What is machine learning?

Before talking about machine learning in marketing analytics, we are going to comment on what machine learning is. It is a class of artificial intelligence methods that are characterized by not providing direct solutions to problems, but training systems to apply solutions.

There are many machine learning methods, but they can be roughly divided into two groups: learning with a teacher and learning without a teacher.

In the case of learning with a teacher, a person provides the machine with initial data in the form of solution-situation pairs. The machine learning system then analyzes these pairs and learns to classify situations based on known solutions. For example, a system can learn when to mark incoming messages as spam.

In the case of learning without a teacher, the machine receives disordered information; situations without solutions and learn to classify those situations based on similar or different signs without human guidance.

Machine learning in online marketing

In psychology, a pattern is a particular set of behavioral reactions or a common sequence of actions. Therefore, we can talk about patterns with respect to any area where people use templates (which is most areas of life).

Consider the example of a pattern used on websites. If the user is not interested in the offer in the popup shown below, they can close this window as follows:

  • Clicking on the X sign
  • Clicking No Thanks
  • By clicking anywhere on the site that is outside of the popup window.

When hundreds of these parameters are collected, the collected data gains value because it contains behavior patterns and dependencies. It hides the huge potential of behavioral data, allowing us to supplement user data with missing parameters based on data we already have for other users.

For example, the easiest way to define a target audience is by gender and age. But what if users fill in this data only in 10% of cases? How can you understand how many of your website users belong to your target audience? Behavior patterns can help.

You can use gender and age data from 10% of users to determine patterns specific to a particular gender and age. You can then use these patterns to predict the gender and age of the remaining 90% of users.

By having comprehensive gender and age data, you can now make personalized offers to all website visitors.

Examples of Machine learning in marketing analytics

recommendation systems

The essence of a recommendation system is to offer customers the products that interest them at the moment.

What a recommendation system predicts: products a customer is likely to buy.

How this data is used: To generate push and email notifications, as well as “Recommended Products” and “Similar Products” blocks on a website.

Result: Users see personalized offers, which increases the likelihood that they will make a purchase.

forecast segmentation

In general, the essence of all types of targeting is to spend the advertising budget only on targeted users.

Most used types of segmentation:

  • Segment targeting: Show ads to groups of users with the same set of attributes.
  • Activate targeting: Show ads to users after they take a certain action (for example, view a product or add an item to the shopping cart).

There is also predictive targeting, where you show ads to users based on how likely they are to make a purchase.

The main difference between these types of guidance is that predictive guidance uses all possible combinations of tens or hundreds of user parameters with all possible values. All other orientation types are based on a limited number of parameters with certain ranges of values.

What forecast segmentation predicts: The probability that a user will make a purchase in n days.

How this data is used:

  • Example 1: Launch advertising campaigns. You do this by creating segments based on the probability of a purchase and uploading those segments to Google Ads, Facebook Ads , and other advertising systems.
  • Example 2: Analyze the effectiveness of advertising campaigns. To do this, create segments based on the probability of a purchase and upload them to Google Analytics and use them to analyze the effectiveness of advertising campaigns (which campaign generates more conversions).

LTV Forecast

The best-known methods for calculating lifetime value, or LTV, are based on knowledge of a customer’s total profit and the length of time the customer has been interacting with the company. However, many modern business tasks require you to calculate the LTV even before the customer leaves. In this case, the only solution is to predict the LTV based on the available data.

How this data is used:

  • Segments are uploaded to email or push notification services and used for mailing to reduce customer churn (the churn rate).
  • Segments are uploaded to Google Analytics and used to analyze the effectiveness of ad campaigns based on expected LTV.

Result: The advertising budget per user is determined based on LTV, which improves the effectiveness of campaigns.

Churn Rate Forecast

In marketing, the concept of churn or exit refers to customers who have left the company and the associated loss of revenue, and is usually expressed in percentage or monetary terms.

Abandonment rate forecasting allows you to respond to a customer’s intention to abandon your product or service before they actually do.

What Forecast Churn Predicts: The Likelihood of Users Leaving by User Segment

How this data is used:

Segments can be loaded into email or push notification services, as well as Google Ads, Facebook Ads, and other advertising systems. You can also pass this information on to the retention department so they can personally contact customers with a high probability of leaving.

Result: retain customers.

Now that we have seen what machine learning in marketing analytics is, let’s see which agencies are the best in this sector.

I will communicate

We are an SEO digital marketing agency that operates throughout Spain. Our team establishes digital strategies with the main purpose of increasing the positioning of the different brands we work with, using tools such as SimilarWeb, Sistrix Toolbox, Google Analytics, Semrush and Google Search Console.

In order for you to achieve the best results in your communication strategy, from Comunicare:

  • We analyze clients, markets, competitors and platforms. As a result of the analysis, we propose a plan with a battery of initial tests. We measure the test results and modify our plan. We don’t like to be wrong
  • We optimize as much as possible to guarantee the success of the campaign. We do not stay in the advertising campaign. We study and improve everything to promote success and recommend implementations.
  • Measurement, monitoring and in-depth analysis of all related KPI’s. We put at your disposal a metric board in real time to follow the campaign very closely. When it finishes, we explain it to you and propose improvements.

Other agencies such as machine learning in marketing analytics

NeoAttack

It is a Digital Marketing agency to sell more. We live in a time where digital marketing is king of customer acquisition for businesses of all sizes.

NeoAttack is a project that helps companies and freelancers of all sectors and sizes sell more.

On their website you can find the methods and strategies they put into practice to help increase the turnover of all these companies day by day and how they always outperform their competitors.

creative wolf

It is a company with a long history in the world of Marketing and other related fields. Its mission is to transmit the message of the different brands that need its services. They offer a wide variety of services, among which we can highlight: web design, Web Design, Video Marketing and Visual Communication, training, SEO, SEM and Social Ads.