At its core, machine learning is a form of artificial intelligence that enables computers to learn on their own by identifying patterns in data. Machine learning is a form of artificial intelligence that enables computers to learn on their own by identifying patterns in data.
Here’s a basic introduction to how machine learning can be used to improve digital ad campaigns.
Machine learning is an intriguing topic. It’s amazing to think that computers can do things that seem almost magical, like US retailer Target discovering a young woman was pregnant before she told her parents or Amazon recommendations.
Did you ever think you could use [social media] for your own marketing campaigns?
It starts with a need
If you are marketing for a business that sells jewelry online, your main strategy should be display advertising.
Your challenge is to create a campaign that gets clicks from potential buyers, particularly those who are willing to spend a lot of money.
Most of your client’s revenue comes from people who spend more than $100. You will need to find more buyers who are willing to pay more than $100, even if it means losing some of the buyers who only pay $100.
You can create ads that will appeal to high-rollers by using a mix of product photos, models, and your brand logo with effective copy.
You then look at the analytics a few days after the campaign has started, only to find that they are not very useful.
Why not?
Marketing analytics can give you some great insights, but you need to know more to really take advantage of it.
To attract high-spending customers, you need to identify what they respond to and then provide more of that and less of what they don’t respond to.
And that’s where your machine learning journey starts.
Machine learning in a nutshell
Machine learning is used to find patterns that we cannot see ourselves.
This means that you use the unbiased power of computers to find things that we humans would not be able to see because we are biased and slow. After this, you create new rules to improve the situation.
The Target story is an example of a company using discounts to attract new customers who they hope will become lifelong, loyal customers.
But they had to find them first. The company wanted to find out what kind of purchasing habits someone who had just become pregnant would have, so they hired a machine learning expert. Once they knew the average age of first-time mothers in their area, they were able to target these women with special offers for pregnancy products.
But how did they do that?
The expert apparently first looked at the buying habits of customers who were already parents, leading up to the birth.
Then he used a machine learning program to detect patterns of purchases made before pregnancy – and fired off an alert to the marketing team when other customers had made similar purchases.
The marketing team was successful in identifying expecting families and sending them direct mail with special offers. They were then able to track whether or not the families took advantage of the offers.
How to get your machine to learn.
How can marketers use this same approach? Where do you start?
To start, focus on the learning and not the machine. This means that you should first find the rules and then worry about automating them.
The process of developing learning rules is fortunately a standard one. The steps are not hard to understand, but it is important to read through them before starting so that you can keep your own marketing tasks in mind.
1) Find your features
To create a spreadsheet model of a real-world problem, you must first identify and map the relevant elements of the problem into spreadsheet format.
The columns in this spreadsheet represent different aspects, or “features,” of your campaign. Things like the platform, the copy, or the photo.
The rows, then, are the data points. The features of each ad which led to the purchase were the colors, the images, and the overall design. Which photo did they see, which copy did they read, or which platform did they click from?
There are a lot of other features that could be used. The time of purchase, pages visited, and other data points are simple examples of how machine learning works effectively.
2) Identify the result
You will need to have a clear, desired outcome as well as a clear, negative outcome. This will allow us to program the computer to recognize the pattern that most often produces the desired outcome.
3) Gather the data
The third task is to gather data that can be used to create features.
But what if you don’t have the right data? You need to know the whole procedure before starting in order to avoid this.
One of the most frustrating parts of machine learning is not having all the data you need to produce a list of features and a result.
4) Pick your machine learning program
OK, this is a bit hard. There are many different types of machine learning algorithms. It’s bewildering and paralyzing when you first start out.
Each machine learning algorithm is best suited for a specific use case, which can produce complex models to help you predict the future.
5) Split your data
It is important to split data into two sets when using the machine learning methodology, with one set for learning and the other for testing. Typically, more data is used for training than for testing. In this case, 400 examples will be used for training, and 100 examples will be used for testing.
We are trying to see if the model the machine creates from the data it learns from will actually work on the data it is being tested with.
In other words, did the machine improve its performance enough to be useful in the future? Did the machine learning algorithm just learn how to predict the training data and is therefore useless on data outside of that?
What are the most effective methods for asking important questions? Most people seem to agree that four times as much data should be used for learning than for testing.
