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so we're here with Mike Nemeth who is
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head of the insurance practice at wns
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global services and peter fader wharton
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marketing professor and most recently
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co-director of the wharton customer
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analytics initiative welcome to both of
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you okay thank you we're here to talk
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about the insurance industry first off
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in what areas of customer experience
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will analytics have the most impact in
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insurance I think the obvious answer is
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that traditionally in the insurance
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world you have three contacts generally
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with your insurance carrier right you
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buy a policy you submit a claim in your
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renewal policy those are the three
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traditional large predictable touch
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points right and and analytics can be
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extremely helpful in all three of those
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instances but I think especially in the
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life insurance industry people are
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thinking more now in terms of predictive
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analytics if you will predicting when
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will you in fact have an interaction
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with your insurance carrier because life
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insurance is really about a journey in
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your lifetime right and various of your
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life experiences trigger the needs that
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insurance carriers provide solutions for
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you graduate from college you get
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married you have children you begin
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preparing for your retirement you have
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grandchildren you want to travel you
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need invested invested monies to pay for
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all of those things so in the life
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industry now they're they're really
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trying to align themselves with the
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events that are going to occur in their
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customers lifetimes both to create a
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better customer experience we know that
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you just got married here's what you
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need and we can help you with that as an
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example but but also in addition to
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improving the customer experience
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obviously it expands the wallet share of
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the supplier of the insurance right too
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often
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producers agents sell the products that
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are easy to sell everybody runs around
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and sells a little term life insurance
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right because that's mandatory everybody
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needs it so they sell that and that's
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all they sell when they make a decent
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living so they're not very well
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motivated to go back and sell the rest
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of the wallet the rest of the lifetime
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experiences and analytics now helps the
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insurance carrier know who are the good
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producers which of their customers are
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getting good service from the companies
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and in what do they need to emphasize
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going forward this is textbook customer
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centricity at least the way that I
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defined it my own book on customer
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centricity which is if we can figure out
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who the right kinds of customers are and
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insurance companies are very good at
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that they know who the good risks are
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they know who the ones who are going to
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be around for a while and pay their
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premiums if we can figure out who the
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right kinds of customers are then just
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opens the door it's what follows that
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really matters if we can figure out
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other ways to enhance the value of those
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customers so it's not just maintaining
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the premiums that we're getting from
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them it's not just selling their maybe
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just some separate unrelated policy but
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if will you be a true trusted advisor
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and find ways to give them unrec amend
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them to other kinds of products and
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services they might actually have
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nothing to do with insurance though I
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have more to do with some of those other
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life events that themselves might be
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associated with changes in their
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insurance again if they can see us as a
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trusted advisor to kind of tell them
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what to do then it's going to be that
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much easier to extract some of that
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created value from them and by the way
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to identify other future policy holders
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who share some of the same kinds of
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characteristics so as we move away from
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just selling that policy you know to
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take in my little piece of it as a sales
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agent and instead focusing on lifetime
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value of how much more can we create
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from an extract from this customer
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that's real customer centricity and
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here's an industry that's in a great
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position to really take advantage of it
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can you talk in greater detail about how
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insurance companies can use analytics to
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improve sales retain clients and improve
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their brand image yes so analytics can
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be a part of that
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entire picture I i think the insurance
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industry is just a fertile ground for
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analytics they applied to all of those
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facets of an insurance carriers business
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but in particular what we're seeing now
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is not only a desire to do a better job
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but a desire to measure how good a job
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am I in fact doing for my customers and
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so analytics are being focused pretty
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tightly on things like customer
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satisfaction metrics and net promoter
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score as ways of measuring how well am i
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doing and then once they've measured how
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well they're doing they can then fine
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tune what they're doing to create better
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Net Promoter scores better customer
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satisfaction scores so you get you get a
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closed-loop effect where you're doing
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analytics up on the front you're testing
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those analytics you're measuring and
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then you're adjusting your behavior as
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you go forward talking about the
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closed-loop one of the really remarkable
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unique things about the insurance
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industry would be actuaries that was
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actually my first job while I was in
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college as I was an actuary just
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measuring these risks and predicting the
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value of customers so here's industry
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that already appreciates the ability to
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to predict and profile and figure out
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what are the right kinds of variables
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what's the right balance between say
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demographics and other kinds of
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behaviors and so on so we have an
