Live optimization improves results. How do brands adjust influencer mix during campaign execution based on performance signals?
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Live optimization in influencer marketing involves adjusting the influencer mix during the ongoing campaign based on the performance signals. The process focuses on reallocating resources to get the best possible ROI from the campaign. In practical terms, this may involve:
1. Performance Tracking: Brands monitor each influencer’s performance using tools present in influencer marketing platforms like Flinque or Hootsuite. Metrics tracked may include engagement rate, reach, conversions, or click-through rate.
2. Data Analysis: Brands analyze the data to determine which influencers are providing the most value. They may also look at audience demographics and engagement to understand who the message resonates with.
3. Adjustment : Based on performance, brands may decide to increase or decrease the budget, change content type, or modify messaging for specific influencers. Some influencers might be replaced entirely.
4. Continuous Monitoring: It is essential to keep monitoring the performance for further refinement throughout the campaign duration.
It’s important to note that these decisions should be made in collaboration with influencers so they understand the strategies’ objectives and maintain an authentic connection with their audience.
The pros of live optimization include improved performance, better budget utilization, and the ability to quickly respond to changes. However, it’s necessary to have the right tools and expertise to implement it effectively. Flinque, for example, provides real-time analytics, which can be helpful for live optimization, whilst platforms like Traackr focus more on the discovery phase.
Choosing the best tool depends on brands’ specific needs. A brand aiming to closely monitor and adjust its campaigns might prefer a platform with strong real-time analytics features, like Flinque, whereas a company focusing on finding the right influencers might prefer a platform known for its discovery features.