Facts About New Media That You Need to Know

Facts About New Media That You Need to Know
It is well-known that the advent of digital technology has radically upended conventional means of public relations and advertising. Finding, attracting, and keeping customers in today’s multichannel economy is impossible without digital marketing.
Results from the 2022 MIT Chief Marketing Officer Summit focus in a digital book released by the MIT Initiative on the Digital Economy.
Before diving into an analysis of digital media, familiarize yourself with this site’s glossary of industry terms.
The significance of data, analytics, and algorithms in reaching today’s always-on clients should stand out most to those in charge of advertising.
According to a group of academics from MIT Sloan, the following will be the most important developments in digital advertising in the next year:
Extreme Users of the Web and Social Media
In order to make a purchase decision, many modern customers turn to online resources like social media and messaging applications.
The Head of the Disruptive Experiences Research Group Since social consumers are swayed by the views of their social network peers on different products and services, Sinan Aral thinks that marketers need to do granular research to understand the effect of social media on the marketing process (a trend known as “social proof”).
Aral claims that when social proof is there, it helps the marketing efforts of any firm. After analysing the purchasing habits of 30 million WeChat users across 71 products and 25 categories, Aral came to the conclusion that adding social proof into marketing tactics led to significant increases in income. With a 271% higher CTR than Disney, Heineken easily triumphed.
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Video Analysis Based on User Content
Impact of TikTok stars is huge, particularly among the younger population. However, whether or not the influx of traffic from these influencer videos really translates into sales is not entirely obvious.
Experts agree that the ad’s visuals and tone are more important than the product’s own merits in attracting customers. Harvard University’s Jeremy Yang, an assistant professor of business administration, found that “product purchases that tend to be more impulsive, hedonic, and lower in price” are particularly vulnerable. In addition, he was pursuing his master’s degree at MIT.
Analyzing Customer Interest using Machine Learning

This method is sometimes referred to as the “chip and dip test.” For a long time, it has been difficult for marketers to determine which combinations of consumer goods are the most likely to result in increased sales via product bundling.
However, such a query may appear daunting because to the breadth and complexity of the accessible data, which is on the scale of billions of potential permutations.
Madhav Kumar, an MIT Sloan researcher and Ph.D. candidate, utilised machine learning to develop a system that searches through millions of scenarios to identify successful and unsuccessful product pairings.
To the tune of 35%, he anticipated revenue growth thanks to the improved bundling strategy.
Machine Learning for Outcomes Prediction
Most marketers concentrate on retention and income, but without accurate calculations, decisions regarding the efficacy of marketing initiatives may be subjective, says Dean Eckles, head of IDE’s social and digital experimental research branch.
Using the latest developments in AI and ML, you may improve your methods and more effectively communicate with your target demographic.
The Boston Globe and the IDE group worked together to analyse the discount’s long-term effect on shoppers. After 18 months, the shorter-term surrogate’s predictions were on par with those of the longer-term surrogate.
Eckles suggested that it was useful to utilise statistical machine learning for making predictions about uncertain and distant future outcomes.
To mitigate the dangers of bias in AI, Good Friction must be put in place.
Digital marketers have been discussing the potential of artificial intelligence and automation to reduce consumer “friction” points. According to Renée Richardson Gasoline, leader of IDE’s Human/AI Interface Research Group, many marketers fail to recognise the gravity of the issue of bias in AI. Marketers should not get caught up in the current “frictionless fever” trend and instead think about the situations when a little friction really helps.
Gasoline recommended incorporating resistance into the use of algorithms so that they wouldn’t be used mindlessly and without examination. Artificial intelligence in marketing may be revolutionary if it were used in a manner that put customers ahead of products.