Evaluating the Effectiveness of Digital Marketing Campaigns through Conversion Rates and Engagement Levels Using ANOVA and Chi-Square Tests

This study investigates the effectiveness of various digital marketing campaign types—Awareness, Conversion, and Retention—on conversion rates and engagement levels. Using a dataset of 8,000 records, we conducted a comprehensive analysis through ANOVA, Chi-Square tests, and OLS regression to understand the impact of these campaign types. The ANOVA results indicated no significant differences in conversion rates across the campaign types, with an F-statistic of 0.4752 and a p-value of 0.6218. Similarly, the analysis for engagement levels, measured by website visits, yielded an F-statistic of 0.3651 and a p-value of 0.6942, suggesting no significant differences among the campaigns. Despite these findings, the Chi-Square test revealed a significant association between campaign types and conversion outcomes, with a Chi-Square statistic of 84.4544 and a p-value of approximately 3.3983e-18. This suggests that while the overall conversion rates do not differ significantly, the type of campaign does influence whether conversions occur. Pairwise t-tests supported these results, showing no significant differences in conversion rates or engagement levels between specific pairs of campaign types. Further, OLS regression analysis for conversion rates resulted in an R-squared value of 0.001 and a non-significant F-statistic, indicating that the predictors such as AdSpend and ClickThroughRate do not significantly explain the variation in conversion rates. Similarly, the regression model for engagement levels, despite an R-squared value of 1.000, highlighted issues of multicollinearity and overfitting. These findings imply that simply altering the type of campaign may not substantially impact conversion rates or engagement levels. Marketers should focus on improving content quality, targeting precision, and user experience to enhance campaign effectiveness. Future research should incorporate additional variables and advanced modeling techniques to provide deeper insights into the factors driving digital marketing success.