What is predictive analytics: definition and concept
Business applications of technology have undergone rapid growth both in adoption and functional complexity. If there is one hot technology dominating the landscape today, it would be analytics, and more specifically, predictive analytics. Below we review what it is and explain some of its practical applications.
Before the advent of business intelligence (BI), data analysis was known simply as decision support. It entailed a descriptive analysis of the business situation based on historical data, which was normally structured and stored in monolithic systems using online analytical processing (OLAP) techniques. During this initial phase of putting our data to work for us, we can typically respond manually. By condensing structured data so it can be understood through visualizations, we are able to understand past events for which we have collected data. By the nature of this process, any actions we might take are reactive and often too late to be effective.
The next step to truly becoming a data-driven company involves predictive and cognitive analytics. The insights we gain through this kind of analysis make the difference between a reactive approach and a proactive approach that anticipates market events and trends, putting us in a position to act fast and effectively.
What is predictive analytics?
In predictive analysis we use historical data to extract an analytical model that can predict future behavior or, as of yet, unknown results.
It uses various statistical modeling, machine learning, and data mining techniques to gather data and transform it into the information needed to make predictions about the future.
Data mining, analytical texts, and statistics are used together to build predictive intelligence models and to identify trends and relationships, both in the structured and unstructured data set.
Types of predictive analytics
Predictive models differ from descriptive and diagnostic models. Descriptive models help us understand what has happened and diagnostic models help us understand the relationships between entities in order to find out why something has happened.
There are various types of predictive models. We are going to look at two: classification and regression models. Classification models put data into categories based on what is learned from historical data; for example, if we want to classify our customers by the likelihood that they will churn. To achieve this, input variables such as credit risk or responses to communications are established. The results of the model are binary—yes or no, in the form of 0 or 1— with the degree of probability. Regression models, on the other hand, allow us to predict a value; for example, what is the profit we will generate from a certain customer (or segment) in the coming months.
Eight ways your company can benefit from predictive analytics:
1. Predict your churn risk and failure rate
The costs associated with customer acquisition represent a sizeable portion of companies’ expenditures.
Therefore, it is important to use predictive techniques to identify potential new customers. In addition, you want to detect those actual customers whose interest appears to be fading (their purchasing or usage activity dwindles) before they ultimately churn.
2. Recommend new items or content to your customers
Most consumer digital media service companies collect large volumes of customer data and store them in databases full of information. They can then use predictive analytics to offer personalized recommendations to their customers
For example, using Jump’s predictive analytics tool you can Leverage specific digital service AI models to predict churn and understand why customers might be at risk, leading to a retention strategy with defined concrete actions. With this statistical analysis we can also apply cross-selling techniques to find out what other items, packages or pieces of content our customers are likely to consume.
3. Adapt the price of your services to each customer
By combining predictive analytics tools with the business’ CRM, customer data can be used to improve the performance of the company’s marketing activities.
In doing so, personalized customer campaigns can be launched according to the different department objectives.
4. Reduce costs and optimize investments
Predictive analytics provides insights into future scenarios, allowing us to make reliable forecasts in the short and medium term, which in turn makes our investments more profitable.
At the same time, companies can use their data to successfully target their marketing efforts to the right customers.
5. Efficient resource planning
By creating different future scenarios, we can estimate the amount of demand to expect at any given time.
As a result, we can optimally assign the necessary resources, thereby reducing storage costs, downtime, investment, etc.
6. Ensure a differentiated market position
Because there is increasingly intense competition across all sectors, especially sharpen in the entertainment industry it is extremely important to focus our efforts on achieving maximum customer satisfaction.
If we position ourselves favorably with our clients, keeping them happy and engaged and earning their loyalty, we will both ensure a differentiated position in the sector and reduce customer acquisition costs.
Some studies indicate that it is between five and 15 times more expensive to attract new customers than to retain the ones we have.
In addition, with the democratization of technology, more and more companies are seeing first-hand the benefits of cognitive analysis and big data as they leave potential competitors behind.
7. Analyze your customers’ social media data
Many consumers use social media platforms to communicate their opinions about fashions, their preferences, and their experience with the products they use, whether from one company or from competitive businesses.
Using predictive methods to analyze this information, we can be proactive and more successful in what we do, offering the right products to the right customers, correcting negative opinions that might be circulating on the web, and anticipating your customers’ desire to buy.
8. Disrupt the market with qualified data
Predictive analysis tools provide large volumes of qualified data that consumer digital media companies can use to decide the best course of action for each customer at any given time.
Only those organizations that embrace disruptive thinking and an adaptive approach to both new consumption styles and organizational trends will persist in this business ecosystem.
Predictive analysis techniques
In descending order of use, the most applied predictive analysis modeling techniques are:
These are classification models that divide the data into subsets based on categories of input variables. They are very helpful when we want to assess the decision-making that occurs throughout the process (along the user beavior funnel, for example). Decision trees are shaped like a tree in which each branch represents a choice from a number of alternatives, and each leaf represents a category or decision. This model, when searching the data, tries to find the variable that allows the dataset to be divided into increasingly different logical groups. They are widely used because they are intuitive and easy to understand.
Linear regression and logistics
This is one of the most widely used methods. Regression analyzes the relationships between different variables. This technique is appropriate when we can assume that continuous data follows a normal distribution; it can find key patterns in large data sets and is often used to determine how much they influence the movement of specific factors, such as the price of a product. In regression analysis we want to predict a value (represented by the dependent variable Y). In a linear regression an independent variable is used to explain or predict the result (Y); while in a multiple regression two or more independent variables are used. It is useful for modeling the probability of an event occurring as a function of other factors. The response variable is categorical, which means that it can assume only a limited number of values.
Last but not least, we come to what is probably the best known technique, given the recent rise in popularity of artificial intelligence and deep learning: neural networks. Extremely complex relationships can be modeled using these techniques, which have become popular because they are very powerful, yet flexible at the same time. They can handle non-linear data relationships, which makes it very interesting the more data we handle, ideal for big data analysis. In other instances, they are used simply to confirm findings from other simpler techniques such as regressions or decision trees. Neural networks are based on pattern recognition and some artificial intelligent processes that allow the parameters to be modeled. They work very well when we do not have a known mathematical formula that relates the inputs and outputs, in this sense the prediction is more important than the explanation. They do need significant volumes of training data to be reliable. As we explained in a previous article, artificial neural networks were developed by researchers trying to mimic the neurophysiological behavior of the human brain.
Trends in big data are demanding greater efficiency and reliability from predictive analytics. Consequently, more companies have incorporated this type of analysis into their core business activities. There will continue to be an adoption of these technologies in the future as demand continues to grow and consumers get more and more demanding.