Multiple Regression and Marketing-Mix Models

Abstract

In this technical note, the concept of regression using a single independent variable is first introduced and then some of the practical challenges associated with it, including multiple independent variables in a regression, are discussed. Particular attention is paid to bias in the regression coefficients in the presence of omitted variables. The concept of the economic significance of a model is introduced and is contrasted with statistical significance. At Darden, this note is used in the course elective Big Data in Marketing. Excerpt UVA-M-0855 Rev. Jun. 20, 2014 Multiple Regression In Marketing-Mix Models The movie Moneyball has a lot to teach us about optimizing a company's marketing mix. In the movie, the management of the Oakland Athletics discovers that the baseball team can get ahead by changing its perspective and looking at data differently than its competitors. The A's know most major-league teams use batting average (hits over real opportunities) as the prevailing metric for determining the worth of a hitter. Traditional baseball wisdom says, “You hit more, you win more.” So the players who have more hits per at bat are generally the most sought after and are paid the most money. But by examining the outcome of decades of baseball games, the A's manager finds a variable he believes to be more predictive of success. It is not only hits that help a baseball team win; walks count too. Getting on base and not making outs is more closely correlated with winning games than hits alone. The team's management takes the analysis of the data and uses it to buy undervalued players—players who don't necessarily have the highest batting averages but who do have high on-base percentages. For a small-market team such as the A's, which has less money to spend on players than other franchises, this strategy changes the game. . . .