Speaker Interview: Vishwa Kolla

Vishwa Kolla
AVP, Head of Advanced Analytics

marni

Marni Edelhart
Director Content & Experience

MOMENTUM TRANSPARENT (1)

Marni: Hi this is Marni Edelhart, Programming Manager for the Direct Marketing Association’s 2016 Marketing Analytics Conference, on the line with Vishwa Kolla, AVP and Head of Advanced Analytics for John Hancock Insurance. Vishwa is the Head of Advanced Analytics at John Hancock Insurance and has 15 plus years cross-industry experience spanning the technology, media, telecomm, banking and insurance industries. His expertise is in leveraging advanced analytical techniques as a means of setting and driving business, marketing and sales strategy.

Vishwa has built several analytical platforms that enable executive decision makers to profitably grow their businesses and drive process efficiencies. He received his MBA from Carnegie Mellon University with concentrations in Strategy, Marketing and Analytics. Additionally, he has an MS in Computer Science and a BS from BITS Pilani, India. Thanks for being here Vishwa.

Vishwa: Thank you Marni.

Marni: Could you start by describing some of the questions you are typically trying to answer or problems you are trying to solve as John Hancock’s Head of Advanced Analytics?

Vishwa: Sure. That’s a great question. In my role I have two distinct sets of problems that I am working on. The first set, has to do with improving the entire customer experience for John Hancock as a whole. It starts with identifying good risks and good prospects from the entire U.S. population, and then it continues to helping streamline the acquisition process and cutting down the time of customer application from six weeks to maybe a few hours. Then after that looking for opportunities to nurture the customer relationship, engage with them and then finally retain the best prospects, best customers.

The second set of problems that I am working on is that I have a big team of individuals, all data scientists who are helping me solve all of these problems. Then my group is laying some of the groundwork and foundation of best practices for all of Manulife and John Hancock. My second set of problems are how can I take this and make it into a repeatable pattern and other functions in other areas around the globe.

Marni: Got it. That seems like some big challenges to be working out.

Vishwa: Yeah. They are very exciting.

Marni: What aspects of the business do you think are most impacted by the implementation of advanced analytics?

Vishwa: I would say across the board there is a lot of impact that advanced analytics can bring. We have actually proven the concept of advanced analytics is by being a cost center and then turning it into a benefit center. We’ve realized that and we’ve in the last few months we are able to show demonstrated savings so that we are paying for a few individuals of ours. And the way we are demonstrating savings are we are trying to tackle where the biggest spends are. Wherever the biggest spends are that’s where we can use advanced analytics to optimize those spends.

And not just John Hancock but across the industry, typically the biggest spending are marketing and then where we are trying to acquire new customers and then growth is a priority. And we are able to cut down significantly on marketing costs by identifying target populations, micro segmentation, micro targeting to cut down on the marketing and mail cost.

So we are getting similar responses from a smaller target population. So this is where we are able to get a significant bang for our buck. That said getting into the acquisition space where they are trying to reduce the cycle time of customers from six weeks to a few hours. This would have next biggest impact where we can actually have a lot more people have better experience coming in, into the company and then getting products.

Marni: Got it. So where do you fit into John Hancock’s organizational chart as head of advanced analytics? How do you work with the varying departments to ensure everyone has access to the data need?

Vishwa: I report to the Chief Analytics Officer of Manulife. And she reports to the CEO of Manulife. In terms of reporting structure, this has worked out really well where we are able to get both the visibility as well as being able to kind of change the culture in the company. In terms of helping varying departments to have the access of data at the right level and also at the right folks and the right level we centralized all of the data into several analytical platforms.

One of them includes traditional data warehouses and then the other includes Big Data platforms such as Hadoop. And then we have two instances of this platform. One is more in the Dev kind of a space where we are, it’s more like sandbox and play area. So people are able to access this data. And this data is always be de-identified. So to protect the innocent that we want to kind of let everybody have access to this data, the more the better. But then at the de-identified level, so that they can do their analysis. The second instance we have everything identified so we can actually do execution of campaigns, be it marketing campaigns or what have you.

Marni: And as information technology, data science and marketing increasing overlaps to drive a seamless customer experience, how do you think sales attribution will or should change to key pace?

Vishwa: Yeah. Sales attribution has always been a challenge. If we look at the product buying cycle, it always starts with awareness, then it turns into consideration and then finally a buy. So the last touch typically gets most of the attribution, which is not always right. And then different companies and different industries have used different methods of aided attribution of sales and so on and so forth.

So this problem I would say will remain. I am not so sure it will change a whole lot or should change a whole lot. It is both product centric and then also industry centric. So the different industries would have different attribution methods. Technology industry would actually like have different attribution methods. There’s not a lot of awareness and consideration there. It is more action so it’s cost per click or cost per action. But in an insurance kind of an industry awareness and consideration, they play a lot of role. So I would say the attribution would differ by industry and by product, at the same time I am not so sure it will change a whole lot.

Marni: That will be interesting to watch. So where do you see missed opportunities in the realm of marketing analytics? Is there one change or initiative you would recommend all brands implement to improve their marketing analytics practice?

Vishwa: So in terms of missed opportunities so there are a lot of tools out there in the marketplace that do portions really well. So for example if you think of marketing campaign management so tools like Marketo or Adobe they do really well. But then they would just do that. And then if you are looking at marketing databases and then looking at propensity data and so on and so forth they have vendors like ChoicePoint which is now part of LexisNexis and KBM, Axiom, they give you all of these data elements.

The challenge or the missed opportunity is there is a big amount of effort that needs to be put into place, to bring all of these views into a centralized database. A lot of people talk about it, but then I’ve seen very few of them use it. And all of these vendors, they do something really well, and then they claim to do everything. But then the one organization that does really well if they go that route and take the high road, is your organization, our own organization. And that’s where I find a lot of the missed opportunities.

A lot of time gets wasted in terms of trying to reconcile and then trying to derive insights and aggregates because you don’t have that single view of customers. I would say the biggest missed opportunity is create a data base that starts with the universe of say we are in the U.S. starts with the U.S. population and then map out of that population which sub segment is our own customer base. And then who are the likes in the customer base. And then anytime we bill campaigns or runs campaigns have that process diligently tied back into this master database if you will.

And I have seen a lot of people talk about it. But then not do enough. And this problem is not easy by any means. This problem requires a lot of discipline. It requires experience with big data, and experience with understanding the business aspects of it. And at the same time what I find is people they … It’s not like one is one and done, so if you set up this platform and then you have it ready to go. You have to kind of constantly maintain it. So I would say if we can crack this nut, then all the other problems are much smaller and then this will give us more bang for our buck when we are running any marketing campaigns.

Marni: That makes a lot of sense. Well, Vishwa I really appreciate you taking the time to speak with me today. This is such valuable information for our audience. And I know they will be excited to hear more from you when you speak about attribution and the future of analytics at the marketing analytics conference taking place June 23 and 24 in Austin, Texas. So again thank you very much.

Vishwa: Thank you very much Marni. And good luck with the conference. I am looking forward to taking part in it.