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New Frontiers in Business Intelligence

Nari Kannan

Auction Price Business Intelligence!

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Have you ever wondered what happens to all those Car Leases when they expire in 3 or 4 years. Those that you see advertised in relentless commercials for automobiles on TV?

Well they all end up back with the manufacturer that auctions them off to be sold as used cars again!

And the typical numbers are staggering - typically 250,000 or more PER MANUFACTURER a year!

And finding the right price at which to sell these cars is an Art, not a science! You sell too many of them in one location, price goes down. You try to sell a convertible in North Dakota, it fetches a lower price than in LA! Do it in winter time rather than summer time, you get different results. Do it when other manufacturers also have a large inventory of similar cars in the same area (like Florida- that Rental Car capital of the US!), you get a lower price! 

Recently, I came to know about a company Opera Solutions that is very heavily into Predictive Analytics. They mix a healthy dose of automated, data driven predictive analytics with human judgment, heuristics and insights to come up with the right answers! And the interesting part is that this intelligence is dependent upon the time, place, season and availability of similar cars in that location at that time!

 

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If you are wondering who Opera Solutions are, they are one of the two finalists in the Netflix Prize competition - a $1M prize to the team that can improve the performance of the Netflix Recommendation Engine by 10%. The one that picks a movie that you would like based on the movies you did like!

But here, they have built this iPad application pictured above using pretty much the same approaches they took there.  This app can help a car company respresentative get an auction price recommendation in real-time, that will maximize their chances of selling them at that price and also maximizing their overall take based on the place, time, season, other similar cars and what they sold for, etc.

It shows in one screen what the other cars sold for, the prices others paid for them, etc that helps the person determine the right price to accept an offer!

Sometimes past data and old BI may not be as useful as data as it is happening or very recent, and this is a  classic example of that kind of predictive analytics!

Opera uses a large combination of Non-Linear predictive models like Neural Networks, Markov Chains, etc to tease out associations between the various pieces of data you have and use them very effectively!

If their results are not compelling, they would not have become a finalist in the Netflix Prize Competition nor would so many large companies like auto companies make their solution part of their complex operations!

For having lived long, I have experienced many instances of being obliged, by better information or fuller consideration, to change opinions, even on important subjects, which I once thought right but found to be otherwise.  - Benjamin Franklin

Nari Kannan's blog explores how new approaches to business intelligence can help organizations improve the performance of business processes--whether these processes are creative or operational, internally-focused or customer-facing, intra-departmental or across functions.

Nari Kannan

Nari Kannan started and serves as the CEO of appsparq, a Mobile Applications development company based in Louisville, KY with offices in Singapore and India. Nari has over two decades of experience in computer systems development, translating product and service strategy into meaningful technology solutions, and both people and product development. Prior to this, he has served as both Chief Technology Officer and Vice President- Engineering in six successful startups, two of which he co-founded. He has proven experience in building companies, engineering teams, and software solutions from scratch in the United States and India. Prior to this, Nari started Ajira Technologies, Inc., in Pleasanton, CA, where he served as Chief Executive Officer for more than six years. While at Ajira, Nari was instrumental in developing service process management solutions that modeled, monitored, and analyzed business processes, initially targeting the Business Process Outsourcing (BPO), Telecom, and Banking verticals in India, and Finance, Insurance, and Healthcare verticals in the United States. Prior to this, he served as VP-Engineering at Ensenda, an ASP for local delivery services. He also served variously as Chief Technology Officer or VP-Engineering at other Bay-Area venture funded startups such as Kadiri and Ensera. He began his career at Digital Equipment Corporation as a Senior Software Engineer. Nari has a long involvement with Customer Support and other customer facing processes. At Digital Equipment Corporation he was involved with their 1800 person customer support center in Colorado Springs, Colorado. He was tasked with coming up with innovative tools to help customer support people do their jobs better. He holds a U.S patent for a software invention that automatically redirected email requests for customer support to the right group by digesting the contents of the request and guessing at which software or hardware support group is best equipped to handle it. At Ensera, he led a 45 person team in developing an internet based ASP service for handling auto insurance claims, coordinating information flow between end-customers, Insurance companies, Repair shops and Parts suppliers. Ensera was acquired by Mitchell Corporation in San Diego. Nari holds a B.S. degree in Physics from Loyola College, and an M.B.A degree from the University of Madras in Madras, India. He graduated with a M.S. degree in Computer Science from the University of Massachusetts at Amherst in 1985. Contact Information: Nari Kannan. Email: nari@appsparq.com Mobile: 925 353 0197. Website: www.appsparq.com View more .

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