In this article we’ll talk about some from the successful applications of analytics. We’ll start this week with 2 different examples that we was reading recently from the podcast of Accenture after which proceed to a few of the other industrial examples.1.       Harrah’s Entertainment Gary Loveman, CEO of Harrah’s, was presenting at an event and sitting in the leading row counseled me of his competitors. He stood up, he showed slide after slide that showed exactly how they use their loyalty card, how they did the analysis, what kinds of metrics they looked at. As well as their competition was writing everything down. Finally, after about an hour of the, someone raised their hand and said, “Dr. Loveman, right bother you that your competition is taking down every word you said?” He just looked quizzically at them for any minute and said, “No, not really, because by the time they figure out how to do things i just described to  you, we are so far in front of them that they will not be able to get caught up.”2.       A. C. Milan: A professional football club located in Milan, Lombardy and something of the very successful organizations. ·         Background:A.C. Milan were built with a situation that was very high-profile and very embarrassing. They hired a player for a lot of money, very costly. Within a couple of weeks, he promptly blew out his knee and was absolutely useless to them throughout the growing season. ·         Objective:They remarked that they needed to take a look at not only what somebody’s previous scoring capability was, they have to look at their possibility of playing and adding to the team. That dropped 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 chance that they were going to become injured. ·         Outcome / Impact:By using their data, they used it initially to decide who to purchase and who to not purchase. But, with time, they started utilizing it inside a different kind of way. They were capable of working with the players to help them understand, “You’re turning your left foot out an excessive amount of. You are going to injure your ankle if you do that.” They actually now talk with each player about two times a month to help them analyze the latest data around their movements. They actually analyze an amazing amount of data; 50,000 data points about a single player; 200 just about their jump. This can be a company that’s really trying to take analytics one stage further.3.       Financial Industry (Charge card) – Marketing Analytics: . ·         Background:A credit card company has a marketing budget of Rs. 1 cr  or 10 lac pieces of mail put aside for delivering direct mails. When they distribute mails to any or all the available lists 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’ll receive at most 50 thousand new customers. ·         Objective:From the 1 cr available pieces they would like to know the 10 lac which are probably to reply to the sale in order it to improve the company’s subscriber base and as a result profitability. ·         Approach:Review data from their past 5 direct mail campaigns and make a predictive model which can help them identify likelihood to respond and differentiate “High probability to respond prospects” vs “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 know the right 1 cr to mail to book 40 thousand accounts. What it means is that throughout 9 cr, they would have booked only 10 thousand accounts (extremely inefficient). This exercise is carried out in most of direct marketing to guarantee wes welker jersey the investment property has optimal impact. By leveraging  the history the corporation could book 80% of accounts with only 10% from 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 when they had the highest risk and needed the greatest price for insurance. They were not a good credit score risk. Everybody knew that and it had been the usual understanding. ·         Objective:Identify segments inside nike patriots jersey the motorcycle insurance owners who’ve lower credit risk than average. ·         Approach:Review data from their past and “DE AVERAGE THE RISK”. They used historic data to find out pockets / segments of customers with “HIGHER THAN AVERAGE RISK” and “LOWER THAN AVERAGE RISK”. ·         Outcome / Impact:The things they found is the fact that while some motorcyclists are actually high-risk, a majority of options are not. Eg a teacher driving a motorbike is a lot lower risk than a senior high school student driving a motorbike. With this in mind, these were in a position to offer reduced price towards the teacher which increased their receptiveness towards the low risk segment and thus increasing their subscriber base. Progressive has made a real art of skimming these sub-segments / pockets off, carving them out, focusing on them, serving them perfectly and moving forward before anyone notices what has happened to them. And that’s truly the heart of the technique for them. That is really what makes analytics a sustainable differentiating technique for these businesses. It is because it is not in regards to a single insight; it is about some processes they’ve, a way of using data and incorporating it to their making decisions, that really helps them transform their business. It makes them a lot more able to maneuver changing business conditions; it makes them tom brady jerseys more likely to anticipate alterations in customers and markets; and, most importantly, it allows these to come up with different scenarios and understand how they need to respond to these changing market conditions.