How To Leverage Your Data To Be Successful Like Amazon And Spotify
At the Marketing AI Institute, we read dozens of articles on artificial intelligence every week to uncover the most valuable ones for our subscribers (become a subscriber today), and we curate them for you here. We call it 3 Links in 3 Minutes. Enjoy!
1. Data = Major Key
What do all of the big tech companies dominating the marketplace have in common?
Loads and loads of data.
According to Inc., companies like Amazon, Google and Spotify are leaders in their space because they effectively leverage the vast amounts of data they gather from their customers and potential customers. This data can range from search habits and products purchases to the music they listen to and the posts they share online. Using this data and artificial intelligence, these companies are able to better understand and market to their users.
Why does this matter to you? Because you don’t have to be a major enterprise to leverage data as a competitive advantage. You simply have to learn how to gather data that is better than your competitors, safely store it and combine it with the right AI tools.
In order to do this, you must first decide what your goal is. Perhaps you want to achieve more sales, increase web traffic or boost brand visibility.
From there, you’ll need to make sure your data is in a format that can be used with machine learning. This basically means taking all the data you’re already gathering and taking it one step further. You need to not only know what your customers bought but also what they didn’t buy. Beyond website visitors, you need to know all the touchpoints a customer experienced before ending up on your site. Dynamic data allows deep learning to draw in-depth conclusions that will ultimately be your competitive advantage.
Jeremy Fain, CEO and co-founder of Cognitiv, explains the importance of developing a strategy around your business’s data and combining it with AI and deep learning as soon as possible.
“The ability to more fully describe and understand a consumer's behavior is more complete than ever before, and that kind of data will make AI marketing tools even more effective over the next few years."
Meet REX Real Estate Exchange—a digital platform that uses AI and machine learning to identify buyers and sell homes, charging a fraction of the commission cost of traditional real estate companies.
CNBC shares the story of Ron and Marilyn Hougardy, a couple from Thousand Oaks, California who recently sold their house using REX. Their home, which was listed at $880,000, sold for $890,500. Instead of sharing their listing on sites likes Zillow and Trulia, REX used AI to identify thousands of likely buyers and target them with ads on websites and social media.
Since launching in 2016, REX has closed 231 transactions and raised $25.5 million in funding from investors such as Best Buy founder Dick Schulze, Sun Microsystems co-founder and former CEO Scott McNealy, former McDonald’s CEO Jack Greenberg, and Crate and Barrel founder Gordon Segal.
REX’s process for selling a home is very simple. The system disseminates an initial round of ads based on a hypothesis of where and whom the likely buyers are. Instantly, REX gathers data on what the 500 people that have clicked on the ad have in common and readjusts its placement to reach more customers similar to them.
REX also tailors ads by determining which features about a home or its surrounding community can increase its probability of selling faster or at a higher price point, such as its proximity to a Starbucks.
Although REX did sell a mega-mansion in Malibu for $44 million in 2015, it typically excels selling homes that are less than $1 million.
“The AI is better at selling a $500,000 dollar home because there's much more data," Jack Ryan, REX’s co-founder and CEO said. "To do probability theory you need lots of data points to say with 85 percent certainty or 92 percent certainty — this is a buyer for a home."
However, REX can’t do it alone. The brokerage has a team of 22 agents spread throughout the United States who serve as a guide to buyers and sellers working with REX.
3. How Humans Are Making AI Biased
Vince Lynch, CEO of IV.AI, explained how AI sometimes takes the fall for human biases:
“Machine learning isn’t bias. Machine learning does the thing that you tell it to do. So you can take a small bias that’s happening inside this pocket that the humans had thought out to begin with, and it can become amplified through the machine learning model. So at the end it looks like it’s real, and the AI, which is in the middle, can get blamed.”
Russ Shaw, co-founder of Tech London Advocates & Global Tech Advocates agreed with the problem but recommended a way for AI to help remedy the issue:
“There are steps we can take now to address some of the biggest concerns about an AI-enabled future. Let’s increase the diversity of AI coders to remove unconscious bias from algorithms; let’s introduce regulation to ensure the technology is fair and safe; and let’s upskill the population to ensure people can make the most of the jobs AI will create, rather than replace.”
However, that is easier said than done. While human bias in hiring has been documented for years, we have to understand that AI is not immune to biases either. There have been cases of women and ethnic minorities being burned by algorithms while job searching or obtaining healthcare, as well.
All panelists agreed that the more effectively we apply AI, the better it will be at eliminating human biases, although it will not happen overnight.
Steven Wolfe Pereira, chief marketing and communications officer at AI tech group Quantcast closed the panel, “Like most forecasts involving technology, people tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”
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