Data science for business: Applications and insights

As a result of the digital transformation, more of our daily activities occur online, providing granular insight into consumer behavior that wasn’t possible in the analog era.

For example, if you walked into a Blockbuster Video, one of a chain of home movie and video game rental stores that was once a dominant force in the entertainment industry, in the 1990s to rent a movie, the store could collect basic data including:

• What movies you rented
• When you rented them
• Whether you returned them on time
• If you followed the “Be Kind, Please Rewind” mantra of the VHS era

At the same time, Blockbuster had no way of knowing if you loved the movie so much you watched it three times or if you turned it off after ten minutes of disappointment.¹

Compare that power and scope of customer data collection to today’s Netflix, which collects massive amounts of data about your viewing habits that it uses to drive its business strategy.

Netflix knows which shows you watch repeatedly and which ones you click away from. It knows how long you watch, where you watch, what device you’re watching on, and where you’re watching from. It also knows this same depth and breadth of information about every other member of your family on your account.²

This storehouse of data lets Netflix create personalized recommendations based on your behaviors and preferences and helps decide which shows to acquire. Data science has even put a price on all of the data Netflix collects. Analysts suggest that Netflix could earn one billion dollars annually by accepting advertisers. Despite this incredible valuation, Netflix believes that data has more value if it’s kept in-house.²

This post explores applications of data science in businesses, including predictive analytics, customer analytics, and process optimization, and covers why businesses such as Netflix tightly guard and highly value their data.

Predictive analytics: Anticipating the future

Predictive analytics allows you to predict what will occur in the future based on past events. We all do this method of analytics instinctively, on some level.

If you’ve noticed that hummingbirds appear in your yard every year around May, you probably start setting up your feeders near the end of April. However, artificial intelligence (AI) and machine learning for business allow you to make predictions based on a much larger dataset and much more refined details with some surprising and unexpected twists that non-data-driven analytics might not uncover. For instance, Walmart discovered that the top-selling item when a hurricane is in the forecast is Strawberry Pop-Tarts.³

So now when the meteorologist notes a tropical storm developing, Walmart knows to ramp up its toaster pastry inventory.

Some other use cases for predictive analytics include the following:

Fraud prevention

Artificial intelligence in business can be used in predictive models to help detect and prevent fraud. These models analyze large datasets for trends in customer transactions and behavior. If you normally spend small amounts of money close to home, but your card is suddenly used for a large transaction halfway around the world, it’s likely to be flagged as suspicious activity.⁴

Price optimization

Customers are willing to pay more for items at certain times–whether it’s due to seasonal or market considerations. Predictive pricing is a big data application that allows businesses to find the optimal pricing strategy based on customer behavior, competitor pricing, market forces, and other trends. These predictions can be used to adjust prices in real-time to maximize profits.⁵

Customer analytics

Understanding your customer base is critical for effective product development and marketing. When you know their pain points, their behaviors and their aspirations, you can tailor your marketing to speak directly to them. Business intelligence tools give you the insight you need to do that.⁶

Some key areas of customer analytics include:⁷

Segmentation

This involves dividing customers into groups based on shared characteristics. Segmentation allows for more targeted marketing campaigns and personalized customer service, as different groups may have different needs and preferences.

Customer lifetime value analysis

Customer lifetime value (CLV) is a prediction of the net profit attributed to the entire future relationship with a customer. Understanding CLV helps businesses to identify the most profitable customer segments and tailor their strategies accordingly.

Churn analysis

This determines the rate at which customers stop doing business with a company. By identifying the factors that lead to customer churn, businesses can take proactive steps to retain customers.

Customer satisfaction

By examining customer feedback, reviews, and social media comments, businesses can gauge customer sentiment and overall satisfaction. This qualitative data can provide much-needed context to quantitative findings and guide improvements.

