Data analytics—the practice of examining data to answer questions, identify trends, and extract insights—can provide you with the information necessary to strategize and make impactful business decisions.
There are four key types of data analytics:
- Descriptive, which answers the question, “What happened?”
- Diagnostic, which answers the question, “Why did this happen?”
- Prescriptive, which answers the question, “What should we do next?”
- Predictive, which answers the question, “What might happen in the future?”
The ability to predict future events and trends is crucial across industries. Predictive analytics appears more often than you might assume—from your weekly weather forecast to algorithm-enabled medical advancements. Here’s an overview of predictive analytics to get you started on the path to data-informed strategy formulation and decision-making.
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Predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.
The predictions could be for the near future—for instance, predicting the malfunction of a piece of machinery later that day—or the more distant future, such as predicting your company’s cash flows for the upcoming year.
Predictive analysis can be conducted manually or using machine-learning algorithms. Either way, historical data is used to make assumptions about the future.
One predictive analytics tool is regression analysis, which can determine the relationship between two variables (single linear regression) or three or more variables (multiple regression). The relationships between variables are written as a mathematical equation that can help predict the outcome should one variable change.
“Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fit that relationship,” says Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics, one of the three courses that make up the Credential of Readiness (CORe) program. “Such insights can prove extremely valuable for analyzing historical trends and developing forecasts.”
Forecasting can enable you to make better decisions and formulate data-informed strategies. Here are several examples of predictive analytics in action to inspire you to use it at your organization.
5 Examples of Predictive Analytics in Action
1. Finance: Forecasting Future Cash Flow
Every business needs to keep periodic financial records, and predictive analytics can play a big role in forecasting your organization’s future health. Using historical data from previous financial statements, as well as data from the broader industry, you can project sales, revenue, and expenses to craft a picture of the future and make decisions.
HBS Professor V.G. Narayanan mentions the importance of forecasting in the course Financial Accounting, which is also part of CORe.
“Managers need to be looking ahead in order to plan for the future health of their business,” Narayanan says. “No matter the field in which you work, there is always a great amount of uncertainty involved in this process.”
2. Entertainment & Hospitality: Determining Staffing Needs
One example explored in Business Analytics is casino and hotel operator Caesars Entertainment’s use of predictive analytics to determine venue staffing needs at specific times.
In entertainment and hospitality, customer influx and outflux depend on various factors, all of which play into how many staff members a venue or hotel needs at a given time. Overstaffing costs money, and understaffing could result in a bad customer experience, overworked employees, and costly mistakes.
To predict the number of hotel check-ins on a given day, a team developed a multiple regression model that considered several factors. This model enabled Caesars to staff its hotels and casinos and avoid overstaffing to the best of its ability.
3. Marketing: Behavioral Targeting
In marketing, consumer data is abundant and leveraged to create content, advertisements, and strategies to better reach potential customers where they are. By examining historical behavioral data and using it to predict what will happen in the future, you engage in predictive analytics.
Predictive analytics can be applied in marketing to forecast sales trends at various times of the year and plan campaigns accordingly.
Additionally, historical behavioral data can help you predict a lead’s likelihood of moving down the funnel from awareness to purchase. For instance, you could use a single linear regression model to determine that the number of content offerings a lead engages with predicts—with a statistically significant level of certainty—their likelihood of converting to a customer down the line. With this knowledge, you can plan targeted ads at various points in the customer’s lifecycle.
Related: What Is Marketing Analytics?
4. Manufacturing: Preventing Malfunction
While the examples above use predictive analytics to take action based on likely scenarios, you can also use predictive analytics to prevent unwanted or harmful situations from occurring. For instance, in the manufacturing field, algorithms can be trained using historical data to accurately predict when a piece of machinery will likely malfunction.
When the criteria for an upcoming malfunction are met, the algorithm is triggered to alert an employee who can stop the machine and potentially save the company thousands, if not millions, of dollars in damaged product and repair costs. This analysis predicts malfunction scenarios in the moment rather than months or years in advance.
Some algorithms even recommend fixes and optimizations to avoid future malfunctions and improve efficiency, saving time, money, and effort. This is an example of prescriptive analytics; more often than not, one or more types of analytics are used in tandem to solve a problem.
5. Health Care: Early Detection of Allergic Reactions
Another example of using algorithms for rapid, predictive analytics for prevention comes from the health care industry. The Wyss Institute at Harvard University partnered with the KeepSmilin4Abbie Foundation to develop a wearable piece of technology that predicts an anaphylactic allergic reaction and automatically administers life-saving epinephrine.
The sensor, called AbbieSense, detects early physiological signs of anaphylaxis as predictors of an ensuing reaction—and it does so far quicker than a human can. When a reaction is predicted to occur, an algorithmic response is triggered. The algorithm can predict the reaction’s severity, alert the individual and caregivers, and automatically inject epinephrine when necessary. The technology’s ability to predict the reaction at a faster speed than manual detection could save lives.
Using Data to Strategize for the Future
No matter your industry, predictive analytics can provide the insights needed to make your next move. Whether you’re driving financial decisions, formulating marketing strategies, changing your course of action, or working to save lives, building a foundation in analytical skills can serve you well.
For hands-on practice and a deeper understanding of how you can put analytics to work for your organization, consider taking Business Analytics, one of three online courses that make up HBS Online’s CORe program.
Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.
