Modeling conversion of Television advertisement for Fast Moving Consumer Goods (FMCG) – (Viewer-to-Buyer Conversion)

Samuel W. Kamande^1, , Emmanuel Ahishakiye¹, Margaret A. Ondeng¹ and Herman T. Wachira¹

¹School of Computing and Informatics, University of Nairobi, P.O. Box 30197 – 00100, GPO Nairobi-Kenya

Cite this paper:
Samuel W. Kamande, Emmanuel Ahishakiye, Margaret A. Ondeng and Herman T. Wachira. Modeling conversion of Television advertisement for Fast Moving Consumer Goods (FMCG) – (Viewer-to-Buyer Conversion). American Journal of Modeling and Optimization. 2017; 5(1):12-23. doi: 10.12691/ajmo-5-1-2

Abstract

There are currently more than 107 TV stations in Kenya (with just over 10 free to air dominating the market), a number that has been growing exponentially since 2001. Further, more than 80% of the country’s population has access to a television. Driven by these two factors and the growing economy, advertising revenue for broadcasters has grown threefold from $107 million in 2007 to $359 million in 2013. With all this money being invested into TV advertising by companies, there has been a limited availability and exposure to tools for measuring the return of such huge investments for Ad spots. Research companies have developed tools to test ads and define the qualities of good advertisement, but none has zeroed down on estimating the conversion rates of those exposed to advertisement; the probability of audiences being converted to buyers of the advertised product. With a special focus on Fast moving consumer goods, a generalized linear model is obtained to estimate the probability of conversion from “viewers” to “buyers” for those that have been exposed to a particular TV advertisement. Data for 120 residents of Nairobi is collected. Demographic characteristics, social economic status, exposure, purchase habits and motivators data are collected. A multinomial logistic model was constructed using this data, with the response being a three-level multinomial variable – “Will buy”, “Will consider buying” and “Will not buy”. Six variables significantly influence the conversion of Television ad viewer to buyers – Gender, income/social class, Level of education, total time spent watching TV in a day, main television interest and most important feature of an advertisement. The model was validated by (a) significant test of the overall model, (b) tests of regression coefficients, (c) goodness-of-fit measures, & (d) validation of predicted probabilities. Three methodological issues were highlighted in the discussion: (1) the use of odds ratio, (2) the Hosmer and Lemeshow test extended to multinomial logistic models, and (3) the missing data problem. Believability and relatability of a television advertisement increase the probability of conversion by three times compared to the length/precision aspect. People with either primary or secondary education are also 3 times more likely to be converted compared to those with tertiary education.

Keywords:
modeling regression analysis generalized linear model multinomial logistic model

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