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we're here with Raj sivakumar who is head of the travel technology and strategy unit at wns a global business
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process management company and Peter fader who is a warden marketing professor and most recently co-director
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of the Wharton customer analytics initiative and we're also joined by Mike Nemeth who is head of the insurance
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practice in north america for wns so welcome everyone good morning good morning so people are talking a lot
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about big data and customer analytics what is customer analytics and why should companies pay attention to that
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so on one in customer analytics has been around forever from the time that marketing as we know it today was born
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let's think about the 1950s or so when we started realizing that customers are
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different from each other and that there's different ways that we can meet their wants and needs and anticipate
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what is that they might want next and get smarter about how we'll deliver it
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so we started collecting a lot of data started with demographics sprinkled in a little bit of behavior start asking
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questions about attitudes started getting physiological measurements as well then let's talk a mix in a little
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bit of social too so a lot of it is both being smart about the kinds of data that
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we should be collecting in order to make better decisions but then the analytics
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part is getting beyond the data or more specifically below the data it's telling
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stories about the true underlying unobservable processes that are driving that data and driving business success
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so if you think about analytics one of the ways that we like to break it down is into three broad buckets we have
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descriptive analytics we have predictive analytics and prescriptive analytics the
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name is a reasonably self-explanatory but it's interesting to see where the the boundaries and the synergies are
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between them so with the script of analytics that's just all about the data so let's collect data let's let's come
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up with with suitable summaries of it let's let's do some data visualization
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let's do some data science to really take the raw data and best frame what's going on then when
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we get to predictive analytics the word predictive is a little bit misleading it's not only about prediction but it is
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this idea of drawing insights that aren't directly observable in the data that's where we want to pull out
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people's true underlying propensity which is going to help us make predictions and it's going to help us
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make better decisions but predictive analytics I mean that's really quite literally the heart of the analytics is
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is the models that we build stories that we tell to really understand what's
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going on and then we layer on top the prescriptive part so now that we know what's really going on and now we can
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project what's going to happen next what do we do about it so how are we going to
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optimize if we have a pile of money to spend how are we going to allocate it across different kinds of activities or
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different customer segments or different geographic areas so it's all about this
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this notion of descriptive predictive and prescriptive and that of course leads to the decision making which is
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what my colleagues here can talk with much more expertise about so what are some of the data sources companies are
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using in customer analytics so as I mentioned earlier there's a lot of generic data sources some of which are
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becoming super hot some of which are tried-and-true so it all starts with demographics not to suggest that that's
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necessarily the best but it certainly is the oldest in the most common there's a
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lot of companies that even when they find better data sources they care so much about just simple observable
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characteristics of the customer so it's going to be things like age and gender &
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geography moving a little bit into media habits what kind of car you own tell me
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more about the characteristics of the zip code that you live in so demographics will kind of spread itself
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out and will sometimes get into things like I said media habits there wouldn't
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be a demographic it would really be more of a behavior but it's still something
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that we often use to label people and then as we move from kind of who people are we move to what is it the thinking
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about so that's what we're going to move into attitudes so things like your wants
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and needs your frustrations one of the real common attitudinal metrics that we focus on today would be net promoter
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score so you know would you reckon and this particular service to someone else that's just one of a myriad
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different attitudinal metrics the other end there will be different kinds of behavioral metrics so we might say
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doesn't matter what people look like doesn't matter what they say it's all
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about what they do and so that's going to be the transactions that people make
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it's going to be their interactions with a website it's going to be their
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interactions with each other it's going to be their responses to inbound and
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outbound marketing activities and then we can that gives us a segue to the next one which would be social so we care a
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lot about who someone is connected with so how many people do you have in your social graph how many of those links are
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inbound people looking at you versus outbound you're looking at other people
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what's the how central are you to the overall social network so as many different kinds of social activities and
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then a real big one that that's really taking off today would be different kinds of physiological measures so if we
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think about the whole a wearables revolution so let's measure heart rate let's let's track people's eyes let's
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let's let's look at that they're at their at their movements not just where
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they're going but how fast they're moving and so on so so is a range of different kinds of metrics and the real
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beauty of analytics isn't just collecting a lot of data but it's figure out ways to do it in a really
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synergistic manner that we can draw insights from these different kinds of metrics collectively that we couldn't
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draw from any one of these types by itself so what are some of the common mistakes and companies make when they
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collect and use big data as well as when they deploy analytics tools so what should they be doing well I think the
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first mistake that people make is in the insurance industry in particular i should say is that they assume that the
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way they have the data organized and and the data they're storing is going to be
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useful in an analytics project and and it isn't always one of the first barriers is how can i reorganize
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information from the various places that I have connections to internal and external how can I organize that data
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and how can i transform the information to make it usable in an analytics project so it comes as a surprise
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sometimes two people they tend to hire a bunch of expert analytics people they buy tools they put them all in a room
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and they say we have a lot of data which insurance companies have massive amounts
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of data and they say tell us some insights and it's just really not that simple the very first step is what data
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are we going to use where are we going to create a new data store that's used
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specifically for analytics purposes how do we manage that data replicate that data over time is is really the first
