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so we're here with Mike Nemeth who is head of the insurance practice at wns global services and peter fader wharton
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marketing professor and most recently co-director of the wharton customer analytics initiative welcome to both of
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you okay thank you we're here to talk about the insurance industry first off
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in what areas of customer experience will analytics have the most impact in insurance I think the obvious answer is
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that traditionally in the insurance world you have three contacts generally with your insurance carrier right you
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buy a policy you submit a claim in your renewal policy those are the three traditional large predictable touch
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points right and and analytics can be extremely helpful in all three of those instances but I think especially in the
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life insurance industry people are thinking more now in terms of predictive analytics if you will predicting when
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will you in fact have an interaction with your insurance carrier because life insurance is really about a journey in
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your lifetime right and various of your life experiences trigger the needs that insurance carriers provide solutions for
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you graduate from college you get married you have children you begin preparing for your retirement you have
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grandchildren you want to travel you need invested invested monies to pay for all of those things so in the life
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industry now they're they're really trying to align themselves with the events that are going to occur in their
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customers lifetimes both to create a better customer experience we know that you just got married here's what you
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need and we can help you with that as an example but but also in addition to improving the customer experience
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obviously it expands the wallet share of the supplier of the insurance right too
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often producers agents sell the products that are easy to sell everybody runs around
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and sells a little term life insurance right because that's mandatory everybody
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needs it so they sell that and that's all they sell when they make a decent living so they're not very well
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motivated to go back and sell the rest of the wallet the rest of the lifetime experiences and analytics now helps the
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insurance carrier know who are the good producers which of their customers are getting good service from the companies
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and in what do they need to emphasize going forward this is textbook customer centricity at least the way that I
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defined it my own book on customer centricity which is if we can figure out who the right kinds of customers are and
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insurance companies are very good at that they know who the good risks are they know who the ones who are going to
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be around for a while and pay their premiums if we can figure out who the right kinds of customers are then just
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opens the door it's what follows that really matters if we can figure out other ways to enhance the value of those
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customers so it's not just maintaining the premiums that we're getting from
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them it's not just selling their maybe just some separate unrelated policy but
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if will you be a true trusted advisor and find ways to give them unrec amend them to other kinds of products and
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services they might actually have nothing to do with insurance though I have more to do with some of those other
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life events that themselves might be associated with changes in their insurance again if they can see us as a
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trusted advisor to kind of tell them what to do then it's going to be that much easier to extract some of that
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created value from them and by the way to identify other future policy holders who share some of the same kinds of
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characteristics so as we move away from just selling that policy you know to take in my little piece of it as a sales
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agent and instead focusing on lifetime value of how much more can we create from an extract from this customer
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that's real customer centricity and here's an industry that's in a great
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position to really take advantage of it can you talk in greater detail about how
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insurance companies can use analytics to improve sales retain clients and improve
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their brand image yes so analytics can be a part of that entire picture I i think the insurance
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industry is just a fertile ground for analytics they applied to all of those facets of an insurance carriers business
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but in particular what we're seeing now is not only a desire to do a better job
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but a desire to measure how good a job am I in fact doing for my customers and so analytics are being focused pretty
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tightly on things like customer satisfaction metrics and net promoter score as ways of measuring how well am i
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doing and then once they've measured how well they're doing they can then fine
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tune what they're doing to create better Net Promoter scores better customer
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satisfaction scores so you get you get a closed-loop effect where you're doing
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analytics up on the front you're testing those analytics you're measuring and
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then you're adjusting your behavior as you go forward talking about the closed-loop one of the really remarkable
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unique things about the insurance industry would be actuaries that was actually my first job while I was in
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college as I was an actuary just measuring these risks and predicting the value of customers so here's industry
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that already appreciates the ability to to predict and profile and figure out what are the right kinds of variables
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what's the right balance between say demographics and other kinds of behaviors and so on so we have an
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industry that already thinks in terms of risk and probabilities and differences among different kinds of customers so it
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may be easier said than done but it's a matter of taking some of the actuarial
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thinking and just bring it over to the business side as well because it's actually quite remarkable though out of
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the research so that I do as a professor I am literally building the same kinds of actuarial models but instead of
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predicting when someone's going to die I'm predicting when they're going to buy
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but it's the same statistical assumptions it's the same kinds of values that go into this kind of work so
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it's actually not a tremendous leap for folks and insurance to embrace what they
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already have in that closed loop ecosystem and to do more with it we're seeing more
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or insurance companies starting to have that conversation across different parts
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of the company where they actually can learn and benefit from each other how can the insurance companies use
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analytics to cut costs and streamline claims processes this one might surprise you it turns out that customer
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satisfaction with the claims process has more to do with the efficiency of the process than it does with what's what's
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my resulting payment when you know what how much money do I get from my claim because because the claims process when
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it's difficult when it's difficult to submit a claim when it's difficult to
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understand what your status is what the next steps are who's taking care of this
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for me when am I going to get my house repaired when is my car going to be repaired that set of interactions when
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they go smoothly is actually more satisfying to the claimant them is how much money did I get what that really
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means is that insurance companies have to do a better job of triage just like in hospital emergency rooms claims need
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to be triaged when they come in to an insurance company is this a simple claim that should be paid today is this a
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claim that can be investigated simply with police reports or the information that's provided by the claimant or do I
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need to assign an adjuster does the adjuster have to go out and see the damage and the repair or can I simply
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refer the customer to a repair shop and have them go ahead and get their car repaired if it was damaged in an
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accident or is it something more serious do I need a serious senior adjuster am I
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going to end up in litigation is this somehow fraudulent do I have to worry about a special investigation for this
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thing so knowing all those different paths that claims can take and if you can know that at the time you take the
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initial claims report and put the claim on the proper path you not only save money but you
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also improve customer satisfaction and in order to know what path to take you must have done your analytics homework
