Machine learning for marketing is often used to effectively improve campaigns and target customers. I remember when I started using machine learning for marketing at my previous company. We constantly tried new ways to optimize our campaigns and get better results. That’s when we came across OWOX Predicted Conversion Lifts attribution. This type of attribution uses machine learning algorithms to predict how likely a conversion will happen based on past data. This helps marketers make more intelligent decisions about allocating resources to get the most ROI possible. After implementing this method, we significantly increased our conversions and overall performance.

What is machine learning?

Machine learning is a class of artificial intelligence methods that train a system to apply a solution.

There are two types of machine learning models: supervised learning and unsupervised learning.

When users supply the system with information about a situation and a solution, the system uses this information to learn how to identify similar problems and match them to their corresponding answers. Over time, as it receives more data about these situations, it learns and becomes better at matching them to their appropriate solution.

A machine learning algorithm can learn to identify messages as spammy.

The machine can learn to sort data sets without any teacher. It’s able to classify the data into different categories on its own.

Machine Learning For Marketing

Machine learning helps marketers find patterns in the behaviors of users on a website. This allows them to anticipate better what actions a user may take in the future and optimize their ads accordingly.

What is the prospect of behavioral data?

In psychology, a pattern is a particular series of behavioral responses or a typical sequence of events. So, we can talk about patterns in any area where people are using templates for behavior (which is most areas in life).

Suppose a user is not interested in the offer presented in a pop-up window on a website. In that case, they can close the window by clicking the ‘x’ in the top right corner, clicking outside the window, or waiting for the window to close on its own after a set period.

If a user closes the window by clicking on the X sign, likely, they’re not interested in the offer. However, if they close the window by clicking No thanks or anywhere else on the site, it’s less clear what their intentions are. Behavioral data can help clarify this by showing how users interact with the site after closing the pop-up window.

In addition to these three options, the pop-up closes independently after a specific time.

So there are four possible outcomes:

Behavioral data is a valuable tool for understanding how users behave on your site or app. For example, you can track which actions are taken and whether or not they succeed. This data can be used to make improvements to the design and functionality of your website or app.

When tens of thousands of such parameters are collected, the data collected becomes more valuable because it contains patterns and behaviors.

Behavioral data is the missing link in user behavior. It allows us to fill in the gaps in user profiles based on the data we already have for other people.

Let’s say you’re creating a target audience based on age and gender. But what if users only fill in this information in 10% of cases?

There are a few ways to understand how many of your website users fall into your target audience. One way is to look at patterns of behavior. Another way is to ask them directly. You could also use a combination of both methods.

Using only 10% of your data, you can determine general patterns in how visitors from a particular gender or age group behave. You can then use this pattern to predict the behavior of 90% of your visitors. With a complete picture of your visitor’s age and gender, you can now personalize the user experience for all visitors to your site.

Now that you’ve collected information about age and gender, you can personalize the offerings you make to all of your website’s visitors.

The quality of data is essential for marketing effectiveness. Models based on company data alone lose out to those built using market data. Having access to high-quality data sets is key to success in marketing.

The potential of behavioral data is vast. It can improve marketing strategies, better understand customer behavior, and predict future trends.

How machine learning can revolutionize marketing

Machine learning allows you to make quick decisions based on big data.

The algorithm that marketers use is: Create a hypothesis, test it, analyze it, and evaluate it. This is long and labor-intensive; results are not always correct because data can change in seconds.

To analyze 20 marketing campaigns, each containing 10 different parameters, for 5 separate market segments, a marketer will need to spend about 4 hours.

If marketers were to analyze their campaigns every single day, then they would find themselves spending half of their day on analysis.

When machine learning systems are used, the evaluation process only takes a few minutes, and there’s no limit to the number of segments or behaviors you can evaluate.

When you use machine learning capabilities for your marketing campaigns, you can respond quickly to changes in traffic quality. This way, you can spend more time developing hypotheses and testing new ideas instead of being stuck in a rut with routine tasks.

The value of your results may decrease as the customer data used for analysis becomes outdated. It is essential to use relevant and up-to-date data to produce accurate results.

People can’t keep up with all the data collected every second by analytics software. Instead, machine learning can sort through thousands of data points, organize them, and present them in a way that’s easy to understand.

What Is a Recommendation System?

The heart of a recommendation system is to provide customers with items they are interested in buying at the moment.

What a recommendation system predicts: Goods that are likely to sell.

