News & Events
5 Brand Machine Learning Case Studies to Motivate Digital Marketers
- June 3, 2022
- Posted by: Shubhankar Gola
- Category: News & Updates
Machine learning is all the rage these days, but how does it actually work in a digital marketing strategy?
If you’ve ever used a website that suggests things based on previous purchases, you’ve come across a machine learning method.
Machine learning is a branch of artificial intelligence that use algorithms to carry out specific tasks like product suggestions.
For digital marketers, it can do a variety of tasks, including:
- Customer journey optimization.
- Customer segmentation.
- Lifetime value modeling.
- Smart bidding.
- Forecast targeting.
For years, machine learning has been used in digital marketing.
In reality, whenever you use a search engine, you are using machine learning.
Many organizations have begun to include this technology in their marketing strategies, despite the fact that it is still a novel tactic for most.
Here are eight digital marketing instances of machine learning.
Chase Bank worked with Persado in 2019 to assist in the creation of marketing text for its campaigns.
They tasked the AI firm to create a copy that generates results.
The following are some examples of machine-generated copy:
Human copy: “Go paperless for a $5 Cash Back reward.”
Machine-generated copy: “For a limited time, we’ll give you $5 cash back if you go paperless.”
Results: AI copy led to an estimated double the number of clicks.
Human copy: With a “Take a look” button, you can “access cash from the equity in your property.”
Machine-generated copy: “It’s real – you can get cash from your home’s equity” with a simple.
Results: The AI copy attracted 47 requests per week, whereas the human copy attracted 25.
Human copy: “Hurry, it’s just till December 31st!” At department stores and wholesale clubs, earn 5% cash back.”
Machine-generated copy: “Regarding Your Card: A 5% Cash Back Offer Is Awaiting You”
Results: Nearly five times as many unique clicks were created by AI copy.
While the machine-generated material may have fared better with customers, it’s worth remembering that it was fed concepts by human copywriters.
Human creatives and machine intelligence can work together to generate and optimize copy that is effective.
Starbucks collects a lot of data from its outlets all over the world.
With the Starbucks loyalty card and mobile app, Starbucks can gain access to purchase data and turn it into the marketing content. Predictive analysis is the term for this method.
Machine learning, for example, records the drinks that each customer buys, where they purchase them, and when they buy them, and then connects this with external data like weather and specials to serve ultra-personalized adverts to customers.
Identifying the consumer using Starbucks’ point-of-sale system that offers the cashier their order of preference is one example.
Based on previous purchases, the app can also recommend new products.
Machine learning can help with product suggestions by removing the guesswork.
Despite the fact that retail behemoths like Starbucks have millions of consumers, they can make each one feel as if they are receiving tailored recommendations thanks to their ability to filter through data rapidly and efficiently.
eBay has a massive email list with millions of users. Each email is required to have enticing subject lines that would entice the recipient to open it.
Human authors, on the other hand, found delivering over 100 million eye-catching subject lines to be daunting.
This is where machine learning comes in.
eBay teamed up with Phrase to help create catchy subject lines that didn’t get caught in spam filters. The machine-generated copy was also consistent with eBay’s brand voice.
Their achievements are as follows:
- Open rates have increased by 15.8%.
- The average number of clicks increased by 31.2 percent.
- Each campaign generates over 700,000 more opens.
- Per campaign, there were over 56,000 additional clicks.
- At scale, machine learning can take the most difficult jobs and perform them in minutes.
As a result, companies may concentrate on long-term marketing rather than microtasks.
Autodesk saw the need for more advanced chatbots.
Consumers are frequently irritated by chatbot restrictions and tend to talk with a live person.
Chatbots, on the other hand, can quickly direct clients to the material, sales, or access financial they require.
As a result, Autodesk turned to AI and machine learning.
Machine learning is used by Autodesk’s chatbot to construct discourse based on search engine terms.
The chatbot can then connect with the customer on either end, resulting in higher conversion rates.
Autodesk saw a threefold boost in conversation engagement and a 109 percent increase in time spent on the page after introducing their chatbot.
Every day, Yelp receives millions of images from around the world.
The business realized it required a more advanced method of matching photographs to specific businesses.
As a result, they created a photo comprehension system that generates semantic data about particular photos.
Yelp can utilize this approach to arrange photographs into categories that are relevant to the user’s search.
To begin, Yelp assigned labels to the photos submitted by users, such as “drinks” or “menu.”
Following that, the firm gathered information from photo captions, photo attributes, and crowdsourcing.
The system then used machine learning to recognize the photo labels, which allowed it to categorize the photos.
This photo classification method aids in the creation of a better Yelp user experience.
It can, for example, assist in the diversification of cover photographs and the creation of tabs that allow users to quickly access the information they require.
Machine learning is merely touching the surface of what it can accomplish for digital marketers.
In less time, humans and algorithms can collaborate to develop more meaningful consumer experiences and better-optimized marketing. It’s a win-win-win situation.