2020 and 2022 Predictions for Digital Marketing
2020 and 2022 Predictions for Digital Marketing
2020 and 2022 Predictions for Digital Marketing

2020 and 2022 Predictions for Digital Marketing

The advent of digital technologies has wholly disrupted conventional advertising and marketing strategies. Identifying, acquiring, and retaining clients in today's multichannel economy is impossible without the aid of digital marketing technologies. If you want to know the ins and outs of digital marketing, then you should definitely Check Out This Fantastic Website.

A new e-book from the MIT Initiative on the Digital Economy compiles key takeaways from the 2022 MIT Chief Marketing Officer Summit.

The most important lesson for those in charge of advertising is that they need simply make more use of data, analytics, and algorithms to reach today's connected consumers.

According to analysts from MIT Sloan, the following will be the most significant developments in digital advertising in 2022:

Those who make extensive use of digital and social networks are also socially active.

Consumers now rely on various digital tools, including social networking and instant messaging applications, to help them make purchasing decisions.

Since social consumers are impacted by what their social network peers think about various goods and services, IDE director Sinan Aral argues that marketers need to employ granular research to properly appreciate the effect of social media on the marketing process (a trend known as "social proof").

Aral analyzed the purchases of 30 million people across WeChat and discovered that using social proof in marketing significantly enhanced sales. While Disney's engagement rate rose by 21%, Heineken's jumped by 271%. Aral claims that there is not a single brand for which the presence of social proof reduces the effectiveness of marketing efforts.

Using User-Generated Video Data From Services Like Tiktok, Youtube, And Others

Among today's youth, particularly those of the millennial age, TikTok stars have tremendous influence. In spite of their popularity, it is not known whether consumers are really persuaded to buy products because of these influencer videos.

Research suggests that a product's compatibility with the aesthetics and tone of an advertisement is more important than the product's inherent attraction. More so for "product purchases that tend to be more impulsive, hedonic, and lower-priced," as an associate professor at Harvard Business School Jeremy Yang found. He was also a graduate student at the Massachusetts Institute of Technology.

The Evaluation of Consumer Interest Using Machine Learning

The "chip and dip" test is another name for this. It has long been a difficulty for marketers seeking to enhance sales via product bundling to choose the right consumer products to combine for co-purchase from a large variety.

However, such an inquiry may be daunting due to the sheer number and complexity of the accessible data, which is on the scale of billions of conceivable combinations.

Madhav Kumar, a research assistant and Ph.D. candidate at MIT Sloan, has developed a machine learning-based system that searches through thousands of hypothetical situations for profitable and unprofitable product pairings.

He anticipated a 35% rise in revenue from the improved bundling strategy.

Machine Learning for Accurate Outcomes Prediction

However, according to Dean Eckles, leader of IDE's social and digital experimental research section, making judgments about effective marketing interventions may be arbitrary without solid estimates, even though most marketers concentrate on retention and income.

In its place, you should update your consumer targeting by using AI and machine learning to create more accurate and timely forecasts.

Using a statistical machine learning approach, researchers at IDE collaborated with journalists from the Boston Globe to examine the effect of a promotional discount over the long term. After 18 months, the short-term surrogate's predictions were just as accurate as the longer-term surrogate's.

Eckles contended that it was to everyone's advantage to use statistical machine learning to foresee distant and elusive events.

Introduce "Good Friction" to AI to reduce the impact of bias.

Discussions on how artificial intelligence and automation may help digital marketers reduce client "friction" are prevalent. According to Renée Richardson Gosline, leader of IDE's Human/AI Interface Research Group, many marketers. Instead of getting caught up in the "frictionless fever" trend, marketers should think about the times when friction helps.


To avoid the automatic and sometimes uncritical use of algorithms, Gosline suggested introducing friction into the system. If AI is applied in marketing in a manner that puts people first rather than treating them like a commodity, it will have a major impact on business.