Bad customer data – databases where customer data is inaccurate, incomplete and inconsistent – causes huge issues for financial institutions.
Accurate customer data informs effective customer communications and personalisation. Without it, communications quickly become ineffective, even detrimental to the customer experience, damaging brand reputation and driving customer churn.
Effective decision making when it comes to the creation of new products and services is also negatively impacted by incorrect data.
In fact, any AI powered initiatives that those in financial services undertake are fed by the quality of the data the AI is accessing. So, for those using or considering using AI tools to deliver better customisation to attract and keep clients it’s vital
that the underlying data is verified, or expensive and embarrassing mistakes may be made which will not forge loyalty.
Also, it’s not possible to be compliant with KYC and AML regulations with wrong and incomplete customer contact data. And in an age of increasing fraud this could lead a growth in such activity, along with the potential for significant fines from regulators
and the associated reputation issues this will cause.
It’s why those with clean customer datasets will benefit from the insight they provide, placing them in pole position to create services customers want, and drive business performance in a highly competitive financial marketplace, while aiding fraud prevention.
Tackling bad data and driving business performance necessitates:
- Recognising that data changes and decays over time: Customer data decays on average at three per cent a month and roughly 25 per cent a year, according to Gartner, as people move home, divorce or pass away. It means data cleaning should
not take place once a year, but on an ongoing basis. Having data cleaning processes in place, not only at the customer onboarding stage, but to clean held user data in batch is the best practice approach.
- Define data quality standards: To be considered accurate, complete, consistent, reliable, valuable and meet global compliance regulations, set defined criteria and best practice guidelines for the data to meet. This is vital because data
quality standards help to ensure that data is reliable and trustworthy, which in turn builds confidence in its use – supporting effective decision-making.
- Undertake data profiling: Doing so enables financial institutions to assess data with a combination of business rules, tools and algorithms to create a report on the condition of the data. This activity is aimed at discovering data types,
recurring patterns, inconsistencies, inaccuracies and gaps in the records, as well as uncovering the structure and relationships between data sources. The reports should be in the form of graphs and tables that help visualise the data condition so that the
source of any issue can be easily identified and corrected. As well as providing more confidence in the data, profiling encourages financial institutions to use it for effective decision making.
- Use an address lookup tool: To collect correct contact data seamlessly at the customer onboarding stage a good place to start is to employ an address lookup or autocomplete service. These technologies ensure accurate address data is obtained
in real-time by providing a properly formatted, correct address when the user begins to input theirs. This is critical when 20 per cent of addresses entered online contain mistakes. Furthermore, the number of keystrokes required when entering an address is
reduced by up to 81 per cent – a key benefit. This results in a faster onboarding process that lessens the probability of the user not completing an application or purchase. The good news is the first point of contact verification can be extended to email,
phone and name, so this valuable contact data can also be verified in real-time. This data further supports the wider ID verification process, and ongoing accurate and quick personalised contact with customers.
- Eradicate duplicate data: Duplicate rates of 10 to 30 per cent on customer databases are not uncommon at many organisations. This data adds cost in terms of time and money, particularly with printed communications and online outreach campaigns.
It can also have a negative impact on the sender’s reputation. The solution is to use an advanced fuzzy matching tool to merge and purge the most challenging records. This results in the creation of a ‘single user record’, which helps to deliver a single customer
view (SCV). The insight from this can be used to improve customer communications. Because multiple communication efforts will not be delivered to the same person efficiency savings are made, and the probability of fraud is reduced with a unified record established
for each user.
- Undertake data suppression / cleansing: Embarking on data cleansing or suppression activity to highlight those who have moved or are no longer at the address on file is very important. These services, as well as removing incorrect addresses,
can include deceased flagging to stop the delivery of mail and other communications to those who have passed away, which can cause anguish to their relatives and friends. Using suppression strategies ensures that those in financial services save money by not
distributing inaccurate messaging, safeguarding their reputations, while enhancing their targeting efforts to overall improve the user experience. Using the National Change of Address (NCOA) database against held customer data is useful because it highlights
those who have moved or passed away. By having fast access to the new addresses of customers who have changed residence banks will be able to maintain a consistent positive customer experience, while improving operational efficiency.
- Source a data cleaning platform: It has never been more straightforward to
deliver data quality in real-time to support improved decision making, enhance the user experience and overall generate wider organisational efficiencies. It’s simple to source a scalable, cost effective data cleaning software-as-a-service (SaaS) platform
that can be accessed in a matter of hours and doesn’t require coding, integration or training. Using this technology it’s possible to cleanse and correct names, addresses, email addresses, and telephone numbers worldwide. Records are matched, ensuring no duplication,
and data profiling is provided to help identify issues for further action. A single, intuitive interface offers the opportunity for data standardisation, validation and enrichment, which ensures high-quality contact information across multiple databases. This
can be provided as new data is being collected and with held data in batch. Such a platform can be deployed via cloud-based API, connector technology like Microsoft SQL Server, and on-premise, as well as SaaS.
To effectively drive business performance, and remain compliant with KYC and AML regulations, it’s vital financial institutions recognise the numerous issues caused by bad customer data, and follow the seven steps outlined in this piece to ensure their customer
databases are accurate and up to date.
