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How a Fitness Brand Used AI to Boost Leads by 218%

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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. Cutting Costs While Trimming Waistlines

High-end gym chain Orangetheory Fitness has cut its cost per lead from $20 to $8 and produced 218 percent more high-quality leads in less than three months—all thanks to artificial intelligence.

As reported by Digiday, Orangetheory began working with The Tombras Group last July to build a custom AI platform for media buying. Since launching in December, the franchise has benefited tremendously from its investment, including getting insights into audiences.

“Usually, there’s a lot of studying and analysis required to ascertain where the sweet spot is to generate leads from,” said Kevin Keith, Orangetheory’s chief brand officer. “Behavior amongst consumers today is so rapid, and there’s so much volume of digital behavior, it’s very hard for a human to track all that in an effective and efficient way. With AI, it’s working 365 days per year intensively studying behavior. There’s no coffee breaks, and it’s happening in many cases in real time.”

Orangetheory’s belief in the power of AI is reflected in its $18 million media budget, which is three times the size of its 2017 budget. Of that, $15 million will be spent using the company’s AI platform. Additionally, the company has increased its in-house marketing team from three to 17 people to support its 600,000 members and nearly 1,000 locations—a testament to AI’s ability to create jobs, not just take them away.


2. Closing The AI Gap

In 2017, MIT Sloan Management Review (MIT SMR) conducted a global survey on the state of artificial intelligence by polling 3,000 managers, executives and analysts. According to VentureBeat, the results showed that while there is still a gap between the ambition and implementation of AI in businesses, the gap is shrinking at a significant pace.

The MIT SMR report explains:

“Our research reveals large gaps between today’s leaders — companies that already understand and have adopted AI — and laggards. The leaders not only have a much deeper appreciation about what’s required to produce AI than laggards, they are also more likely to have senior leadership support and have developed a business case for AI initiatives.”

While the survey showed that three out of four executives believe AI has the power to propel their businesses into new ventures and 85 percent trust it will give them a competitive advantage, less than 20 percent have actually integrated AI into their processes.

Comparatively, in 2015, 34 percent of businesses feared artificial intelligence, only seven percent planned on incorporating it within the next year and 49 percent of companies had no intentions of using AI in the future.

While it’s true that AI is growing rapidly in a very short amount of time, the more conversations we have regarding its capabilities and future implications, the more positive developments we can expect to see.

3. AI Jobs: Somewhere Between Utopia and Dystopia

TechRepublic shares the highlights from The Economist Intelligence Unit (EIU) and Google’s recent report, Risk and Rewards: Scenarios Around the Economic Impact of Machine Learning. By combining economics, statistical modeling, secondary research and interviews with industry and academic experts, the EIU was able to develop scenarios on the impact of machine learning for several industries and countries.

"The debate over the impact of machine learning, and artificial intelligence is an important one, and like all important debates it needs to be reasonable and informed,” said Chris Clague, editor of the report. “Our objective with this report is to help with that cause by charting a path between the techno-utopians who believe these technologies will solve all the world's problems and the pessimists who warn that they are dooming us to a jobless, dystopian future."

The first scenario assumes an increase in governmental funding for training in machine learning, as well as an escalation in complementary human and machine work. In this scenario, most economies would not see much growth. However, Australia would be an exception as a growth in services would benefit their export-dependent economy.

In the second scenario, all countries for the report studied would see an increase of at least one percent in their GDP by the year 2030 as a result of greater access to open source data, private sector tax credits and advances in computing efficiency.

The third scenario predicts insufficient policy support and intelligent machines completely replacing humans jobs. As a result, the UK and Australian economies would shrink and the US, Japanese and Chinese economies would continue to grow, but fall below baseline.

Moving forward, the report recommends increased communication between developers and policymakers to better manage expectations, acknowledge potential risks and rewards, improve transparency and educate the public. To prepare for the rise of AI, policymakers in all countries must focus on investing in skills and training, keeping data safe and promoting the development of technology.

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