6 Industries Using Data Science for Better Performance Reporting
Data science encompasses numerous technologies, including big data analytics and artificial intelligence (AI). Entire sectors and individual companies can depend on it for a variety of reasons.
An increasingly common trend is for businesses to apply data science for better performance reporting. Here are six industries taking that approach.
1. Food Service
Companies in the food service sector have a wealth of opportunities to use data science for improved performance reporting. For example, they can use the information to show a menu item caused a certain percentage of increased attendance at restaurants during the first two weeks of its rollout.
In one case, a large restaurant chain had downward-trending performance metrics, and its executives could not identify the cause. They hired a company that investigated company data and revealed six drivers of outcomes, such as communications, ethics and job fit.
The analysts also determined that if a restaurant location achieved a mean rating of 4.00 or above for those six categories, it would serve 18,000 more customers per year and notice a 16% boost in customer satisfaction. As such, the company improved its performance by viewing reports that showed it where to focus managerial efforts for better results.
Starbucks is also a champion of using data science for better performance reporting. It analyzes information before moving into new markets, releasing its products into grocery stores or changing its menu offerings. There are 87,000 possible drink combinations at Starbucks, but the company keeps tabs on local trends when deciding which options to highlight.
In one local market that had a heatwave, the company spotlighted its cold Frappuccino drinks to cater to people who wanted to cool off. It also monitors how customers interact with its app, and which incentives they take advantage of most often. Then, when it’s time to create performance reports, Starbucks can back up its decisions with hard data to please investors and other stakeholders.
2. Travel and Hospitality
The internet has dramatically changed the travel and hospitality industry by empowering everyday consumers to book hotel rooms and flights themselves without going through travel agencies. This shift means brands in the sector must be aware of what it means to give consistently high-quality service to guests. Failing to do so could cause profits and overall performance to plunge.
That’s because it’s easy for people to go on social media or hotel review sites and air their grievances to an audience of thousands in a matter of minutes. Keeping guests happy and maintaining brand profitability gets simpler with help from data science.
One way this sector can utilize data science for better performance reporting is to automatically adjust rates based on what analysis platforms indicate. Marriott International invested millions into its Revenue Optimizing System (ROS), which made room prices appear differently based on factors that impacted customers’ willingness to pay more during given conditions.
Starwood Hotels merged with Marriott International soon afterward and achieved a 5% increase on per-room revenue thanks to the ROS. The company realizes that happy customers are more likely to be repeat visitors to the hotels under its umbrella. It also depends on data science to figure out which new amenities to offer in certain locations or when making improvements to its loyalty program.
3. Higher Education
Entities in the higher education sector know there is fierce competition to face. If the public hears a learning institution has an above-average percentage of students that drop out before earning their diplomas or the campus has an unaddressed crime problem, it’ll arguably be challenging to attract incoming students and urge donors to give financial contributions.
Watermark Insights is a company that links data collection to continual reflection and growth from students, faculty and administrators. California Baptist University is one of Watermark Insight’s clients, and it knew that understanding learning outcomes was crucial for enhancing performance reporting. After all, these improvements indicate a higher-education facility is functioning as intended.
Now, the university can look at student performance at an individual level, plus see how each assignment given in a course affects the learning objectives someone should meet before earning a degree. Using data science in this way also allows determining whether course content requires adjustments for maximum effectiveness.
Data science also has worthy applications in the banking sector, particularly concerning how to appeal to new customers and when to deliver targeted messages to existing ones. After a bank reaches out to a segment of the audience, big data analytics can determine what most resonated with people from certain age groups or those associated with other demographics.
Bank of Ireland recently upped its investment in using data science to predict which messages to send its customers based on what platforms they use to interact with or learn about the company. The marketing staff monitors more than 15 channels, including social media feeds. They then depend on data for better decision-making that drives performance.
That doesn’t always mean the bank makes more sales to its audience. Instead, improved performance might manifest via better customer retention rates. Bank of Ireland often analyzes data to figure out if its current methods adequately meet customer needs and, if they don’t, how to remedy the problem.
5. Health Care
Hospitals in the U.S. that receive government funding must give reports on more than 100 quality measures that get published on a publicly accessible site. If a facility has a long history of poor performance and does not address known issues, it’s at risk of losing funding and developing a bad reputation that causes patients to go elsewhere.
Researchers came up with a new way to apply big data to hospital performance reports and found that using such a system gave a more nuanced look at how a hospital stacks up to others.
Also, researchers working with big data at Seattle Children’s Hospital want to apply data science to avoid future complications for intensive care unit (ICU) patients at that facility, which marks another crucial measurement of performance. The technology spots patterns in information from critically ill patients and combines it with information about which treatments worked well for people with similar data trends.
The project is still in its early stages, but if it demonstrates the expected success, patient outcomes could improve. In turn, hospital readmission rates and unexpected complications could go down.
Manufacturing companies rely on data science to streamline their operations and output. For example, some platforms predict when a piece of equipment needs maintenance, stopping it from causing an unexpected shutdown. That’s important, since many companies measure performance by tracking a manufacturing plant’s productivity.
A manufacturing company can also indicate how it’s doing by reducing issues with recalled products. Daimler Trucks Asia (DTA) did that by deploying a product that analyzed structured data, such as call center records and customer engagement on social media platforms. Looking at the information strategically like that should save $8 million in warranty costs during the first two years of use.
Additionally, the high-tech system allows the company to be more proactive by predicting and prioritizing quality issues more than a year earlier than previous methods could. When manufacturers can use data analysis tools to indicate money saved, they’re using performance reporting in a way that makes sense for the bottom line.
Spending less on recalled products and other quality issues lets companies focus on using their financial resources to excel in difficult or new markets.
Promising Performance Improvements
This list gives a snapshot of the various ways companies and industries can tap into data and publish more authoritative performance reports that show where the entity does well and where room for improvement exists. Then, the enterprises can spend money wisely and conclude which changes to make to keep quality high.
About the author: Kayla Matthews is a journalist and writer focusing on automation and big data. Her work has also appeared on The Week, InsideBIGDATA and KDnuggets, among other publications. To read more from Kayla, please consider visiting her tech blog: Productivity Bytes.
Tags: Banking performance, Data analysis, Food and Beverage performance, Healthcare performance, Higher Education, Hospitality and Tourism, Manufacturing performance, Organizational Strategy, Reporting Performance