Predictive analytics is a branch of advanced analytics, which predicts future outcomes by studying past events and results based on statistical analysis.
Using data allows you to know in advance the behavior of customers in reference to the object of study. The ultimate goal of predictive analytics is to define how you can influence the situation with your actions and what can be changed before the predicted event.
Predictive analysis is often viewed in the context of big data — transaction data, customer feedback, sales results, marketing campaign information, etc. And since more and more businesses make decisions based on valuable data sets, let’s look at what data to consider while conducting predictive analysis.
Structured and unstructured data
The core of every research is data. To create a business intelligence strategy, it’s necessary to collect as much information as possible and know how to differentiate the data that adds value to the research. The information that you can collect and study can be divided into structured and unstructured data:
- Structured data can be adequately classified and stored in an ordered manner, for example, gender, age, income level, marital status, etc.
- On the other hand, you can’t do that with unstructured data. It has no definite structure, e.g., publications on social media, emails, audio, video, image files, etc. Unstructured data also includes the elements that can be derived from the content, such as sentiment information. In other words, it doesn’t usually fit in a table with columns and rows.
Using relevant data, you can find out and predict results and behaviors, which allows you to be proactive. Proactivity is a great advantage, too — it helps you make practical decisions based on data and not on assumptions.
How to do predictive analysis: Process and components
Like any process, predictive analytics is made up of different phases:
- Definition of the project. Here you must establish what your objectives are. In addition, you must determine the data sources you will use, the scope of the analysis, and the results you hope to get.
- Data collection. This is the stage where you obtain the needed data. Once collected, it needs to be processed and organized to become understandable so that it can be used later.
- Data processing. It consists of inspecting, transforming, and classifying the data to find trends and draw conclusions about the information it contains.
- Statistical analysis. Through analyzing descriptive statistics, you can understand better what your data is trying to tell, see the first results and conclusions, and identify behavioral probabilities.
- Predictive modeling. As you analyze your data, you will be able to create predictive models automatically.
- Implementation of predictive models. During the last stage, you can implement analytical results in your everyday business decisions and study the outcome, building a process that helps reach the set goals and making reports that will further allow you to automate decisions.
Advantages of predictive analytics for your sales and marketing
The main advantages of predictive analytics are as follows:
✓ It helps prevent losses by detecting early signs of customer dissatisfaction. With the help of predictive analytics, you can create customer segments based on a higher or lower risk of loss. This way, you can apply timely corrective actions, thereby increasing retention and revenue.
✓ It allows you to maximize customer lifetime value (CLV). You will identify customer segments with high value and thus plan marketing activities, establishing the most appropriate cross/upselling strategies.
✓ It lets you identify new customer segments with high potential. In other words, if you know which of your clients are likely to increase their purchasing frequencies and quantity, target them with timely actions, and increase revenue.
✓ It allows you to properly plan your campaigns and develop an ideal outreach approach for each of your segments. By analyzing all the data you have, such as purchasing patterns, online customer behavior, interactions on social networks, etc., you will define the best moments and channels through which to communicate with your customers.
✓ Speaking of campaigns… Predictive analytics enables you to predict the performance of each campaign based on the communication channel.
✓ It helps create product recommendations (e.g., for cross/upselling) based on the purchase history of each client. You can use the history of purchases made by customers and identify products or services with high sales potential.
✓ It allows you to predict a drop in sales and thus carry out counter-campaigns to reduce it as much as possible.
✓ With predictive analytics, you can reduce the rate of closing accounts or shopping cart abandonment. As we’ve already mentioned, it lets you detect early signs of dissatisfaction. By identifying which customers are most likely to abandon their accounts or purchases, you can work on recovery campaigns to prevent this.
✓ And finally, it helps identify the probability of a purchase decision.
Predictive analytics applications
Predictive analytics is used in sales, marketing, banking, insurance, pharmaceuticals and healthcare, telecom, travel, retail, etc. Let’s take a look at some of its most common use cases:
Customer relationship management (CRM) analytics
Now, this analytics plays an important role throughout the customer lifecycle — from the moment of acquisition and relationship-nurturing until after closing the deal when you’re trying to retain the client. In this sense, predictive analytics will allow you to achieve CRM objectives in terms of marketing, sales, and customer service campaigns.
Data collection analytics
Predictive analytics results can be used to optimize the allocation of data resources, identifying data collection sources, contact strategies, and legal actions to increase the recovery of information and reduce data collection costs.
Although it may seem strange, predictive analytics is essential in detecting fraudulent online and offline transactions, identity theft, and false insurance claims.
Predictive analytics can be used efficiently in the healthcare sector to determine patients at risk of developing diseases such as asthma, diabetes, heart failure, and other conditions. It can also be used to learn if a new treatment is more effective.
Even though predictive analysis is usually carried out by a Business Intelligence department, knowing how to minimize business risks is helpful for everyone. Regardless of your industry, predictive analytics can help you solve the problems you aren’t even aware of yet!