Missing data is the bane of every industry and profession that aspires to be data-driven. Few things are worse, however, than having data and then having access to the data revoked by a third party. This is the situation that content marketers find themselves in today as social networks restrict more and more access to data about how content is shared. On February 7, 2018, LinkedIn removed its sharing counts, blinding many to the performance of their content on the network.
Trust Insights set out to find alternatives for the missing data. With none commercially available, we instead built a methodology for inferring, or imputing, the missing data using machine learning technology with 98.2% accuracy. After extensive testing and validation with a quarter-million-row dataset, we deployed it to help marketers once again understand how their content performed on LinkedIn.
When paired with reliable training data, machine learning imputation of missing data is an effective way to fix missing or broken data, from simple social media shares to complex datasets.
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