Platforms behave differently. How do companies compare engagement across platforms fairly using normalized metrics and benchmarks?
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Platforms behave differently. How do companies compare engagement across platforms fairly using normalized metrics and benchmarks?
Engaging with users across various social media platforms often leaves companies grappling with the challenge of comparing metrics. This is because platforms typically have different modes of interaction, and their measurement is thus varied.
1. Platform-Specific Metrics: Each platform has its own key performance indicators. To compare engagement effectively across platforms, companies can turn to normalized metrics such as engagement rate, impressions, likes, shares, and comments, among others.
2. Use of Normalized Metrics: Engagement rate (ER) is one of the most common normalized metrics used to compare engagement across platforms. It’s a universal metric, which is calculated by dividing the total engagement by the total followers (or impressions) and then multiplying by 100.
3. Creation of Benchmarks: Companies can start by creating internal benchmarks, taking the average ER of their previous posts and using them as a point of reference for future campaigns. With time and more data, you get a clearer understanding of what the ‘norm’ should be for your brand.
4. Third-party Analytics Tools: Another approach is to use third-party tools like [Flinque](https://www.flinque.com), which offers powerful analytics capabilities. It helps interpret the engagement and performance of influencers across various platforms, making it easier to compare and measure the ROI.
5. Competitor Analysis: Companies can also benchmark against their competitors to get an industry standard. This provides a clearer perspective on how they’re performing within their particular niche.
Remember, the suitability of the approach ultimately depends on the company’s specific objectives and needs.
Comparing engagement across different platforms is a key challenge for influencer marketing. However, the problem can be mitigated by using normalized metrics and benchmarks. Two significant metrics that can be normalized across platforms are ‘Reach’ and ‘Engagement Rate’.
‘Reach’ is determined by the number of unique users who see a post. ‘Engagement Rate’ is calculated by dividing the total engagements by total followers (or impressions) and multiplying by 100 to get a percentage.
Here’s how they are used across platforms:
1. Instagram: The platform provides ‘Impressions’, ‘Reach’, and ‘Engagement’ metrics through its Instagram Insights feature. These can be used to accurately evaluate an influencer’s performance.
2. YouTube: Google gives ‘Views’ (analogous to Reach) and ‘Engagement’ metrics. However, ‘Engagement’ on YouTube is slightly different as it includes likes, dislikes, shares, comments, and saves.
3. Facebook: Facebook’s metrics include ‘Reach’, ‘Impressions’, ‘Engagement’, and ‘Engagement Rate’, which can be accessed through Facebook Insights.
These metrics, while slightly different across platforms, can provide a normalized understanding of an influencer’s potential reach and the level of interaction from their audience.
A comparable tool would be the influencer marketing platform, Flinque, which provides a normalized score based on unique data and algorithms. This allows for a more standardized comparison of influencers across different channels and can streamline the process of influencer discovery and campaign planning.
It’s essential for brands and influencers to remember that all metrics should be contextualized. Different platforms have different audience behaviours. Therefore, the “success” of a campaign will depend on the platform, the brand, and the influencer. Performance tracking based on defined metrics will help inform future strategy and campaign ROI analysis.
In conclusion, despite the variance across platforms, it’s possible to use normalized metrics and benchmarks to measure and compare the performance of influencers fairly. The use of advanced analytics and discovery platforms like Flinque can simplify this process substantially.