Big Data for Marketing: Retail Store Example

Big Data

Cost and Size scales up and down based upon need

Big Data grew from a need for speed and size. Now, it's everywhere - running Netflix, iTunes, Ebay, Amazon, our banks, schools. We can get information on customers from re-marketing and affiliate companies like Dotomi, Commission Junction and more. 

It's easier to identify businesses that aren't using Big Data than to list the ones who are. The "Taco's Ensenada" restaurant in Duarte, CA where I frequent for fish tacos is probably not using Big Data, except to blast music from a Mexican Pandora channel. Even my tacos are Big Data influenced.

Big Data is everywhere but its influence and how it benefits our services is not very well understood by consumers, or even by experts in the information industry. 

How different is Big Data? -- A retail clothing store example:

Old System: Relational / Queuing Systems:

• You're standing in line at H&M and your arms are full of clothes it took an hour to find. There are three lines, and about 30 people waiting. But, there are only 2 cashiers.
• Everyone waits one at a time for one of the cashiers to free up. The cashier's availability is governed by how quickly they can check out the individual transactions for each person. 
• One guy in line has 10 shirts and a return -- he's the line hog and everyone is going to have to wait for him. Not his fault.
• A teenage girl is in line to buy one bracelet and will wait the same amount of time everyone else does. Not her fault.
• In this model each person waits in a queue to check out. Each is treated as a separate transaction. The time it takes you to check out once you're at the register is determined by  the number of clothes you have, returns, sale items, or your credit card company -- these are sub-transactions. 
• Checkout Time p/p = Multiply time per sub-transaction by clothes, returns, sales items, credit or cash, and number of people, then divide by 2

New Big Data clothing Store: 

• Imagine you walk up to the counter at H&M ready to buy 10 items, and there are no cashiers. Then instantly, 10 cashiers pop up -- one for every item.
• Then, you are physically multiplied by the number of your items and you stand in front of each cashier.
• All of the transactions are done at once. Each takes no time. 
• At the end, you're reassembled into one person, you have your receipt, and you leave the store.
• Imagine this happens for every single customer who will ever buy things.
• Checkout Time p/p = time to do one transaction, divided by endless computers.

That's Big Data -- cost & performance scale up & down for high & low demand

Big Data: scales up or down in cost and size and performance based upon needs.

Big Data: performance is great at large and small data storage sizes

Big Data: is cost effective because it is designed to scale to needs

Big Data: powers nearly every large online business.