What do you do if your influencer marketing analytics are affected by biases?

1 Sampling bias

Sampling bias occurs when your data is not representative of the population you want to study. For example, if you only track the engagement of your influencers' posts on one platform, but ignore the other platforms where they have followers, you are missing out on a large segment of your audience. To avoid sampling bias, you need to define your target population clearly, use multiple sources of data, and apply appropriate weighting or stratification methods to adjust for differences in sample size or characteristics.

2 Attribution bias

Attribution bias happens when you assign credit or blame to the wrong factors for your outcomes. For example, if you attribute the success of your influencer campaign solely to the influencer's personality, but ignore the quality of your product, the timing of your launch, or the competitive landscape, you are overestimating the influencer's role and underestimating the other factors. To avoid attribution bias, you need to use a robust attribution model that accounts for all the touchpoints and interactions in your customer journey, and test different scenarios and assumptions to validate your results.

3 Confirmation bias

Confirmation bias occurs when you seek or interpret data that confirms your pre-existing beliefs or expectations, and ignore or dismiss data that contradicts them. For example, if you have a positive impression of an influencer, you might focus on the positive feedback and metrics from their followers, and overlook the negative or neutral ones. To avoid confirmation bias, you need to be objective and open-minded, use multiple sources of data and perspectives, and challenge your own hypotheses and biases.

4 Recency bias

Recency bias happens when you give more weight or importance to the most recent or memorable data, and neglect the older or less salient data. For example, if you base your influencer marketing strategy on the latest trends or viral posts, but ignore the long-term patterns or historical data, you might miss out on the bigger picture or the underlying causes of your performance. To avoid recency bias, you need to use a balanced and comprehensive data set that covers different time frames and levels of detail, and use trend analysis and forecasting tools to identify and anticipate changes.

5 Survivorship bias

Survivorship bias occurs when you focus on the successful cases or examples, and ignore the unsuccessful or missing ones. For example, if you only look at the influencers who have high engagement rates or conversions, but ignore the ones who have low or no results, you might overestimate the effectiveness of your influencer marketing campaigns. To avoid survivorship bias, you need to include all the relevant data and cases in your analysis, and use appropriate benchmarks and comparisons to evaluate your performance.