The power of analytics.....Let the numbers speak !!

Data is one of the biggest assets for an organization in today’s era. We leave behind trails and data is collected in every transaction we make, every activity we do. If we go to a store to buy something, the receipt is data generated. We withdraw money from an ATM, we leave behind the transaction details. We go to shopping malls and mega stores, our shopping list generates data and our buying pattern and then there are video cameras which even record our movements. Data is everywhere. There is just too much of data. The rate at which data is being generated is also on an increase. Over 90% of the online data has just been created in the last 2 years. If companies can learn to make sense out of the data, if they can generate insights from the data, there is a real gold mine to be unlocked. Data is thus a game changer.

An in-depth data analysis can generate insights on customer behavior, market movements, prevailing trends, product performance, competitor performance etc. etc. Data can help a company reach-out the intended audience in a much more efficient and targeted manner. Thus companies should encourage a data-centric approach to understand customers and make better customer oriented strategies. The 1^st step is to have a Data Warehousing strategy so that the data gets stored in an efficient manner.




Once the Data Warehouses/ Data Marts are created, it is the turn to implement Business Intelligence measures like various reports, dashboards, performance metrics, predictive modeling etc. to generate insights from the data. Business Intelligence is the art of transforming raw data into meaningful and actionable information.

Let us have a look at some of the amazing practical examples of Analytics:

1) Obama’s election campaign:

      Business Intelligence and Analytics played a key role in helping Obama win the American 2012 Presidential elections. A team of data scientists was setup to assist Obama in the electoral campaign. The team collected information about the voters from all available sources and created a central data warehouse. The data was thus organized so that it could be analyzed. A profiling of the voters was done and it was determined how to approach which category of voters. For example women of a particular state would spend any amount of money for a dinner party with George Clooney (and Obama). Such insights helped raise money for the elections. Further a lot of simulations were carried out to predict the election results. Metrics were created to measure the impact of various emails/ Social Media campaigns/ telephone calls etc. as agents to raise money. Various analytical models were developed to determine voter preferences and thus strategies were formed to lure such votes. BI and Analytics helped Obama win the elections in style.   
 

    2) Harrah’s casino:

      Harrah’s is a popular casino chain in USA/UK. Harrah’s established a Data Warehouse and started collecting all sort of data about its customers. The data was collected with the use of membership cards. The data collected helped the company analyze the customers’ spending patterns, gaming habits, reward point’s redemption habits etc. By collecting and analyzing all the data, Harrah’s came to the shocking conclusion that its most profitable customers were not the high rollers (as contrast to the traditional thought) but middle class lawyers, doctors, bankers etc. were the most profitable customers. Harrah’s could find out what their gaming preferences were, what kind of deals they liked. Thus will the help of data, Harrah’s could find who its target customer were and was able to create superior customer oriented strategies and in turn transformed itself into a profitable casino loved by its customers.

3) New York Police Department:

      The New York Police Department used BI and analytics to predict crimes and were successful to quite an extent. The police collected data on all the crimes/burglaries and tried to find out trends and patterns. They also collected a lot of video footage and carried out video analytics. Analysis of all the crime data showed that the criminals were more likely to commit crimes at certain locations at certain times only. This gave the police a head on what the likely crime targets were and when crimes were expected to happen. They were thus better prepared for any crimes, sent patrol parties at the predicted locations and the crime rate fell by up to 30% thanks to data mining and analytics.  

4) Car Insurance Company:

      A car insurance company in South Africa could improve upon its business by using analytics. They used analytics to predict which insurance claims were false and saved a lot of money on false claims. They collected all the historical data and analyzed the same and found patterns/trends in the false claims. For example, they found that most of the accidents happened in the day time and the accidents claimed between 10 pm and 5 am were mostly fraudulent. This helped them validate the insurance claims and the company saved 2.3 Million dollars in just 4 months after incorporating analytics in their business.

PS: With inputs from Harvard Business Cases and IBM Analytics Case Studies.

PPS: Must see this video on analytics (One of the best by IBM):


Cheers,
~Ankit