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industry that already thinks in terms of
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risk and probabilities and differences
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among different kinds of customers so it
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may be easier said than done but it's a
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matter of taking some of the actuarial
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thinking and just bring it over to the
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business side as well because it's
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actually quite remarkable though out of
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the research so that I do as a professor
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I am literally building the same kinds
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of actuarial models but instead of
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predicting when someone's going to die
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I'm predicting when they're going to buy
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but it's the same statistical
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assumptions it's the same kinds of
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values that go into this kind of work so
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it's actually not a tremendous leap for
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folks and insurance to embrace what they
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already have in that closed loop
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ecosystem and to do more with it we're
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seeing more
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or insurance companies starting to have
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that conversation across different parts
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of the company where they actually can
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learn and benefit from each other how
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can the insurance companies use
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analytics to cut costs and streamline
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claims processes this one might surprise
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you it turns out that customer
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satisfaction with the claims process has
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more to do with the efficiency of the
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process than it does with what's what's
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my resulting payment when you know what
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how much money do I get from my claim
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because because the claims process when
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it's difficult when it's difficult to
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submit a claim when it's difficult to
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understand what your status is what the
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next steps are who's taking care of this
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for me when am I going to get my house
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repaired when is my car going to be
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repaired that set of interactions when
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they go smoothly is actually more
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satisfying to the claimant them is how
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much money did I get what that really
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means is that insurance companies have
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to do a better job of triage just like
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in hospital emergency rooms claims need
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to be triaged when they come in to an
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insurance company is this a simple claim
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that should be paid today is this a
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claim that can be investigated simply
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with police reports or the information
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that's provided by the claimant or do I
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need to assign an adjuster does the
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adjuster have to go out and see the
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damage and the repair or can I simply
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refer the customer to a repair shop and
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have them go ahead and get their car
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repaired if it was damaged in an
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accident or is it something more serious
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do I need a serious senior adjuster am I
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going to end up in litigation is this
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somehow fraudulent do I have to worry
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about a special investigation for this
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thing so knowing all those different
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paths that claims can take and if you
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can know that at the time you take the
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initial claims report and put the claim
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on the proper
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path you not only save money but you
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also improve customer satisfaction and
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in order to know what path to take you
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must have done your analytics homework
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to understand the characteristics of
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every kind of claims report that comes
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in the door I love Mike's answer I don't
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like the question and here's the
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difference the question asked about
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costs but Mike's answer was more about
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enhancing value and I think that's what
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we really want to focus on I mean not to
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ignore cost that's that's certainly a
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big piece of the equation you know
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companies have been pretty cognizant of
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cost forever you know we have this
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visceral reaction we know what the costs
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are but it's a little bit harder to
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measure to anticipate to really
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appreciate the value that we create by
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by handling claims the right way so
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again we do want to be efficient don't
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get me wrong but I think there's there's
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more needle moving opportunity to
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value-enhancing than there is to cost
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cutting and I think a lot of the steps
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and the analytics underlying those steps
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that might just spoke about are ways to
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primarily enhance value while at the
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same time of you know keeping costs in
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check I think it's very important to
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recognize both opportunities through
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analytics and in many cases including
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this one it's more about value creation
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and it is about cost minimization so
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cost savings become a byproduct of good
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customer service and what could possibly
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be a better way to do business so how
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can ensure use analytics to figure out
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the optimal optimal mix of distribution
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channels you know this is a question
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that's a little bit premature actually
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in the maturation of the industry the
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trend today so maybe I'll go back in
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time the trend used to be that she chose
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a singular distribution channel there
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are the well-known very large insurance
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companies that we see advertised all the
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time who had their own company agents
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right you would go to one of their field
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offices and speak to a human being and
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sign up for insurance and and that was a
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trusted distribution channel for decades
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then
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we began to see all of this
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disintermediation the effective online
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capabilities in the internet the effect
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of being able to call a call center
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speak to someone over the telephone and
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and that really had an impact on how
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people thought about distribution and so
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their reaction today is I need to be
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everywhere and so everybody now wants to
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have their own agents independent agents
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an online presence a call center
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capability everybody wants to be
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everywhere and so a future step will be
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now analyzing do you really want to be
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everywhere given the kind of customer
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you have and the kind of products you
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are selling to your customer what are in
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fact the optimal mixes of distribution
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channels for your particular business so
