Today’s interview is with Anil Kaul who is the CEO and co-founder of Absolutdata, a big data and predictive analytics firm. They describe themselves as decision engineers, where they apply decision sciences to help improve decisions at the world’s largest companies. Anil joins me today to talk about what they are up to and how predictive analytics can be used to help solve the problem of silent customer churn.

This interview follows on from my recent interview – How to use data and analytics to improve the 3R’s with your customers – Interview with Evan Carroll – and is number 169 in the series of interviews with authors and business leaders that are doing great things, helping businesses innovate and delivering great service and experience to their customers.

Highlights of my interview with Anil:

• Anil has been doing predictive analytics work for the last 15 years with Absolutdata.
• Silent customer churn is different from the traditional view of customer churn.
• Anil defines ‘silent customer churn’ as the situation where a customer might (not) cancel your service or they might (not) stop their subscription but any additional expenditure they make or are planning to make will be go to your competition. So, you might think that they are a happy customer because they have renewed or continued to spend with you at the same level but, in fact, there is a lot going on that you don’t know about.
• One way to think about it is to think about a friend that you gradually lose touch with.
• Predictive analytics can help you gain a better understanding of what is happening and, just as importantly, what is not happening with the customers and their use of your product/service such that you can build up a picture of a customer’s DNA.
• Given the explosion of the amount of data that is now available, data is becoming less and less of a problem.
• The challenge is figuring out what is the data telling us, what is happening with a customer base, what the best customers are doing, what average customers are doing, what they are likely to do in the future and what can be done about it.
• Anil makes this real by using an example of a music subscription business, one of their clients, where they have found out that by the time that someone cancels their subscription it is too late to get them back because by this time they have already signed up with someone else. Therefore, predictive analytics is all about figuring out when is that ‘moment’ many, many weeks prior to cancellation that is the time when the firm could do something to turn the customer around. That moment could be when they see a decrease in the level of engagement as compared to their regular patterns or that of other customers.
• When you have a good predictive model built then it will tell you way before something has happened that it is likely to happen. This gives firms the time and opportunity to influence a decision rather than just seeing it happen and having no control over it.
• Predictive analytics gives companies the opportunity to be proactive rather than reactive.
• A good model and dataset will also provide guidance on what to do and how a customer should be contacted.
• Anil uses an example of the NSA in the US that uses these techniques in their anti-terrorist work. However, given the 1,000s and 1,000s of conversations going on their challenge is to use predictive analytics to figure out the intent that comes out of the conversations that they are listening to. That helps them figure out where the real threats are.
• We need to understand the intent of the customer based on the information that we have.
• Predictive analytics is based on two things:
  • 1.Learning from past behaviour, what has worked and what hasn’t; and
  • 2. Conducting experiments and tests to figure out the effect of something new that hasn’t been tried before.
• Anil says that predictive analytics is very good and much better than humans at using the past to help figure out what we should do in the future.
• However, humans are a lot better at the creative stuff i.e. new and innovative stuff that we haven’t done before but that we could try and test in the future.
• Anil relays two examples of firms that they have worked with:
  • The first is the music client (as above) which competes with iTunes. Using predictive analytics they were able to help them figure out a few things:
    • Which customers to spend no time or effort on as they wouldn’t stay, based on their behaviour,and regardless of what the company did to try and keep them;
    • Identify those customers that were about to churn;
    • Identify a series of steps and actions that they could take that would help build loyalty and retention as opposed to just one step that was to stave off churn; and
    • Most people when faced with losing a customer would offer them a discount or something free to try and persuade them to stay. However, they were able to help the music subscription company understand that there was a certain group of customers where money or a gift was not the point. The point was that they hadn’t seen the value in the service. That helped the music subscription business construct a series of actions to help those customers experience the ‘Aha!’ moment. Following that they would then see the value and decide that this is their music subscription service and they had no need to go anywhere else. In this case, the series of actions that they identified and were implemented reduced their customer churn by 25% over a period of 6 months.
  • The second example involves a bank where their customers took all of the usual services (savings, current/checking accounts etc).
    • However, what they noticed was that when their customers wanted other services like stock-trading, for example, they hardly ever considered the bank. This was despite the fact that the bank offered these and other services too.
    • So, in this case, they used predictive analytics to figure out when was the right time to start talking to customers about the next product that they could buy and which was the most likely next product to be bought.
    • Using predictive models they were able to help the bank predict with a 65-70% accuracy rate the next product that a customer would be looking for next and when (month of the year) they would be looking for it. This helped them better plan their marketing and communications to those customers.
    • As a result, over the course of two years, they were able to help the bank increase the average number of products held by the bank’s customers by 25%.
• This is similar to the case cited by Evan Carroll in his interview – the example of Westpac, the fourth largest bank in Australia and their ‘No Me’ programme.
• Customer centricity isn’t about selling someone something that they don’t want. Rather, it is about providing people with products at the right time and the right place based on their needs. If you do that then of course customers are more likely to buy from you.
• Normally, when getting started with this type of endeavour people often recommend starting with your data. However, that’s not what Anil recommends. He recommends that you start with your business problem, figure what information and data you would need to help solve that problem, and compare that to what information and data that you have.
• You will never have 100% of the data that you need but you can still get tremendous value from the data that you have.
• In short, don’t start with the data because you don’t know what you are looking for.
• There are a lot of people talking about data and analytics but Anil is unsure how many are really getting the value out of it as yet.
• Absolutdata have developed a product called Navik Converter that uses data that already exists within companies and combines this with their methodologies to provide a set of recommendations around what campaign to run next, what offer and to which particular type of customer etc etc.

