Let me ask you a simple question. What is the most difficult task for a bank? …..Acquiring new customers? … Retaining customers? …. Growing share of customer wallet? No. I would say being customer centric is the most difficult task for any bank. But why being customer centric is very important? The answer is simple. A customer who is better engaged is easy to retain, easy to acquire and easy to do business with him.
The next question is how can a bank be more customer centric? It is not as easy as you think. That is where the importance of big data analytics comes in to the picture. Banks need to exploit the big data of customers to derive value for their customers. The equation is very simple. Big data + Analytics = Big Opportunities.
Just imagine a scenario where I use my credit card to buy some stuff in a shop. Now my bank gets to know data about me like what I bought, how much I spent, when did I spend, where did I spend etc., Like this, bank offers a number of services and it has millions of customers, ultimately resulting in billions of data simply getting piled up in bank’s database. This huge amount of data is called big data. So the big challenge for the bank here is proper utilization of these big data and make sense out of these customer information so that they can be more customer centric.
Sadly many banks are unable to exploit big data because of various reasons. Data about customers typically gets stored in silos and there is no single common server of database to get 360 degree view of customers. Classic example for this case would be when Deutsche bank attempted to implement a big data project they faced problems in extracting data from legacy systems.
Also the time associated with implementing a data analytics project is considerably huge. Almost 75% of banks do not have sufficient talent for data analytics. More importantly the lack of interest shown by top management is also one of the biggest challenges for big data analytics. They need to come out of their traditional approach and think about new technology. Last but very important of all is unstructured data problem. Let me explain this in detail as this is where many banks finding it difficult to overcome.
When we talk about big data, we first need to discuss about the types of data i.e, Structured and Unstructured data. Structured data is data that can be easily organized. For instance, any data that is entered into a computer like age, gender etc. in prescribed format is a structured data. Unstructured data is data that does not follow a specified format for big data. These are very difficult to analyse and this constitutes about 90% of the total data. For instance, Let us assume that you are not happy with your bank’s services and you want to give them a feedback about the same.
Since you are more active in social media, you will always prefer to do it in Facebook or Twitter rather than following the feedback format provided by the bank in their website. Let us assume that you are giving your feedback in the bank’s official Facebook page and you give your feedback as comments like “I am not happy with your credit card service. I am receiving offers which are always expired and irrelevant to me. Please look into it.” Now this is given entirely in text format so it becomes very difficult to analyse it and most of the time it simply gets dumped in the database. This is one of the main reasons why your feedback is not considered by your bank and in turn they are unable to provide you a pleasant banking experience.
The conversion of unstructured data into meaningful data is very important to derive the solution. This can be done by some new technologies like natural language processing, text mining, stochastic based algorithm etc. The ‘raw’ text, which is typically in the form of tweets, Facebook comments and emails, is analysed by converting the text into words or phrases. These words or phrases are categorized as ‘good’ or ‘bad’ to analyse the mood of the customer. For instance the word “good” gets a “+1” and the word “bad” gets a “-1” and “neutral” get “0”. Subsequently, all the unstructured data in the form of text is converted in to structured numeric, which make up the input for analytical algorithms and then it can be processed further easily.
But one should be very careful in implementing a big data project. There are series of steps needs to be followed while implementing the project as suggested by IBM. First of all in the education stage focus should be on knowledge gathering and market observations. In explore stage, banks should develop strategy and roadmap based on business needs and challenges. In engage stage, they should pilot big data initiatives to validate value and requirements. In execute stage, deployment with continuous application of analytics is done. Best way would be by adopting a ‘Start small and add more complexity step-by-step’ strategy. Rabobank adopted this strategy and learned valuable lessons. Controlled journey is always preferred than a giant leap.
- Bank of America adopted big data to provide consistent, appealing offers to well-defined customer segments
- S. Bank adopted big data to improve lead conversion rate by over 100%
- Rabobank adopted big data to analyse criminal activity in ATMs and it was ranked as 10th World’s safest banks by global finance
To sum it up all, I would like to stress that most of the banks are struggling to be more customer centric. Understanding the customer needs is the key to achieve customer centricity. They face a huge challenge of big data to achieve that. Technology has developed very much these days to make use of this big data and make meaningful insights. So it is high time for them to shift focus to customer analytics so that it gives mutual benefit to both customers and banks. Choice is very clear for the banks now. They could either develop a big data culture, capabilities & technology in the organization and grow in the market or live at the mercy of customers. Let us hope they wake up and make the right choice.
Try deep learning using MATLAB