4 Examples of Predictive Analytics That Help Your Business Predict the Future

While descriptive analytics, diagnostic analytics, and prescriptive analytics all deliver valuable insight, predictive analytics is the only type of data analytics that can help businesses predict what will happen next.

Of course, if you’re a small business leader who has never used predictive analytics before and isn’t sure where to start, you might feel more like you’re grasping in the dark rather than peeking into the future. In fact, according to our recent Martech Research survey (methodology below), 41% of startup marketers find it challenging to keep up with emerging marketing technology like BI and analytics tools.

As you can imagine, the ability to predict the future rather than relying on trial and error is incredibly important for businesses. A small business that is capable of accurately forecasting future conditions obviously has a huge advantage over businesses that are just making their best guess. So it’s crucial for business leaders to understand this technology if they want to stay competitive.

In an effort to take some of the mystery out of predictive analytics, let’s look at several real world examples of predictive analytics in action, along with a peek under the hood of the technology that makes predictive analytics work.

Ready to get started with data analytics at your business? Here’s a short video to help you get started on the right foot:

Predictive analytics is the practice of studying historic data to help predict future events and trends. Predictive analytics typically uses tools like big data, machine learning, and regression analysis–which examines the relationships between historical data points–to improve the accuracy of the predictions.

To visualize how predictive analytics works, imagine standing on the back of a cruise ship looking out at the wake of the ship. Over several miles you’ll gain a very rough orientation of which way the ship is headed, but any minor turns or course corrections will wildly skew this sense of direction. But track this course over hundreds of miles and data points on a map, and you’ll eventually have a fairly accurate idea of where the ship is headed, even without knowing the destination or seeing the view from the front of the ship.

1. Predictive analytics in customer service

What if a company could predict issues customers will face before they happen, and preemptively work on solutions to prepare customer service reps to help? 

For example, an energy supplier might use predictive analytics to determine that customers will consume more power during the winter, so they can expect more complaints from those customers about rising bills. The company could then provide its customer service reps with clear explanations of charges, information on payment relief options, and strategies for reducing consumption (and bills) during the winter.

Challenge: Predicting the issues your customers will have before they happen.

Solution: Use customer service software that leverages machine learning to gather and process customer analytics data—like call volume, resolution success rate, and customer satisfaction—while your customer service team works to help forecast future issues and ultimately reduce customer churn.

The agent performance dashboard in Five9 customer service software (Source)

2. Predictive analytics in marketing

For all of its advances over the past several decades, marketing technology doesn’t always do the best job of helping buyers. Anyone who has searched online for new shoes only to have their computer relentlessly hammer them over the head with ads for new sneakers for the next several months can attest to this. 

But predictive analytics is helping marketers get better at anticipating customer behavior so that they can tailor a marketing campaign for the right audience at the right time on the right channels.

Challenge: Predicting trends in customer behavior so that marketing campaigns are targeted strategically.

Solution: Use marketing automation software that includes advanced analytics features to help your marketing teams track buyer behavior through every step of their customer journey, giving them insight on which actions buyers are most likely to take next.

Here’s an example of a campaign insight dashboard in SharpSpring by Constant Contact, one of GetApp’s Category Leaders for Marketing Automation:

Campaign insights in SharpSpring by Constant Contact (Source)

3. Predictive analytics in retail

Staying ahead of the seemingly unpredictable trends in fashion and retail seems like an impossible task. And as we’ve seen over the past several years, a sudden spike in customer demand for a specific product can be devastating for retailers trying to keep shelves stocked with the right items. While no one seems to have predicted the combination of factors that led to the great toilet paper shortage of 2020, better data analytics models might be able to anticipate a similar situation in the future.

Challenge: Predicting which items will be in demand in the months and years ahead in order to supply adequate inventory to meet customer demand.

Solution: Use modern inventory management software that includes forecasting features. This helps anticipate supply needs, not only to ensure that you’ll have enough inventory to meet customer demand, but also so that you don’t over invest in certain products. The last thing you’ll want are products that take up valuable shelf and warehouse space without selling.

Here’s an example of the inventory forecasting dashboard in Netstock inventory management software:

The inventory forecasting dashboard in Netstock inventory management software (Source)

4. Predictive analytics in recruiting and hiring

Wouldn’t it be great if you could predict which candidates are most likely to be successful in your organization, how long they are likely to stay, and when an employee is likely to leave for a new opportunity? 

Recruiters and hiring departments process lots of people data from current and previous employees that can be used to help predict future outcomes. As businesses endure The Great Resignation, predictive analytics can be useful in helping hiring managers narrow down candidate lists and gauge the impact of staff turnover before it happens.

Challenge: Narrowing down the best candidates from a virtual stack of resumes and predicting the impact of employee turnover before it becomes a major problem.

Solution: Use recruiting software with advanced analytics features like performance and attrition forecasting to hire the best candidates and set aside time and resources to make new hires ahead of time.

Here’s an example of the candidate score report feature in Criteria, one of GetApp’s Category Leaders in recruiting software:

The candidate score report feature in Criteria recruiting software (Source)

As you can see, predictive analytics has a wide range of useful applications no matter which industry you’re in. But to successfully take advantage of predictive analysis, you will need to leverage the right technology.

• Start by exploring your current software stack for predictive analytics capabilities.

• If you’re having trouble finding predictive analytics capabilities in your software stack, talk to your software vendors’ support representatives. 

• For a more targeted approach to business analytics, consider investing in a dedicated business intelligence platform.

Ready to gain new insight into your business using business intelligence and predictive analytics? GetApp has you covered. Our Business Intelligence Buyers Guide has all the information you need before starting your BI software buying journey, and our Category Leaders in Business Intelligence highlights 15 top options in BI software based on verified user reviews.

GetApp’s Category Leaders in Business Intelligence (Source)