In this article we will talk about some of the successful applications of analytics. We will start with 2 different examples which I was reading recently from a podcast of Accenture and then move to some of the other industrial examples.
1. Harrah’s Entertainment
Gary Loveman, CEO of Harrah’s, was presenting at an event and sitting in the front row were all of his competitors. He stood up, he showed slide after slide that showed precisely how they use their loyalty card, how they did the analysis, what kinds of metrics they looked at. And their competition was writing everything down.
Finally, after about an hour of this, someone raised their hand and said, “Dr. Loveman, doesn’t it bother you that your competitors are taking down every word you said?” He just looked quizzically at them for a minute and said, “No, not really, because by the time they figure out how to do what I just described to you, we will be so far ahead of them that they will never be able to catch up.”
2. A. C. Milan: A professional football club based in Milan, Lombardy and one of the most successful organizations
Background: A.C. Milan had a situation that was very high-profile and very embarrassing. They hired a player for a lot of money, very expensive. Within a couple of weeks, he promptly blew out his knee and was absolutely useless to them for the rest of the season.
Objective: They realized that they needed to look at not just what somebody’s previous scoring capability was, they needed to look at their potential for playing and contributing to the team. That came down to keeping them healthy.
Approach: So, they actually formed a research group that analyzed every aspect of a player’s moves—how they run, how they jump—and analyzed the likelihood that they were going to become injured.
Outcome / Impact: Using that data, they used it initially to decide who to purchase and who not to purchase. But, over time, they started using it in a different kind of way. They were able to work with the players to help them understand, “You’re turning your left foot out too much. You are going to injure your ankle if you do that.” They actually now meet with each player about twice a month to help them analyze the latest data around their movements. They actually analyze an incredible amount of data; 50,000 data points about a single player; 200 just about their jump. This is a company that is really trying to take analytics to the next level.
3. Financial Industry (Credit Card) – Marketing Analytics
Background: A credit card company has a marketing budget of Rs. 1 cr. or 10 lac pieces of mail set aside for sending out direct mails. If they send out mails to all the available lists that they have, they would need to spend Rs. 10 cr. or 1 cr. pieces of mail. They also know that by running this marketing campaign they will receive at the most 50 thousand new customers.
Objective: From the 1 cr. available pieces they would like to identify the 10 lac which are most likely to respond to the offer so as it to increase the company’s customer base and in turn profitability.
Approach: Review data from their past 5 direct mail campaigns and build a predictive model which can help them identify likelihood to respond and differentiate “High probability to respond prospects” v/s “Low probability to respond prospects”.
Outcome / Impact: Using the data from past campaigns, they were able to build a logistic regression (statistical predictive) model. This model looked at the history and helped them identify the right 1 cr. to mail to book 40 thousand accounts. What it means is that for the rest of 9 cr., they would have booked only 10 thousand accounts (extremely inefficient). This exercise is carried out in majority of direct marketing to ensure the money spent has optimal impact. By leveraging the history this company was able to book 80% of accounts with only 10% of the mail and hence really reducing their cost to book the accounts.
4. Progressive Insurance Industry (Motorcycle Insurance) – Pricing & Risk Analytics
Background: A few years ago everyone was treating motorcyclists the same as if they had the highest risk and needed the highest price for insurance. They were not good credit risk. Everybody knew that and it was conventional wisdom.
Objective: Identify segments within the motorcycle insurance owners who have lower credit risk than average.
Approach: Review data from their past and “DE AVERAGE THE RISK”. They used historic data to determine pockets / segments of customers with “HIGHER THAN AVERAGE RISK” and “LOWER THAN AVERAGE RISK”.
Outcome / Impact: What they found is that while some motorcyclists are really high risk, a majority of them are not. Eg a teacher driving a motorcycle is a lot lower risk than a high school student driving a motorcycle. With this in mind, they were able to offer much lower price to the teacher which increased their receptiveness to the low risk segment and thus increasing their customer base. Progressive has made a real art of skimming these sub-segments / pockets off, carving them out, focusing on them, serving them very well and moving on before anyone notices what has happened to them. And that’s really the heart of the strategy for them.
That is really what makes analytics a sustainable differentiating strategy for these companies. It is because it is not about a single insight; it is about a set of processes they have, a way of using data and incorporating it into their decision making that really helps them transform their business. It makes them much more able to maneuver changing business conditions; it makes them much more likely to anticipate changes in customers and markets; and, most importantly, it allows them to come up with different scenarios and understand how they ought to react to these changing market conditions.
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