Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.
“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics. “It’s the way the world is going.”
Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.
What Is Business Analytics?
As described in the Harvard Business Review video below, business analytics is “the use of math and statistics to derive meaning from data in order to make better business decisions.”
There are three key types of business analytics: descriptive, predictive, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. In the case of prescriptive analytics, testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.
Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.
Business Analytics in Action
According to a recent survey by McKinsey, an increasing share of organizations report using analytics to generate growth. Here’s a look at how three companies are aligning with that trend and applying data insights to their decision-making processes.
1. Improving Productivity and Collaboration at Microsoft
At technology giant Microsoft, collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.
Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by cutting down on the number of employees per building and by reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft, which found that people are more likely to collaborate when they’re more closely located to one another.
In an article for the Harvard Business Review, the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members, and an estimated savings of $520,000 per year in employee time.
The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.
For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions, and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.
2. Enhancing Customer Support at Uber
Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.
COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.
For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing—a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.
Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.
At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.
Related: 5 Things to Remember When Looking at a Dataset
With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.
3. Forecasting Orders and Recipes at Blue Apron
For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents its subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand, with the aim of using data to avoid product spoilage and fulfill orders.
To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, and these typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s recipe preferences from the past, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.
Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has been able to measure the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.
By employing predictive analytics to better understand customers, Blue Apron has been able to improve its user experience, identify how subscriber tastes change over time, and recognize how shifting preferences are impacted by recipe offerings.
Related: Business Analytics: What It Is and Why It's Important
Developing a Data Mindset
As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.
“Using data analytics is a very effective way to have influence in an organization,” Hammond says. “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”
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