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Online Data Capture Chapter 6 Chris Bralczyk Gayle Bradshaw Michelle Guthrie Rachel Donovan Jason Kniebel Bridget Locke Chris Bralczyk Gayle Bradshaw Michelle.

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Presentation on theme: "Online Data Capture Chapter 6 Chris Bralczyk Gayle Bradshaw Michelle Guthrie Rachel Donovan Jason Kniebel Bridget Locke Chris Bralczyk Gayle Bradshaw Michelle."— Presentation transcript:

1 Online Data Capture Chapter 6 Chris Bralczyk Gayle Bradshaw Michelle Guthrie Rachel Donovan Jason Kniebel Bridget Locke Chris Bralczyk Gayle Bradshaw Michelle Guthrie Rachel Donovan Jason Kniebel Bridget Locke

2 Introduction  Chapter 6: Understanding the Internet Consumer  Online Data Capture  2 types of data  Nature of the data  How it is acquired  Chapter 6: Understanding the Internet Consumer  Online Data Capture  2 types of data  Nature of the data  How it is acquired

3 Third-Party Internet Usage Data  Variables include:  Length of time on a website  Pages visited  Destination site after leaving  Number of Internet sessions per day/week/month  Average length of each session  Average number of sites in each session  Watch Out, Google! Amazon Gets Search  A9.com is a powerful search engine, using web search and image search results enhanced by Google, Search Inside the Book, results from Amazon.com, reference results from GuruNet, movie results from IMDb, etc.  Provides a unique set of powerful features to find information, organize it, and remember it all in one place  Variables include:  Length of time on a website  Pages visited  Destination site after leaving  Number of Internet sessions per day/week/month  Average length of each session  Average number of sites in each session  Watch Out, Google! Amazon Gets Search  A9.com is a powerful search engine, using web search and image search results enhanced by Google, Search Inside the Book, results from Amazon.com, reference results from GuruNet, movie results from IMDb, etc.  Provides a unique set of powerful features to find information, organize it, and remember it all in one place

4 Transactional Data  Customer account data  Customer name  Billing address  Acquisition source (catalog, banner ad, search engine)  Ship-to address  Purchase-specific data  SKU numbers for each product  Date of purchase  Total number of items  Total value of purchase  Channel (retail, catalog, Web, etc.)  Method of payment  Customer account data  Customer name  Billing address  Acquisition source (catalog, banner ad, search engine)  Ship-to address  Purchase-specific data  SKU numbers for each product  Date of purchase  Total number of items  Total value of purchase  Channel (retail, catalog, Web, etc.)  Method of payment

5 Who Captures the Data?  The Enterprise itself  Limited by enormous amount of data which must be extracted from operating systems and analyzed  Very expensive  Marketing Data services  Specializes in click stream data capture  Acquires site-specific statistics for only the company website; not for competitors  The Enterprise itself  Limited by enormous amount of data which must be extracted from operating systems and analyzed  Very expensive  Marketing Data services  Specializes in click stream data capture  Acquires site-specific statistics for only the company website; not for competitors

6 How much is too much?  Level of detail at which the data is captured depends on the enterprise’s current and future marketing plans  Tempting to capture the largest possible number of variables at the finest level of detail (“granularity”)  the marketer doesn’t know exactly what he will need  possible to aggregate very detailed data but usually impossible to break out data that was collected at a higher level of aggregation  Level of detail at which the data is captured depends on the enterprise’s current and future marketing plans  Tempting to capture the largest possible number of variables at the finest level of detail (“granularity”)  the marketer doesn’t know exactly what he will need  possible to aggregate very detailed data but usually impossible to break out data that was collected at a higher level of aggregation

7 How much is too much? Cont.  Collection and maintenance of data is a significant marketing expense item; unnecessary detail is expensive to store and cumbersome to handle  “The IT shop usually has a kind of pack-rat mentality, where they want to collect data by default:’If storage is cheap, let’s go ahead and collect it.’ The IT unit generally has no sense of how the line-of-business manager needs to use that data.”  PeopleSoft aligns business strategy with operations to help companies react quickly to unexpected changes in business conditions.  Collection and maintenance of data is a significant marketing expense item; unnecessary detail is expensive to store and cumbersome to handle  “The IT shop usually has a kind of pack-rat mentality, where they want to collect data by default:’If storage is cheap, let’s go ahead and collect it.’ The IT unit generally has no sense of how the line-of-business manager needs to use that data.”  PeopleSoft aligns business strategy with operations to help companies react quickly to unexpected changes in business conditions.

8 www.iwon.com  People register and customize a personal home page  iwon.com tracks people’s behavior and sells the information  iwon.com gives away $10,000 every weekday, $1,000 on the weekend and $1 million every tax day  People register and customize a personal home page  iwon.com tracks people’s behavior and sells the information  iwon.com gives away $10,000 every weekday, $1,000 on the weekend and $1 million every tax day

9 The Dark Side of Online Shopping  Internet investigator Don Garlock’s story  Thieves held web development jobs in Thailand  Debit card number stolen from Amazon.com or randomly generated  Avoid using debit cards  Transactional data is NEVER 100% safe  It CAN happen to anyone!!!!  Internet investigator Don Garlock’s story  Thieves held web development jobs in Thailand  Debit card number stolen from Amazon.com or randomly generated  Avoid using debit cards  Transactional data is NEVER 100% safe  It CAN happen to anyone!!!!

10 Conclusion  Used to understand consumers better  Provides ways to segment consumers through their online behavior  Next Step: implement marketing strategies based on captured information  Provides edge in competitive marketplace  Imposes vulnerability on consumers’ personal information  Beware!!!! Nothing is completely secure!!!!  Used to understand consumers better  Provides ways to segment consumers through their online behavior  Next Step: implement marketing strategies based on captured information  Provides edge in competitive marketplace  Imposes vulnerability on consumers’ personal information  Beware!!!! Nothing is completely secure!!!!


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