The advent of digital tools has upended age-old processes in marketing and advertising. Digital marketing technology is now a requirement for identifying, engaging and retaining customers in the omnichannel world.
The MIT Initiative’s new e-book on the digital economy highlights lessons learned from the 2022 MIT Chief Marketing Officers Summit held this spring. The top message for marketing executives is: Add data, analytics, and algorithms to better reach the modern, socially-linked consumer.
Here are the top digital marketing trends for 2022, according to MIT Sloan researchers:
Social consumers in a wide range of digital and social media networks
Consumers today base their brand decisions on a vast array of digitally connected networks, from Facebook to WhatsApp, and the mix is always in flux.
According to IDE Director, social consumers are influenced by what their social network peers think about various products and services (a trend called “social proof”), so marketers are using detailed analytics to We really need to understand the role of social media.
Aral studied 71 different products in 25 categories purchased by 30 million people on WeChat and found that inserting social proof into ads had a significant positive effect, although the effect was There was variability. For example, Heineken’s click-through rate increased by 271%, while Disney’s interactions increased by 21%. Aral said social proof made advertising less effective for any brand.
Video analytics on TikTok, YouTube and other social media
TikTok influencers are particularly influential with Gen Z. The question is whether those viral influencer videos actually transcend attention and lead to sales.
Research shows that engagement and product appearance aren’t important factors, but whether the product complements or syncs well with the video ad. According to research conducted by Jeremy Yang, an associate professor at Harvard’s School of Business during his Ph.D. more pronounced in
Measuring consumer engagement with machine learning
We call this the “Chip and Dip” challenge. Marketers have long grappled with how to bundle products, finding the right consumer products to combine for co-buying from a vast assortment. With billions of choices, this research is rigorous and extensive, and data analysis can be challenging.
MIT Sloan PhD candidate and researcher Madhav Kumar has developed a machine learning-based framework. This framework examines thousands of field he scenarios to identify pairs of successful and unsuccessful products.
“Our optimized bundling policy is expected to increase revenue by 35%,” he said.
Predict outcomes using machine learning
Most marketers are concerned about retention and revenue, but without proper forecasting, decisions about effective marketing interventions can become arbitrary. Leader of IDE’s Social and Digital Experimentation Research Group. Instead, it uses AI and machine learning to update customer targeting and predict outcomes faster and more accurately.
In collaboration with The Boston Globe, IDE researchers employed a statistical machine learning approach to analyze discount offer results on customer behavior beyond the first 90 days. Short-term proxy predictions were as accurate as predictions made 18 months later.
“There is a lot of value in applying statistical machine learning to predict long-term, hard-to-measure outcomes,” said Eckles.
Add “good friction” to reduce AI bias
Digital marketers often talk about reducing customer “friction” points by using AI and automation to facilitate the customer experience. But many marketers don’t realize that bias is a very real factor in AI. Leader of the IDE’s Human/AI Interface Research Group. Instead of getting caught up in the “frictionless fever,” marketers must think about when and where friction can actually play a positive role.
“We use friction to break the automatic and potentially uncritical use of algorithms,” said Gosline. “Using AI in a human-centered, non-exploitative way will be a real strategic advantage for marketing.”
Read the 2022 MIT CMO Summit Report