Operational efficiency through business analytics

Data-driven decision-making can improve all aspects of operational efficiency in an organization. According to the global consulting firm McKinsey & Co., almost every aspect of work will be optimized by 2025 through real-time business analytics powered by “vast networks of connected devices.”⁸

Some of the ways data drives operational efficiency now and will in the future include the following:

Process optimization

Creating standard, efficient processes for all of your business operations can increase efficiency, reduce costs, and improve quality. Data analytics can drive process optimization by identifying bottlenecks and inefficiencies, quality control issues, and areas where resources can be better used. In turn, business leaders use these insights to make changes to increase efficiency. Process optimization is an ongoing cycle of continuous improvement.⁹

Supply chain management

In the wake of pandemic-related supply chain disruptions, businesses have focused more attention on creating resilient supply chains.¹⁰

Data science improves supply chain visibility and coordination, leading to more accurate demand forecasting, better supplier selection, and improved logistics planning. The Internet of Things (IoT) allows for a real-time end-to-end picture of the supply chain, providing opportunities for inventory control, route optimization, and warehouse automation.¹¹

Big Data applications

The vast and constantly growing body of data about customers and their needs is creating ever more opportunities for businesses that are prepared to use it.¹² Whether your business is using big data to predict your customers' needs and demands, or to optimize its own functioning, the information is out there and ready to be used.

Data visualization tools

It's important to be able to share the data you have in a way that makes it clear what the necessary action items are. Data visualization allows you to see the relationship between what you know and what you need to do about it.¹³

Elevating business strategies through advanced data science initiatives with data science tools

In the dynamic landscape of contemporary business, the role of data science initiatives cannot be overstated. Beyond the conventional understanding of data analysis, businesses are increasingly recognizing the strategic value embedded in these initiatives.

To step into the future of business analytics, organizations need to deploy cutting-edge data science tools, backed by skilled data scientists who can navigate the complexities of the modern data landscape.

Navigating the data science process for informed decision-making

The journey of data science for business involves a structured approach known as the data science process. This systematic method encompasses everything from data collection to the deployment of sophisticated machine-learning models.

The ability to interpret data goes beyond basic analysis; it requires a nuanced understanding of the intricacies of big data and unstructured data. As businesses aim for data-driven decision-making, the emphasis on a robust data science process becomes pivotal.

Unraveling the potential of predictive modeling in business dynamics

Within the realm of predictive analytics, a more granular aspect deserving of attention is predictive modeling. This involves not just foreseeing future trends but constructing models that simulate potential scenarios. Through the integration of machine learning algorithms, businesses can conduct predictive analysis that goes beyond simple forecasting. By identifying critical data points and assessing risk management strategies, organizations can proactively shape their responses to unforeseen challenges.

Data storage solutions for data analysis: Beyond warehousing critical data

In the age of information, effective data storage is a cornerstone of successful data management. Beyond the sheer volume of data collected, businesses must implement strategic solutions for storing and retrieving information.

From manufacturing data to customer insights, the architecture of data storage plays a pivotal role in ensuring that the right data is accessible at the right time. This is especially critical when dealing with analyzing large data sets and conducting data science projects that span diverse business functions.

As businesses embark on the exciting journey of incorporating data science into their operations, the fusion of technological prowess and strategic acumen becomes the driving force. The success of data science in business hinges on the seamless integration of advanced tools, skilled professionals, and a forward-thinking approach that adapts to the evolving landscape of market trends and industry dynamics.

Championing success: The role of data scientists in advanced analytics

In the intricate realm of data science initiatives, the unsung heroes are undoubtedly the skilled individuals known as data scientists. These professionals wield a unique combination of analytical prowess and strategic thinking, making them indispensable in the journey toward advanced business analytics.

The data science process is not merely a series of steps; it's an odyssey guided by the expertise of data scientists. From the initial stages of data collection to the deployment of sophisticated machine learning models, data scientists navigate the complexities of big data and unstructured data. Their nuanced understanding ensures that the journey towards data-driven decision-making is not just a process but a strategic expedition, with each step orchestrated towards unlocking business insights.

As businesses recognize the transformative potential of data science, the spotlight on data scientists intensifies. Their skills in interpreting, analyzing, and modeling data contribute not only to the success of individual projects but to the overarching triumph of data science in business. In the intricate dance between technological innovation and human expertise, data scientists emerge as the architects of success, shaping the future of advanced analytics and strategic decision-making.

Prepare yourself to be a global business leader in a data-driven world

The University of Kansas’ online Master of Business Administration (MBA) program is top-ranked nationally and will equip you with the knowledge and skills you need to lead today’s top businesses in an era of digital transformation.¹⁴

As a part of the extensive Jayhawk network, you’ll make key business world connections with your cohort during your online studies.

KU's top-ranked program, expert faculty, and affordable tuition make investing in your future an easy decision. Contact a KU admissions outreach advisor today to get started.