Implications of the New Age Analytics for the Banking Sector
Data-driven decision making is not new to the banking sector. Data warehouse, data marts, and business intelligence (BI) have been an integral part of the banking IT infrastructure. However, the advent of digital channel for customer engagement has created new set of challenges for traditional IT systems. Whether banks turn this challenge on its head and create new set of opportunities out of it, is the critical question. Customers prefer to carry out their banking transactions digitally, as far as possible. This stands true for large cities and metros today, however, the trend is rapidly spreading to tier II and tier III cities. “As a bank, am I equipped to handle this?” is a question that every bank is asking itself quite often these days. To make additional investment on systems and applications to handle this data is just one side of the coin. If a strategy to take advantage of the humongous digital data is not in place, the investment will turn into expenses very quickly rather than opportunities. Correlating this digital data with data created by traditional applications will give the banks – comprehensive insights about customer’s behavior and preferences. These insights will create valuable inputs for marketing and alternate channels to devise focused campaigns and ensure targeted and consistent messaging. Analytics is not limited to only creating and applying statistical models and waiting for miracles to happen. On the contrary, it’s all about creating a ‘data culture’ within the organization, where every piece of data is respected, aggregated, compared and used to create insights. A successful analytics strategy is all about building a structure, component-by-component and should encompass the following: Identification of a business area Identifying and defining a business problem Identifying data sources (inside & outside) Data cleaning, mapping and massaging Building data visualization layer Building and applying models Working closely with other stake-holders to convert this data into insights, and in turn, revenue With the changing times, customer behavior and business process, it’s ideal to move into new age analytics. New age analytics requires the analytics team to work closely alongside marketing, product development and customer teams. They also need to take complete responsibility until the hypothesis is tested and fine-tuned, and results into better customer engagement. The analytics team needs to have in-depth knowledge of the bank’s product development process and capabilities, and needs to work pro-actively to tweak data insights and model hypothesis in real-time to get better results. So, where do you to start from? Although, social analytics is a buzz word today, it’s better to start with the data generated by customers within the banking domain. For example: Customers are using the bank’s debit cards, and with every swipe, they create critical digital information. Analyzing usage patterns on larger data sets will not only reveal her/his buying preferences, but will also highlight their engagements with the bank’s affiliated merchants. The bank’s product development and partnerships teams are better poised with these insights to decide if they should enhance partnerships with existing merchants or go for merchants with new and innovative products. They can also leverage the insights to decide on locations where a particular offer from a merchant may grab more eyeballs and mindshare. The new age analytics model needs to be kept well fed with data and churned perennially for insights across dimensions. The more questions the various teams in a bank ask the analytics model/ tool, the better will be the returns of their analytics investment. The more important point is – how does one handle the digital data created by the customers outside your bank? Banks can very well leverage this data and also link it to other details of the account holders to get a 360 degree view of the customer, their preferences, interactions, behavior on digital channels, etc. Linking of the customers social profiles also adds a very important dimension. While it is possible to achieve this for the entire customer base, the bank should start by identifying customers who might be active digitally and then take the first step by linking their social profiles to the available information. If the bank can manage to achieve this for even 5% of the digitally active customers, it can open numerous opportunities. For example: Some customers are actively talking about their holiday plans or investment plans on social networking sites. Once the bank links the customer’s digital profile with his customer ID, it can proactively track his needs such as travel cards, loans, etc., and pitch for relevant services proactively; much before a competitor can jump in. But, what about your competitors? How about tracking their social footprint and giving valuable insights to your product development and customer engagement teams? A negative comment about customer service on a competitor’s Facebook page should be an alarm for the bank’s customer support teams as well. Whereas, a positive comment on their new product should be a cue for the marketing and product development teams to check if they have ‘missed the bus’ and also decide on any action that needs to be proactively taken by them. I would like to summarize the entire new age analytics eco-system as below: The new age customers are generating considerable digital data from within as well outside the bank’s systems. The new age bank needs to take the lead and leverage the new age analytical tools and models to capture, analyze and use it to its advantage. Analytics team should not work in isolation. They should work closely with alternate channels and product development teams as also with critical departments such as marketing, finance, human resources etc. In fact, analytics team should be well entrenched into customer engagement and product development as well. The more it connects internally, the more efficient it becomes. Focus on data captured internally within your system such as debit card usage, and then build practical analytics models on top of it to give valuable insights to various teams to enhance revenue and augment brand recall. Many a times, in an attempt to capture external digital footprint of all your customers, the focus is lost. Instead, the bank should start with a focused set of customers and keep improving their model by increasing the volume of data captured The bank should pay serious attention to the competitor’s activities on social network. It can provide valuable insights on which the bank should act proactively to create a great brand for itself.
Data-driven decision making is not new to the banking sector. Data warehouse, data marts, and business intelligence (BI) have been an integral part of the banking IT infrastructure. However, the advent of digital channel for customer engagement has created new set of challenges for traditional IT systems. Whether banks turn this challenge on its head […]
Data-driven decision making is not new to the banking sector. Data warehouse, data marts, and business intelligence (BI) have been an integral part of the
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["Go", "API", "AI", "Git", "RAG", "ViT", "analytics", "GAN", "R", "data warehouse"]
["AI", "analytics", "RAG", "R", "Go", "Git", "API", "data warehouse", "GAN", "ViT"]
Director - Analytics and Big Data, Clover Infotech Pvt Ltd