- 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
- 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.
- 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.
- Brayton, F., Levin, A., Tryon, R., & Williams, J. C. (1997). The evelotion of macro models at the Federal Reserve Board. Federal Reserve Board. Retrieved from https://www.federalreserve.gov/pubs/feds/1997/199729/199729pap.pdf
- Hoptroff, R. (1992). Principles and Practice of Time Serires Forecasting and Business Modeling Using Neural Nets, Nueral Computing and Applications, Springer-Verlag, 1(1).
- Hoptroff, R., & Kudyba, S. (2001). Data Mining and Business Intelligence: A Guide to Productivity (pp. 1-180). Hershey, PA: IGI Global. doi:10.4018/978-1-930708-03-7
- Liu, Y., Laguna, J., Wright, M., & He, H. (2014). Media mix modeling–A Monte Carlo simulation study. Journal of Marketing Analytics, 2(3), 173-186.
- Pophal, L. (2014). The technology of contextualized content: What’s next on the horizon? EContent, 37(7), 16. Retrieved from http://www.econtentmag.com/Articles/Editorial/Feature/The-Technology-of-Contextualized-Content-Whats-Next-on-the-Horizon-99029.htm
- Reiss, P. C. & Wolak, F. A. (2007) Structural Econometric Modeling: Rationales and Examples from Industrial Organizations. Retrieved from https://web.stanford.edu/group/fwolak/cgi-bin/sites/default/files/files/Structural%20Econometric%20Modeling_Rationales%20and%20Examples%20From%20Industrial%20Organization_Reiss,%20Wolak.pdf