Machine learning is all the rage, but how does it actually perform in practice as part of a digital marketing strategy?
If you’re using a website that recommends products based on previous purchases, you’ve hit a machine learning strategy.
Machine learning is an aspect of artificial intelligence (AI) that uses algorithms to accomplish specific tasks, such as product recommendations.
It enables a variety of functions for digital marketers, including:
Machine learning has been in digital marketing for years.
In fact, you are using Machine learning whenever you use a search engine.
While still a new tactic for most, many businesses are already starting to incorporate this technology into their marketing campaigns.
Below are eight examples of machine learning in digital marketing.
1. Chase
In 2019, banking giant Chase Bank partnered with Persado to create marketing copy for its campaign.
They challenged the AI company to generate copy that would generate more clicks—and they did.
An example of a machine learning-generated replica is as follows:
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.”
result: AI copy generates almost double the number of hits.
human copy: Use the View button to “Get cash from your home’s assets”.
machine-generated copy: “It’s true – you can unlock cash from assets in your home with a quick ‘click to apply’.
result: AI replicas attract 47 applicants per week, while human replicas attract 25 applicants per week.
human copy: “Come on, end December 31 and earn 5% cash back at department stores, wholesale clubs.”
machine-generated copy: “About Your Card: 5% Cash Back Awaits You”
result: AI copywriting generates nearly five times as many unique clicks.
While machine-generated copywriting may perform better with clients, it’s important to remember that it works alongside human copywriters to provide them with ideas.
Human copywriting and machine learning can work together to create and optimize copy that resonates.
2. Starbucks
Through its stores around the world, Starbucks has access to a lot of data.
Starbucks can access purchase insights and turn this information into marketing materials through the Starbucks loyalty card and mobile app.This strategy is called predictive analytics.
For example, machine learning collects what beverages each customer purchased, where and when, and matches it with external data such as weather and promotions to deliver hyper-personalized ads to customers.
An example includes identifying customers through Starbucks’ point-of-sale system and serving baristas their favorite orders.
The app can also recommend new products based on previous purchases (which may change based 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.
3. eBay
eBay has millions of email subscribers. Every email needs an engaging subject line to get customers to click.
However, delivering over 100 million compelling subject lines is overwhelming for a human writer.
Enter machine learning.
eBay partnered with Phrasee to help generate engaging subject lines that won’t trigger spam filters. Additionally, machine-generated copy is in keeping with eBay’s brand voice.
Their results show success:
- The open rate increased by 15.8%.
- Average clicks increased by 31.2%.
- Over 700,000 incremental opens per campaign.
- Each campaign added over 56,000 clicks.
Machine learning can do the toughest tasks and do it at scale in minutes.
As a result, businesses can focus more on macro activities rather than micro tasks.
4. Door panels
Doordash runs thousands of marketing campaigns across its marketing channels.
Their team manually updates bids based on the performance of the ad.
However, the team found the task time-consuming and laborious.
So Doordash turned to machine learning to optimize its marketing spend.
It builds a Attribution data.
This data tells the company which channels and activities customers convert from.
However, with thousands of simultaneous campaigns, it is difficult to collect such data in a timely manner.
Machine learning helps with this task by collecting data and creating spending recommendations so they can optimize their budgets quickly and efficiently.
5. Autodesk
Autodesk saw a need for more sophisticated chatbots.
Consumers are often frustrated with the limitations of chatbots and therefore prefer to talk to humans.
However, Chatbots can help guide customers effectively to the content, salesperson or service page they need.
So Autodesk turned to machine learning and artificial intelligence.
Autodesk’s chatbots use machine learning to create conversations based on search engine keywords.
The chatbot can then connect to the customer on the other end, resulting in a faster conversion rate.
Since implementing the chatbot, Autodesk has seen a threefold increase in chat engagement and a 109% increase in time spent on the page.
6. Baidu
In 2017, Chinese search engine Baidu built a system called Deep Voice, which uses machine learning to convert text into speech. The system can learn 2,500 sounds, each requiring half an hour of data.
Baidu explained that Deep Voice could bring a more immersive experience to video games and audiobooks.
Baidu Deep Voice aims to teach machines to speak more like humans by imitating thousands of human voices.
Soon, search engines hope the system can pick up on 10,000 or more different accent voices.
When perfected, Deep Voice can improve things we use every day, such as:
- Siri.
- Alexa.
- Google Assistant.
- Live translation.
- Biometric security.
It can even help people who have lost their voice communicate again.
Although there haven’t been any recent updates, Baidu still hopes that Deep Voice will revolutionize our technology.
7. Tailor Brands
Tailor brand use Machine learning helps its users create logos.
The machine, “This or That,” helps Tailor Brands use decision-making algorithms to understand user tastes.
By choosing a sample they like, users can tell the logo generator 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 a style similar to what they’ve used before.
8. Shout
Yelp receives millions of photos around the world every day.
The company realized it needed a sophisticated way to match photos to specific businesses.
so they Developed a photo understanding system Create semantic data about a single photo.
The system allows Yelp to categorize photos into categories relevant to user searches.
First, Yelp created tags for the photos they received from users, such as “drinks” or “menu.”
Next, the company collects data from photo captions, photo attributes, and crowdsourcing.
It then implements machine learning to identify photo tags, from which the system can classify photos.
This photo classification system helps create a better user experience on Yelp.
For example, it can help diversify cover photos and create tags that let users jump 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 Experience And run more optimized campaigns in less time. It’s a win-win situation.
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