Business Intelligence: Online Profiling

Online profiling is using a person’s online identity to collect information about them, their behaviors, their interactions, their tastes, etc. to drive a targeted advertising (McNurlin et al., 2008).  Online profiling straddles the point of becoming useful, annoying, or “Big Brother is watching” (Pophal, 2014).  Profiling can be based on simple third-party cookies, which are unknowingly placed when an end-user travels to a website and depending on the priority of the cookie, it can change the entire end-user experience when the visit a site with targeted messages on banner adds (McNurlin et al., 2008).  More complex tracking is when some end user uses a mobile device to scan a QR code or walks near an NFC area, where the phone then transmits about 40 different variables of that person to the company, which can then provide a more precise or perfect advertising (Pophal, 2014).

This data collection is all to gain more information about the consumer, to make better decisions about what to offer theses consumers like precise advertisements, deals, etc. (McNurlin, 2008).  The best way to describe this is through this quote by a current marketer in Phophal (2014): “So if I’m in L.A., and it’s a pretty warm day here-85 degrees-you shouldn’t be showing me an ad for hot coffee; you should be showing me a cool drink.” But, advertisers have to find a way to let the consumer know about their product, without overwhelming the consumer with “information overload.” How do consumers say “Hey look at me, I am important, and nothing else is… wouldn’t this look nice in your possession?”  If they do this too much, they can alienate the buyer from using the technology and from buying the product altogether. These advertisers need to find a meaningful and influencing connection to their consumers if they want to drive up their revenues.

At the end of the day, all this online profiling is aiming to collect enough or more than necessary data to make predictions of what the consumer is most likely going to buy and give them enough incentive to influence their purchasing decision.  The operating cost of such a tool must be done so that there is still a profit to be gained when the consumer completes a transaction and buys the product.  This, then becomes an important part of a BI program, because you are aiming to drive consumers away from your competitors and into your product.

The fear comes when the end-user doesn’t know what the data is currently being used for, what data do these companies or government have, etc.  Richards and King (2014) and McEwen, Boyer, and Sun (2013), expressed that it is the flow of information, and the lack of transparency is what feeds the fear of the public. Hence, the “Big Brother is watching”.  McEwen et al. (2013) did express many possible solutions, one which could gain traction in this case is having the consumers (end-users) know what variables is being collected and have an opt-out feature, where a subset of those variables stay with them and does not get transmitted.

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Big Data Analytics: Advertising

Advertising went from focusing on sales to a consumer focus, to social media advertising, to now trying to establish a relationship with consumers.  In the late 1990s and early 2000s, third party cookies were used on consumers to help deliver information to the company and based on the priority level of those cookies banner ads will appear selling targeted products on other websites (sometimes unrelated to the current search).  Sometimes you don’t even have to click on the banner for the cookies to be stored (McNurlin, Sprague, & Bui, 2008).  McNurlin et al. (2008) then talk about how current consumer shopping data was collected by loyalty cards, through BlockBuster, Publix, Winn-Dixie, etc.

Before all of this in the 1980s-today, company credit cards like a SEARS Master Card could have captured all this data, even though they had a load of other data that was collected that may not have helped them with selling/advertising a particular product mix that they carry.  They would help influence the buyer with giving them store discounts if the card was used in their location to drive more consumption.  Then they could target ads/flyers/sales based on the data they have gathered through each swipe of the card.

Now, in today’s world we can see online profiling coming into existence.  Online profiling is using a person’s online identity to collect information about them, their behaviors, their interactions, their tastes, etc. to drive a targeted advertising (McNurlin et al., 2008).  Online profiling straddles the point of becoming useful, annoying, or “Big Brother is watching” (Pophal, 2014).  Profiling began as third party cookies and have evolved with the times to include 40 different variables that could be sent off from your mobile device when the consumer uses it while they shop (Pophal, 2014).  This online profiling now allows for marketers to send personalized and “perfect” advertisements to the consumer, instantly.  However, as society switches from device to device, marketers must find the best way to continue the consumer’s buying experience without becoming too annoying, which can turn the consumer away from using the app and even buying the product (Pophal, 2014).  The best way to describe this is through this quote by a modern marketer in Phophal (2014): “So if I’m in L.A., and it’s a pretty warm day here-85 degrees-you shouldn’t be showing me an ad for hot coffee; you should be showing me a cool drink.” Marketers are now aiming to build a relationship with the consumers, by trying to provide perceived value to the customer, using these types of techniques.

Amazon tries a different approach, as items get attached to the shopping cart and before purchases, they use aggregate big data to find out what other items this consumer would purchase (Pophal, 2014) and say “Others who purchased X also bought Y, Z, and A.”  This quote, almost implies that these items are a set and will enhance your overall experience, buy some more.

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