The best way to gain a competitive edge in any industry is by knowing more about your operations and the market dynamics affecting them than your competitors. Competing with greater intensity and the ability to get more done in less time is what differentiates market leaders from other companies. One of the best ways to make this goal a reality is to implement a business intelligence tool with predictive analytics. But what is predictive analytics? How is it implemented? How is it different from other kinds of analytics? Read on to answer these questions and more.
What is Predictive Analytics?
As the name implies, predictive analytics focuses on making predictions about possible futures. It is defined as the use of data, algorithms and machine learning to identify the likelihood of future results based on historical information.
Analytics software captures relationships among a variety of factors and assigns them a score or weight to guide its analysis. It uses data mining and modeling to make visualizations so users can more easily identify patterns and trends. Predictive analytics mostly uses structured data such as age, gender, income, sales quotas, etc. If users want to analyze unstructured data like audio, video, images or text, they have to use some kind of text analysis program first.
After incorporating and cleaning the data, the software analyzes it with artificial intelligence and machine learning to make predictions about how the data will look in the future if trends continue. It can identify threats, help users manage risk and highlight opportunities for action. It can make predictions seconds, days, weeks or even years into the future if the data foundation is large enough. It can also draw from big data sources like Hadoop, Hive or an organization’s proprietary database depending on the questions the user is trying to answer.
Other Types of Analytics
So what are the differences between predictive analytics and all the other types of analytics? We’ll do a brief overview here, but this article about diagnostic, descriptive, prescriptive and predictive analytics explains it in much more detail if you’d like to learn more.
Predictive vs. Prescriptive Analytics
Like predictive analytics, prescriptive analytics deals with the future based on historical data. But where predictive analytics predicts a likely future, prescriptive analytics allows users to predict various futures based on different actions. It’s sort of like time travel without any universe-altering paradoxes.
Prescriptive analytics performs a “what if” function to measure the potential outcome of new initiatives by leveraging related information. For example, if we increase our sales expenditures in the northwest, then what will the likely outcome be? Prescriptive analytics might predict the answer to this question by using data collected when we increased sales funding in the northeast combined with current data from the northwest.
It allows users to take business decisions for a test drive, calculating likely outcomes to better prepare themselves for the reality of their choices so they can make the most informed decision possible. Basically, prescriptive analytics helps users prescribe the best action for a given set of data.
Predictive vs. Descriptive Analytics
To perform predictive analytics, users must first utilize descriptive analytics. Descriptive analytics is made up of the foundational analytical processes that identify what is happening at a given moment in a business organization.
Users can set the parameters for what questions they hope to answer through descriptive analytics. For example, you might ask, “hat was our profit for October 2019?”, “Did this employee perform up to their expected standards in January through March?”, “How many employees are on the payroll, and how much do they cost the company?”, etc.
These questions aim to answer the questions what’s happening, when it happened and where it happened?” Descriptive analytics forms the basis for answering business questions through data and provides a jumping-off point for the other types of analytics.
Predictive vs. Diagnostic Analytics
While descriptive analytics provides the answer to what’s happening in your business, diagnostic analytics deduces why it’s happening. It utilizes data mining, data association and drill-down. It also diagnoses issues based on data relationships and identifying patterns.
This type of analytics highlights and identifies anomalies in the data to help users answer questions that require deeper analysis than something that could be solved by quick descriptive analysis. For example, where descriptive analytics would ask how much a marketing campaign went over budget, diagnostic analytics would look at why it went over budget?”
Similar to a doctor diagnosing a patient’s illness, diagnostic analytics aims to investigate your business questions so that predictive and prescriptive analytics can resolve them.
Benefits of Predictive Analytics
The main benefits of predictive analytics is that it positions businesses to take advantage of every opportunity that might arise. It encourages organizations to be proactive and forward-thinking, anticipating outcomes before they become a problem and staying ahead of trends in the market.
Users can apply these tactics in a sales environment to more efficiently cross-sell or upsell based on previous successes. It can also be useful in healthcare to track patient health records and predict potential issues. Furthermore, it can detect trends in fraudulent online behavior to reduce your company’s risk for theft and fraud, as well as make portfolio predictions to manage financial risk. These are just a few real-world examples of predictive analytics benefits, as there are many ways this tool can be a boon to your business.
Features of Predictive Analytics
Some of the main capabilities offered by predictive analytics platforms are:
- Data Collection
- Data Mining
- Data Analysis
- Data Modeling
- Analysis Deployment
- Machine Learning
- Artificial Intelligence
The State of Predictive Analytics
- By 2020, predictive and prescriptive analytics will attract 40% of new investment in business intelligence and analytics by enterprises.
