Maintaining the status quo is as good as falling behind in today’s business world. The law of accelerating returns (i.e. more technological advances lead to faster changes over time) creates a situation where rolling adaptation is a necessary part of any business’ success. Those that can adapt, anticipating future events and predicting customer needs, will expand their market share and improve their bottom line – and the difference often depends on their predictive analytics solutions.
You’ve heard of predictive analytics as a way to outpace the competition, but what does it mean in practice and what’s the actual ROI? In this blog, we provide an overview of predictive analytics in business and how you can harness this technology for quick and satisfying wins.
What is Predictive Analysis
At its essence, predictive analytics is a type of data analysis designed to foresee the future through in-depth evaluation of current and historical data points. To give you a sense of the amount of data out there, the world is on track to create 463 exabytes or 463,000,000 terabytes a day by 2025. Data patterns are often buried beneath layers of white noise. You can master these near imperceptible patterns – if you have the computing power to analyze for them.
A combination of data mining, machine learning, and statistical modeling helps businesses to identify deep patterns and trends they can use to take action. Thanks to advancements in data storage and processing technology, you can analyze structured data, unstructured data, or a combination of the two without any deceleration in your results.
What started as the domain of R&D firms and mega corporations has grown into a data science solution that most businesses can leverage. And through increased sophistication and user-friendliness, modern predictive analytics solutions are just as easy for CEOs to harness as it for data analysts. Now, as many as 49% of U.S. businesses are using advanced and predictive analytics – and most others recognize the potential of these tools.
One key consideration to keep in mind is that the predictions made by these methods are only as reliable as the data they’re fed. That means without effective quality data management solutions and a well-defined enterprise data strategy, predictions will be incomplete and imprecise at best.
What Industries Can Do with Predictive Data Analytics
The potential of predictive analytics is celebrated, but what are the practical applications? More than just shot-in-the-dark predictions, predictive analytics solutions are able to make reliable forecasts across industries. Here are four examples where the payoff is incredible:
Retail – Predicting Shoppers’ Needs
Can retailers predict customer purchases before they happen? Amazon thinks so. They are identifying a method called anticipatory shopping to foresee customer needs and ship those products to a consumer’s closest distribution center – all prior to the actual purchase. The online retailer will further improve upon their recommendations and product delivery using customer history, data from similar customers in a demographic, and monitored buying signals.
The above is the latest innovation in predictive analytics. With all of the data we’re gathering on consumers, predicting future behavior and purchases has been simplified. Retailers already track purchase histories and demographic information in their CRM and other databases. The next step is to harness that data jackpot to maximize sales. Customizing promotions, increasing upselling and cross-selling, and timing marketing campaigns with buying intent thrive with predictive analytics solutions.
Sales Organizations – Reading Buying Signals
Experienced sales professionals know the telltale signs of buyer readiness, yet they don’t have time available to monitor every possible opportunity. With predictive analytics solutions, sales organizations can laser-focus their outreach on buyers who are ready to make moves, ultimately boosting the success of their sales campaigns. By categorizing leads based on buying readiness signals and sending sales reps real-time notifications, this predictive analytics solution allows them to perfectly time any reengagement efforts.
Insurance – Enhancing Fraud Detection
The ability to decrease the risk of fraud detection is the Holy Grail of insurance industry pursuits. Across all lines of insurance, $80 billion a year is lost through fraudulent claims. Yet you don’t want to produce false positives that send auditors and investigators barking up the wrong tree.
With predictive analytics (and well-defined historical data), businesses can incorporate a sophisticated array of data to determine the warning signs of fraudulent claims filers. Beyond that, they can identify new products and consumer demands before the competition does.
Healthcare – Anticipating Readmission Risks
For healthcare providers, one of the greatest opportunities is in preventive medicine. Researchers from the University of Texas Southwestern reviewed extensive patient data from electronic health records (EHRs) to determine the likelihood a person would be readmitted to the hospital within 30 days. For a C. difficile infection specifically, vital sign instability upon discharge and longer hospital stays correlated with a higher risk of readmission. And the proof for preventing other infections and ailments is building each day.
For healthcare payers, the drive is for value-based care models. Predictive analytics helps to identify the members who, when proactively engaged, result in the greatest improvements, as well as the types of strategies that will yield the best results for increased appointment attendance and preventative self-care.
How to Make It Happen
Before you can enact predictive analytics in your own organization, you need the right foundation. That involves everything from your data model and data architecture to the practices that govern them. Make these actions top priorities before you launch any predictive analytics project:
Define Your Objectives
Do you want to prevent customer churn? Reduce fraud? Model your risk? From the outset, you need to know what your stakeholders and end users want and need. Then, you can determine the types of projects to pursue as early use cases.
Build a Foundation and Process for Your Historic Data Set
Accurate predictions require an ongoing consistent, structured, and clean source of historical data. If you can’t trust your historical data, your predictive results will cause more harm than good. A well-designed data model and integration solution not only puts your predictive analytics on the right footing, but it provides all of your reporting with greater accuracy and results. When done right, data required for predictive solutions can come from the same model used to feed executive dashboards and detailed granular reports.
Explore and Identify Relevant Data
Which data points will yield the best results? Even a straightforward goal can benefit from an extensive range of data sources and information. That means every predictive analytics project should begin with a full assessment of your source applications, integration strategy, and data model to determine what’s required for accurate predictions.
Accomplish Thorough Management
Data quality and accurate business interpretations do not remain reliable for long when left unmonitored. Before launching your predictive analytics platform, ensure that the data you are accessing remains accurate and clean to produce actionable insights that deliver the best results.
Integrate with Your System
The quality of predictive analytics depend on how well your platform integrates with the goals of the business. Your data integration strategy must be impeccable if your forecasts are going to capture future events.
Want to use predictive analytics in business applications that grow your business and bottom line? Schedule an Aptitive Whiteboard Strategy Session to learn your current state and your opportunities.
Fred Bliss is the CTO at Aptitive. He brings over 15 years of experience solving complex business problems through data solutions including cloud integration, data warehouse modeling, ETL, and front-end reporting implementations.