While the individual ways that data science can help your organization are innumerable, they can be broken down into three major categories: business efficiency, product creation, and customer experience. Product creation and customer service are important in their own rights, but business efficiency is the foundation for better business built by data science.

Its ability to increase efficiency is the highest value opportunity that data science brings to an existing business model. Here’s how data science can help you improve your own business efficiency.

Data Science and Data Creation

Traditionally, data science has focused on existing data, but improving business efficiency requires more than analyzing the most readily available data. We must also focus on the creation of new, useful data. Data creation and capture are moving data science beyond analysis and into the realm of managing when and how data is collected, annotated, and organized. For example, A/B tests on your business’s website are a simple way to create new data that measures how your website performs, how customers interact with it, and how you can improve those interactions.

Data creation doesn’t always require new tests or new methods of collection, though. It can also require using the data your business already has—and if you’re running a successful enterprise, you probably have a lot of it—in new ways to find the hidden productivity. With innovations in data science, previously underutilized data can result in major capability increases.

Responsive organizations take advantage of two universal improvement methods detailed below: real-time reporting and learning from historical data.

Real-Time Reporting

For organizations with a heavy customer service component, real-time reporting made possible by data science can improve business efficiency immediately.

Real-time reporting makes customer interactions more effective by allowing service representatives to better understand consumers while interacting with them. Think about how many times you’ve made a call to a customer service line and waited on hold only to answer a bunch of questions, be put on hold again, and then have to answer all the same questions with everyone else you talk to. With real-time reporting, customer service agents have instant access to the questions callers have already answered, shortening the call and hold times while improving the overall experience.

Learning from Historical Data

While real-time reporting enhances interactions in the moment, historical data can ensure that you interact with the correct customers in the first place. With historical data, you can analyze how your customers have behaved in the past and build predictive models to learn how they will likely behave in the future. You can use historical data to properly staff your business with more employees at peak times and fewer at less busy times. You can also use your historical data to determine which website designs best serve your customers, or which products you should recommend to certain customer types.

By taking historical data and examining it through a new lens, you can create new data to improve your business efficiency in countless ways.

A Data-Driven Culture

To ensure your organization is properly taking advantage of all the efficiencies data science offers, you must create a data-driven culture from leadership down. Focusing on and using data can’t just be the purview of a small data science team—it must be part of the mentality of leadership, with every employee understanding data’s inherent importance in their job role.

How do you best make sure data science is properly integrated? Incentivize data-driven decision-making at every level.

When your organization has a data-driven culture from the top down, the results speak for themselves. Just one example from Clearlink and SYKES is the use of data-based automation that turns PPC ads on and off based on the availability of sales agents so the company is paying for ads only when agents are available to answer the phone calls generated by said ads. The machine learning algorithm used by the PPC team saves the company money through the effective use of ad purchasing and sales agent availability and provides a better experience for customers, who won’t have to wait on hold or make calls that will go unanswered.

Examples like this are evident throughout the business world. Las Vegas Sands Corp., which owns casinos in Las Vegas, Pennsylvania, Singapore, and Macao, uses customer analytics to determine high-value guests and decide which guests to attract and comp. Progressive Insurance collected 10 billion miles of driving data from its customers in order to better optimize rates for its safest drivers.

Embracing data analytics gives these companies an edge in crowded fields. Doing the same can mean big results for your business.

Landon Starr
Author
Landon Starr

Landon Starr leads the data science organization at Clearlink, which includes the information management, advanced analytics, reporting, and CRO teams. His organization builds AI/machine learning capabilities, manages experimental design and creative A/B testing processes, optimizes UX, and architects the information management backbone to support the breadth of data capabilities that enable Clearlink’s intelligent customer experience platform.