How are Big Data & Analytics really being used by Insurers?
Another week, another conference, this time it was Big Data & Analytics for Insurance.
To start, I was running a pre-conference workshop (as mentioned previously) on how Customer Insight can help mitigate Conduct Risks for insurers. Good to get active engagement from delegates and once more see how relevant analytics & research are to embedding best practice marketing that provides (as BAU) better outcomes for your customers.
After that workshop on Tuesday, this two day event included speakers from across the insurance industry. Mostly UK-based, they were sharing how big data & analytics were making a difference in their companies. Helpfully, to compliment the lessons learn at the last conference I attended (sharing the progress of Insurance Marketing leaders), these presenters were leaders of analytics or insight functions. So, it was an opportunity to hear the perspective of these customer insight leaders & progress being made towards marketing visions.
As ever, there is much you forget after the enthusiasm of such an event. So, I find it helps to take notes (to tweet if the event is organised for that) and to only try to take away one lesson from each speaker.
As studies have shown for training, one of the biggest predictors of lasting benefit from attending such a learning event — is whether or not you commit to an immediate action. If you can commit to do just one thing differently over next few weeks, you are far more likely to recall what you learnt & benefit from improved skills & knowledge.
Hopefully, this list of single lessons, will help inspire you to a relevent action as a result of reading this blog post:
Kirill (Zurich), shared the importance of a good relationship with IT. Plus evidence of improved value across the customer journey from applying analytics (including understanding them for marketing, smarter pricing from risk modelling for acquisition & renewal and improve claims decisioning. The key lesson here seemed to be about thinking across the customer journey. How might you apply more data & analytics to improve acquisition, education, cross-sell and what Kirill called “recovering the relationship”?
Daniel (Swiss Re), focussed on the variety of R&D tests and applications they are implementing. From A/B testing of comms for behavioural biases (as this blog has advocated), to application of IBM Watson machine learning to reinsurance problems. Some of the most interesting applications were use of new datasets created by customers (inc. PatientsLikeMe.com) and the opportunity for reciprocity in data sharing & services provided by wearables and other IoT devices. The key lesson I heard was the need to build trust with customers and recognise them as owners of their own data, so instead of big data by stealth, create apps that provide services they would value in return for data sharing. What could you offer your customers as a service or insight into their lives in response to data you’d value as an insurer?
Having an opportunity to present during the main conference as well, I shared a short overview of our training on “Consultancy Skills for Analysts”. It was encouraging to see again the interest from many of the data & analytics leaders there in developing their teams softer skills (like questioning, stakeholder management, planning, facilitation, communication & influencing). The 9 step model we use for structuring this training still appears to have wide application. Key lesson? Do you need more data/technology, or would you get more return by investing in developing your analysts to have more impact in your business?
Paola (Groupama), gave us an Italian perspective. She echoed previous comments on the importance of context, both for analysis and for communication & services for your customers. Using Telematics data, combined with other public & social data, has enabled the development of services to help their customers when needs arise (collision for example). The main lesson was the importance of context, or as Groupama consider it, being a “Proximity Company” for their customers. A trusted partner to help when things go wrong. How could your data enable closer help in context for your customers when they need it, but without intrusion at other times?
Steve (Covea), came from a different perspective, as an ex-policeman with responsibility for detecting financial crime. Here the need for data sharing between insurers was a key theme, even after years of efforts from CUE, MIAFRA etc. The breakthrough for them appears to have e been real-time analytics during a claims call. Using questioning & validation using internal & big data to create fraud alerts where needed or confirm those who can be paid quickly. A key lesson was that sometimes realtime really does matter, reducing customers down to segments or batch scored propensity scores is not as powerful as the data being able to provide active listening & prompting to a customer conversation. Do you have customer or business needs that would benefit from realtime insight?
Howard (Standard Life), shared the journey of this leading pension provider. Entitled “The Art of Good Conversation”, Howard explained their cultural journey to go from ‘Customer First’ as a vision, to analytics & customer insights really being embedded in decision-making across the organisation. A key lesson I took from this, as well as recognising many of the challenges, was the theme that every interaction matters. This has been true for them both with regard to customers (moving to A/B testing as norm & better coordination of messaging journey) and in embedding use of insight into leadership behaviour (moving to pull not push analytics & demonstrating ROI through stronger marketing measurement). How are you influencing your senior leaders to see the value of insight-led decision-making?
Reza (AIG), comes from a more academic background and so takes an R&D approach within AIG’s Science team. Good to hear his call for the importance of storytelling, plus the importance of visualisation in communicating analytics results. There were interesting points on Simpon’s Paradox, optimal use of your Data Scientists & the potential for Insurance to be the next frontier for FinTech. But for me the key lesson was very similar to that shared by Daniel, the opportunity of Me2B economy (buying apps to help me in my life through the ‘payment’ of sharing data I generate in my life). Are there new commercial models for your business whose currency could be customers’ own self-generated data?
Kiran (Zenith) offered a somewhat contrarian approach to using data to gain a commercial advantage. Some was clearly beneficial, using big data to improve ID & verification of customers and to identify those attempting to “game” quote engines by altering the data they provide (as apparently condoned by Martin Lewis). However, the key lesson for me was to be careful how you position your data & analytics plans. Kiran presented their potential use of social media and other big data to help with profiling & risk rating customers as “weaponising Big Data”. Given the planned FCA thematic review on just this topic, that was at the least ballsy. Are you careful in the language you choose to envision your business with the potential of big data & analytics?
Barry (Axa), provided the wisdom of a lifetime spent working within the Insurance industry. There were a number of tips & wise warnings against being sold on vision or shiny new technology, rather than the analytics which actually matters commercially. I also found myself agreeing with Barry on the need to bring data & analytics understanding ‘in house’. Like Howard, there were also war stories from the work needed to move from a product-centric to customer-centric business. A good question proposed was: What is an acceptable intrusion into people’s personal data to provide underwriting advantage? But for me the key lesson was to develop a workable Customer Lifetime Value score and use that to prioritise your other models. Are you making sufficient use of value-based-segmentations (whether for underwriting or marketing purposes)?
Colin (Aviva), reminded us of the role such work can also play in Solvency II projects. Although potentially a dry subject that people avoid (a bit like data regulation), there is an opportunity here. The key lesson was to spot where something that needs to be built for Solvency III (and thus is more likely to get budget), can also have wider business benefits. Are you joining up with your Solvency II team to see how your intended data strategies could be rationalised, to avoid duplication and reduce time & money?
Dilip (1st Central), was mainly hilarious and should be booked as an insurance industry stand-up comic (perhaps in a double act with Barry, his mentor). To help us end with a more human perspective, he shared his own career journey as an analytics leader and the benefits of working for a smaller private insurer like 1st Central with a start-up mentality. If you get a chance to speak with Dilip, do ask him about Dilip’s 5 laws, but for me the key lesson was maintaining an inquisitive attitude to internal data. He will wander around in his business, see what people are doing & always be asking “can I use that data?” Do you still have a restless curiosity about data opportunities within your business? (To date the biggest returns shown for “Big Data” have come from previously unused internal data)
Apologies for a long post, but I hope that helps those leading data, insight or analytics teams within insurance businesses. A number of those lessons also have much wider application across FS and other sectors.
If you have been to conferences at the start of 2016, what did you learn to help your insight leadership?
Great recap. Thanks for this. I need to get Dilip’s 5 laws onto a t-shirt or something.