
Changing business requirements
Most businesses have a history.
Customer data may have been accumulated over years, or decades.
Data that was captured in the past may not have complied with the requirements of the modern business. Two simple examples:
Enhanced customer analytics
Modern, data driven marketing and product development is driving interest in our customer that goes beyond the basic need to know data points of years ago.
Our marketing department may want to know about our customer’s interests, employment history or education (for example) at a level of detail that may not have been catered for in the past.
New channels of communication, and social media outlets, also generate data points that earlier data sets could not possibly have catered for in the past, as they did not exist., or were not relevant.
A focus on customer segmentation or experience may find that existing data does not cater for these new requirements, Data may be adequate for operations, but is of poor quality for analysis.
The race toward artificial intelligence and machine learning applications to drive better decision making, and even to advise clients, is particularly impacted – with more and more acknowledgement that data quality is essential for AI
Highly regulated industries – think financial services – are used to navigating a minefield of data related compliance.
Over the last few years regulations and standards such as BCBS 239, AML and FATCA have forced banks and insurance companies to learn more about their clients. Existing data may not have been sufficient.
Regulations drive new data standards that may not have been catered or in the past. Historically accurate data may fail data quality checks when measured against these new requirements.
Growing focus on customer privacy and the ethical use of data means that every industry is becoming highly regulated. Do your systems allow you to track and measure customer’s needs with respect to regulations such as GDPR and PoPIA?
Customer data degrades at a rate estimated at +- 2% per month.
People changes jobs, move house, get married, have children, etc.
Over 1 year this means that +- 30% of your contacts and leads have lost quality – making them harder to contact, harder to support, and harder to understand.
What is the impact on your business if you recognize that one in three customers, leads or accounts data are no longer relevant.
Poor capture standards
Of course, both the above examples assume that data was captured correctly in the first place.
This is often not the case.
The race to digital is often seen as a means to improve efficiency and accuracy of data capture. Yet without attention to data, digital may not deliver quality.
We need to understand that our digital strategy must plan for quality. if quality customer data is indeed our desired
Data quality is the missing piece of the customer puzzle
Customer data quality controls can help to ensure qualify data is captured correctly the first time, and is maintained over time. More on this next week.