Filtering out 65+ in Studies

Summary

We explore the effects of age exclusion in our advertising campaigns. We specifically focused on the 65+ age group and its impact on the performance of our ads for a school-based app.

We ran series of ads featuring different features & value propositions.

All the ads and the subsequent analysis presented in this report were generated using our tools.

Test Groups:

Regular Audience: This group was exposed to ads with an audience age range set from 18 to 65+.
Test Audience: This group was exposed to ads with an audience age range set from 18 to 64.

The Results:

The Regular Group achieved a peak Click-Through Rate (CTR) of 3.50%. However, the Test Group outshone them with a remarkable CTR of 6.93%.

VALIDATED AI CASE STUDIES | FILTERING OUT 65+ FROM CASE STUDIES

Demographics:

Age: The 55-64 & 65+ age groups dominated in reach, impressions, and overall results. The 35-44 age group showed the third-highest level of engagement.

Gender: Females showed higher engagement in older age segments, almost twice that of males. Male engagement tended to decrease as the age group increased.

VALIDATED AI CASE STUDIES | FILTERING OUT 65+ FROM CASE STUDIES
VALIDATED AI CASE STUDIES | FILTERING OUT 65+ FROM CASE STUDIES

Our A/B test provided valuable insights into the effectiveness of our ad campaigns when we adjusted the age range of our audience. The Test Group (ages 18 to 64) demonstrated a twofold increase in Click-Through Rates (CTR) compared to the Regular Group (ages 18 to 65+). Despite similar reach and impressions for both groups, the cost per result was notably higher for the Regular Group. These findings suggest that adjusting the age range of our audience can significantly affect engagement rates and campaign costs. As we move forward, we will use these insights to guide our strategy and help us optimize our advertising efforts for maximum impact.

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