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Four Ways to Improve Marketing with Machine Learning

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Posted 2 months ago on April 27th, 2021. Last modified on May 18th, 2021

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Artificial intelligence (AI) and machine learning (ML) were once buzzwords relegated to the world of science fiction but now are commonplace in everyday business. In fact, Gartner reports that in 2019, 37% of organizations used AI in the workplace, and IDC predicts spending on AI and cognitive systems will reach $57.6 billion in 2021. 

As AI and ML platforms have become more accessible to mainstream, non-coding audiences, marketers have embraced the technology, recognizing its ability to quickly parse data, identify patterns, predict future outcomes, and enhance the customer experience. How can your business use machine learning and artificial intelligence to improve your marketing efforts? Here are four examples.

1. Evaluate lead-generation sources to optimize marketing spend

If you’ve been tracking where leads come from—such as social media channels, search engines, and events—you can use ML to analyze all of that data and quickly determine which sources generate leads with the highest likelihood of purchase (or another desired action). So instead of having to wait for new leads to work their way through an entire marketing funnel to see how they did, you can rapidly adjust and optimize marketing spend.

Take DoorDash, for example. According to one of their recent posts, “Our marketing team currently manages our campaigns manually by regularly updating bids and budgets on our channel partners’ dashboards...at any given time we are operating tens of thousands of campaigns across all our marketing channels. At our scale this is both time-consuming and sub-optimal in performance...managing the spend across all these campaigns is a complex, multidimensional optimization problem, exactly the kind of thing that machines excel at.”

DoorDash has begun using ML to optimize marketing spend, and its data scientist says they expect this new approach to lower costs by 10% to 30% while still enabling them to reach the same number of customers.

2. Segment your audiences to create more personalized messaging

If you have historical data on which kinds of customers purchased specific products or services from your company, you can use machine learning to predict which leads are likely to buy those same offerings. You also can use ML to predict which of your current customers are likely to purchase additional offerings based on past customers’ behavior. With these insights, you can better segment your audiences and send more pertinent, personalized messaging for better campaign performance.

Major retailers—including Walmart, Target, and Amazon—are using machine learning to make better product recommendations to shoppers. Rakuten, often described as the “Amazon of Japan,” uses the data it gathers to help third-party sellers increase their conversions on the Rakuten platform. And yes, it works: A machine learning-powered test performed with 1,100 Rakuten merchants was able to help sellers achieve an average conversion rate of 43%.

3. Increase engagement on social media

From Facebook and Instagram to LinkedIn and Snapchat, all the social media platforms are using ML for everything from facial recognition (e.g., making it easy to tag your sister in photos) to job and friend recommendations. And now that ML has become more accessible, you can use it to optimize your social media efforts, too. For example, you can use ML to analyze your past social media posts’ performance, including post content and how many likes, comments, shares, and clickthroughs they generated. Then, the ML model can predict how well a new post is likely to perform. You can use this kind of model to increase overall social media engagement and fine-tune your messaging within specific channels.

4. Improve the customer experience with chatbots

Customers who have a positive experience will buy again or recommend your brand to a friend. Research suggests that AI-powered chatbots are increasingly becoming an integral part of business’ marketing and customer service offerings, as consumer spend via chatbots is anticipated to reach $142 billion in 2021.

Customers love chatbots because of their self-service nature—they can quickly get answers without long hold times and continue multitasking in the process—and can seamlessly connect to a human agent if they need additional assistance. Companies appreciate that AI-powered chatbots constantly learn and improve, serving the business as a continually-expanding knowledge database and a customizable tool capable of personalized customer engagement. 

Take Bitaeble for example, a SaaS business which helps other businesses quickly and easily create brand and product videos by leveraging stock images, animations and photos. They were spending hundreds of hours fielding inbound customer questions on their website. By leveraging Intercom’s AI enabled chatbot, they were able to automatically answer the most common customer questions as fast as their customers were able to type them in. Answering customer questions faster and with less effort is the definition of a win-win. 

Connecting the Dots

Machine learning and artificial intelligence can alleviate much of the heavy lifting involved in parsing data and finding patterns, so it’s no surprise that marketers are already embracing the technology. From optimizing your marketing spend and improving your personalization with advanced segmentation to boosting social media engagement and enhancing your customers’ experience, there are numerous ways that machine learning can augment (and supercharge) your marketing efforts. 


About the Author: Jon Reilly is co-founder and COO of Akkio, where he focuses on driving the company’s product led growth (PLG) mission of democratizing AI. Prior to Akkio, Reilly was VP of Marketing at Markforged and led the Music Player product management team at Sonos."

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