Redefining Customer Experience In Banking & Financial Services Using Data And Analytics
Gone are the days when customers relied on a single bank to fulfil all their financial needs. Today, as financial products become increasingly commoditized, customers are quick to switch providers. In such a context, banks and financial services firms primarily rely on customer experience to differentiate themselves and improve customer loyalty.
Leading firms are tapping into valuable, but underleveraged data sources to better understand customers, identify gaps in experience, and deliver personalized experiences. However, this is easier said than done. Many face challenges with legacy systems, data siloes, accessing the right data, inability to generate actionable insights, and much more.
Here’s how analytics is helping leading firms overcome these challenges and effectively leverage data to drive better decisions around customer experience.
The rise of digital has made vast amounts of data available across multiple sources. This may be in the form of structured and unstructured data, such as transactional data, survey answers, social media posts, CRM platforms, financial filings, email exchanges, customer service logs and much more. Several firms are leveraging listening tools and technology platforms such as Qualtrics and UserTesting to access the right information needed to improve customer experience.
A good example is when Barclays launched its mobile app, they leveraged social listening tools to source data from user activity on social media. This not only helped them understand customer sentiment, but also make real-time adjustments to the app to meet customers expectations and needs.
After collecting data, the challenge lies in bringing data from multiple sources and channels together for further analysis. Finding relevant information from vast amounts of data is often time-consuming, unreliable, and costly. Leading firms are leveraging artificial intelligence (AI) to effectively synthesize data. AI technologies like Natural Language Processing (NLP) help review and uncover affinities, patterns, and themes from large amounts of data quickly.
Analysing data for actionability:
A common challenge for many firms is using synthesized data to connect the dots and drive actionability for a better customer experience. Here’s where advanced analytics techniques can help analyse the voice of customers through topic and sentiment analysis. These techniques help generate deeper, actionable insights to inform targeting, customer acquisition, customer retention, cross-selling, customer service and much more.
For example, analysing chatbot data can help identify issues faced by customers, such as ‘Failed money transfer’. A deep dive into this issue can provide actionability across various banking teams:
Fraud risk team: Investigate the failed transaction for transfer fraud
Product team: Check functionality of features for transferring money
Marketing team: Verify if there is a need for a campaign to educate customers on how to transfer money
Hence, by analysing data and enabling actionability, businesses can fix customer issues and deliver better experiences, which in this case is seamless money transfer.
In an increasingly competitive environment, banks and financial services firms need to make it worthwhile for customers to maintain long-lasting relationships with them. While everyone has access to vast amounts of data, the challenge is to not get lost in the data but to use it to your advantage. In the end, winners will be those who leverage data at their disposal to deliver truly differentiated experiences and de-commoditize themselves in the eyes of the customer.
The author of this article is Kartik Poduval, Assistant Vice President at Ugam, a Merkle company.