Econsultancy and RedEye published a report on Predictive Analytics a couple of weeks ago, stating that 40% of companies already use predictive analysis. And 99% of the survey participants agree on the importance of this technique for the future of their businesses. "It's always good to know what your colleagues are doing and more when you can be missing trends that can be very useful for the development of your company," they explain in the article that presents the study.
But really, what is predictive analytics, and what can it do for you. This is what we will try to reveal in this post.
What is predictive analytics?
Predictive analysis is a technique used by the consulting research market about 15 years ago. Banks and insurance companies were pioneers in its use. Today, it has been extended to all sectors of the economy interested in discovering new business lines, improving customers' experience by customizing offers and services or saving costs with the optimization of internal processes of companies.
Examples of predictive analytical use
One of the examples we can use to understand what predictive analysis entails is the marketing actions that banks or insurance companies needed to perform at the beginning of the use of this technique - either to apply loyalty or business growth models or to prevent abandonment.
Suppose that one of these companies had a budget to send a letter in which to offer its customers a new credit card or other insurance other than the one already contracted. And that this budget was not enough to be able to send it to his million clients, but they had to choose only 50 thousand of them. The natural question that arises in this situation is 'who should they send it to'? How to choose the most appropriate profiles or more likely to buy these products?
The answer comes from the hand of predictive research. Well, thanks to it, we can avoid choosing at random and look for niches of clients that best allow us to target our business objectives.
Another example is that of market research for mobile phone companies in Latin America, where the prepaid card system is very common. Predictive analytics is often used to know a very valuable data for these companies, such as knowing when customers are going to carry out the following recharge and thus be able to launch promotions.
Predictive analytics gives us a much deeper understanding of our business. It offers data that the human brain cannot comprehend. With the available data, a data science company can predict or anticipate customers'" future behaviors as accurately as possible and thus optimize costs and make marketing activities more productive.
If there is something that distinguishes a seller from a neighborhood store, it is the deep knowledge and closeness he has with his regular customers. A personal treatment that is usually offered whenever a buyer comes to purchase any good from him.
This is something that big companies lack. Although they do have data that can somehow supply the knowledge that personal relationships give you: how often they buy and what they buy, if they call customer service and for what, what are their claims, etc.
One of the ways of analyzing and ordering all this data is through learning from history to predict future behaviors. And these predictive models can be reached through different types of algorithms:
Decision tree, logical regressions (more complete numerical formulas), and random forest are three of those algorithms that provide us with different criteria, which help us separate positive cases from negative ones. Depending on which of them we choose, it can be used to predict the actions of clients. It not only gives us the probability but also the reason, which is very recommended for business areas, as it helps us define the marketing action we have projected.
Predictive analysis can be used to accompany a marketing action, and its purpose is to order customers as they will meet the objectives set out in that action. Now, with new technologies and new communication channels, the budget may no longer be as important as at the beginning of predictive analytics. But it is so as not to bother customers with products that do not interest them.
Let's go to another example of the use of predictive analysis, such as telephone marketing campaigns. Who wants to be called about an offer that does not interest them? And the consequent discomfort generated by the brand name in the recipient of the message? With predictive research, a market research company can make calls that are more effective. They will only contact the people who are more likely to buy, customizing the offers based on the patterns studied and thus saving efforts, costs and, something even more important in today's environment, improving the customer experience.