Changing the Landscape of Customer Experience with Advanced Analytics

Changing the Landscape of Customer Experience with Advanced Analytics

That timeless principle – “Know Your Customer” – has never been more relevant than today. Customer expectations are escalating rapidly. They want transparency in products and pricing; personalization of options and choices; and control throughout their interactions.

For an insurance company, the path to success is to offer those products, choices, and interactions that are relevant to an individual at the time that they are needed. These offerings extend well beyond product needs and pricing options. Customers expect that easy, relevant experiences and interactions will be offered across multiple channels. After all, they get tailored recommendations from Amazon and Netflix – why not from their insurance company?

Carriers have significant amounts of data necessary to know the customer deeply. It’s there in the public data showing the purchase of a new house or a marriage. It’s there on Facebook and LinkedIn as customers clearly talk about their life changes and new jobs.


One of the newest trends is dynamic segmentation. Carriers are pulling in massive amounts of data from multiple sources creating finely grained segments and then using focused models to dynamically segment customers based on changing behaviors.

This goes well beyond conventional predictive analytics. The new dimension to this is the dynamic nature of segmentation. A traditional segmentation model uses demographics to segment a customer into a broad tier and leaves them there. But with cognitive computing and machine learning an institution can create finely grained segments and can rapidly change that segmentation as customer behaviors change.

To pull off this level of intervention at scale, a carrier needs technology that works simply and easily, pulling in data from a wide variety of sources – both structured and unstructured.

The technology needs to be able to handle the scale of real-time analysis of that data and run the data through predictive and dynamic models. Models need to continuously learn and more accurately predict behaviors using cognitive computing.

Doing this well allows an carrier to humanize a digital interaction and in a live channel, to augment the human so they can scale, allowing the human to focus on what they do best – build relationships with customers and exercise judgment around the relationship.

Sophisticated carriers are using advanced analytics and machine learning as a powerful tool to find unexpected opportunities to improve sales, marketing and redefine the customer experience. These powerful tools are allowing carriers to go well beyond simple number crunching and reporting and improve their ability to listen and anticipate the needs of customers.

Open source, analytics and the pace of change

Open source, analytics and the pace of change
I love spotting ironies such as how this years Strata | Hadoop World conference (the UK one) spent more time discussing Apache Spark and whether it was a successor to Hadoop or another tool in the box than it did discussing Hadoop and it’s applications. It was great to see members of the insurance industry there amongst the retailers and banks as well. “But wait?!?!!” I hear you say, “Hadoop isn’t all that old is it?” Herein lies the great challenge for the CIO faced with requests for open source tools. These are dynamic, social projects without the same stickiness as those legacy systems insurers spend time worrying about. Not only do users / consumers / fans of open source software shift between projects but the contributers / developers do too. With the rising use of tools like R, Python, Linux, GIT, Hadoop, Spark, Docker, Capistrano and all manner of wacky projects on the go and being adopted by insurers how should a CIO respond? Prohibition tends to lead to shadow IT and surprises down the line far more unwelcome than managing some new software. The key advice is to understand these types of projects can be more transient than other enterprise software. Experiment with them but be careful of expensive, enterprise installations that are hard to extract later down the line. In truth insurer adoption of some of these technologies will outlive the fashion for them but it still requires planning for their removal or worst case, their ongoing support. I promised analytics in the title too didn’t I? Well Spark is all about real time analytics and is having an interesting impact in the machine learning and predictive modelling space. It gets around some of the issues with interacting with Hadoop while still delivering performance. With open source projects survival of the fittest is the order of the day, far more so than in classic insurance software markets. Hadoop has it’s place, with many insurers globally investing in it.We will see new fashions in analytics approaches and more opensource tools I’m sure. Some will follow the Dodo. For those interested in Hadoop have a look at my report from 2011, when Hadoop was new and cutting edge. It seems it requires an update.

