6 Industries Warming up to Predictive Analytics and Forecasting

Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.

Predictive analytics and forecasting tools help companies have more confidence about what to anticipate for the future instead of just taking educated guesses and hoping for the best.

Despite the valuable insights extracted from such technology, some sectors lagged when embracing the options. Here are six that are realizing how beneficial predictive analytics could be.



1. Energy

Electricity is something most people in developed parts of the world take for granted. But, when outages happen, business owners and consumers alike deal with inconveniences ranging from spoiled food to productivity losses.

Power companies are realizing predictive analytics and forecast data can help them plan for energy demand and supply enough power to the grid to stop it from getting overloaded. Moreover, such information can help them assess the performance of their equipment and avoid unplanned shutdowns.

Some companies attach sensors to their most crucial equipment to keep tabs on operating specifics. In one case study, a power company switched from manual monitoring to a continuous monitoring system made possible with wireless sensors.

The technology detected an issue with one of the generator cooling fans, and the company moved forward with a planned equipment shutdown that cost $100,000. However, failing to fix the problem could have led to downtime and repairs easily exceeding $1 million.

Many energy providers also put predictive analytics information in the hands of their customers. They offer apps that let people see their expected energy usage for the week or month, and how small changes might allow them to save resources.


2. Higher Education

Higher education institutions increasingly use predictive analytics and forecasting methods to better accommodate needs related to prospective and current students.

Some of them use predictive analytics when choosing which athletes to recruit for university programs. One company's tool assessed whether a potential recruit would join Northwestern University's football team with a 94% accuracy rate.

Higher education facilities also use forecasting with early-warning alerts that pinpoint which characteristics make a person enrolled in online or in-person classes exceptionally likely to drop out before earning a diploma. Then, administrators or educators can intervene and get to the bottom of situations — potentially before students give up on their educations.

Countless other opportunities exist for higher education facilities that want to use predictive analytics. They could dig into data to determine the impact of incoming classes, then decide whether to extend the hours of campus libraries and dining halls or expand parking areas, for example.


3. Apparel

In today's society, apparel designers and retailers seem to know and provide what people want to buy like clockwork. Two months before a person goes on an early summer vacation to the beach, swimsuits appear on the racks of their favorite stores.

Apparel brands commonly use enterprise resource planning (ERP) software. It integrates processes across multiple business functions and allows companies to see things like sales and manufacturing data, then make decisions accordingly. Statistics show 84% of mid-sized businesses integrated ERP technology into their operations.

Using an ERP solution that has a predictive analytics component can help companies plan how to respond to customer demands. Failing to do so could mean garments don't generate enough interest, or that clothing is perpetually sold out and causes frustrations for users. Predictive analytics can also help companies get ahead of trends, such as consumers' desire to wear sustainably made pieces.

Prediction can also come into play when ensuring clothing on the market suits the weather. Some people studying fashion design evaluate climate change data during their classes. That allows them to think ahead and make things that people will want to wear based on the likely weather for their regions.


4. Dining

Factors ranging from a person's amount of disposable income to whether the weather's sunny or rainy could determine whether they decide to go out for dinner or stay at home to eat. But, dining establishments need to understand more about those factors when planning their staffing needs, menus and more.

Some predictive analytics platforms give forecasts about the projected number of diners at a restaurant on a given day or within particular timeframes. Others show real-time tracking about how many ingredients the establishments use in their kitchens, then predict when it's time to reorder supplies.

There are also predictive analytics options that connect to point-of-service terminals and show things like the average amount a person spends at a restaurant on a Friday night or whether specific menu options are more popular than others. Then, those tools can crunch the numbers and help restaurant executives decide whether to make price changes or introduce new dishes.


5. Music Festivals

The recent news about a top investor for the Woodstock 50th Anniversary Festival dropping out and leaving the festival's future uncertain show even the largest and most anticipated festivals need to weigh various factors before and during the planning phases.

That investor says the festival won't happen, but Woodstock's organizers insist the show must go on.

One of the complications about planning a festival is that people have so many choices when deciding which ones are their must-attend events and which ones they should avoid. So, festival organizers have to make timely decisions about when to announce their top acts, the prices to set for tickets and which months of the year would attract the most festival lovers.

At a top music festival in Denmark, IBM used data-driven technology for forecasting during the festival in numerous ways. The festival grounds spread over 148 acres, and as people moved around, their festival wristbands sent tracking statistics that allowed staff to gain information while respecting privacy.

One component of the system showed which areas of the site were most crowded or likely to become more densely populated. Knowing that data allowed festival organizers to prevent overcrowding at stages and send more security teams to specific areas. Another metric measured transactions at food vendors, then gave information to streamline customer service and reduce waiting times.

The festival landscape offers substantially more options for people ready for a few days full of music and good times. But, word spreads quickly about poorly executed gatherings — such as the infamously disastrous Fyre Festival. Predictive analytics and forecasting can reduce mishaps and boost the probability that attendees have their expectations met or surpassed.


6. Human Resources

Statistics indicate the average cost per hire in 2017 was $4,129. If a human resources professional ultimately invests that amount toward a person who only stays with the company for a couple of months, that's a problem companies understandably want to solve. Many aim to do so through forecasting data that identifies how likely a person is to accept a job offer and stay with the company long-term.

Others predict how likely a person is to leave, allowing human resources members to have conversations with them to try and make things right. Predictive analytics can also make companies more diverse and decrease hiring bias.


Analytics Paying Off

These six industries concluded predictive analytics and forecasting benefited them. More sectors will likely make that decision as they look for new ways to conquer challenges.

Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.