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challenge that people tend to tend to face under the travel industry of just tried what Mike said with the with the
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data collection becoming so much cheaper storing data becoming so much cheaper unfortunately the emphasis on data
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collection okay has overshadowed the emphasis on analytics and so a lot of lot of companies lot of lot of people
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collect data to what purpose and the key is to be able to ask the right question
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to get to the right answer okay you know the asking the right question is so much
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more important okay and because we asked the wrong question with the technology that we have we can really quickly get
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to the wrong answer so the emphasis on analytics the emphasis on interpreting the data the emphasis not realizing that
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it's all about trade-offs is so much more important in the current environment I surely agree and and I
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think about the old days again I'm a real historian of marketing and business
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there's so much that we can learn when we didn't have all of this data when
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there really was more focused on decision-making than on data collection and data management and companies were
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pretty good at taking the limited amount of data and squeezing as much value out
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of as possible unfortunately today a lot of companies are saying well we have all
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of this data that we didn't have four years ago so therefore whatever we knew
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back then is irrelevant so think it's important to understand to think before
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collecting data to think about what kind of data you need in order to address the
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specific questions and hypotheses that you have in mind rather than this idea of if we build it that is a data
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warehouse amazing things are going to happen so we're all on the same page about that well some companies as
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particularly once in the banking and insurance industries sit on a lot of data as you guys mentioned but they
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don't really mine it to great effect can you give more details and information
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about the barriers that stop companies from mining the data to great effect sure I think it begins with the fact
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that the data that's been traditionally collected in the insurance industry is
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not about the customer we have elementary information like Peter mentioned earlier gender age location
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things of that nature but really most of the data that is being sat upon by insurance companies is information about
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the risk not about the customer so it's about the house it's about the car it's
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about business as opposed to about the customer and we can Intuit a certain amount of customer information from
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information about the risk but most of it is not about the customer so collecting information specific to the
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customer it's a relatively new thing in the insurance industry and so these doors are suddenly wide open and like
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Peter mentioned just a moment ago all of a sudden we have this influx of information that insurance companies
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have not had a lot of experience with and so they don't really know how to interpret the meaning of some of that
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information how to combine it with information they do have experience with to come up with good analytic results
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and and and I think one of the keys to making that work is to add domain expertise to the analytics project teams
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and this is sometimes overlooked unfortunately we hire analysts we buy tools we have data we think those are
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the three components to to produce these fantastic results and they forget about
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the domain expertise that needs to go into the mix and Raj hit it right on the nail when he said and and I I think we
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have more questions about this so i won't go too deeply into it yeah at the moment but analytics is all about asking
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the right questions and the people who know what the right questions are are the domain experts I do want to take
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everything that might just said take out the word insurance and financial services and plug in pretty much any
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other industry and this name applies in fact in many ways insurance it might be a step ahead of many other sectors
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because traditionally they have looked at say risks differently for different kinds of customers as opposed to a lot
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of other sectors that have looked at the customer in some kind of singular way there is the customer but but indeed the
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idea that our data collection has been much more focused on the products that we develop and the activities that we do
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to develop and serve those products as opposed to those previously faceless nameless customers out there that we're
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creating the demand for them that is a change and I like to believe that a lot of the activities that I'm doing and
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that happened at say in academia in general are trying to get companies to kind of wake up and realize that it's
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not just a matter of collecting more data about your products it's about changing the kind of data the kinds of
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questions that you're asking in a very transformational way and just to add to
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what Peter said and Mike said and unfortunately like Peter exactly referred to the the issues that Mike
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talked to board he cannot take ownership just in the insurance industry its industry agnostic and perhaps what kind
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of rares that head in the travel industry and particularly with Airlines is the issue of trade-offs okay let me
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give you a very simple example right so the marketing department would like to ensure that they're the customer the
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highest year gets the preference in terms of seat assignment and travel where is the revenue management
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department would like to make sure that every single passenger pays the highest price so this trade-off between what do
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you charge a customer vs you alligator cuz allocate a high-value customer who may not be paying a high
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value sorry high value on that flight becomes a classic trade-off so you know the companies that understand the
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trade-offs better again leverages the data to understand the trade-offs better is going to be well served so what's
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next for customer analytics so right now it's been so much about the data and I
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think we've we've made it very clear and I hope that that people resonate with
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ASD is that it's not just a matter of collecting more data so let's go back to
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the basic rubric of the descriptive the predictive and the prescriptive there's
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been so much attention these days on the descriptive part which is let's collect
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lots of data let's make lots of pretty pictures let's do a lot of what we call
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data science the problem is when we talk about data science there's been too much
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emphasis on the data and not enough emphasis on the science and so I think that the next generation as we start
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seeing that there's limits first of all not only to how much data we can collect
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but the quality of data we collect is going to start saying let's not collect
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any more let's think more carefully about that data let's understand the
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processes that are driving in the first place and let's get smarter about the
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ways that we can layer on top different kinds of prescriptive or optimal elements so I think we're going to see I
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don't want to say a shift i'm not saying we're moving away from data by any means
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but a broadening of our horizons a little bit more of the science to balance out the focus that we've had on
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data so far you