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How Machine Learning Works in Paid Marketing?

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Paid marketing is getting more complex and competitive as more players are entering the online domain. Knowing how to run ads is not enough to thrive online. To ensure you get the maximum benefits and ROI from your ads, you have to optimize them.

There are several manual ways to optimize your ads and outperform your competitors. However, you can also leverage advanced technologies like machine learning to hone your paid marketing. There are several automation tools available online that help you manage the ads, but when you integrate ML, it can take off a major burden from your shoulders. Before you get into how to optimize your ads with machine learning, let EZ Rankings make you understand what machine learning is.

What is machine learning?,

Like robotics, algorithms can be taught to understand and process the collected data through machine learning technology. Leveraging machine learning for managing your ads will help you process large chunks of data faster with higher accuracy and precision.

Whatever paid marketing you run, be it your Google ads, LinkedIn ads, or Facebook ads, machine learning can help you identify important data sets. Using machine learning technology, you can get multiple benefits that will help you save both time and money.

The key to run successful paid marketing is to identify the right data sets and use them to cut the paid ads cost and run it with more efficiency. Data points can help you complete your business goals and outperform your competitors. When you know what you need to control and where to cut costs while bidding on keywords. It’s only the tip of the iceberg about what ML can do for your business and for optimizing paid ads.

How to use machine learning in paid ads

There are several examples available that guide us on how to use machine learning in paid ads. Some of the key examples are:

Recommendation systems

By using this machine learning advancement, sellers can let customers see products that they’re interested in buying at the moment. This system predicts goods that customers would like to buy.

How it is used – It is used for generating emails and push notifications along with recommended products. It is implemented using a K-means clustering algorithm. By using this algorithm, sellers can show personalized offers to customers and improve their retention rate.

Forecast targeting

This machine learning feature lets sellers efficiently spend their ad budget and get maximum results. This feature lets sellers show ads once the customers have taken some actions. Users can implement this machine learning feature using algorithms like Decision tree, XGBoost, and CATBoost.

How it is used — By integrating any of the above algorithms with the ad targeting, companies can determine hot leads that are likely to buy their products or services.

LTV forecasting

Machine learning can help in finding out the lifetime value of customers in an accurate manner. To use machine learning on LTV forecasting, there are several algorithms available like SVM, Random Forest, and Logic Regression.

How it is used — By using machine learning-based algorithms, company users can determine the exact reach they will get on a given budget.

What are some aspects of ML in ads?

We’ve understood the importance of machine learning in optimizing business operations, especially in the paid ads aspect. Let’s now understand how it actually helps you outperform competitors.

Bidding on budget

The first and foremost aspect of paid ads is optimizing your budget to thrive online. There are certain manual ways to save on budget by optimizing the bidding strategy. However, using machine learning, you can automate various aspects of budget optimization and save you money. Once you enter the keywords, budget, and target audience, machine learning will help you excellently optimize the bidding by doing the calculations by itself.

If you are new to ads and you need a little assistance, machine learning can prove itself as a boon for you. As it allows you to only enter the key parameters and it will optimize the bid by itself. It is especially important when you are running multiple campaigns, it becomes easier for you to manage them efficiently with minimum time and effort.

Audience management

Earlier, you used to change the audience to adjust to the performance of the aids and to improve the accuracy of the targeted audience. Using machine learning, you can improve the overall audience targeting and increase the ROI and conversion rate. To optimize the overall ROI, you earlier had to adjust the audience targeting and define the exact demographics. However, with machine learning, the algorithm will help you target the right audience every time you run the ad.

This feature becomes handy and useful especially if you are dealing with a wide range of audiences. For example, if you are running six different campaigns with different audience demographics. Then the machine learning algorithm will automatically manage the audience for all your campaigns. It will help you understand the audience set with more clarity.

Changing campaigns status

One thing that you need to take care of while running the ads often is the status of the ad, whether they are running, stopped, or they are paused. While managing the ads manually, it becomes difficult to keep track of them all. In many cases, individuals miss checking certain statuses, and they end up paying more money for their campaigns.

It’s better to use machine learning for managing the status of the ad and make sure to get the best outcome from them. As there are several integrations available that you can use, machine learning understands and learns the pattern that makes it more accurate over a period of time.

Negative keywords

Negative keywords play a pivotal role in optimizing your campaigns and get you the best results and ROI for your money spent. Using machine learning, the algorithm learns your preferences for negative keywords and then keeps the data in its NLP memory for further use. It becomes handy when you manage a wide range of negative keywords for multiple ads.

Wrapping up

Machine learning is the future and running ads will become smarter in the upcoming time. Whether you are starting out or an expert, ML can help you in saving both time and money. In addition, professionals can run and manage multiple ads at the same time.

About Author –

 With over 13 years experience as a leader in digital marketing, Mansi Rana is Managing Director of EZ Rankings. Passionate about all things data; providing actionable business intelligence in digital, future tech; and venture bubbles categories for everyone, everywhere.

Source: Plato Data Intelligence

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