8 Machine Learning Cases From Brands To Inspire Digital Marketers

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8 Machine Learning Examples From Brands To Inspire Digital Marketers

This is a hot topic, but what does machine learning mean in real life?

You’ve encountered a machine learning strategy if you’ve used a website that recommends products based on previous purchases.

Artificial intelligence, also known as machine learning or AI, is an aspect that uses algorithms to perform specific tasks such product recommendation.

This data can be used for various purposes by digital marketers, including:

Since many years, digital marketing uses machine learning.

It is actually you that are using Search engines employ machine learning for machine learning.

Even though this strategy is new, many businesses incorporate it in their marketing strategies.

We have eight cases of machine-learning being used in digital advertising

1. Chase

Persado Bank and Chase Bank joined forces in 2019 to create marketing copy.

They challenged the AI company to generate copy that yields more clicks — which they did.

These are examples of machine-learning generated copy

Copy by Human: “Go paperless and earn $5 Cash Back.”

Automated copy: “Limited Time Offer: We’ll reward you with $5 Cash Back when you go paperless.”

These are our resultsClick rates for AI copy were nearly double that of original.

Copy by Human: “Access cash from the equity in your home” with a “Take a look” button.

Automated copy: “It’s true – You can unlock cash from the equity in your home” with a quick “Click To Apply.”

These are our resultsAI Copy attracted 47 people per week, while Human Copy attracted just 25.

Copy by Human: “Hurry, It Ends December 31 Earn 5% Cash Back At Department Stores, Wholesale Clubs.”

Automated copy: “Regarding Your Card: 5% Cash Back Is Waiting For You”

ResultsArtificial intelligence produced five times more clicks per unique than AI copy.

While the machine-generated copy may have performed better with customers, it’s important to remember that it worked with human copywriters feeding it ideas.

The combination of machine learning with the human copywriter can make copy more memorable.

2. Starbucks

Starbucks is a global company with many stores and has access to a lot of data.

Starbucks is able to track customer purchases and then use that information in its marketing collateral via the loyalty card or mobile application. This is called Predictive Analysis.

Machine learning can be used to collect data on customers’ drinking habits. This includes where and when they purchased their drinks. The data is then used to match it with additional information, such as weather or promotions, to produce highly targeted ads that are delivered directly to the customers.

One instance includes identifying the customer through Starbucks’ point-of-sale system and providing the barista with their preferred order.

Based on previous purchases, you can receive new suggestions for products. You can modify this depending on the weather and holiday.

Machine learning makes it possible to remove the guesswork involved in product recommendations.

Starbucks is an international retail company with many millions of customers. They are able quickly to sort the data and make each customer feel unique.

2.

eBay claims that it has over a million subscribers to its email lists. To get customers to click on your emails, you must include engaging subject lines.

But, when the subject matter had to be over 100,000,000 eyes-catching, it was overwhelming for human writers.

It is possible to use 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 eBay’s brand voice.

Their success is evident in the results

  • An increase of 15.8% in open rates
  • An increase of 31.2% for clicks
  • Over 700,000 incremental opens per campaign.
  • Over 56,000 incremental clicks per campaign.

Machine learning makes it possible to simplify even the most complicated tasks in just minutes.

Companies can focus now on larger-picture campaign rather than microtasks.

4. Doordash

Doordash uses thousands of different campaigns across its channels.

Their team manually updates bids based on the ads’ performance.

The task became so overwhelming that it was becoming too tedious for the team.

Doordash has decided to utilize machine learning in marketing optimization.

It created a platform to automate marketing based on this. attribution data.

This data allows the company to identify which channel has been used and what campaign was it.

When there are many campaigns going at once, it can be difficult to collect the information quickly.

Machine learning is a great tool to help you accomplish this task. The machine learns from the collected data to make spending suggestions, optimizes budgets quickly, and does so efficiently.

5. Autodesk

Autodesk identified the requirement for chatbots of greater sophistication.

Customers get frustrated by chatbots’ shortcomings and would rather talk to someone real.

However, Chatbots can efficiently help customersThe appropriate salesperson will direct them to the service page, content and/or product pages.

Autodesk is now focusing on machine learning, AI, and Augmented Reality.

Autodesk’s chatbot uses machine learning to create dialogue based on search engine keywords.

The chatbot is able to connect directly with customers at the other end, allowing for faster conversions.

Autodesk witnessed a threefold increase in chat engagement after implementing the chatbot, and an additional 109% spent time on their website.

6. Baidu

Baidu (the Chinese search engine), created the Deep Voice system in 2017. It used machine learning and text-to-speech transformations to create the Deep Voice system. The system recognizes 2,500 voices with just half an hour’s data.

Baidu said that Deep Voice might lead to more rich experiences in audiobooks, and video games.

Baidu’s goal with Deep Voice is to teach machines to speak more human-like by imitating thousands of human voices.

The search engine should soon be able to record voice and accent recordings from 10,000-10,000 people.

Deep Voice can be used to enhance everyday life by improving things such as:

  • Siri.
  • Alexa.
  • Google Assistant.
  • Real-time translation.
  • Biometrics for security

It can also be used for helping someone with a lost voice to talk again.

While there haven’t been any recent updates, Baidu remains hopeful that Deep Voice will revolutionize our tech.

7. Tailor Brands

Tailor Brands This machine-learning tool creates logos.

The machine, “This or That,” helps Tailor Brands understand a user’s taste using decision-making algorithms.

By selecting samples, the logo generator will tell you which fonts or styles are most popular.

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 show styles similar to what they’ve used before.

8. Yelp

Yelp receives millions of photos every day from all over the world.

It was recognized by the company that it required more advanced methods to match images to specific businesses.

They did. The first system to understand photos was inventedIt is possible to create semantic information for each photograph.

This system allows Yelp to sort photos into categories relevant to the user’s search.

First, Yelp created labels for the photos they received from users, such as “drinks” or “menu.”

We next needed to gather data from crowdsourcing, photo attributes and captions.

The machine learning was then applied to the photo labels, allowing it to place them in the appropriate categories.

The photo classification allows for a better user experience on Yelp.

It can be used to create tabs to allow you to quickly jump to the relevant information.

Machine learning is still a very powerful tool for digital marketers.

Humans and machines can work together to create more meaningful customer experiencesThis allows you to create more productive campaigns within a short time span. It’s a win-win-win.

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