We live in a time when data is the new gold. They are driving innovation, enabling personalized customer experiences, and changing the way businesses operate and make decisions. One area where data has a particularly strong impact is marketing. And in the midst of all these developments, a new, emerging field has emerged: Predictive Analytics.

Predictive analytics, or predictive analytics, uses historical data, statistical algorithms and machine learning to predict future outcomes. It gives marketers the ability to look into the future and make informed decisions about what to do next.

In this blog post, we take a closer look at the future of marketing and the role that predictive analytics plays in it. We will look at the basics of this technology, explain how it works and why it is so important for modern marketing strategies.

We’ll also look at some practical use cases and show you how predictive analytics can help personalize customer experiences, increase sales, and ultimately ensure the success of your business.

Whether you’re a marketing professional looking to expand your knowledge or a business owner looking for ways to increase sales and improve customer retention, this blog post will give you clear insight into the future of marketing: Predictive Analytics. Join us on this exciting journey into the future of data-driven marketing!

Definition: What is Predictive Analytics?

Predictive analytics is a process that uses historical data sources to create a mathematical model that predicts events in the future. It sounds complicated at first, but you will encounter the basic principle almost every day: it is comparable to forecasting the probability of rain on a certain date.

It is important to note that the probability of hurricanes or temperature is not comparable to this. Even with predictive analytics in marketing, a mathematical model can only be created if data of the same type is used. Read more about the requirements for predictive analytics.

Why is predictive analytics so important?

Predictive analytics plays such a big role in online marketing because it can be instrumental in ensuring that your marketing campaigns are crowned with success. Predictive analytics allows you to predict the buying behavior and habits of your target audience. You can then use these predictions to better target your marketing campaign to increase your conversions. However, not only marketing campaigns, but also sales processes and customer services can be optimized with predictive analytics. Predictive analytics is therefore an important area of activity, particularly in the field of customer relationship management (CRM).

But how does predictive analytics actually work?

Predictive analytics tools can identify purchase intentions, or the ideal customer, by analyzing available past data and using the results to find people whose data matches that of ideal customers. Similarly, leads can be evaluated using predictive analytics. Historical data as well as intent data is also used for this purpose to identify potential customers. This includes measuring the likelihood of purchase, how customers should be contacted, and what information should be sent to them.

You can use predictive analysis for this purpose:

  • Personalized Customer Experience
  • Acquisition of new suitable customers
  • Optimization of the online marketing budget
  • Optimization of logistics and inventory quantity

How does predictive analytics work in online marketing?

So weathermen are basically doing nothing more than predictive analytics when they tell us it’s going to rain over the next few days. But how does predictive analytics work in online marketing? How can you apply the predictive analytics model to make your marketing campaign successful?

Prerequisites for predictive analytics

Before you can get started with predictive analysis, you first need to get the basics straight. Because predictive analytics doesn’t work without you meeting some prerequisites in terms of data and co. We would like to explain to you in more detail what these requirements are:

Data is the foundation of any predictive analysis and at the same time sets its framework. You can probably guess that the better the quality of your data, the better your forecast. The quality of your data is characterized by its relevance. This means that you need to determine in advance which topic your predictive analytics will deal with. You can then select the data that provides information relevant to the topic.


Identifying relevant data also requires that you organize your data in a meaningful and clear system. Such a data management system helps you to quickly find the appropriate data. It is the infrastructure of your predictive analytics. Think about how best to build your infrastructure – it may look different in every company. For example, you can collect your data in a large data warehouse or in a small database. In addition, you need to consider whether you program your algorithms yourself or use software?


Predictive analytics is a very comprehensive topic and not something you can just deal with on the side. You need a team that includes people who manage and store the data, and people who understand, apply, and interpret the algorithms – for example, data engineers, data analysts, or data scientists.

Guideline for a Predictive Analysis

Predictive analysis involves a step-by-step approach. It is also an iterative, or repetitive, process. This means you will probably have to repeat the process several times to achieve success. We want to show you the theoretical steps of Predictive Analysis with a simple practical example:

Predictive Analysis Prozess

Step 1: Objective

Set the goals of your predictive analysis.
In practice, you should ask yourself the following question: Which potential customers will sign up for my offers within the next 30 days?

Step 2: Data acquisition

Check what data sources are available to you and merge them.
For our example, you need historical data, demographic data, and data on the channels used, as well as a list of potential customers.

Step 3: Data checking and processing

As mentioned earlier, the quality of your data is critical to your predictive analytics. So review the data at hand and clean up what doesn’t fit if necessary.
Review your data to determine facts such as whether average conversion times vary between channels and whether demographic characteristics correlate with those times.

Step 4: Create predictive model

Create the predictive model, which is the actual predictive analysis.
As described above, you should determine whether you will create the model yourself or with the help of software.

Step 5: Model test and optimization

Enter the data in your model and test it. Then you can optimize it accordingly.
Test your model and historical and demographic data, etc. to verify they are still current.

Step 6: Integration

By the time you integrate it into your business processes, you may need to have made a few runs of your predictive analytics. Finally, you can use the insights gained for your online marketing.
Target your online marketing more intensively to the potential customers who will sign up for your offers within the next 30 days.

  • When dealing with data, always keep in mind that it can be strongly influenced, for example, by the season, crises or recent events.

Advantages of predictive analytics

  • Cost-effective

  • Conserving resources, both materially and temporally

  • Risk minimization

  • Optimization of your marketing campaigns

  • Improvement of the operating business

Predictive Analytics and Big Data

Big Data and Predictive Analytics are often confused, but they are not the same thing. Big Data involves the collection of huge amounts of disparate data. Since this involves data from billions of people, it must first be analyzed. Predictive analytics is used to capture patterns and correlations in the data. Only with this predictive analysis can the potential of Big Data be further exploited and the data made useful. But predictive analysis also benefits from Big Data. This is because the value of predictive analysis increases as the amount of data increases.

Application examples for predictive analytics

Here are three examples of how predictive analytics can be used in practice:

  1. Predicting customer churn: Many companies, especially in the telecommunications and financial industries, use predictive analytics to determine which customers are likely to terminate their business relationship. By analyzing customer behavior, purchase history, and interactions with the company, models can be created that predict which customers are at high risk of churn. These insights can then be used to take targeted customer retention measures and reduce customer churn.
  2. Demand forecasting in the supply chain: Predictive analytics is used in manufacturing and retail to predict future fluctuations in demand. By analyzing historical sales data, seasonal trends and market information, companies can better plan which products to produce or order and in what quantities. This can help avoid overstocks or stockouts and improve supply chain efficiency.
  3. Credit risk assessment: financial institutions use predictive analytics to assess the risk of credit default. By analyzing data such as credit history, income, employment status, and other factors, models can be created that predict the risk of loan default. These predictions can help institutions make more informed lending decisions and minimize financial losses.


Within the industry, there is already a firm assumption: Predictive analysis is the future of marketing. Who doesn’t want to know what will happen in the future? Or at least ideally tailor your own marketing campaign to your own target group and potential customers? That’s exactly why you should use predictive analytics.

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