Few months ago, I received a call from an insurance aggregator (to demystify, an online insurance broker who helps you with the best choice comparing multiple insurer offers in the market). The person who spoke requested some of my time that could result in big savings on my car and home insurance premiums. 15 minutes into it, I realized that by switching to another insurance company, I could save 50% on my insurance premium for identical terms. I got the process going further and in an hour’s time, had a new car and home insurance policy at half the price.
Getting a bit intuitive, I asked how he was able to target me. To which he mentioned that he had my requisite “data” - my driving history, the car insurance premium I am paying, my residence zip code in US and my household data etc. that suggested I can be a good hit and I was indeed!!
On introspection, I could sense how the right customer data powered by robust analytics tools can enable such small sized firms to successfully switch customers for cost savings in Insurance premiums. Prior to this, I was never approached by any of the big auto and home insurers in the market.
The reason? Data on a customer’s financial well-being, purchase behaviour, family status etc. that’s such a powerful source is not still put to its fullest use by many of the financial services giants. Whenever we think of brands who have leveraged data so successfully, what comes to our mind are the Amazons, Googles etc. and not any from the financial services space.
It may not make real sense to compare the shopping experience of a roku enabled smart tv online vs a capital guaranteed variable life insurance policy with an insurance organization; for the simple reason, an investment linked insurance product calls for focused presales advice followed by years of post-sales service while the contract remains in the insurer’s books. However it’s a lot relevant to look into how data and analytics powered by technology can enhance the product & services offerings across the financial services value chain.
A cursory look at the business strategy of any financial services organization such as an insurer reveals they aren’t any different from other industries, the high level objectives being:
- Grow topline (Gross Revenues) and retain/achieve market leadership by introducing new products and services.
- Control operational costs (thereby grow net income) through business processes and technology optimization – customer facing and internal.
- Enhance customer experience by leveraging technology to the fullest extent.
Though not a solution by itself, customer data and analytics is the foundation. Here you have various front facing technologies such as Web (Portal) & Mobility, mid office services such as APIs and Transaction intensive back office processing systems act to enable firms to achieve their objectives.
Data and analytics strategy for an insurer, like any other IT strategy, should focus on three key questions:
- Why rather easy one to answer given the importance customer centric technology commands today and no organizational strategy exists that doesn’t embody this in some form.
- What --- the business processes that need to be optimized and/or automated and those that have a dependency on data and inferences.
- How --- more than one approach/technologies exist for organizations to harvest, mine and report data. The best to adopt depends on the organizational need, target processes, budget etc.
Though not to the extent of retail, Financial services has seen some interesting use cases where analytics enabled technology solutions have emerged as game changers. Some of them are:
- A leading life & pensions provider wanted to differentiate its post sales retirement services to customers in a competitive market place.
In order to prioritize the customer services that have to be provided online (through the customer portal), the firm engaged in an intensive data collection and analysis exercise. Key customer services such as – loans, withdrawals, annuity payouts, fund transfers, revaluation method switches etc. were prioritized for this online implementation based on the inferences from the analytics exercise.
- A retirement provider wanted to implement a innovative payout product to differentiate itself from competition.
Coming from the part of the globe where “try and if not satisfied, get your refund” is the retail golden rule, this provider indulged in data collection/analysis on their customer needs as well as competitive market offerings to determine what this new product should comprise of.
The conclusion was to introduce a “Try it out” retirement annuity product that would enable the customer to enrol and start receiving the payouts for an initial period during the accumulation phase itself without committing themselves to a payout annuity.
- We are living today in a world of data (organized and unorganized), where each of our daily activities such as
- our morning walk and workouts in the gym
- the number of miles we drive to work and back, the route we take and the way we drive our cars
- the number of hours we spend in our offices and our work activities
- the time we spend in the evenings before going to bed
is getting tracked by the various devices (such as Mobile, laptops, ipads etc.), the mobile apps (Health, Traffic maps, Drive etc.) and the social media platforms (facebook, twitter etc.) we use. Insurers can leverage customer data available through these sources to implement various services, such as:
- Preferred pricing auto and health insurance with rewards for safe driving and health habits
- Mobile services for reporting of auto accidents, alternate cars, access to emergency hospital services etc.
- Loss adjustment services on the fly that would enable timely investigation of insurance claim events and settlement, avoiding costly litigations later on.
Above are instances of how the retail experience influences financial services offerings and what technology powered by analytics makes possible today!!
However adopting big data and analytics for business decision making is not without its own challenges. A key one being, collection of the right data from all relevant sources for an organization to come up with timely, correct inferences.Decisions that are based on incorrect, incomplete or irrelevant data can result in inappropriate and ill timed product offerings/services to customers that can be equally detrimental to the organziation as right data is to its growth.