Category Archives: digital transformation

WHAT LIFE STAGES ARE YOUR CUSTOMERS?

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Insurers and Wealth Managers have long aspired to segment customers based on their life stage. Big Data can finally make this happen

by Ai Meun Lim, Chief Product Officer, Percipient, and Dr Shidan Murphy,  Senior Data Scientist, Angoss Software Corporation

Shakespeare describes the “The Seven Ages of Man” as the Infant, Schoolboy, Lover, Soldier, Justice, Pantaloon (roughly translated as foolish old man) and Old Age. While today our view of one’s life stage is perhaps less cynical (and poetic!), the desire for such a clear classification remains a strong business goal across a range of industries.

Most businesses implicitly recognize that an individual’s life stage contributes towards a multifaceted perspective into the needs of a customer. Further, in some tightly regulated financial businesses, such as wealth management and insurance, determining a customer’s life stage is required to determine risk tolerance and financial objectives. Understanding a customer’s life stage is essential for any customer-centric business strategy.

However, despite the importance, most financial institutions have failed to adequately monetize the concept of a life stage. Major limitations to such monetization are:
– Outdated perceptions of a customer life stage.
– Unavailable or outdated customer data
– Inability to track customer life changes
– A lack of predictive analytics

Why a Dynamic Segmentation of Life Stages?

Traditional methods have based life stage assessments on easily quantifiable factors such as age and income. Yet an age or income-only lens has proven inadequate for detecting financially-important events such as getting married, starting a family, launching a business venture, or taking care of elderly parents.

While the automatic and fluid tracking of a customer’s life stage is sought after in financial institutions today, most still rely on ad hoc updates from customers themselves, and at best, annual customer surveys. Such passive tracking methods point to difficulties financial institution have meeting their “duty of care” obligations, let alone running profitable and targeted life stage campaigns.

The development of a dynamic segmentation of life stages is therefore a generational-leap in customer understanding. By digesting real time data from traditional and non-traditional data sources and using advanced algorithms to analyse this data, it is now possible to create a granular customer segmentation that is capable of evolving with the customer’s lifecycle.

Life Stage Analytics

Percipient and Angoss are collaborating to offer dynamic life stage segmentation that applies the tools capable of unifying such data, and technological advances in big data analytics.

The foundation of this approach is data – and lots of it. For the segmentation to be meaningful, the data must include both conventional and unconventional data sources.  Conventional sources include demographic data, spending patterns, and both assets and liabilities. Although these data are readily available in financial institutions, they are often underutilized.

Advancements in data technology means there is also scope to incorporate unconventional data sources such as social media (think LinkedIn), wearable devices (think Fitbit), digital footprints and third-party data aggregators.  Some financial institutions may already be collecting this data, but have not been put it to use for this purpose.

Customer segmentation is then created using cutting-edge analytics. The analytical approach combines business rules and next-generation tools and techniques to create granular-level customer categorizations. The life stage categorizations are dynamic and regularly updated to reflect changes in the data feeds.

Such dynamic categorizations create the opportunities for, among other requirements, next product recommendations, customer retention and calculating the actual and expected profitability of the customer base.

A Revolution in Needs Based Selling

The financial industry’s mantra is that product sales be underpinned by their customer’s life stage. Today, data and next-generation tools are in place to create more accurate, dynamic and granular view into the needs of their customers. An accurate, analytics-driven understanding of life stages are set to revolutionize needs-based selling.

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Banks, Declutter Your Data Architecture!

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Banks do not need to be wedded to complexity, says Navin Suri, Percipient’s CEO 

Marie Kondō’s bestseller, The Life-Changing Magic of Tidying Up: The Japanese Art of Decluttering and Organizing, is sweeping the world. Her message that simplicity pays off applies as much to a bank’s data architecture as it does to a person’s wardrobe.

Few bankers would argue with the notion that the IT architecture in banks is overly complex and as a result, far less productive than it could be. So how did we get here? Rather than a single blueprint, most banks’ IT evolved out of the global financial industry’s changing consumer demands, regulatory requirements, geographic expansion, and M&As. This has led to a tangled web of diverse operational systems, databases and data tools.

Rapid Digitisation

But rapid digitisation has put this complex architecture under further stress. Amid dire warnings, such as the one from Francisco Gonzales, then CEO of BBVA, that non-tech ready banks “face certain death”, many rushed to pick up the pace of their digital transformation.

Banks rolled out their mobile apps and digital services by adopting a so-called “two-speed infrastructure”, that is, enhanced capabilities at the front, built on a patchwork of legacy systems at the back. Now over a third of all banks, according to a 2015 Capgemini survey, say “building the architecture/ application infrastructure supporting transformation of the apps landscape” is their topmost priority.

Fragmented Infrastructure

Meanwhile a key reward of digitisation – high value business intelligence – remains elusive. Banking circles may be abuzz with talk of big data, but the lack of interoperability across systems makes this difficult to achieve. In some cases, cost effective big data processing technologies like Hadoop have actually deepened the problem by introducing yet more elements to an already unwieldy architecture.

To address the problem, financial institutions have opted for two vastly contrasting approaches. Either paper over the cracks with a growing number of manual processes, or bite the bullet, as UBS is doing. The world’s largest private bank announced in October last year that it will be spending US$ 1 billion on an IT overhaul to integrate its “historically fragmented infrastructure”.

Attack On Complexity

However, for those banks unable or unwilling to rip out and replace their existing sytems, there is a third way. The availability of highly innovative open source software offer banks the option of using middleware to declutter and integrate what they have.

