Data Quality: The New Power in Digital Banking

Melissa AU Team | Data Quality | , , ,

As the pandemic locked down cities, like other businesses, banking and financial institutions too had to find digital ways to connect with customers. In the first half of 2020, the volume of banking transactions and digital payments increased by 10% and 21% respectively. Having a glitchy web and app interface could now put the institution’s reputation and customer loyalty at risk. Along with technological upgrades, financial institutions also recognized the need for an enhanced customer experience and, in turn, better quality data.

The Relationship Between Data And Customer Experience

Today, it isn’t product differentiation and pricing that determines customer loyalty as much as it is the experience offered to customers. In the digital world, this means that customers expect prompt responses, they want personalized services and they want their preferences to be remembered. This is where the quality of data becomes important. A customer would not be pleased if a customer service agent was not able to call him the right name based on his customer ID!

When it comes to banking and finance, safety is another high priority for customers. If the customer service agent cannot get the customer’s name right, it’s safe to assume that the customer would not be very confident about how safe his data is. Security too is based on data quality. If all the data in your database meets high-quality standards, it would be accurate, valid and unique. Thus, there’s no risk of duplication and a much lower chance of fraudsters hacking into the system.

Measuring Data Quality

Collecting data is easy. Making sure it meets quality standards is the more difficult part. According to a Harvard Business Review, only 3% of the data collected by companies meets basic data quality standards.

Dirty data or poor quality data is inaccurate, outdated, incomplete or simply inconsistent with the rest of the database. On the other hand, good quality data is accurate, complete, consistent, valid, unique and formatted according to set standards.

How Does Better Quality Data Empower Better Customer Experiences?

How customers feel about a banking or financial institution impacts their ability to maintain long-term relationships. Let’s look at some of the ways good quality data can improve customer experiences.

Trend Spotting For Personalized Service

Today, it isn’t enough to meet customer expectations, businesses must exceed them. You need to know what your customer wants before they ask for it. Good quality data helps create a wholesome persona for every customer. The details are complete and there’s no duplication, hence, no confusion. If a customer is looking for a car loan, it’s safe to say he/ she will also need insurance. Hence, your campaigns can be directed towards loans and insurance offerings. Similarly, if a customer’s income shows a marked increase, you can market investment schemes to him/her. Offering such proactive personalized services will make the customer feel valued and maintain their loyalty.

Balance Security With A Seamless Customer Experience

Data can play a very helpful role in spotting trends and identifying a customer’s behavior pattern. You know a customer usually operates his/her account from a web interface. Good quality data can also help you assess the average value of online transactions. Now, if there’s any sudden activity that does not conform to this pattern, an alert may be sounded. For example, the account may be accessed from a mobile phone in a different city or an unnaturally high amount may be credited or debited from the account.

If your data is reliable, these alerts may help detect fraudulent activities early. But, if the data is unreliable, you may call the customer or halt transactions unnecessarily thereby inconveniencing them.

Stay Connected With Targeted Campaigns

By now, it’s been well proven that a one-size-fits-all approach is not very effective for marketing campaigns. The audience must be segmented and campaigns must then be designed to target the specific needs of each segment.

Now, if you’re relying on poor quality data, segmentation may not be correct and you may not understand the needs of your customers. For example, a person who’s just started working may not be interested in a home loan but he may be looking for a vehicle loan.

Accurate knowledge of customer demographics can help you place such customers in the right category and target them accordingly. When your campaigns are relevant, customers are likely to engage more with the institution.

Consistent Experience Across Channels

Today, customers can interact with banks by visiting a branch, through the web interface, through an app or through telebanking. What they need is a consistent customer experience irrespective of the channel of communication. It doesn’t bode well for a banking institution if the customer has a different account for the app and a different one for the web interface. Having dual accounts is not only a fraud risk but also means that the customer does not get the same experience through both channels.

For example, let’s take a case wherein a customer opened an account with his phone number as a reference on the app and his email as a reference on the website. In both cases, supplementary data would also have been collected at the time of opening the account.

In the first case, this would have included an email address and in the second, it would have included a phone number. If the data was properly formatted, it would be recognized as the same account but if it were not properly formatted, a duplicate account may get created.

Thus, it is only when you can verify and validate data to be accurate and unique that you can offer consistent experiences across channels.

In Conclusion

Improving customer experiences is proving to be the defining point for customer loyalty and brand growth. The quality of customer experience one can offer depends on the quality of data held. Hence, it is important to use data verification tools to filter poor quality data out before it can enter the database and to ensure that the data held is updated and valid.