Customer Experience
Aug 16, 2021

The retail banking revolution: 

How data analytics is improving customer experience in an omnichannel environment

Social distancing. Work-from-home. Contactless purchases. These and other lifestyle changes arising out of the pandemic have created an unexpected opportunity for retail banks to accelerate their digital transformation.

Banks must be able to respond to and capitalize on this opportunity quickly. But before they can effectively address such dynamic developments, they first need a holistic view of the new customer journey across all channels. And that's only possible if robust analytics frameworks are part of the underlying digital architecture.

What does omnichannel mean for customers?

For customers, an omnichannel experience means being able to seamlessly transition between channels – without losing context – and enjoying a consistent experience across channels.

For example, imagine a customer who has previously registered for autopay with their bank. They receive a text message on their mobile phone, alerting them that their credit card bill is past due. To check on things, they log into the bank's mobile application. They can't figure out why the payment wasn't made in a timely manner. So, they request support from a virtual agent. The virtual agent tells them that there were some issues with fund clearance and helps them complete the payment. For the customer, this means avoiding a late payment penalty, which enhances customer satisfaction (figure 1).

Figure 1: An omnichannel environment means customers can seamlessly transition between channels

What does omnichannel mean for banks?

For the bank, this means the customer has used three distinct channels – mobile phone, mobile application, and virtual assistant – all of which generate useful customer data.

The greater the number of channels a customer uses on a particular journey, the higher the volume of customer interactions. The more customer interactions, the more data that's generated.

Eliciting the right insights from this data is critical. For example, if a customer tries to pay their credit card bill through a mobile application but fails and then retries through the web and succeeds, the customer's ability to switch between channels has improved their experience.

But is switching between channels always a good thing? How can a bank find out, especially when there are millions of transactions happening every second? And how can a bank differentiate between a customer who switches channels due to a channel failure and one who is simply trying to start a new transaction in a different channel?

Three steps to data-led transformation

The answers to all these questions lie in data-led transformation, which is at the heart of the retail banking revolution and is the cornerstone of omnichannel customer experience in retail banking.

There are three steps to successful data-led transformation:

1) Establish the analytics framework

First, the bank must establish its analytics framework. This involves defining the time it should take for a customer to complete a particular workflow activity, such as paying a credit card bill.

Then, the bank must identify the point in time at which it would consider this same activity to have been a success, a failure, or abandoned by the customer. Finally, the bank must tag each transaction with a unique customer ID, timestamp, workflow activity, and channel (figure 2).

With such a framework in place, the bank can determine both failure rates and channel-switching rates. On the other hand, if the digital architecture doesn't take these factors into account, the bank has no way of determining whether there is a channel breakage. This means the bank can't measure the customer experience in an omnichannel environment, let alone develop strategies to improve it.

Figure 2: Measuring and improving customer experience in an omnichannel environment requires an analytics framework

2) Clean and transform the data

The next step is fixing or removing inaccurate, corrupted, incorrectly formatted, duplicate, or incomplete data and ensuring the data is in the proper format. Wrong data and wrong data formats can lead a bank to wrong conclusions and business decisions, especially if huge quantities of big data are at play.

Multiple, interconnected channels generate vast amounts of data. There will be a mix of structured data (such as clickstream data from the web channel) and unstructured data (such as speech transcripts from support calls).

So, the bank's infrastructure should be big data ready. This means that even before a bank can apply the analytics model it's developed, it first needs to clean up and transform these raw datasets.

3) Apply the framework and link it to key performance indicators (KPIs)

Lastly, banks should apply the framework they've developed to their big data architecture and link it back to their KPIs. For example, a bank's success criteria may include the rate of digital adoption, customer experience as gauged by its Net Promoter Score, the cost to serve, or the customer churn rate (figure 3).

Additionally, the framework should synthesize the insights from structured and unstructured data to identify points of high customer effort and process inefficiencies so that the bank can address them. Analytical models can use machine learning algorithms to predict customer behavior and promote proactive customer engagement.

Figure 3: Applying a framework to big-data architecture and linking it to KPIs is important for retail banks

About the authors

Max Feil

Max Feil

Head of customer support for consumer banking

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Raj Kumar Subramanian

Raj Kumar Subramanian

Customer analytics practice leader for consumer banking

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Mohan Raj

Mohan Raj

Customer analytics solution architect for consumer banking

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