How this customer data is collected: To generate emails, push notifications, and “recommended” and “similar” product blocks on a website.

Personalized marketing increases the likelihood that users will make a purchase.

A standard algorithm used for this is kmeans.

What Is Forecast Targeting?

In general, all targeting is spending the marketing budget on target customers.

Most commonly used methods of targeting:

Predictive marketing is when you target users based on their likelihood to purchase.

Predictive marketing is unlike other forms of targeted advertising because it utilizes every possible combination of dozens of user attributes with any potential value.

All other forms of targeting use a limited set of parameters with set values.

Forecast Targeting predicts the likelihood that someone will buy from you within n number of days.

How this information is used:

Forecast targeting is a method of predicting future behavior patterns to better target advertising campaigns. Segments are created based on the probability of purchase, and these segments are then uploaded to Facebook Ads, Google Ads, and other advertising systems. This allows for more accurate targeting of potential customers and leads to more sales.

OWOX BI can automatically import audience lists from Google Analytics into Google AdWords. This lets you easily create new, updated, and uploaded lists of users for AdWords.

Forecast Targeting allows you to manage your bids based on data, boosting your ROI and conversions while saving your advertising budget.

LTV Forecasting – How to Predict LTV

The most well-known way of calculating lifetime value is by knowing the total profit a customer makes and how long they have interacted with your business.

But calculating customer lifetime value (LTV) before a customer even walks out the door is becoming increasingly common.

In this case, the only possible solution is to predict customer lifetime value (LTV) using available data sets.

How this information is used:

The effectiveness of campaigns is improved by using LTV to determine an advertising budget for each user.

To predict LTV, you can use standard algorithms such as XGBoost, SVM, Random Forest, or Logistic Regression.

Churn Rate Forecasting

In marketing, churn or outflow refers to customers who have stopped using your product or service or left your company. This is typically expressed in terms of percentages or monetary amounts.

By predicting customer behavior, you can better prepare for their next move and prevent them from abandoning your product.

What Churn Rate Forecasting predicts: The probability that users will leave by user segments.

This data can be used to target customers with a high risk of leaving with email or push notifications, as well as ads on Google and Facebook. This information can also be passed on to the retention department to reach out to these customers directly.

Benefit: Retain your customers.

Some standard algorithms that could be used for churn rate forecasting include support vector machines (SVM), logistic regression, and other classification algorithms.

OWOX BI ML Segmentation for Your Business

OWOX has developed a machine learning solution that uses customer behavior to predict which customers are more likely to make a purchase.

By calculating this metric, you can identify audiences that are more likely to convert to customers and target them with advertising for a greater return on investment.

Machine learning capabilities of OWOXs for segmenting users.

The OWOX BI platform can be trained using customer data from any source: your CRM system, website, or mobile application. Our tool allows you to select any desired behavior: purchases, completed orders, or phone calls.

You can set a time frame for when you’d like your prospect to convert. This can be useful if you know when your prospects typically make purchases.

The results of your calculation can be used in different ad platforms, such as Google AdWords, Facebook Ads, and Instagram Ads. This helps you to target your ad campaigns better and increase your conversion rates.

Conversion Prediction Results can be used to help you:

OWOX’s predicted conversion lifts feature

In OWOX, you can integrate any traditional marketing analytics software with your reports. You can also use OWOX predictive analytics tool to forecast conversions. Or, you can build your own custom model for tracking your rules.

You can analyze your marketing campaigns from different perspectives, compare them across various models, and then choose the one that best meets your objective.

AttributionOutput: OWOX’s predicted conversion lifts feature lets you see which channels are contributing to future and ongoing sales.

The predicted conversion lift attribution model is a great tool for seeing how each of your channels and campaigns contribute to a sale.

With Wowox, you can instantly analyze your newly launched campaigns and draw a conclusion right away, rather than waiting months or even years. Even if the sales take place only in the future, the predictive analytics will accurately tell you how your marketing campaigns are performing.

Let’s say you’ve run a campaign at the top of the sales funnel, and you want to know how it’s impacted your sales.

This conversion lift analysis can be done as soon as a week after launching. You bought some paid search ads yesterday.

It’s too early to say whether it will bring in sales, but already it can predict how much it will contribute to the future.

Conclusion

Overall, using machine learning for marketing can be extremely beneficial. It can help you automatically improve campaigns and target customers more effectively. If you’re looking to get the most out of your marketing efforts, we recommend giving OWOX Predicted Conversion Lifts attribution a try.