Predictive Analytics, or predictive analysis. What is it, and how does it work? But, most importantly, how does it empower marketers to anticipate the needs of their audience? Let's unveil the secrets of these impactful strategies.
Predictive Analytics is an advanced data analysis technology. It uses historical data and machine learning algorithms, artificial intelligence, and statistical models to make plausible forecasts about future events. Futuristic science fiction? Not exactly; it's the pure reality of the present.
Once data collection is complete, the statistical model is activated and trained to generate predictions based on the provided or selected data. The entire process can be summarized in just 5 steps:
1. Define the problem of interest: What do we want to ask this electronic brain? The data provided will undoubtedly be in vast quantities, so we must have a clear idea of what we want to derive from it. The best products to sell at Christmas? A list of potential spammers or industrial spies? Geographical areas at higher risk of catastrophic events than others? The possibilities might seem as endless as the database we managed to create.
2. Organize the data: It depends on how good we were at collecting and archiving them in historical records, but the more data we have, the more organized we must be. Even before developing Predictive Analytics models, we must set up the data in such a way that they are easily accessible and readable with centralized access that is valid for the entire system.
3. Level and process the data: Raw data itself is essential and highly valuable. But while it might be okay to view them individually, perhaps by saving them in an Excel spreadsheet on your computer, they might need to be "rearranged" for easy integration into a central system. They may require cleaning, leveling, and processing in such a way that the settings match in a single common flow.
4. Model development: This is undoubtedly the work of an expert technician. We should let Data Scientists do their work, utilizing a series of tools and techniques to develop predictive models depending on the problem we have chosen. As mentioned earlier, the most commonly used models are based on machine learning systems, artificial intelligence, and statistical models.
5. Data validation and distribution: After the initial decisions, accumulation, cleaning, and data processing, and especially after developing predictive models, we are ready to make them usable for anyone we think needs them. To make things easier, these models are usually transformed into web interfaces, such as applications or a website, much more user-friendly and easy to consult.
We have everything, and we're ready? Well, what do we do with these Predictive Analytics, and what benefits can they bring us? Let's find out.
By analyzing the data collected from our customers' online behavior and flow, Predictive Analytics allows marketers to offer highly personalized experiences, such as recommending genuinely interesting products, suggesting undoubtedly valuable content, and even creating engaging promotions tailored for each individual.
Predictive Analytics enables marketers to optimize their advertising campaigns by identifying the right channels, times, and messages that are most effective for each selected customer segment. An "each to their own" approach, performed excellently and without annoyance to our followers.
Having the ability to predict potential issues and needs of our online visitors, Predictive Analytics helps customer support teams provide timely and proactive assistance, leading to greater customer satisfaction and loyalty.
Predictive Analytics assists sales teams in prioritizing leads based on the probability of conversion, enabling more focused and efficient lead nurturing. Ultimately, it's less effort and more sales.
Predictive Analytics provides invaluable real-time insights, allowing marketers to respond quickly to changes in customer behaviors and market trends.
Predictive Analytics helps in predicting sales trends very accurately, helping businesses plan resources, set goals, and make data-driven decisions rather than emotional ones.
As mentioned earlier, with Predictive Analytics, the marketing team can make decisions based on verifiable data and mathematical models, reducing guesswork, subjective opinions, emotional choices, and thus improving the overall effectiveness of campaigns.
It seems like we've covered a lot in this article, and I hope it transforms into something useful for your future steps. Are we ready to gain a deeper understanding of our customers and offer experiences that exceed their expectations? Then, Have a Happy Marketing!
Matteo is an experienced freelancer who serves as an outsourcing manager for multiple international projects. He has a passion for programming, content creatin, eating pizza, and developing innovative solutions. A native Italian, he resides in Bali and is a devoted family man and nature enthusiast. When he's not in front of the computer, you can likely find him trekking through the heart of a jungle or unwinding on a secred beach.
