How Senior Leaders Use Predictive Analytics to Gain Competitive Advantage

predictive analysis

How Senior Leaders Use Predictive Analytics to Gain Competitive Advantage

Effective manipulation of big data, which now plays a role in practically every sector of the global marketplace, has become integral to the successful operation of businesses. Once processed, these large pools of data can be leveraged in a variety of ways; one such method is predictive analytics, which allows senior leaders to stimulate growth within their firms by enhancing their ability to predict consumer behaviors, recognize market trends, understand customer needs, and identify opportunities to improve existing business processes. Consequently, professionals who intend to advance into a senior leadership position should refine their understanding of how predictive analytics may be used to gain a significant advantage over the competition.

How Does Predictive Analytics Work?

Predictive analytics allows analysts to forecast the likelihood of an outcome by using a variety of tools and methodologies to process historical data, such as transactional data from human resources or a company’s financial records. If this data already exists within the firm’s internal systems, implementing a predictive analytics strategy is much easier, as the firm has an established starting point to build upon.

While predictive analytics is usually facilitated by various software products designed to access and manage company data, the operators of this software still need to be professionals who can process and interpret data under the direction of an experienced leader. In fact, senior leaders are the ones tasked with identifying the best ways to organize, analyze, and process the data. In doing so, their firms become more capable of coordinating reasonable hypotheses based on the data, allowing them to develop actionable strategic decisions.

How Senior Leaders Apply Predictive Analytics

One opportunity to create a competitive advantage using predictive analytics hinges on using relevant data about potential customers to identify the market’s current demand for a product or service. Predictive analytics can accomplish this in several different ways, most of which involve interpreting web search data, like the location of visitors to the company’s website or how many people searched online for the company’s website or products. Leaders can use these predictions to get an accurate estimate of true product demand and counteract uncertainty in the global economy. With that information, senior leaders can plan production accordingly and ensure that inventory and resources are committed to the right locations.

After identifying demand by analyzing the purchasing habits of their customers, leaders can then apply predictive analytics to structure their pricing strategies. This process is more applicable to online stores, as they automatically track when customers make a purchase, how long they browse the catalog and, to an extent, how they respond to different prices. Using this data, firms can estimate the success of certain discounts and promotions, allowing them to experiment with different pricing models. Furthermore, this data can also be useful for generating advertisements, as understanding customer demographics will enable marketing teams to focus their advertising efforts.

Predictive analytics is also particularly useful for helping business leaders optimize equipment management. Through a process often referred to as “predictive maintenance,” data scientists compile and process data that has been generated by production assets linked to the Internet of Things (an interconnected system of wireless computing devices). By implementing machine learning to find trends in this data, firms can produce an accurate forecast for when equipment will need routine maintenance, in-depth service, or even replacement. Access to such information can prove highly valuable, because it allows senior leaders to recognize potential threats to the supply chain and eliminate them before they can compromise the efficient delivery of goods and services.

Potential Barriers to Effectively Using Predictive Analytics

  • Defining a Problem—Business leaders need to know what to look for when identifying problems. If they define a problem in accurately, their efforts may be wasted and the real issue may become worse.
  • Data Quality—Poor quality data can be riddled with missing values or other invalid information. If forecasts are made using such data, business leaders may be influenced to make poor decisions based on inaccurate data.
  • Employee Talent—Firms often struggle to find specialists who understand how to extract insights from highly detailed yet often unstructured data logs. Such skills come with expertise in computer science, data engineering, statistical analysis, and behavioral science, but a single employee will rarely have familiarity with all four areas. Therefore, senior leaders must ultimately assume the sometimes difficult responsibility of hiring and managing an interdisciplinary team of individuals who possess a strong mixture of these skills.

Industries That Benefit from Predictive Analytics

The practical applications of predictive analytics are far reaching, which is why firms hire leaders who can use data to develop their marketing strategies, make sound financial choices, refine human resources protocol and even plot government actions. The following industries benefit the most from predictive analytics.


New technologies are constantly being developed to optimize energy infrastructure while lowering costs and emissions. Technologies like smart meters monitor and regulate energy use, while also collecting valuable customer data points and usage statistics. The information about consumer energy habits can then be processed using predictive analytics. This data has the potential to reduce costs and improve services, as well as alert customers of when energy bills are likely to rise so they can prepare accordingly.