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we're we're actually talking a little
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bit about something that's going to
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happen tomorrow I'm trying to make that
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future happened today at least in my
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academic work which is one way to sort
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this out because yes every company wants
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to be everywhere but that's expensive
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and so we got to figure out where is it
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that we're going to get the best ROI and
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it's not just a matter of just booking
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getting more policies tomorrow it's a
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matter of creating sound like a broken
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record customer lifetime value so if we
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can look at each agent that we have or
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each office or each channel and say
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what's the CLV of the the customers of
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the policyholder's whom we acquire
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through that channel or through the
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activities of those agents how have they
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enhanced by being a trusted adviser the
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the value of existing customers we can
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start to use that as kind of a as a gold
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standard metric to start to say we have
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this incremental dollar to spend which
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kind of channel or which specific agency
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should we be spending it on so I think
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using forward-looking metrics which of
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course arises from this this push
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towards analytics is going to make it I
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want to say it's going to be easy but
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it's going to give us at least an
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objective way to figure out how we can
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allocate this this importance
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Ben decision and I think it all fits
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hand-in-hand the kinds of calculations
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that lead to clv will arise quite
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naturally from the other kinds of
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analytic activities that Mike was
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talking about earlier oh great so what
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are some best practices that companies
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should follow in setting up date and
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analytics governance programs and how do
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you get company-wide support for such
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initiatives yeah I think there those are
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there are two questions there but
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there's a connection between them so I
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think one best practices understanding
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that there's a preparatory phase in
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aggregating organizing transforming data
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for use but that that isn't the end game
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I know I know Peter would agree with me
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that there's probably a little too much
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emphasis on that preparatory step and
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not enough emphasis on let's do
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something with that data the second best
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practice i think is incorporating domain
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expertise into the analytics teams i
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believe that what this means in practice
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is having different analytics teams for
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different domains within a business for
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example a typical property and casualty
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company is going to have a personal
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lines business where they sell insurance
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to us also a commercial lines of
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business where they sell insurance to
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businesses and and those are really two
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different domains and require different
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analytics teams and that probably means
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that you need some sort of an umbrella
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over the top of those domains but on the
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actual project teams you need domain
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expertise because because the key to
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having a successful analytics practice
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within an insurance company is really
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being able to generate a return on
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investment in order to generate a return
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on investment you need the domain
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experts because they're the people who
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understand what questions should be
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answered I call it right to left
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thinking so for four
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our viewers that would be this way on a
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whiteboard where we start with what are
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the answers we're looking for and then
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we work back through how are we going to
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find those answers what data do we need
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what domain expertise what are the right
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analytics tools what are the right
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analytics approaches methodologies to
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apply to get those particular answers
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and if we get valuable answers then
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we'll generate a return on investment
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and if we regenerate a return on
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investment we will then get a adoption
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within the organization and support
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within the organization for what we're
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doing let me pick up on the last point
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that my grazed and he talks about going
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right to left I'm going to talk about
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going from top to bottom which is
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getting that by it I like the idea of
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having that domain expertise of having
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these kind of local experts in each of
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the different product lines doing there
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Alex thing but then you have to have
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this umbrella then you're going to have
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this overall center of excellence it's
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going to be helping to coordinate all
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them here's the issue you can't do that
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from bottom up what happens with a lot
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of companies is very often it's the
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marketing people say hey listen we got
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all this data and predictive analytics
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when you do all this stuff here we can
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we can kind of make marketing better and
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the rest you're going to say okay
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marketing do whatever you want knock
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yourself out that's great but unless you
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can create it that it's truly
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enterprise-wide unless it's going to
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involve that the people in all the
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different functional areas it's going to
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have limited impact has to come from the
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top it has to come from the sea level it
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has to be sea level people not just
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tolerating these analytics is going to
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keep the marketing people happening
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happy but it has to be them embracing it
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and here's an industry again given the
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actuarial heritage an industry that that
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isn't afraid of data that understands
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risks and probabilities let's do it from
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the top let's have a high level
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analytical vision and let's build that
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umbrella and let's kind of sow the seeds
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for the different domain expertise
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throughout the organization instead of
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just waiting for it and hoping that it's
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going to bubble up so Mike is thinking
00:17:39
right to left I'm thinking from top to
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bottom you get all those directions
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right and then good things are going to
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happen
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you