- Forrester forecasts a 15% compound annual growth rate (CAGR) for the predictive analytics and machine learning market through 2021, according to its study, The Forrester Wave™: Predictive Analytics And Machine Learning Solutions, Q1 2017.
- Between 2017 and 2019, spending on real-time predictive analytics grew three times faster than any other type of analytics, according to a recent Gartner study.
- The global predictive analytics market is projected to reach $10.95 billion by 2022, according to Zion Market Research.
- 69% of decision-makers believe analytics will be crucial for business success in 2020, and 15% consider it essential for operating their businesses today.
The bottom line is that market leaders across all industries need to know how to make predictive analytics more profitable. Most have moved beyond managing their businesses using only descriptive and predictive analytics. Now, they leverage insight and data to predict elements such as when to define specific prices for individual customers, launch new products, discontinue legacy products, offer promotions and implementcross-selling and upselling.
A study from ConversionXL examines how the simultaneous growth of the scale of data available to organizations and its accessibility via cheap cloud storage has solved some of the initial barriers to implementing predictive analytics. This puts previously disadvantaged organizations back on the playing field with those that could afford expensive analytics programs. The rise of affordable analytics has made business intelligence much more accessible to a range of organizations that historically would not have been positioned to utilize them.
How to Use Predictive Analytics
So now you have an understanding of what predictive analytics does, how it works, where it fits into the world of advanced analytics and what kind of impact it’s having on various industries. But how do you use predictive analytics and how can it be helpful to you personally? Here are some examples of ways to use predictive analytics:
Model Customer Behavior
A great way to get more familiar with how your client base is likely to respond to product changes, marketing campaigns, website optimization and other actions is to utilize predictive analytics. You can take historical data, A/B testing results, heatmaps and a variety of other conversion testing tools and run predictive models with your BI software to forecast how your customers are likely to respond to a given variable. There’s never a guarantee that your predictions will be accurate, but making data-informed decisions puts your business in an informed and prepared position for most possibilities.
Predictive analytics is uniquely suited for risk assessment and management. Let’s use the banking industry as an example here. By using predictive analytics to account for variables such as credit score, financial history, age, income and other factors, users can make predictions about how likely a customer is to pay back a loan. This helps employees more adequately determine interest rates, payback options and other terms that help protect the bank from the threat of nonpayment. Predictive analytics allows users to follow many trails of possibility to ensure they have an accurate view of what might happen so they can plan for it.
Accurate sales forecasts are very important for setting budgets, establishing quotas, handing out bonuses and preparing a business for its future. Predictive analytics facilitates forecasting by offering multiple possible futures based on specific parameters and historical data. This positions companies to make more accurate predictions and be better prepared for all possible outcomes rather than just the most likely or most recent.
How To Select an Analytics Solution
If you want to get on board with the benefits of predictive analytics, you might be wondering how to get a business intelligence solution of your very own. Follow these steps to find the perfect match for your organization’s unique needs.
The first, and potentially most crucial, step is to identify requirements that you need from an analytics tool. Not all business intelligence solutions offer predictive analytics, so if that’s an important feature for you, make sure to take note of it. This BI requirements template will help you familiarize yourself with some available capabilities and understand which are the most vital for your business.
Next, you can use your requirements to find the right vendor to match your needs. This comparison report breaks down different industry leaders based on how well they perform in different requirement categories. Match the products to your most vital requirements to create a shortlist of best-match vendors — we recommend identifying between five and seven.
After creating your shortlist, you can request proposals. These are official documents that include a personalized price quote, detailed product information and usually a free trial or demo that allows you to familiarize yourself with how the system operates. This RFP guide will walk you through the process of drafting and sending a formal RFP. Vendors are more than happy to offer proposals, so don’t be shy in sending them to all your shortlist vendors.
Predictive analytics seeks to use past data to predict future opportunities and risks. It allows businesses to position themselves advantageously and act on opportunities in advance to gain an edge over the competition. It also helps identify risks and potential pitfalls, making businesses more agile and prepared for problems.
Predictive analytics offers data collection, mining, analysis and modeling. It uses statistics and machine learning, as well as artificial intelligence, to forecast potential futures.
In this guide, we gave you tips for selecting a predictive analytics solution and answered the basic questions around the definition of predictive analytics.
Still have more questions? Make sure to leave them in the comments and we’ll dive even deeper.