Identifying real long-term trends in influencer performance data requires a robust analysis that should go beyond merely looking at short-term spikes. Various methods can be employed:

1. Historical Data Analysis: Companies should analyze the historical data of an influencer, observing their performance over an extended period, preferably over a year. This helps to smoothen out the effect of one-off factors leading to short-term spikes.

2. Benchmarks Comparison: Comparing an influencer’s data against industry benchmarks can provide valuable insights. An influencer performing consistently above or at par with industry standards would be more likely to produce better long-term results.

3. Engagement Rate: Focusing on the engagement rate instead of just follower count is crucial. It gives a better sense of the influencer’s overall effectiveness and audience quality.

4. Continual Monitoring: Regularly monitoring the influencer’s performance metrics for a set period post-campaign is essential for gauging long-term effects.

5. Seasonal Adjustments: Adjusting for seasonal fluctuations in engagement or performance can help in filtering out spikes due to temporal factors.

Platforms like [Flinque](https://www.flinque.com) offer granular analytics that enable companies to track the performance of influencers comprehensively. Flinque provides easy-to-understand data visualization tools to spot trends in your influencer performance data, from plotting changes in engagement levels to conducting deep-dives into audience analytics and sentiment analysis.

However, it’s essential to align these analytical insights with your specific brand objectives and marketing goals. Remember, the ‘right’ influencer isn’t a universal truth, but rather a decision based on your brand’s unique needs and target audience.