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to understand the characteristics of every kind of claims report that comes in the door I love Mike's answer I don't
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like the question and here's the difference the question asked about costs but Mike's answer was more about
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enhancing value and I think that's what we really want to focus on I mean not to
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ignore cost that's that's certainly a big piece of the equation you know
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companies have been pretty cognizant of cost forever you know we have this visceral reaction we know what the costs
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are but it's a little bit harder to measure to anticipate to really appreciate the value that we create by
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by handling claims the right way so again we do want to be efficient don't get me wrong but I think there's there's
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more needle moving opportunity to value-enhancing than there is to cost cutting and I think a lot of the steps
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and the analytics underlying those steps that might just spoke about are ways to
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primarily enhance value while at the same time of you know keeping costs in check I think it's very important to
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recognize both opportunities through analytics and in many cases including this one it's more about value creation
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and it is about cost minimization so cost savings become a byproduct of good customer service and what could possibly
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be a better way to do business so how can ensure use analytics to figure out the optimal optimal mix of distribution
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channels you know this is a question that's a little bit premature actually in the maturation of the industry the
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trend today so maybe I'll go back in time the trend used to be that she chose
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a singular distribution channel there are the well-known very large insurance companies that we see advertised all the
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time who had their own company agents right you would go to one of their field offices and speak to a human being and
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sign up for insurance and and that was a trusted distribution channel for decades
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then we began to see all of this disintermediation the effective online capabilities in the internet the effect
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of being able to call a call center speak to someone over the telephone and and that really had an impact on how
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people thought about distribution and so their reaction today is I need to be everywhere and so everybody now wants to
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have their own agents independent agents an online presence a call center capability everybody wants to be
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everywhere and so a future step will be now analyzing do you really want to be everywhere given the kind of customer
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you have and the kind of products you are selling to your customer what are in fact the optimal mixes of distribution
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channels for your particular business so we're we're actually talking a little
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bit about something that's going to happen tomorrow I'm trying to make that
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future happened today at least in my academic work which is one way to sort this out because yes every company wants
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to be everywhere but that's expensive and so we got to figure out where is it
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that we're going to get the best ROI and it's not just a matter of just booking
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getting more policies tomorrow it's a matter of creating sound like a broken
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record customer lifetime value so if we can look at each agent that we have or each office or each channel and say
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what's the CLV of the the customers of the policyholder's whom we acquire
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through that channel or through the activities of those agents how have they enhanced by being a trusted adviser the
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the value of existing customers we can start to use that as kind of a as a gold standard metric to start to say we have
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this incremental dollar to spend which kind of channel or which specific agency should we be spending it on so I think
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using forward-looking metrics which of course arises from this this push towards analytics is going to make it I
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want to say it's going to be easy but it's going to give us at least an objective way to figure out how we can
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allocate this this importance Ben decision and I think it all fits hand-in-hand the kinds of calculations
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that lead to clv will arise quite naturally from the other kinds of analytic activities that Mike was
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talking about earlier oh great so what are some best practices that companies should follow in setting up date and
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analytics governance programs and how do you get company-wide support for such initiatives yeah I think there those are
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there are two questions there but there's a connection between them so I think one best practices understanding
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that there's a preparatory phase in aggregating organizing transforming data
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for use but that that isn't the end game I know I know Peter would agree with me
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that there's probably a little too much emphasis on that preparatory step and
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not enough emphasis on let's do something with that data the second best practice i think is incorporating domain
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expertise into the analytics teams i believe that what this means in practice is having different analytics teams for
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different domains within a business for example a typical property and casualty company is going to have a personal
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lines business where they sell insurance to us also a commercial lines of business where they sell insurance to
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businesses and and those are really two different domains and require different analytics teams and that probably means
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that you need some sort of an umbrella over the top of those domains but on the actual project teams you need domain
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expertise because because the key to having a successful analytics practice within an insurance company is really
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being able to generate a return on investment in order to generate a return on investment you need the domain
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experts because they're the people who understand what questions should be answered I call it right to left
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thinking so for four our viewers that would be this way on a whiteboard where we start with what are
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the answers we're looking for and then we work back through how are we going to
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find those answers what data do we need what domain expertise what are the right
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analytics tools what are the right analytics approaches methodologies to apply to get those particular answers
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and if we get valuable answers then we'll generate a return on investment and if we regenerate a return on
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investment we will then get a adoption within the organization and support within the organization for what we're
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doing let me pick up on the last point that my grazed and he talks about going right to left I'm going to talk about
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going from top to bottom which is getting that by it I like the idea of having that domain expertise of having
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these kind of local experts in each of the different product lines doing there Alex thing but then you have to have
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this umbrella then you're going to have this overall center of excellence it's
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going to be helping to coordinate all them here's the issue you can't do that
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from bottom up what happens with a lot of companies is very often it's the marketing people say hey listen we got
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all this data and predictive analytics when you do all this stuff here we can we can kind of make marketing better and
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the rest you're going to say okay marketing do whatever you want knock yourself out that's great but unless you
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can create it that it's truly enterprise-wide unless it's going to involve that the people in all the
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different functional areas it's going to have limited impact has to come from the
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top it has to come from the sea level it has to be sea level people not just tolerating these analytics is going to
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keep the marketing people happening happy but it has to be them embracing it and here's an industry again given the
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actuarial heritage an industry that that isn't afraid of data that understands
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risks and probabilities let's do it from the top let's have a high level
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analytical vision and let's build that umbrella and let's kind of sow the seeds
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for the different domain expertise throughout the organization instead of just waiting for it and hoping that it's
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going to bubble up so Mike is thinking right to left I'm thinking from top to
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bottom you get all those directions right and then good things are going to happen
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you