Social Media Optimization through A/B Testing for Brand Awareness: A Reach Analysis of A/B Creatives

Abstract

Aim of the Study: This research aimed to explore the relationship between Social Media Optimization (SMO) and A/B testing for brand awareness on Facebook. The analysis was done through the reach of A/B creatives of social media posts on a newly created Facebook page of Ahad Montessori and School Furniture. The objective was to determine the impact of A/B testing on brand awareness in terms of reach among the target audience.

Methodology: A/B testing was used to compare the performance of two visual creatives on Facebook. The researcher selected an audience on Facebook and run the A/B testing for 2-3 days through the visual creatives for the targeted audience (both male and female audiences aged 25-55) with interests related to Montessori, nurseries, parenting, kg, schools, furniture, and education. The reach and engagement metrics were analyzed for each creative to assess their effectiveness.

Findings: The researcher used the reach metric to analyze the success of the A/B testing, however, results indicated that in the A/B testing engagement was a more crucial factor than reach in determining the success of A/B testing for brand awareness. Therefore, one visual creative won in this test as engaging content encouraged users to interact with this creative for improving brand recall. Hence, A/B testing proved valuable for optimizing social media content, leading to increased reach and engagement for brand awareness.

Conclusion: The study emphasized the significance of A/B testing for social media optimization. Content creators and marketers could get benefit from the A/B testing to see the result before launching any campaign. Therefore, content creators and marketers are advised to focus on creating engaging ads to improve reach and increase brand awareness. The findings of this study provided insights for advertisers and marketers to implement effective strategies for content creation and optimization using A/B testing.