About Anil (taken from his Absolutdata bio)

Dr Anil Kaul is the CEO and co-founder of Absolutdata. A prominent and well-known personality in the field of analytics and research, Anil has over twenty years of experience in marketing, strategic consulting and quantitative modeling. Before starting Absolutdata in 2001, Anil worked at Personify and McKinsey & Company. He has a PhD in quantitative marketing from Cornell University.

Anil is dedicated to the cause of exploring the best practices for implementing analytic solutions in progressive organizations and building capabilities towards robust decision-making. What distinguishes him is his lens, painted by the brush of analytics, that enables him to scope out opportunities across a wide range of scenarios, be it the performance of fortune 500 companies or like the time he used his base in classical analytics to crunch an entire undergraduate semester’s syllabus into two weeks’ worth of study.

He is a recognized thought leader in the industry, having published articles in leading management and academic journals such as the McKinsey Quarterly, Marketing Science, Journal of Marketing Research and International Journal of Research in Marketing. One of his papers was nominated for the Paul Green Award by the American Marketing Association. He has also been an invited as a speaker to McKinsey and the universities of Dartmouth, Cornell, Yale, Columbia, and New York University.

In his spare time, Anil indulgences his keen interest in gadgets, apps and the latest in technology. He is as proud of having cut down his paper usage to a bare minimum (a mere 25 pages this year) as he is of his bargaining skills when it comes to airline ticket prices—ask him how to get a flight at prices less than those available on Internet sites and prepare to be amazed.

Anil thrives in high-energy environments both at work and outside. He is an avid hiker and enjoys the satisfaction of a grueling hike. He has scaled 20,000 feet (6,000 meters) in Ladakh, as well as climbed Mount Whitney (the highest peak in the continental US) and the Grand Canyon both in one day. Anil loves the break from routine that hiking allows, the communion with nature, and that it pushes the limits of what he thinks he can accomplish. He finds that a promising challenge—whether it is leading the company towards the heights of success or literally conquering lofty peaks—just has to be accepted.

Check out Absolutdata’s new product: Navik Converter, say Hi to Absolutdata and Anil on Twitter @Absolutdata and @anil_kaul and connect with Anil on Linkedin here.

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