DATA ANALYTICS: DECODING CUSTOMER NEEDS IN A DIGITALIZED MARKET PLACE

By Sayandeb Banerjee, Cross-industry Delivery Lead at Mu Sigma

As e-commerce and mobile technology continue to grow and drive greater convergence, organizations are having to adapt to a new way of ‘doing business’. They are faced with an increasingly digitalized market place, where automated payments and interactive eco-systems are more and more commonplace, and barriers for customers to switch are ever lower.

According to the latest TNS Current Account Switching Index, consumers are continuing to switch loyalties in droves, looking “beyond the ‘traditional High Street banks’, focusing instead on the individual bank offering.” With more demanding customers across all sectors and a plethora of choices available to them, it is not surprising that service issues remain the biggest reason for customers to opt out from a bank.

Sayandeb Banerjee

Sayandeb Banerjee

The World Retail Banking Report 2014 (WRBR) shows that tech-savvy Generation Y population (those born between 1980 and 2000) is driving demand for greater digital servicing. The report reveals that the number of customers interacting with banks via bricks-and-mortar retail branches has fallen from 16% last year to 14% this year. Conversely, those using mobile phones have risen from 13% to 22%, and those using social media from virtually zero last year to 10% in 2014. Despite the strong trend toward social media as a channel, 42% of banks say they have no intention of offering transactional capabilities through social media. Concerns over data privacy and security were cited as the main reasons, as well as regulatory constraints and inadequate back-end infrastructure.

For many, the biggest challenge in this digital evolution lies in understanding how to meet the high expectations of customers in an often ‘faceless’ environment. Many banks are increasingly turning to big data for answers. With the right tools, talent, platforms and processes in place, banks can not only better strategize their sales and marketing efforts to prevent customer churn, but also identify the profitability of particular customer segments, help them prioritize these relationships and align channels accordingly.

Central to mining the right data and making better informed decisions is an understanding of different customer lifecycles, which broadly speaking can be broken down in to three stages: Entry, Engagement and Exit. An understanding of each of these three E’s will enable banks to adapt marketing, sales and relationship management approaches to each stage. This in turn will help not only with setting pricing policies more accurately, but also with defining the services and offerings that best respond to increasingly high customer expectations, while avoiding/minimizing ‘exits’ to the best possible extent.

Many retail businesses resort to a variety of marketing tactics to drive customer interest or loyalty in their brands through a spectrum of entry points, both online and offline, and track the associated data. Supermarkets like Tesco are shining examples of this, and many banks would do well to tap into this cross-industry knowledge bank. Paid searches, mobile apps, loyalty cards, promotional offers and incentives for referrals could all be applied more creatively in the banking sector.

While channel entry points provide valuable insights – particularly in defining the best mediums to use for any communication programme – the biggest answers to customer needs, behaviors and priorities lie in their actual brand engagements. Traditionally these insights will have been generated through surveys, reviews and feedback through face-to-face encounters in-branch. Today’s increasingly digital marketplace however, has opened the door to quicker and far richer sources of data. Banks now have the potential to mine data in real-time by tracking online customer engagements through click-streams, social media posts and location intelligence data offered up through most smartphones and GIS aided mobile devices.

Social media in particular, with the added dimension of sentiment that is often attached to social dialogues, offers banks a potentially huge and rich source of data not only from existing customer base but also from potential ones.. And the evidence for getting social media right is compelling – according to the WRBR report around half of all retail bank customers surveyed claimed that they would be more likely to stay at banks that offered social media as an engagement platform.

By using the right analytical tools and tracking these engagements, banks can gather data on customer demographics, income patterns, spending and saving habits, as well as interest areas – and build valuable customer profiles in the process.

Leveraging this trove of data however, requires people who can not only create the algorithms needed to gather the right data, but also possess the skills to translate insights from this data into sound business decisions. As most banks are aware, business problems are constantly in flux and often ill-defined, which means they need to take an interdisciplinary and adaptive approach that combines the appropriate technology with mathematical proficiency, business acumen, design thinking, and behavioural sciences – in short, ‘Decision Sciences’.

Decoding the digital marketplace pays handsome rewards when it is done well. Banks can utilize customer data profiles to shape and streamline strategies to best communicate with their customers through a cohesive omni-channel approach rather than the oft-used multi-channel approach. This can help them enhance their success rates by providing the right financial products or services to their customers. These capabilities sit right at the heart of the buzzword of the moment, ‘personalisation’. Banks need to embrace the digital marketplace as a means to meet customer expectations and differentiate themselves from competitors in what is a fiercely competitive and continuously evolving business landscape.

About the Author:

Sayandeb Banerjee is the Cross-industry Delivery Lead at Mu Sigma. He has over 15 years of experience in decision sciences, including business intelligence and quantitative modeling using advanced data mining techniques. He also has significant domain expertise in Financial Services and Insurance business verticals. He has an MS in Quantitative Economics from Indian Statistical Institute (ISI), Kolkata & New Delhi, where he was the recipient of the ISI Alumni Association gold medal.

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