| dc.description.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. | en_US |
