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Video analytics on TikTok, YouTube, and other social media

TikTok influencers loom huge, particularly with Gen Z. The challenge is whether or not those popular influencer videos truly transfer beyond attention into revenue.

Research demonstrates that engagement and product appearance isn’t the essential thing — it’s more about if the product is complimentary or well-synched to the video ad. And the impact is more significant for “product purchases that tend to be more impulsive, hedonic, and lower-priced,” according to research performed by Harvard Business School associate professor Jeremy Yang when he was a PhD student at MIT.


Measuring consumer engagement with machine learning

Call it the “chip and dip” challenge: Marketers have long wrestled with how to bundle goods, selecting the perfect consumer items to combine for co-purchase from a large range. With billions of alternatives, this study is rigorous and huge in scope, and data processing may be overwhelming.

Researcher Madhav Kumar, a PhD candidate at MIT Sloan, devised a machine learning-based system that churns through thousands of field situations to discover successful and less successful product combinations.

“The optimized bundling approach is likely to improve revenue by 35 percent ,” he stated.


Using machine learning to anticipate outcomes

Most marketers are worried about retention and income, but without reliable estimates, judgments regarding successful marketing interventions may be arbitrary, saidDean Eckles, social and digital experimental research group head at IDE. Instead, update consumer targeting via use of AI and machine learning to estimate results more rapidly and correctly.


In partnership with the Boston Globe, IDE researchers employed a statistical machine learning technique to examine the impact of a discount offer on consumer behavior after the first 90 days. The short-term surrogate forecast was equally as accurate as a prognosis given after 18 months.

“There’s a lot of utility to employing statistical machine learning to anticipate long-term and hard-to-measure outcomes,” Eckles added.


Adding “good friction” to decrease AI bias

Digital marketers speak regularly about lowering consumer “friction” points by employing AI and automation to simplify the customer experience. But many marketers don’t recognize prejudice is a very real concern with AI, said Renée Richardson Gosline, lead for the Human/AI Interface Research Group at IDE. Instead of being caught up in “frictionless fever,” marketers must think about when and when friction might really play a constructive role.


“Usage friction to halt the automatic and perhaps uncritical use of algorithms,” Gosline stated. “Using AI in a manner that’s human-centered as opposed to exploitative will be a genuine strategic advantage” for marketing.

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