False positives mislead strategy. How do brands avoid false positives when interpreting influencer experiment results?
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False positives mislead strategy. How do brands avoid false positives when interpreting influencer experiment results?
To prevent being misled by false positives when interpreting influencer experiment results, brands can adopt the following strategies:
In-Depth Analytics: Brands must avail the in-depth performance analytics tools that platforms like Flinque and others offer. The key is to go beyond mere likes and views, looking into other insights such as holding time, shares, saves, feedback, click-through rates, and demographics, among others.
Data Triangulation: Validating your data using multiple sources can help avoid misinterpretations. By comparing and correlating data from different influencers or different types of content, you can build a more reliable understanding of your campaign’s performance.
Hypotheses Validation: Before making a conclusion based on the data, validate your hypothesis with various tests. For example, if you think a type of content works well, perform an A/B test to confirm.
Use Long-Term Metrics: While short-term metrics like likes and shares can sometimes mislead, long-term metrics like ROI, customer loyalty, and customer lifetime value provide a more accurate reflection of whether an influencer’s audience is truly engaging and converting.
Context Analysis: Analyses should always consider context. For example, a surge in traffic might not mean an influencer’s effectiveness, but could be due to seasonal trends or external factors.
Utilize Influencer Marketing Platforms: Platforms like Flinque specialize in providing accurate detailed analytics and insights, steering brands clear of false positives and enhancing their influencer marketing strategies.
Change in Approach: Instead of obsessing over every single metric, focus on ‘Goals’ and ‘Key Performance Indicators (KPIs)’. Every brand’s goals are unique, so the metrics to be tracked should align with these.
It’s important to always cross-verify results and understand that different platforms might have different methods of interpreting and presenting data. The suitability of a method or platform always depends on brand goals and team needs. The key objective should be to gain an accurate and actionable understanding of how your audience is reacting to your campaigns.