Due to the immense volume of data that can be generated during on-board and off-board flight procedures, predictive analytics can be applied to improve the speed, efficiency, and overall operational integrity of an airline. With all operations connected via unified data management software, airlines can reduce delays by predicting airspace traffic and crucial maintenance. Aviation executives can also use predictive analytics to identify the most in-demand flights throughout the year and adjust their pricing strategy accordingly to maximize profits.


Businesses in the agricultural industry have begun using advanced digital technology, such as devices connected to the Internet of Things (IoT), to improve the efficiency of their agricultural systems. Using data collected by these systems, the business leaders of large-scale farming operations can analyze the agricultural and environmental conditions of their farms. With that information, they can deduce optimal planting, fertilizing and harvest times relative to each crop. Farming businesses can benefit from predictive equipment maintenance options as well, as it will improve their ability to schedule maintenance outside of intensive working periods, such as harvesting times.


Retailers can use predictive analytics to develop insights about the customers who enter their shops and visit their websites, allowing business leaders to decide where to build new stores, how to improve customer service, and what aspects of their business spark their customers’ interest. The futuristic combination of predictive analytics strategies and machine learning is certain to offer a suite of competitive advantages to retailers, such as leveraging data about store traffic trends and sales metrics in order to set prices and construct marketing.

Supply Chain Operations

Many firms collect data routinely throughout the entire lifetime of inventory—from the point of origin, during transit, when it reaches its location, and once it is sold. Using this data, senior leaders can ensure that shipments are being handled appropriately, and if not, they are equipped to promptly inform the recipient of any delays and use historical data to predict when the shipment will arrive. Integrating this technology into the supply chain primarily benefits massive national and international organizations, as they have far more to gain from streamlining their logistical efforts. As predictive analytics solutions become more affordable and accessible, smaller firms will be able to utilize such innovations in their supply chain networks as well.

What The Future Holds for Predictive Analytics

Experts continue to refine the process of using machine learning to aid predictive analytics, and the resulting applications are expected to improve the operational efficiency of businesses across numerous industries. As data scientists continue to make this technology more flexible and customizable, the price and bandwidth required to operate big data processing infrastructure should fall. This would make predictive analytics far more accessible to companies that cannot currently afford to invest in it. As more companies adopt this technology, thought leaders will continue identifying new ways to use predictive analytics to enhance their operations. In doing so, they will likely spur greater creativity and innovation in their respective industries.

Regarding examples of progress currently being made in predictive analytics, experts in the medical field believe that massive data sets containing patient histories have the potential to be entered into analytics systems and used to enhance diagnostic and treatment processes. In the automobile industry, predictive analytics has been combined with machine learning in order to improve driverless cars’ analytical abilities. As the technology continues to advance, predictive analytics will enhance a leader’s ability to tap into knowledge of past, present, and future market trends, allowing them to rapidly make well calculated decisions for improving the quality of their products and services.

Data is the future for most businesses. Whether they are online or physical, the businesses who swiftly and intelligently incorporate analytics into their operations will gain the greatest competitive advantage in their respective industries. However, if these techniques are not guided skillfully by a knowledgeable leader, efforts to use analytics will likely fall short of their intended targets. Therefore, business leaders should pursue a Master of Science in Entrepreneurial Leadership degree in order to learn how they can use big data analytics to capitalize on favorable market conditions. In these degree programs, professionals can learn how organizational leaders combine creativity and communication to create a favorable work environment in which professionals are comfortable furthering research and innovation to streamline business practices.

Learn More

As the nation’s oldest private military college, Norwich University has been a leader in innovative education since 1819. Through its online programs, Norwich delivers relevant and applicable curricula that allow its students to make a positive impact on their places of work and their communities.

Norwich University’s online Master of Science in Executive Leadership program offers an advanced level of graduate-level coursework that is designed to help address the specific challenges senior leaders regularly face. The program focuses on providing tools for enhancing one’s ability to leverage human capacity for strategic results and accomplishes this by taking a four-dimensional approach to leadership, with an emphasis on leading the self, leading others, leading organizations and leading in service.


December 2017