Predictive analysis is an advanced analytical technique that uses data, algorithms, and machine learning to anticipate trends and make business projections.
Thanks to computational advancement, it is already possible to analyze large volumes of data (Big Data) to find patterns and evaluate future possibilities from its history.
The concept originated in the 1940s when governments started using the first computers – those that occupied an entire room and served warlike purposes.
But, predictive Analytics has gained far more relevance today, driven by powerful processors and new technologies.
Here you have some very interesting examples:
Customer Churn forecast
Making a churn forecast means identifying the signals that precede your customers’ cancellation request, calculating the probability in each situation.
With predictive models, you can cross-check data such as customer service quality, customer satisfaction level, and churn rate to determine which factors influence cancellation.
The goal is to understand the main reasons for the customer’s loss and reverse this process.
Campaign optimization
Your entire history of marketing campaigns can be used to project better results in the future.
Just use predictive analytics to identify the best channels for each content, the most successful language for each target audience, and other variables predicting consumer acceptance.
Thus, you aim directly at the target when engaging and winning over your audience.
Lead segmentation
Predictive analytics is also great for creating lead segmentation strategies.
After all, one of the biggest challenges of marketing is to map the profile of these potential customers to offer tailored content and create infallible nutrition campaigns.
With data and machine learning, you can generate segmented groups based on sophisticated analysis, predicting what leads want to receive the smallest details.
Customer Relationship Management (CRM)
In CRM strategies, you can use predictive models to understand customers’ every moment during the life cycle and purchase journey.
In this case, there is no lack of data to create multivariate models and analyze the most diverse possible relationships between behaviors, profiles, purchase history, interactions, and customer perceptions.
With these powerful insights in hand, you can revolutionize your customer relationship with personalized content, promotions, and offers.
Fraud detection
Analytical methods also allow companies to detect patterns of fraud and prevent security breaches.
With the discussion of cybersecurity on the rise, more and more organizations are concerned with correcting vulnerabilities and identifying any abnormalities in time to prevent damage.
Predictive models make it much easier to identify threats in real-time and anticipate scams.
Risk management
Risk management is another area that directly benefits from predictive analysis.
It would be much easier to make decisions with a complete view of the risks and opportunities ahead, right?
Therefore, predicting the probabilities of profit or loss is the great differentiator of advanced data analysis, whether to analyze a customer’s credit risk or the possible consequences of an investment.
Conclusion
Predictive analysis is a great way to help you see your business’s future, helping you to map the possibilities for making better decisions and going beyond your competitors.
Remember, human intelligence is indispensable in this process, as you need to feed the predictive models with quality data to obtain good results.
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