Percipient’s data technology solutions, for example, enable banks to pull together all their data without the need for data duplication, enterprise data warehouses, an array of data transformation tools, or new processes and skills. These solutions are, at their core, an attack on the architectural complexity that banks have come to grudgingly accept.

Visible Order

As Marie Kondō points out, “Visible mess helps distract us from the true source of the disorder.” In the case of most banks, the true source of the disorder appears to be an IT infrastructure derived, rather than designed, to meet the huge demands placed on it by digitisation. There is now a real opportunity to turn this visible mess into visible order.

This article was a contribution to, and originally appeared in, finews.asia

When the BACK end isn’t running as smoothly as the FRONT

Many organisations’ impressive new digital apps are underpinned by slow and cumbersome backends

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The dollar value of the digital transformation market is impressive indeed. IDC’s January 2016 report suggests that worldwide spending on digital transformation technologies is growing at around 17% pa to reach over $2.1 trillion by 2019.

However, it is clear that this spending is not uniform across enterprises’ digital infrastructure. While mobile app development tends to receive the lion’s share of an enterprise’s attention, the focus on back end improvements, such as in data security and data integration, appears to lag. For example, a 2016 Gartner survey found that of the enterprises expecting to increase their mobile app spending by an average of 30%, their overall mobile budget was only expected to grow by 10% over the same period.

This trend is worrying for several reasons. Outdated and disjointed systems run a greater risk of failures and security breaches. And coupled with the rapid release of new digital channels, any malfunction is likely to have a wider, more immediate and higher profile impact on customers.

However, system disconnects are not wholly related to cost. Here we look at three underlying reasons for an enterprise’s mismatched IT growth.

  1. The two-speed approach: Inevitable… and useful?

 Firstly, the process of digitisation has caused a shift in the enterprise IT decision-making process. According to a Bain & Company 2014 survey, nearly one third of technology purchasing power has migrated from CIOs and technology teams to business stakeholders.

This shift in power and accountability is in line with the need to respond promptly to customers’ fast changing digital preferences. As a result, business stakeholders and marketing executives are now better able to make “quick fixes” to customer-centric apps and digital channel features, without waiting to ensure long term alignment with back-end systems. Some business stakeholders even resort to “Shadow IT” ie new technologies not yet sanctioned by their IT teams.

The consequence of this is a “two-speed” IT infrastructure. Despite the risks, this is a situation some analysts would deem inevitable and even desirable, in order to ensure that customer experience demands are met without delay. The problem starts when, having been launched, interest in the project wanes in favour of the next big thing. Under such circumstances, neither business owners nor IT teams work to ensure that back-end systems “catch-up” and are able to sustainably support front-end innovation. The lesson here is that a two-speed approach is acceptable, but only if the arrangement is temporary.

  1. Which comes first: the skills or the project?

Another weak link in the synchronisation of front and back end systems development is the prevailing skills shortage. According to a survey of 2,600 CIOs conducted by Gartner, the biggest obstacle to digital transformation is the lack of technology talent. In fact, so deep is the crisis that, according to Gartner, of 1.4 million computer specialist job openings in 2020 projected by U.S. Department of Labour, there are not enough qualified graduates to fill even about 30 percent of these jobs.

So which IT sectors are most affected? While front end and back end developers are equally in vogue, the key need is to bring the two together. Across a variety of HR surveys, project management and solutions architecture are among the top five most hard-to-find IT skills today. These skills encapsulate the ability to scope out a business need, dimension it, identify the frontend solution, and manage its backend delivery.

While enterprises remain committed to launching new digital offerings, this shortage is dampening their enthusiasm for large scale IT upgrades. Albert Ellis, CEO of the Harvey Nash Group, notes that, “In the past, CIOs would set their IT strategy first, followed by a resourcing plan. Now, it’s all changed. Certain skills are in such demand IT executives are facing up to the reality there’s no point in having the right technology platform if you don’t have the right people to build and support it.”

The solution, suggests HR specialists, is to focus on access rather than ownership. In a world undergoing digital tansformation, a business model that relies only on inhouse talent appears to no longer be tenable. The question then is where, not whether, to locate external talent.

  1. Embedding new technology: Out with the old?

 A third barrier to back end IT growth is the difficulty in incorporating new technology into existing enterprise infrastructure. While front ends can be created afresh, back end upgrades are often a question of merging the old and the new. Which is no easy task. It is perhaps not surprising that a new A.T. Kearney report found that the failure to integrate new technologies with legacy systems was cited by 59% of respondents as the reason they were not able to achieve their digitisation plans.

This is driving enterprises to find creative solutions to the problem. One way is for them to adopt new platforms altogther by leveraging on cloud computing services such as PaaS (platform as a service). This service means enterprises are free to develop their own apps, while PaaS vendors provide the key backend tools such as hosting, operating systems, databases, etc, needed to support such applications. Today the global market for PaaS is projected by Global Industry Analysts to reach USD 7.5 billion by 2020, with Asia Pacific the fastest growing market at a projected CAGR of over 20%.

Of course, enterprises that have spent large amounts on their back systems will find this solution harder to swallow. However, many are using application programming interfaces (APIs) to leverage on new software components that have been developed internally and externally. According to one study, 67% of “digital disruptors”, that is, enterprises most successful at their digital transformations, put a high emphasis on the managed use of modern APIs techniques, standards and protocols to integrate their front and back end systems.

Over the next few years, there is likely to be a concerted shift of focus from already polished digital interfaces to an equally polished back end infrastructure. Turning digital offerings into primary profit drivers is unlikely unless enterprises also deliver data security, operational efficiency, and processing speed.

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