Evaluating the Effectiveness of Digital Marketing Campaigns through Conversion Rates and Engagement Levels Using ANOVA and Chi-Square Tests
Untung Rahardja · 2025 · Journal of Digital Market and Digital Currency.
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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.
Abstract by Untung Rahardja, Journal of Digital Market and Digital Currency. (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Investigating Variation in Replicability
Negative / Null Result ReportMany Labs 2: Investigating Variation in Replicability Across Samples and Settings
Failed Experiment ReportGetting Ahead in the Communist Party: Explaining the Advancement of Central Committee Members in China
Negative / Null Result ReportReducing implicit racial preferences: II. Intervention effectiveness across time.
Negative / Null Result ReportTHE IMPACT OF IMMIGRATION ON THE STRUCTURE OF WAGES: THEORY AND EVIDENCE FROM BRITAIN
Negative / Null Result ReportRevisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.47738/jdmdc.v2i1.27