Barley Laing, the UK Managing Director at Melissa
Having bad customer data – databases where customer data is inaccurate, incomplete inconsistent – causes huge issues for those in financial services.
Access to accurate customer data informs effective customer communications and personalisation, so without it communications rapidly become ineffective, even detrimental to the customer experience, damaging brand reputation and driving customer churn.
Incorrect data also negatively impacts on an organisation’s decision making when it comes to the creation of new products and services. In a highly competitive financial marketplace it’s those with clean customer datasets that will quickly benefit from the insight they provide, leaving them in pole position to create services customers want, driving business performance.
Then of course there’s the issue of KYC and AML compliance. Wrong and incomplete customer contact data will cause compliance issues, and in an age of increasing fraud this could lead a growth in such activity, along with the potential for huge fines from regulators and the associated reputation issues this will cause.
To tackle bad data and drive business performance:
- Recognise that data changes and decays over time: According to Gartner, customer data decays on average at three per cent a month and roughly 25 per cent a year, as people move home, divorce or pass away. Therefore, data cleaning should take place on an ongoing basis. Not once a year. The best practice approach is to have data cleaning processes in place, not only at the customer onboarding stage, but to clean held user data in batch.
- Define data quality standards: Set defined criteria and best practice guidelines for the data to meet to be considered accurate, complete, consistent, reliable, valuable and meet global compliance regulations. This is so important 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: This 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 exercise 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. This provides more confidence in the data and encourages financial institutions to use it for effective decision making.
- Employ an address lookup tool: A good place to start to collect correct contact data seamlessly at the customer onboarding stage is to use an address lookup or autocomplete service. Such technologies make it possible to obtain accurate address data in real-time by providing a properly formatted, correct address when the user starts to input theirs. This is vital when 20 per cent of addresses inputted online contain mistakes. An additional benefit is the number of keystrokes required when entering an address is reduced by up to 81 per cent. The result is a faster onboarding process that reduces the probability of the user not completing an application or purchase. Additionally, 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 also supports the wider ID verification process, and ongoing accurate and quick personalised contact with customers.
- Remove duplicate data: In many organisations duplicate rates of 10 to 30 per cent on user databases are not uncommon. Such data adds cost in terms of time and money, particularly with printed communications and online outreach campaigns, and it can have a negative impact on the sender’s reputation. The answer is to use an advanced fuzzy matching tool to merge and purge the most challenging records. The result is the creation of a ‘single user record’, which helps to deliver a single customer view (SCV), with the insight from this used to improve communications. Efficiency savings are made because multiple communication efforts will not be delivered to the same person. Furthermore, the likelihood 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. Along with removing incorrect addresses, these services can include deceased flagging to prevent the delivery of mail and other communications to those who have passed away, which can cause distress to their friends and relatives. 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. Ideally use the National Change of Address (NCOA) database against held customer data, because it highlights those who have moved or passed away. By having quick 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: Delivering data quality in real-time to support improved decision making, enhance the user experience and overall generate wider organisational efficiencies, has never been more straightforward. 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 can be simply sourced. This technology can 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 delivered as new data is being gathered and with held data in batch. As well as SaaS, such a platform can be deployed via cloud-based API, connector technology like Microsoft SQL Server, and on-premise.
For financial institutions to effectively drive business performance, and remain compliant with KYC and AML regulation, it’s vital they recognise the numerous issues caused by bad customer data. Following the seven steps outlined in this piece will help ensure their customer databases are accurate and up to date.
