Financial Services
Data analytics driving super personalized customer experience for banks
Customers across all sectors wish for a personalised experience. The banking industry is no different. Offering a personalised customer experience requires banks to understand their customers' needs and pain points and provide solutions to address them.
What is a personalised customer experience for banks?
Customers choose the banking services of a bank that provides them value for their investment. They prefer banking services that address their specific needs instead of offering generic services. Customer services offered by the bank have a great impact on how clients perceive the bank. Customers like banking services that solve their problems promptly and interact with them with politeness. Another important aspect of customer experience is their digital interactions with the bank. Banking customers do not like complicated and time-consuming paperwork. Customers expect banking professionals to provide services with minimal wait times. Further, banking customers prefer to work with banks that take cybersecurity measures to secure their personal data.
While these are generalised expectations of banking customers, banking professionals need to dig deeper into the specific needs of their customers in order to be able to tailor their services accordingly.
Offering bank customers a personalised customer experience has become a vital factor for success because of the availability of multiple options to customers. It makes customers feel seen, heard and understood and helps retain customers. It also helps attract new customers as the existing clients become brand ambassadors for the bank when they share their experience with others.
Data analytics and automation technologies enhance the quality of services offered and help personalise the customer experience. Robotic process automation (RPA) technology automates several rule-based processes to boost their speed and accuracy. It, therefore, reduces wait times and rework caused by inaccuracies.
Technologies like artificial intelligence (AI) boost customer experience in multiple ways. AI-based chatbots are available 24/7 to address customer queries. Machine learning (ML) algorithms predict future trends based on existing data. This helps to address issues and enhance customer experience. Natural language processing (NLP) technology provides sentiment analysis, which offers a better understanding of customers to banking professionals.
At the core of all these technologies lies data. All the technologies need to be trained with the help of data. Data analysis is a critical aspect of intelligent automation.
Role of data analytics in banking
Data analytics refers to the analysis of raw data to make inferences from that data. Raw data comes from multiple sources and may be structured or unstructured. Data analytics in banking includes customer analytics, business analytics, predictive analytics, and more.
Data analytics in banking helps in the following ways:
- Helps banking teams understand their customers better
- Optimises existing processes for better outcomes
- Builds predictive models
- Forecasts growth opportunities
Data analytics facilitates personalised customer experience
How does data analytics help banks build personalised customer experiences? Here are the details.
Provides an accurate picture of customers
Data analytics provides detailed information about bank customers, such as which banking products they are using, what motivates them, what matters to them, etc. Sentiment analysis tools help banks understand how customers feel about their services.
Data analytics provides insights into the specific needs of customers instead of generic information. All these insights help banks develop personalised sales and marketing strategies that deliver the right product/service to the right customer at the right time.
Helps banks attend to customers promptly
As service providers, banks need to value the time of their customers. Hence, banks must connect with them where they are instead of expecting them to seek banks out. Also, banks must ensure that they have a consolidated source of data that provides all the information about the customer. This data should be easily accessible to banking professionals whenever required. Data analytics facilitates secure storage of data with easy access. By providing specific details about customers, it helps banks connect with their customers at the right time in the right place.
Identify gaps
Data analytics provides information about churn rates. This data helps banks identify the areas of improvement in customer interactions. Banks can refine their marketing strategies accordingly and enhance customer experience.
Tailor unique customer experiences
Customer analytics helps banks segment their customers based on their creditworthiness and thereby improve credit risk management. Customer segmentation also facilitates offering the best products/services as per the specific needs of the customers.
Mitigate risks
Predictive analytics minimises the risk of fraud by analysing customer behaviour and sending alerts in case of anomalous behaviour. The automated system notifies the customer so that they can take action to protect their account.
In a nutshell, data analytics is the most effective strategy for banks to provide personalised customer experience. This is because data analytics in banking not only provides banks with valuable insights about customers, but it also helps create a secure environment with seamless banking transactions.
