4. Define the duration of the social media experiment

[shaownhasan]

Don’t fall into the common mistake of not defining a time frame for your social media experiment. Remember that social media strategy is a long game–give time for new initiatives to grow and develop.

Your reporting window depends on your budget, audience size and KPIs, but the most important factor is to reach statistical significance.

Statistical significance refers to the likelihood your test ig data results are the outcome of a defined cause and not chance. To reach statistical significance, you’ll need a large sample size and a control. For example, a sample size of 1,000 is stronger than 100, and your control would be the piece of content you do not change.

Set a duration and look for statistical significance. What are the significance changes? After your testing period, consider optimizing content that didn’t work during that timeframe instead of hitting the breaks on posts that aren’t resonating immediately.

While experimenting with TikTok, the social team reported results after four months since there was enough data available to analyze. They also set a weekly update to our internal social dashboard to continue testing and learning, along with iterating strategy, if needed.

During the first four months, we discovered views for every TikTok remained consistent, with an average of 535 views per video. We were also able to confirm our thoughts/assumptions about the For You Page (FYP) and the TikTok algorithm—each consistently pushed out content to our target audience (social media specialists, managers, digital marketers, etc.).