Tag Archives: insurance



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– Outdated perceptions of a customer life stage.
– Unavailable or outdated customer data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.


Healthcare and Health Insurance: Big Data’s Sweet Spot


Nowhere is the utility of big data greater than in healthcare, and by inference, health insurance. Venture capitalists appear to have caught on to this, and in the first quarter of 2016, put USD 1.4 billion into the hands of healthcare IT companies. This was almost a third more than in the previous quarter, with much of it directed towards wearables, data analytics and telemedicine.

#1: The Cost Challenge

Three key trends are driving the big data revolution in healthcare. The most important of these is the desperate need, in a world that is rapidly aging, to stem ever-rising healthcare costs. This is a need shared by all healthcare stakeholders, from consumers and providers, to insurers and government agencies.

While cost saving is not a new item on the global healthcare agenda, the recent spotlight on “value-for-money” health services is in part the result of the U.S.’s Affordable Care Act. Faced with ACA requirements, “merger mania” has broken out among healthcare insurers, according to the PwC Health Research Institute.

It would be fair to say that these proposed insurance mega-mergers have been met with some scepticism. Opponents fear that the much-touted economies of scale may not materialise. However, proponents and opponents alike agree that insurance company mergers do offer a key opportunity: to optimise their IT infrastructure, and leverage on innovations in mobile health applications and analytics.

#2: Monitoring on the move

Fitness Coach USA Running

This brings us to the second unmissable trend – the rise of activity trackers and medical devices. Increasingly, health monitoring is shifting from the hospital and clinic to the home and on-the-go. Given that the management of chronic diseases is already the largest component of U.S. healthcare spending, Goldman Sachs suggests that remote health monitoring is “at the bulls-eye of the healthcare cost challenge”.

But the power of health monitoring devices is not just the ability to save money. Health and fitness monitoring devices are enjoying a huge surge in popularity. According to IDC, 78.1 million wearables were shipped in 2015, up from 28.8 million the previous year. PwC’s recent survey suggests that one in three American consumers now have at least one medical, health or fitness app on their mobile devices.

What this means is that the healthcare industry is no longer just reliant on clinical data. Health-related digital data is now (potentially) on tap from medical and fitness devices. The question is whether the data is being put to good use. To date, many healthcare and device providers have been reticent to do so, amid concerns that personal data sharing would be unpalatable to patients and users. The PwC survey appears to lay this to rest, with 83% of respondents indicating their willingness to share their data to help their own diagnosis and treatment, and 73% to help others.

#3: Prevention not cure


This level of acceptance may stem from the already signifiant strides in healthcare analytics, particularly in the area of patient risk forecasting. For example, by analysing patients’ everyday behaviours, socio-economic factors, and clinical updates, hospitals are now predicting and where possible, preventing, their post-operative health deterioration. It is perhaps not surprising that, in a recent survey, advanced analytics was found to be more prevalent among mid-sized U.S. hospitals, ie those between 100 – 500 beds, where there is a strong incentive to minimise patient re-admissions.

Furthermore, in the last few years, healthcare analytics have expanded to include healthy individuals with specific health vulnerabilities. Individuals at risk are offered wearables that track their vital statistics and surroundings, and analytics help primary care doctors to determine if a change in preventive medication or lifestyle is required.

Then there are the individuals who are tracked and rewarded simply for staying healthy. Employers, partnering with insurance players like United Health, Kaiser Foundation Group, Humana Group and Aetna, now account for the fastest sales of wearables. By agreeing to have their activity levels monitored and analysed, employees are able to lower their health insurance premiums as part of so-called “corporate wellness programs”.

A data-hungry future


Given the pace of adoption of wearables and healthcare analytics, there is an appetite for data that may well grow to point of becoming insatiable. Already, insurance companies are said to be busy approaching app developers in an attempt to directly access their fitness data.

By applying sophisticated analytics to large and varied datasets, insurers are hoping to refine their insurance plans. On the horizon looms a cut-throat health insurance marketplace, with new players such as hospitals and primary healthcare providers joining the fray. To stay competitive, health plans could evolve from annual and generalised premiums to daily-adjusted, personalised rates, based on an individual’s real time fitness data. This data will be generated by devices that insurers will offer at discounted rates or for free, further elevating data volumes and the performance of healthcare analytics.

Can you think of a more compelling virtuous cycle?