Digital Marketing Trends for 2022

Digital Marketing Trends for 2022

The advent of digital tools has disrupted age-old processes in marketing and advertising. Digital marketing technology is now a requirement for identifying, attracting and retaining customers in an omnichannel world.

A new e-book from MIT’s digital economy initiative highlights what has been learned from the 2022 MIT Chief Marketing Officer Summit held this spring. The main message for marketing executives: add data, analytics and algorithms to better reach socially connected modern consumers.

Here are the top digital marketing trends from MIT Sloan researchers for 2022:

Social consumers in large social and digital media networks

Today’s consumers make brand decisions based on a very large set of digitally connected networks, from Facebook to WhatsApp, and the mix is ​​constantly changing.

Because social consumers are influenced by what social networking peers think about different products and services (a trend called “social proof”), marketers need to use granular analytics to truly understand the role of social media in marketing. , according to the director of the IDE

Aral looked at 71 different products across 25 categories purchased by 30 million people on WeChat and found significantly positive effects from putting social proof into an ad, although effectiveness varied. For example, Heineken had a 271% increase in click-through rate, while Disney’s interactions increased by 21%. There were no brands for which social proof would reduce the effectiveness of the ads, Aral said.

Video analytics on TikTok, YouTube and other social media

TikTok influencers loom, especially with Generation Z. The question is whether those viral influencer videos actually translate beyond attention into sales.

Research shows that product engagement and appearance are not the crucial factor, but more whether the product complements or is well-synchronized with the video ad. And the effect is most pronounced for “product purchases that tend to be more impulsive, hedonistic, and cheaper,” according to research conducted by Harvard Business School assistant professor Jeremy Yang while a doctoral student at MIT.

Measuring consumer engagement with machine learning

Call it the “chip and dip” challenge: Marketers have long grappled with how to group goods, finding the right consumer products to combine for co-buying from a huge assortment. With billions of options, this research is challenging and large-scale, and data analysis can be daunting.

Researcher Madhav Kumar, a PhD student at MIT Sloan, has developed a machine learning-based framework that moves through thousands of field scenarios to identify successful and less successful product pairs.

“The optimized bundling policy is expected to increase revenue by 35%,” he said.

Using machine learning to predict outcomes

Most marketers are concerned about retention and revenue, but without good predictions, decisions on effective marketing can be arbitrary, he said. head of the research group on social and digital experimentation at IDE. Instead, it updates customer targeting through the use of artificial intelligence and machine learning to predict results faster and more accurately.

In collaboration with the Boston Globe, IDE researchers took a statistical machine learning approach to analyze the results of a discount offer on customer behavior after the first 90 days. The short-term surrogate forecast was as accurate as a forecast made after 18 months.

“There is a lot of value in applying statistical machine learning to predict long-term, hard-to-measure outcomes,” said Eckles.

Added “good friction” to reduce AI distortion

Digital marketers often talk about reducing customer “friction” points by using artificial intelligence and automation to facilitate the customer experience. But many marketers don’t understand that bias is a very real factor with AI, he said head of the research group on human / artificial intelligence interface at the IDE. Instead of being overwhelmed by “frictionless fever,” marketers need to think about when and where friction can actually play a positive role.

“It uses friction to stop the automatic and potentially uncritical use of algorithms,” Gosline said. “Using AI in a human-centered way instead of exploitation will be a real strategic advantage” for marketing.

Read the MIT 2022 CMO Summit report

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