How Predictive Analysis Can Skyrocket Marketing Strategies

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Can you predict the future?

Decision making may be a tough business. At least once, we have been stuck between many options, uncertain of what to do.

In the marketing field, it happens very often. Marketers have to make tons of decisions every day. Many of them could be crucial for the success of the next campaign – or even of the company. In private life, it’s common to go with your gut. In a business situation, it might be too risky.

What could you do, then?

Predictive analysis is the answer. In this article, we will see how predictive analytics can become a triumphant look at the future for your company. We’ll also see how Artificial Intelligence can skyrocket decision capacity and minimize risks.
The key to do it isn’t crystal balls and hocus pocus, but pure and straightforward well-combined data.

Predictive Analysis. How Data Can Help to Predict The Future

Predictive analysis is a hot topic in the modern technology business, but what does it mean? It happens to be a branch of the field of data analytics. To simplify, we could say that algorithms analyze a set of known data to make reliable predictions about future events (unknown data). It is a fascinating process that can help a lot our world – and marketing business.

Nowadays, data are everything – and they are the foundation of predictive analysis, too.

If we manage to obtain the right amount of them, we can improve marketing strategies in many different ways. They can be any kind of it, from newsletter conversion rate to amount of CTR on an e-commerce website.

Data is useful to understand what customers like the most, improve a marketing campaign, or select the most valuable target.

However, this process only works if data has meaning. Marketing managers collect thousands of bits of information from different sources. Statistics and Business Intelligence usually are an excellent point to extract information useful to predict some future behaviors, the base o to achieve predictive analysis.

However, statistics and business intelligence present some difficulties.
Data is often retained in silos in a fuzzy and unstructured way. It is hard to get out something from it without further analysis.

Labeling and structuring data is an effort that requires a lot of time and competences. Fortunately, Artificial Intelligence is demonstrating is incredible power in doing these activities according to different AI models. For example, your IT team can use data modeling and machine learning algorithms, a subset of the AI field to have the job done.

Once that you have polished data, you can go ahead and leveraging on artificial intelligence to do predictive analysis. The foundation concept is that the system relies on several models of known data to understand the process in the present moment. After building a picture of what’s happening, they make an informed prediction based on various techniques.

Naturally, machine-based predictions can’t provide a 100% secure scenario of how things are going to be. However, even if those algorithms cannot predict precisely the future, they can discover trends and extract coherent patterns of data.

Moreover, while the old method based on Business Intelligence (BI) needs a periodical reboot with new queries, Artificial Intelligence (AI) technology allows a real-time free update. Amazing. You can save a lot of time and money to do even better what can give you astonishing support to your strategy.

managers predict the future with crystal ball

Predictive Analysis And AI: Taking Predictions To The Next Level

One of the most critical innovations Artificial Intelligence brings is the chance to combine predictive analysis and machine learning. This technology can learn and work without the help of human beings. A so powerful ability means that it can process a massive amount of data, and it can do it real-time.

Let’s see some examples of how AI can improve predictive analysis.

Understanding customer buying behavior. Consumers’ decision and customer journey are all but logic-based. Emotions, feelings, and irrational thoughts play a significant role in the process, but they don’t run it. Instinct and emotions are huge players in people’s mind, but they also follow some behavioral paths.
Lucky for us, AI algorithms are more and more capable of identifying these emotions and predict which prospects are more likely to become clients. They can also analyze behavioral paths, thanks to previously stored data, and extract insights from them.

Fitting and custom adv. The one-for-all method is out of fashion and wholly ineffective. On the other hand, personalized advertising guarantees a more efficient result. Predictive advertising can use a set of different attributes to anticipate which users might positively respond to a specific message. We can see the big picture of our advertising activities, understanding at first glance what works at best and what can be improved. It can also be used for pre-targeting, selecting potential customers thanks to data we already know about our current clients.

Lead scoring. We already discussed why lead scoring is so vital for a brand. Moreover, predictive analysis can reduce any risk and allows to make decisions based on a solid wall of data.

After all, maybe a machine can’t predict the future yet. At least, not in the sense that we understand predicting.
However, it can help us in crafting strategies and making decisions. It’s already a great goal, even if we’re all eager to know which the next step will be.


  • Decision making may be complicated. Predictive analysis can help marketers to figure out how campaigns and marketing activities are going to develop.
  • Algorithms analyze a set of known data to make reliable predictions about future events. All these predictions are possible because the software can handle a considerable amount of data.
  • It’s possible to combine predictive analysis and machine learning. In this way, AI algorithms can process a massive amount of data in real-time and boost marketing strategies.


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