The rise and fall (and rise) of Artificial Intelligence

The rise and fall (and rise) of Artificial Intelligence
Artificial intelligence has been around nearly as long as humans have been able to think about themselves, about thought and what they do. Empathy is wired into us – some more than others but we are all capable of thinking from another’s point of view. This capacity leads us to anthropomorphize things that aren’t human, to imbue things in our daily lives with human qualities like moods, characteristics and personality. When we build puppets, robots, models that look sort of human it is easy to for us to assign it with greater power, ability and promise than is really there. For marketers in other fields, to have consumers attribute their products with ‘magical’ properties would be a dream come true but for artificial intelligence it is a nightmare – one the industry has expended funds marketing against. Artificial intelligence has delivered many great tools which today we take for granted. Our phones listen to us and understand our requests in the context of our calendar, our camera’s recognise faces and social networks tell us who those faces belong to, machines translate words from one language to another (although don’t get the translations tattooed just yet) and the list goes on. We chuckle at these mistakes these learning and adaptive systems make, we see the huge strides and investment and we expect a new human like intelligence to emerge in the short term. Around the middle of every decade since the 60’s there has been a peak in excitement for AI, a frustration with it’s lack of progress, and a reduction of funding or AI winters as they are called. In the eighties it was LISP machines, in the nineties it was expert systems. Now in the twenty-tens (I thought it was teenies but that’s a kids show apparently) we are seeing a resurgence of AI, a blending of machine learning, predictive modelling and cognitive computing along with self driving cars. This raises some rare and interesting questions:
  • Are we headed for a new AI winter?
  • Or an AI apocalypse?
  • Also, will I still be cleaning my home in 2020?
It is certainly true to say the set of tasks we can expect software and physical computing systems to do is vastly increased compared to just a decade ago, and massively so since the 60s. Doing all the things humans can do and living in our society, empathising and understanding us in that broad context is still well beyond computers – but engaging with us in specific, well-defined domains such as about our calendar or what we would like to buy from the shop is well within their grasp today. Previously difficult tasks such as searching a database for information, reading that from a screen and keying it into another screen is now entirely possible – see the earlier blog post on bots. Having a drone fly itself around an obstacle to reach an objective is still very hard. Having a vehicle drive itself on the road is in fact easier, albeit most humans don’t benefit from lidar sensors, ultrasonics and eyes in the back of their head (alright, bumper). It is good to see AI on the rise again – I loved the topic ever since getting into programming and getting involved in a cognitive psychology course some years ago. I recall writing an expert system in Pascal back in the 90s. I am concerned, as the insurance industry should be, by a new AI winter. Self driving cars and vehicles have the potential to make the roads safer for all. We will when we see them, imbue them with more power than they have – this is human nature. We will, in the not too distant future, hear people say things like, “the car likes to give cyclists a lot of room on the road” or “the car prefers to take this corner at a fair speed” – imbuing a complex machine with sensors and programming with preferences, desires and likes – human qualities. When the first death comes we will ask how could it do such a thing. When an automated car is put in a position where it must decide between a set of actions – each leading to injury, we will hear people discuss why it chose to do what it did, people may say, “it did the best it could” or worse, “no person would ever have done that, this is why machines shouldn’t be able to choose.” The latter of course revealing the human construct, an unspoken contract – our expectation that smart or intelligent systems will operate like us, share our values, our culture, that we can predict their actions in our context. This is the greatest threat to AI and always has been – the expectation, the contract that the new intelligence will be like human intelligence. Some winters are due in part to that contract being broken, to these systems not living up to the expectation and making inhuman mistakes. There are a set of tools available now that are not intelligent but they are smart and they are powerful. We would be remiss in our duty to our customers and shareholders if we do not leverage them. Manage expectations about these powerful tools and understand the very real limits that exist on them. If we can do this we may benefit from the AI boom and avoid another AI Winter. Will we see an AI apocalypse? Ironically it’s not the human like intelligence that may be our greatest threat but simpler intelligences. A human like intelligence could empathise, could act in acordance with values and could be relatively predictable (in a human way). There are many stories across science fiction of smart robots that act like insects and replicate, in fact that only make copies of themselves, that pose a great threat to any civilisation. They are not intelligent, they don’t want to kill off all life in the galaxy – they just turn all the available resources into copies of themselves which would have that effect. We are much closer to building that threat frankly (with drones, 3d printers, etc.), than a super intelligence that decides all human life is worthless. For now though – I expect these things to stay firmly in the space of science fiction. I include this discussion here because it does demonstrate a key difference between smart with unintended consequences and ‘intelligent’ – a lesson worth bearing in mind for those adopting AI. Finally – will we see robots cleaning our homes by 2020? Well roomba is out there and sort of does that. Stairs and steps are still a huge challenge to robots. Frankly differentiating furniture, pets, clutter, magazines, rubbish, dust and recycling in a moving environment is still a very complex issue. As in insurance, I think smart things will make cleaning easier and assist those who invest but there’ll be a role for human intelligence in ensuring the pets aren’t recycled and the customer ultimately gets the service they expect.