The advantages of machine learning in digital marketing
There are many ways to leverage machine learning in digital marketing to make the most of your data:
Improved user segmentation
Digital marketers can learn about their customer’s preferences and behaviors over time by monitoring their activities in detail.
There are a lot of metrics available to measure for mobile apps, but most marketers only measure up to 25. A machine can take in all of that information very quickly and use it for marketing.
A more personalized customer experience
Improved segmentation enables more personalized experiences.
If you know where your customer is in their buyer’s journey, you can target them with ads that are most relevant to their stage and preferences.
The app can use data such as search history and typical actions to suggest more products or services.
Creative optimization
A/B testing is a great way to figure out the optimal location, color, size, etc. of a CTA button or an image. However, this is usually limited to one variable. Machine learning can help marketers test multiple variables and see which is most effective.
Automation of processes
Machine learning is about more than just being able to better target users. This text is discussing how to learn to do tasks without human oversight. A marketing campaign involves a lot of different processes, many of which can be quite tedious and repetitive.
Marketers can save time by automating certain tasks so they can focus on other issues that need human intervention. In other words, the end result is a more efficient and less time-consuming process with fewer mistakes, leaving marketers free to focus on more strategic tasks.
Putting machine learning into action
How does machine learning impact mobile marketing? Here are some of the main examples and use cases.
Ad fraud
Fraud has been a problem on the internet and will continue to be a problem in the future. $1.6 billion was lost to app install fraud in the first half of 2020. Outsmarting fraudster is a never-ending challenge.
Machine learning is a valuable technique for combating fraud by analyzing common user behavior patterns and using these insights to identify any activity that may diverge from these patterns.
A machine can analyze data trends across dozens of indicators much faster than a human can.
In addition, the larger the available data set is, the quicker the machine can identify existing and new fraud trends as they emerge. Machine is better at blocking spam emails than humans.
Chatbots
Many businesses aim to deliver excellent customer service, and app developers are no different. Chatbots are computer programs that can engage in human-like conversations. The most advanced versions of these chatbots are driven by machine learning. They offer the opportunity for 24/7 customer service.
More advanced machine learning-driven bots can help customers to purchase items and answer any questions they may have. You can reduce your customer support costs by escalating any queries that the algorithm can’t answer.
Dynamic pricing
With the help of PAR, they can analyze their competition and aim to be the best in the market. Travel apps use PAR to compare their prices to their competitors and ensure they are offering their products and services at competitive prices. An example of this would be a hotel booking app not wanting to price a hotel room much higher or lower than what its competition is pricing it at. Dynamic pricing uses machine learning to adjust prices based on real-time market data. This ensures that customers always receive the most accurate price quote possible.
The app can also predict which users are close to making a purchase, and offer them timely discounts. Offers like discounts can be given to customers who are almost done converting, but may need an additional push to finish. This means that discounts can be offered in marketing campaigns on a limited basis to people who would not normally buy the product.
User Acquisition
Media sources that have a lot of data can use machine learning to get more people to download and use their apps. It is important for marketers to understand how their media partners are using machine learning to optimize campaigns.
Facebook developed a tool called Automated App Ads (AAA), which uses machine learning to allow marketers to test audiences in a quick and efficient manner. They can test different keywords and ads to see which ones have the best conversion rates.
The Google App Campaigns (AC) product provides real-time analysis of hundreds of signals so that your app appears in front of the user most likely to convert. AC also uses this technology to make the most of its bids across Google’s properties. This allows advertisers to optimize their campaigns for specific in-app events that are most likely to lead to conversions, such as level completions or adding items to a shopping cart. The software takes campaign types, in-app events, and in-app conversion values and incorporates them with other variables to create a custom optimization plan for each advertiser.
Understanding customer behavior
Machine learning can help app marketers understand customer behavior. The ability to predict how a customer is likely to behave based on their previous interactions is an incredibly useful tool for marketers.
The takeaway
Before we get too comfortable in our marketing jobs, it is important to familiarize ourselves with new technology and know what is possible. Although it is unlikely that machine learning experts will steal our jobs anytime soon, it is still good to stay ahead of the curve.
This simple example demonstrates the preparation necessary to use machine learning with a marketing program so that you can take steps toward computer-assisted marketing automation.
A machine learning program can help identify what is and is not working with your campaigns, though it may not be able to identify your customers as well as Target did.
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