Machine learning is all the rage, but what does it look like in practice, as part of a digital marketing strategy?
You’ve encountered a machine learning strategy if you’ve used a website that recommends products based on previous purchases.
Machine learning is a facet of artificial intelligence (AI) that uses algorithms to perform specific tasks, such as product recommendations.
It can perform a multitude of functions for digital marketers, including:
Machine learning has been a part of digital marketing for years.
In fact, you are using machine learning every time you use search engines.
Although still a new strategy for most, many companies have started implementing this technology in their marketing campaigns.
Below are eight examples of machine learning in digital marketing.
In 2019, banking giant Chase Bank partnered with Persado to help it create marketing copy for its campaigns.
They challenged the AI company to generate copy that gets more clicks, which they did.
Here are examples of copy generated by machine learning:
Human Copy: “Go paperless and earn $5 cash back.”
Machine-generated copy“Limited Time Offer: We’ll reward you with $5 cash back when you go paperless.”
Results: AI copy generated almost double the clicks.
Human Copy: “Access your home equity money” with a “Peek a look” button.
Machine-generated copy: “It’s true – You can unlock money from the equity in your home” with a quick “Click to Apply”.
Results: The AI copy attracted 47 candidates per week, while the human copy attracted 25 candidates per week.
Human Copy: “Hurry it ends December 31 Earn 5% Cash Back at Department Stores, Wholesale Clubs.”
Machine-generated copy“About your card: 5% cash back awaits you”
Results: AI copy generated almost five times more unique clicks.
While machine-generated copy may have worked better with clients, it’s important to remember that it worked with human writers who gave it ideas.
Together, human writers and machine learning can create and optimize copy that resonates.
With stores around the world, Starbucks gets a lot of data.
Starbucks can access purchase information and turn that information into marketing materials through the Starbucks Loyalty Card and mobile app. This strategy is called predictive analytics.
For example, machine learning collects what drinks each customer buys, where they buy them, and when they buy them, and combines them with outside data such as weather and promotions to deliver highly personalized ads to customers.
An example is identifying the customer through the Starbucks POS system and providing the barista with their favorite order.
The app may also suggest new products based on previous purchases (which may change depending on weather conditions or holidays).
Machine learning can take the guesswork out of product recommendations.
Retail giants like Starbucks have millions of customers, but they can make everyone feel like they’re getting personalized recommendations because they can sift through the data quickly and efficiently.
eBay has millions of email subscribers. Every email needed engaging subject lines that would entice the customer to click.
However, delivering over 100 million catchy subject lines has proven overwhelming for human writers.
Enter machine learning.
eBay partnered with Phrasee to help generate engaging subject lines that didn’t trigger spam filters. Additionally, the machine-generated copy aligned with the eBay brand voice.
Their results show success:
- 15.8% increase in open rates.
- 31.2% increase in average number of clicks.
- More than 700,000 additional openings per campaign.
- Over 56,000 additional clicks per campaign.
Machine learning can take the toughest tasks and finish them in minutes at scale.
Therefore, companies can focus more on global campaigns than on microtasks.
4. Door dash
Doordash manages thousands of marketing campaigns across its marketing channels.
Their team manually updates bids based on ad performance.
However, the team found this task to be time-consuming and overwhelming.
Doordash therefore turned to machine learning to optimize its marketing spend.
He built a marketing automation platform based on attribution data.
This data tells the company on which channel the customer converted and with which campaign.
However, it can be difficult to collect this type of data quickly with thousands of campaigns running at the same time.
Machine learning helps tackle this task by collecting this data and creating spending recommendations so they can optimize their budget quickly and efficiently.
Autodesk saw the need for more sophisticated chatbots.
Consumers are often frustrated with the limitations of chatbots and therefore prefer to speak with a human.
However, chatbots can help guide customers efficiently to the content, seller, or service page they need.
Autodesk therefore turned to machine learning and AI.
Autodesk’s chatbot uses machine learning to create dialogue based on search engine keywords.
Then the chatbot can connect to the customer on the other end, allowing for faster conversion rates.
Since implementing its chatbot, Autodesk has tripled chat engagement and increased time on page by 109%.
In 2017 Baidu, the Chinese search engine, built a system called Deep Voice that uses machine learning to convert text to speech. This system can learn 2,500 voices with half an hour of data each.
Baidu explains that Deep Voice can lead to more immersive experiences in video games and audiobooks.
Baidu’s goal with Deep Voice is to teach machines to speak more humanely by mimicking thousands of human voices.
Soon, the search engine hopes the system will be able to master 10,000 or more voices with different accents.
When perfected, Deep Voice could improve the things we use every day, like:
- Google Assistant.
- Real-time translation.
- Biometric security.
It can even help people who have lost their voice to communicate again.
Although there have been no recent updates, Baidu remains hopeful that Deep Voice will revolutionize our technology.
7. Bespoke brands
Tailor Brands uses machine learning to help its users create logos.
The machine, “This or That”, helps Tailor Brands understand a user’s tastes using decision-making algorithms.
By choosing examples of what they like, users tell the logo maker their preferences for styles, fonts, and other design aspects.
Tailor Brands uses linear algebra.
Each user’s decision is fed into an equation that helps the machine learn the user’s preferences.
The next time someone generates a logo, Tailor Brands can display styles similar to ones they’ve used before.
Yelp receives millions of photos every day from around the world.
The company realized it needed a sophisticated way to match photos to specific companies.
So they developed a photo understanding system to create semantic data about individual photographs.
This system allows Yelp to sort photos into categories relevant to the user’s search.
First, Yelp created tags for photos received from users, such as “drinks” or “menu.”
Next, the company collected data from photo captions, photo attributes, and crowdsourcing.
Next, it implemented machine learning to recognize photo tags, from which the system could classify photos into categories.
This photo classification system helps create a better user experience on Yelp.
For example, it can help diversify cover photos and create tabs that allow users to jump straight to the exact information they’re looking for.
Digital marketers are only scratching the surface of what machine learning can do for them.
Humans and machines can work together to create more meaningful customer experiences and more optimized campaigns in less time. It’s a win-win-win.
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