As a response to the title of this post, we could say that predictive analysis allows us to use data to know in advance the behavior of consumers in reference to the object of study. It seems logical, right? However, that is not its only purpose. The ultimate goal of predictive analysis is not so much to predict, which is essential, but to know how we can influence actions and what probabilities have to be decisive in the face of that predicted event. Are you confused? The professionals at Exnovation, a leading content marketing agency in the USA, help you with the knowledge you must have.

Crystal Ball

To clarify the topic, let’s first define predictive analysis. Predictive analysis is a subset of advanced analytics that aims to forecast future events by examining past occurrences through statistical analysis. Many Big Data professionals and digital content agencies refer to it as a “crystal ball” since, much like psychics, it seeks to predict future outcomes using data models. However, this metaphor isn’t entirely accurate. In marketing, the goal of predictive analysis is not just to forecast outcomes; it is to inform actions that lead to desired results. Let’s explain further.

To build practical business intelligence, it is essential to collect as much data as possible and differentiate valuable information from irrelevant data. The data we encounter can be categorized into two types: structured and unstructured data.

Structured data is orderly and can be easily processed. Common examples of structured data include age, gender, marital status, and income range. In contrast, unstructured data cannot be organized or classified without a specific structure, such as social media content, including elements derived from that content, like the sentiment expressed in posts.

By leveraging the correct data, we can discover and anticipate results and behaviors, allowing us to be proactive. This proactive approach is a significant advantage because it enables us to make decisions based on data rather than assumptions.

Predictive analysis takes things a step further by suggesting specific actions that can be implemented based on predictions and their implications. This aspect is precious in marketing. Knowing that an undesirable event is likely to occur is not helpful unless we also study the actions required to change that behavior to align with our interests.

Thus, we can conclude that the ultimate goal of predictive analysis is not merely to know what might happen; it aims to develop predictive models using mathematical and artificial intelligence techniques. These models help content marketing agencies in the USA predict how certain variables are expected to behave in the future based on a set of predictor variables.

Now that we have a clear understanding of predictive analysis, let’s explore the process, its advantages, and its applications.

The Predictive Analytics Process

Like any process, this one is made up of different phases.

1. Project Definition

Here, you must establish what your objectives are and why you are going to do this. In addition, you must determine the data sources you are going to use, the decisions, the results and the scope you expect to obtain as a result of your efforts.

2. Data Collection

This is the moment in which we obtain the data. Once we have collected it, we process the information and transform it into a comprehensible structure so that we can use it later.

3. Data Processing

It consists of inspecting, cleaning, transforming, and classifying data to discover helpful information that can be used to reach conclusions.

4. Statistical Analysis

This will allow you to determine the first results and conclusions through descriptive statistics and identify behavioral probabilities.

5. Predictive Modeling

This phase gives you the opportunity to create predictive models automatically.

6. Implementation of Predictive Models

This is the last phase, where a content marketing agency in the USA can deploy the analytical results of daily decisions, building a process to obtain results and reports that allow us to reach decision automation.

Main Advantages of Predictive Analytics

Now that we know what predictive analytics is and what the process is, you will have an idea of ​​its advantages. These are the ones that come to mind.

1. It helps you prevent churn by detecting early signs of dissatisfaction

In this way, you can create customer segments based on the higher or lower risk of loss. In this way, you can apply timely corrective actions, thereby increasing retention and revenue.

2. It allows you to maximize customer lifetime value (CLV)

With predictive analytics, content marketing agency services will be able to identify high-value customer segments and thus plan marketing actions by establishing the most appropriate cost/up-selling strategies.

3. Identify new high-potential customer segments

What does this mean? If you know which of your customers can increase their purchases, target them with timely actions and increase revenue.

4. Properly plan your campaigns, giving them the ideal focus for each of the segments

By analyzing all the data you have, such as purchasing patterns, behavior, web browsing, interactions on social networks, etc., and content marketing agencies in the USA, you will be able to define which are the best moments and channels through which communicate with your clients.

5. Predict the performance of each campaign based on the channel

Predictive analysis allows you to analyze online purchasing habits and behavior, helping you predict the campaign’s performance on each channel.

6. Create product recommendations (cross/upselling) based on each client’s purchase history

You can use this historical knowledge to identify products or services with high sales potential per client.

7. Predict off-peak moments and thus carry out campaigns to reduce this drop in sales

Thanks to these analyses, we can predict the moments in which sales drop and thus act on them in advance, reducing this circumstance as much as possible.

8. Reduce customer or shopping cart abandonment rate

This follows the same line as the first point, where we talked about preventing churn by detecting early signs of dissatisfaction. By identifying which customers are most likely to abandon their purchase, you can intervene to prevent this from happening.

9. Identify purchase probability

Create customer segments based on their purchase probability and thus communicate with them differently based on this.

Final Words

Interesting, right? I do not doubt that if you have come this far, it is because you are very interested in putting these predictive models into practice in your business. At Exnovation, we have a great team of experts who create opportunities and use advantages for companies through customer intelligence and predictive analytics. Contact us in case of any assistance.