In its 2012 Predictions: Competing for 2020 report, IDC states that 40 percent of banks will ramp up to launch big data and analytics. This is expected as the financial services industry has always been ahead of the technology curve; and nothing defines the landscape more than big data and analytics today.
Despite this, the banking industry continues to be flooded with generic products and services, targeted at pre-defined, broad market segments. Customers continue to be elusive, as banks struggle to define the innovation that will boost their bottom line.
Bankers agree that analytics is a differentiator that can drive revenue and foster customer loyalty. The challenge is in understanding how to harness analytics to boost productivity and enhance its value to banks.
Analytics is strategic, not tactical
With a wealth of information at their fingertips, today’s connected consumers are providing banks with a smaller window of opportunity. Customers who walk into the bank already have a set of preconceived ideas about their options.
The main objective of meeting a bank officer is no longer to consult on what to purchase, but rather, to negotiate the products the customers have decided on, based on their research online and conversations with peers through various social media outlets.
This shifting scenario is putting banks under greater pressure to make faster, more accurate and profitable decisions. Most analytical tools today help banks target products to loosely defined market segments by comparing prospects to known criteria, and matching them to available bank products. This is no longer adequate.
Today, bank associates must be empowered to dynamically shape offers, even as they are discussing with the customer across the table, based on contextual intelligence that evolve during the interaction. In short, analytics must not only be proactive (determining the personalised offering bundle that will see higher acceptance rate) but also predictive (suggesting something that the customer has not even thought of). This is real-time interactive
The goal of real-time analytics is to create a pull strategy for today’s discerning customers. Retail banking must evolve from just managing static segments to addressing the dynamic behaviours among customers. This involves analysing current clients and prospects to determine how market changes have altered their context of ’perceived value’ derived from banks.
These insights are then leveraged when designing the appropriate offers, at appropriate service and pricing levels. By focusing on the value of the bank’s relationship with a customer, banks can reward their loyal customers while prompting less profitable segments to adjust their behaviour. Customer analytics is an important part of behaviour-based pricing.
Now, more than ever, crucial insights must be embedded into processes and systems to generate comprehensive
intelligence that will enhance the quality and flexibility of products and services. Analytics need to be present throughout all of the key functions of a bank: from distribution to conception, and through to production.
For analytics to work from a firm foundation, master data needs to be aggregated and consolidated across the entire organization. This provides a single point of truth that can be harnessed effectively to manage pricing and targeting rules.
Building value-enhanced client relationships
Customer engagement for banks cuts through three important stages known as distribution, conception and production.
Distribution focuses on the client relationship to develop client intimacy and deep understanding of what the client really needs and wants. The main aim of analytics at this stage is to precisely segment the customer based on interests, needs, transactions and other demographics, and estimate the value of the client relationship.
This approach is enhanced by recommending next best actions, considering customer’s context at a given point in time. In the eyes of the customer, the bank has understood the customer’s situation perfectly.
The bank not only recognises the customer’s needs and the products that are suitable, but also anticipates and recommends other solutions and alternatives that eventually help in building the customer’s trust in the bank’s recommendations. Armed with real-time analytics, the bank is well-positioned to personalise offers for customers, recognising each customer as individuals rather than part of a market segment.
The conception stage is dedicated to designing the right offers and product bundles for the client through the right channel, for a customised price. This specific layer is the key because it serves as the bridge between the distribution and production layers – the link between the clientand the end products.
To support this stage, marketing, sales, legal and finance must closely collaborate to bring the required flexibility and dynamism to provide the perfect option, based on insights and interaction with the customer.
Design means customisation of the products, offers, price and channel in various possible permutations and combinations. This stage needs an agile platform like Master Data Management (MDM) to support multiple iterations in the design, on a real-time basis.
One of several ways would be to build the product and offerings catalogues separately in order to work towards speedy customisation. In addition, dynamic interaction with the customer is brought to action using rules devised through analytics and embedded in the decision-making engine.
Last, but not least, is the production stage. This is dedicated to the development of productsand services in the back office. Development could be proprietary to the bank or created through alliances with external partners. Production manages the bank's complex ecosystem through optimised processes and innovative business models, so as to bring about a mutually beneficial relationship between banks and their partners.
Analytical tools enable banks to readily digest the huge amount of collated data impacting both the banks and their partners, such as market intelligence and regulatory limits. With these collated insights, banks can then determine and develop the innovative products at the right costs, to create a win-win situation for all.
The challenge for banks is to integrate and synergise these three stages efficiently, while allowing for flexible deployment, to enhance the customer experience, ensure cost efficiency and improve profitability.
The path to customer centricity
While it is fairly established that customer centricity is a holy grail in the banking industry – with almost all the banks recognizing its existence – the banks face many challenges in attaining it. Many recognize the answer is in analytics but don’t understand the way to unlock the value.
Outlined above is a starting point, where a strategic path has been laid out for achieving customer centricity through an analytics-led approach. Value from analytics is achieved by driving insights from the framework and models, and ensuring those are implemented effectively through the technology infrastructure.
The good news is that such business analytics, data management tools and the requisite technology platforms are available today and proven. The success is dependent on strategic operational design and implementation across the entire value chain within the bank. Those banks which make the first right move are going to be the leading ambassadors of customer centricity in our times.
The views expressed in this column are the author's own and do not necessarily reflect this publication's view, and this article is not edited by Asian Banking & Finance. The author was not remunerated for this article.
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