Influencer Marketing and Machine Learning
Influencer marketing has since been established as an effective way of increasing brand awareness. With all the data freely available on multiple platforms, it’s also now easier to determine the actual reach of any influencer. This is especially important because of the temptation to relate a large following to impact, and since it’s been proven that this is not always the case, the application of data science here can be very useful to save your business from wasting resources.
Here are a few ways machine learning and data science can help with influencer marketing:
Influencer reach can be defined as the number of unique views on a piece of content. Therefore, an efficient reach is one where advertising on a sponsored post, for example, will be seen by multiple different people. As an alternative to approaching an influencer to ask about the success of various campaigns, they have done in the past, an organization with a data science team can predict this reach. For example, an Instagram influencer with loyal and committed supporters will often have high engagement on their posts. Thus data from likes and comments can be captured for analysis and prediction.
Sentiment analysis is the analysis of the emotion behind a text. Organizations with the resources to do so can use it on reviews to understand when customers feel upset or happy. This machine learning technique can also be applied to influencer marketing to know how people feel about an influencer or a particular campaign they have done. This is useful for choosing which influencer to use for a particular campaign because it’s possible that people react differently based on the person and their delivery. It’s also possible that users react differently to the same campaign on different platforms, i.e. Instagram vs Twitter. Knowing the right influencer and platform to choose is crucial for increasing the campaign’s success.
Time series analysis
According to various psychologists, time can and does affect human decisions. In the same way, online sentiment may also be affected by the time of day. Thus, time series analysis can be an important factor to consider for influencer marketing because then an organization may be able to predict the right time to deploy a campaign that will positively affect consumers’ decisions. An example of this is companies increasing email marketing towards the end of the month when it’s expected to be a salary week.
Influencer marketing can be much more effective with the help of data science. Companies without a data science team can outsource the service to us to help them achieve their goals without wasting valuable resources.