Predicting future outcomes is possible when you have access to data. Predictive analytics uses data and advanced statistical techniques to make projections based on what’s happened in the past.
What is Predictive Analytics?
Predictive analytics is a type of data analytics that’s used to make predictions about future performance and outcomes based on current and historical data. Put simply, it is a data-driven assessment of what will likely happen in the future based on what’s already happened.
The idea of using data to make projections isn’t a new concept. However, predictive analytics has dramatically changed in recent years as data has exploded, both in terms of sheer volume and the types of data that can be collected. Current and historical data, statistics and modeling are all key components that make predictive analytics possible.
Essentially, you are looking for patterns in the existing data that point toward regular occurrences. In other words, if something occurs repeatedly over the course of a month you can expect it to keep occurring in the coming weeks. An example of this would be a SaaS company noticing that once customers max out the users on their account they are highly likely to upgrade to the next service level. This information can help you determine tier-pricing that maximizes profitability.
Ultimately, predictive analytics is a decision-making tool that’s based on objective information. It can be both quantitative and qualitative, although it is largely based on quantifiable data.
Why Businesses Need Predictive Analytics
Predictive analytics is a fast-growing sector. The global predictive analytics market is expected to grow over 22% between now and 2024 as more companies invest in advanced analytics. That kind of growth tells you predictive analytics is considered an important element of success for many businesses.
Part of the growth can be attributed to advances in technologies like artificial intelligence that make predictive analytics more accurate and accessible. Automated software that improves ease-of-use is also opening the doors for more businesses to make use of predictive analytics.
Being able to predict how well a product will sell or how customers will use a service is extremely valuable. In our fast-paced economy, being able to cut down on any trial and error can make a huge impact on the bottom line.
Other universal benefits include:
More Informed Business Decisions
Predictive analytics can be paired with business intelligence to make more informed business decisions based on current trends and projections. This gives decision-makers and stakeholders assurance that choices are data-driven rather than instinctual.
Get to Market Sooner
In addition to saving money, predictive analytics can help you bring products and features to the market sooner. You’ll have more time in the product lifecycle to capture a greater share of the market and start generating revenue sooner.
Predictive analytics is used to pick up on trends and patterns in the data that suggest a reoccurring theme of what will happen in the near future. That insight can help a business identify opportunities from feature updates that will improve user reviews to ways to cut down on shipping costs.
Lots of Applications
Predictive analytics can be applied to virtually every aspect of a business. The solutions range from the supply chain to marketing to product development. Even internal operations and your workforce can be enhanced by predictive analytics.
Improved Products and Services
By analyzing user behavior trends you can figure out ways to improve products and services based on how customers are likely to respond after making a purchase.
Makes a Business More Competitive
In fast-moving markets and industries, businesses that can make accurate projections stay ahead of the curve. In doing so, your business will have a competitive advantage.
Assess Customer Behavior
Predictive analytics can be applied to customers when you analyze user behavior. It can tell you how big of a risk a customer will possibly be, their potential lifetime value, purchasing frequency, etc. based on how others in a cohort have behaved. You can even see trends in how customers react during certain economic events.
Of course, predictive analytics has its shortcomings. Any major change within the industry, such as changes in regulations or a new competitor, can throw off projections no matter how much data has been analyzed. It’s also not a foolproof method. Predictive analytics can be highly accurate at projecting the likelihood of what will happen, but it’s far from a guarantee.
How Predictive Analytics Works
Another reason predictive analytics is growing is ease-of-use. It used to be a job that only statisticians and mathematicians could handle. But today software and applications have dramatically eased the workload by doing most of the complex calculations for users. As a result, any manager or business analyst can conduct predictive analysis with the right tools and techniques.
In order to conduct predictive analysis, you must first gather the data. This is known as data mining. There are a few techniques that are commonly used for predictive analytics data mining:
- Time series data mining – Data is collected and time-stamped for a specified time period. For example, number of sales for a month.
- Sampling – A data sample is created that can be applied during predictive analytics to reveal a pattern or trend.
- Clustering – This is when you break data into subsets of similar information.
Text mining is similar to data mining except you are examining text to look for patterns and trends. This type of predictive analytics would be used to source information from customer reviews or complaints handled by the customer service department.
The foundation of any type of analytics is data structuring. Approximately 90% of data is unstructured, meaning that it isn’t organized. Structuring data makes it easier to derive information and uncover trends.
Modeling is also a key factor in predictive analytics. Predictive models are developed using existing data (known results) and tell the software or computer how to ingest and parse the data, helping ensure as much data is processed as possible. The model uses that data to project values for new scenarios or variables. The projections will note the probability of a predicted outcome.
There are several types of predictive models that can be used. Examples include:
- Classification Models – These are used to classify customers or users based on their likely behavior. For example, a customer’s likelihood of churning.
- Regression Models – This is a number-based model that is often used to predict revenue growth, product adoption, etc. over a certain period of time.
- Customer Lifetime Value Model – The customer LTV model aims to better predict how much revenue will be generated by a particular customer or cohort.
There are three primary types of predictive techniques:
- Neutral networks – They help model complex relationships. It is a new practice that focuses on deep learning by attempting to recreate how the human mind thinks.
- Decision Trees – This is a tree-shaped schema that represents a likely course of action. It’s also used to figure out statistical probability. Paired with the branching technique decision trees can also show the potential outcomes of a decision.
- Linear and Logistic Regression – This technique is often used in the financial sector to determine the relationship between variables. It also helps in forecasting asset value.
This is the use of computers to analyze data and pick out patterns using algorithms.
Predictive Analytics Software
Third-party software makes predictive analytics possible for many businesses. Analytics platforms give businesses the ability to automatically collect data and structure it in a way that’s readable without the need for advanced in-house development and IT specialists. Set up is simple and allows companies to start gathering data for analysis immediately.
5 Steps to Start Using Predictive Analytics
Step 1. Identify a Question You Want to Answer
In other words, what do you want to predict? It’s also good to know how you will act on the prediction.
Step 2. Find an Analytics Tool for Data Mining
You’ll need to collect and store data from numerous sources for predictive analytics. There are a number of software applications that can do this for you. You’ll also need to determine who will be in charge of managing the data mining process.
Step 3. Prepare the Data
This can be a time-consuming endeavor, but analytics tools that automatically generate reports and identify trends in the data help lighten the workload.
Step 4. Build a Predictive Model
Once you have the data, a predictive model must be created to make a projection. Here again, analytics software can make the process much easier. However, whoever is managing the process will still need to refine the models and interpret the results.
Step 5. Share the Information and Put It Into Action
Once the predictive model has done its job, it’s time to share the findings with stakeholders and decisionmakers. From there, they can decide what actions should be taken based on the predictions.