DemandJump Is Looking At Content Analytics In A Different Way

July 12, 2016 Brennan Walker

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In this week’s video, Tyler dives into DemandJump’s content feature in AIM.

“The way we're going about content is quite unique in that we are not relying on supervised machine learning techniques,” says Tyler.

What he means is that DemandJump is not relying on humans to manually label and curate data sets. Rather, unsupervised techniques are used, looking for structures, which essentially let the data organize itself.

In the video, Tyler explains a few of the many benefits associated with the way we are analyzing content. First, he describes how the resulting recommendations are much more robust than traditional techniques. DemandJump looks at interrelated, grouped content built around more than just relevant or trending topics given a query.

Second, he evaluates the scalability of such an approach and how the lack of human involvement equates to a quicker analysis of bulk data.

Lastly, he touches on how other, large organizations are catching onto the impressive benefits of DemandJump’s approach to content.

That is, an approach allowing for more robust suggestions through unsupervised, self-organization and quickly curated data resulting in actionable insights and suggestions.

If you are interested in any of Tyler’s or DemandJump’s other videos, be sure to check them out here.

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