Tag Archives: big data



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– Outdated perceptions of a customer life stage.
– Unavailable or outdated customer data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


2016 Revelations


Big data can be funny too…no…seriously! 


As the year draws to a close, and we treat ourselves to some well-earned merry making, here is a look back at some 2016 big data events that prove big data isn’t all dull and serious.

A World of How, What and Why


Google’s annual list of most googled terms are always revealing and this year’s is no different. Australia’s Top Ten “How to” searches in 2016 suggest that Australians are still concerned with the challenges of daily life: “How to tie a tie?” and “how to get rid of pimples?”

UK-based Googlers, on the other hand, were concerned with a somewhat more esoteric question: “How to make slime?”. Their Top Ten also included the more sadly philosophical: “How to accept myself for who I am?”.

Meanwhile, the “Why” question uppermost with Swedish Googlers in 2016 was “Why are eggs brown or white?”. Clearly, the world is still full of innocents.

 (Extra) Ordinary Gifts


This year’s Singles Day has been deemed Alibaba’s most successful yet. Sales this year reached USD 17.79 billion, compared to USD 14.3 billion last year. Unsurprisingly, top sellers were phones and appliances.

But everyday household items are also big hits on Singles Day. Hera BB cream from Korea and Laurier sanitary napkins from Japan are traditional favourites, as is milk.

Last year, one German supplier alone accounted for 2.35 million litres (USD14.3 million) of Singles Day sales of liquid milk. On the same auspicious day this year, an Australian manufacturer was able to shift 350,000 goat soap items worth over USD 1 million.

Deep Learners?


Alongside top trending searches, Google’s 2016 Breakout Searches (ie yoy searches that rose by 5,000% or more) were equally eye catching.

While “Severe Weather” featured in Germany’s list, “Nobel Prize” in Sweden, and “Melania Trump” in Slovenia, China’s list contained only two: “Deep Learning” and “Machine Learning”.

This presumably has something to do with ordinary people wanting to understand what organisations – from the government to corporates – are doing with their data. For example, e-retailer Shangpin.com has determined that Chinese consumers prefer to shop for underwear in the late evening. While it is still unclear how this will drive future marketing, the retailer says such “subtleties” provide valuable insights into the Chinese psyche.

 Better Restroom Service


2016 also provided some insight into Japanese restroom habits and preferences. A report by the Japan Times revealed that highway operator, Central Nippon Expressway Co. (NEXCO Central) had installed 3,000 sensors, including motion detectors for toilet bowls, in 51 restroom locations along the Shin-Tomei Expressway.

Analysis of the data collected suggest that cubicle use is gaining on urinals, and an average cubicle visit is four minutes and four seconds, up 35 seconds in seven years, which NEXCO puts down to the increased use of mobile phones in restrooms.

NEXCO expect this data to help it improve its service. For male commuters faced with long cubicle waiting times, this will be a relief.

Its Been Weird


Spotify dug deep into its data stores to come up with one of the year’s most interesting end-of-year campaigns. Displayed on billboards at select locations around the world, Spotify highlighted the behaviour of some of the site’s users, and their reaction to it.

Under a general tagline of “Thanks, 2016. Its been weird”, examples included:

  • Dear 3,749 people who streamed “Its The End Of The World As We Know It” the day of the Brexit vote. Hang on in there.
  • To the 1,235 guys who loved the Girls’ Night playlist this year. We Love You.
  • Dear person who played “Sorry” 42 times on Valentine’s Day. What did you do?

Developed by Spotify’s in-house team, Chief Marketing Officer, Seth Farbman said their data was inspiring and gave an insight into people’s emotions.

We agree – where can you detect more emotion than in a person’s playlist?.

Are You Open To Open Source?


Navin Suri, Percipient’s CEO, gives six compelling reasons why organisations should leverage open source software