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How AI enables Customer Centricity

By Vishwa Kolla, AVP, & Head of Advanced Analytics, John Hancock Insurance

Vishwa Kolla, AVP, & Head of Advanced Analytics, John Hancock Insurance

Traditionally, companies were more product-centric. Today, regardless of industry, sector or the way business is conducted (b2c vs b2b vs b2b2c) companies cannot do away without being customer-centric. It is only reasonable for us (as buyers or consumers or customers) to have a tall order of expectations. We expect options, expect a seamless experience, and expect to be treated well. We expect our service to be fast and relevant. We want our preferences remembered. We expect the interactions to be personal. We might start with a notable brand, but don’t mind switching for the right value. In summary, we all are a segment of one. It costs four times to acquire a new customer than to retain an existing one. No wonder furthering customer centricity has become one of the top goals for most CEOs.

Fortunately, if there is one thing there is no dearth of, it is data. Firms that have embedded data and algorithms (ML and AI) into everyday decision making have enjoyed sustained Cumulative Average Growth Rate (CAGR) of 7-12 percent over 1.3 decades starting 2001. Their CAGRs handily beat respective industry averages of 1-5 percent (Source: Revenue figures from Capital IQ). If a firm is not thinking along these lines, it may be leaving a lot of money on the table and might suffer from an obsolescence risk.

It is not surprising to see why AI is on top of everyone’s mind. Digital Assistants (Siri, Alexa, Google Home, Cortana), chat bots, self-driving cars, face passwords, algorithmic trading, personalized news feeds, personalized product and movie recommendations and for the techno-geeks Eugene Goostman who arguably passed the Turing test—AI is ubiquitous.

“The role of AI has rapidly evolved from being talked about to becoming an integral part of how business is done today.”

AI was founded as an academic discipline and as a branch of Computer Science, 61 years back, in 1956. Although related, AI methods differ from traditional machine learning (ML) methods. Traditional ML methods generally include supervised and un-supervised learning, dimensionality reduction, anomaly detection and non-linear learning. AI use cases include image recognition (facial passwords on iPhone X), reasoning (medical diagnosis), building knowledge banks (Watson), working with speech and natural language constructs of processing, generation and translations (Digital Assistants). In both realms, the list is not nearly exhaustive, but is illustrative.

The role of AI has rapidly evolved from being talked about to becoming an integral part of how business is done today. It may help to think along the continuum of the customer life cycle. As a case study, we will anchor the rest of the conversation to Insurance.

First: Customer Prospecting

An old adage in this realm is “Half of marketing dollars go waste. And we don’t know which half.” Today, new technologies (Cloud) and methods (ML and AI) can help cull through massive quantities of data. For e.g., a propensity to respond algorithm can be built in just a few hours despite having to cull through several thousand dimensions on an entire US adult population database (involving several hundred million observations). Consequently, targeting becomes very efficient and marketing dollars can be spent more optimally.

Second: Customer Acquisition

The price of the financial product (e.g., Life Insurance) is determined by the risk profile of an individual. Behind the scenes firms use trolls of data from non-invasive sources (driving records, prescription history and medical history) and invasive sources (blood, urine, labs) to underwrite policies. The body fluid collection process contributes about 90-95 percent of the decision-making time. In any buying process, the longer it takes to move from consideration to a purchase, the lower the likelihood is of a conversion. Advances in (ML and AI) algorithms, access to digitized versions of traditional data and access to new non-traditional data sources (activity for e.g.) are allowing carriers to either reduce/eliminate invasive underwriting especially for the healthier block. While carriers may not satisfy 100 percent of prospects, such a process still satisfies the healthier group which otherwise would likely be averse to considering insurance purchase, given the inconvenience.

Taking customer-centricity idea a little further, advances in wearables have allowed for more customers to benefit from wellness programs, especially in the Life space. Until recently, individuals with certain pre-existing conditions (for e.g., Diabetes or AIDS) found it extremely hard to buy insurance. These conditions are only bad if not properly managed. Carriers in S.Africa underwrite people with AIDS because they prescribe to strict medication adherence and carriers in US are starting to issue policies to the diabetics. This is a win-win for both the individual as well as the carrier. The individual stays healthier, longer. A healthier population generates actuarial surpluses that some firms can and are willing to share back with the individual. As Michael Porter mentions, creating shared value is the new way for companies to achieve economic success and more and more firms are moving in this direction. Data and sophisticated algorithms (for what one could call as continuous risk scoring) make such business models feasible.

Third: Customer Nurture

The holy-grail for the auto-insurance industry is to bring about a behavioural change towards better driving. Behavior is most difficult to change, but with timely coaching, is possible. Simple adjustments to breaking, jerking or driving w/o holding a phone can significantly drive down crash risk. Telematics and/ or apps on a mobile phone can make such a process feasible. These apps either give instant or timely feedback. With minimal technology investments and likely bold thinking, outcomes can be dramatically favourable. Sharing some of the benefits back with the individual for better driving can help with retaining a customer longer, reduce their disruption risk, and reduce loss and combined ratios. Again, sophisticated algorithms make this model feasible.

In a nutshell, advances in data collection devices (wearables and telematics), advances in technologies (cloud and big-data) and advances in the algorithms (AI and ML) coupled with a good business models around sharing incremental value generated, can all work well together to further a firm’s customer-centricity goals.

Analytics Solutions Special