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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Business Intelligence: Targets, Probabilities, & Modeling

  • Target Measures are used to improve marketing efforts through tracking measures like ROI, NVP, Revenue, lead generation, lag generations, growth rates, etc. (Liu, Laguna, Wright, & He, 2014). The goal is that after a marketing effort is conducted, there should be a change in Target Measures. Positive changes in these measures should be repeated.  Hoptroff and Kufyba (2001) stated that these measures could also be defect rates, default rates, survey ranking results, response rates, churn rate, the value of lost to the business, transaction amounts, products purchased, etc.
  • Probability Mining is data mining using Logit Regression, neural networks, linear regression, etc. Using this helps determine the probability of an event, in our case meeting or failing to meet our Target Measures based on information on past events. (Hoptroff & Kufyba, 2001)
  • Econometrics Modeling is a form of understanding the economy through a blend of economic theory with statistical analysis. Essentially, a way of modeling how certain independent variables act or influence the dependent variable using both economic and statistical theory tools to build the model.  Econometrics Modeling looks into the market power a business holds, game theory models, information theory models, etc.  It is rationalized that economic theory nor statistical theory can provide enough knowledge to solve/describe a certain variable/state, thus the blending of both are assumed to be better at solving/describing a certain variable/state (Reiss & Wolak, 2007)

In the end, an econometric models can contains elements of probability mining, but a probability miner doesn’t have to be is not an econometric model.  Each of these models and miners can track and report on target measures.

Econometrics Modeling is a way to understand price and the pricing model, which is central to generating profits through understanding both economic and statistical/probability principles to achieve a targeted measure.   Companies should use big data and a probability miner/econometric modeling to help them understand the meaning behind the data and extract actionable decisions one could make to either meet or exceed a current target measure, compare and contrast against their current competition, understand their current customers.

Two slightly different Applications

  1. Probability mining has been used to see a customer’s affinity and responses towards a new product through profiling current and/or new customers (Hoptroff & Kufyba, 2001). Companies and marketing firms work on these models to assign a probability value of attracting new customers to a new or existing product or service. The results can give indications as to whether or not the company could met the Target Measures.
  2. We have Marketing Strategies Plan A, B, and C, and we want to use econometric modeling to understand how cost effective each marketing strategy plan would be with respect to the same product/product mix at different price points. This would be a cause and effect modeling (Hoptroff, 1992). Thus, the model should help predict which strategy would produce the most revenue, which is one of our main target measures.

An example of using Probability Mining is Amazon’s Online shopping experience. As the consumer adds items to the shopping cart, Amazon in real-time begins to apply probabilistic mining to find out what other items this consumer would purchase (Pophal, 2014) based on what has happened before through the creation of profiles 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.  For instance, buyers of a $600 Orion Telescope also bought this $45 Hydrogen-alpha filter (use to point the telescope towards the sun to see planets move in front of it).

The Federal Reserve Bank and its board members have been using econometric modeling in the past 30 years for forecasting economic conditions and quantitative policy analysis (Brayton. Levin, Tryon., & Williams, 1997).  The model began in 1966 with help of the academic community, Division of Research and Statistics with available technology, which became operational in 1970.  It had approximate 60 behavioral equations, with long-run neoclassical growth model, factor demands, and life-cycle model of consumption.  Brayton et al. in 1997 go on to say that this model was used for primarily the analysis of stabilization of monetary and fiscal policies, as well as other governmental policies effects onto the economy.

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