As more businesses undergo digital transformations, adopting data science is increasingly important to maintain a competitive edge. Not only does successful implementation of such tools drive revenue, but it also allows decision makers to reimagine and revamp their processes in ways that are more efficient, effective, and innovative.
If you are like many of your peers, implementing a data science project can seem overwhelming. And, knowing where to start is arguably one of the hardest parts. But there’s a strong need for businesses to start implementing data science initiatives now to avoid getting left behind by the competition.
In fact, according to a recent Forrester Consulting study, early adopters of data science are seeing a 5.8x ROI, while later adopters are seeing a 3.8x ROI. To help you get started, we’ve outlined some data science applications that have proven to drive impact.
10 Impactful Data Science Applications [+ Examples]
Here are some applications that highlight how data science can generate results (a.k.a. revenue) for your enterprise. While these are specific examples, you can pull inspiration from outside of your industry or expertise area and apply them to your business.
1. Gain deep customer insights
Understanding your customers as best as possible translates to improved campaigns and branding, and by extension, increased profits. Though you may think you know your buyers, there are always insights to gain by digging deeper into the abundance of data already at your fingertips.
Demographic information like age, income, and geography are latent commonalities between customers that those businesses can leverage to tailor offers, make service interactions more relevant, and design more impactful products. Through a combination of structured and unstructured data, businesses can discern more meaningful characteristics about their customers and segment them into groups accordingly.
By uncovering the nuances between different segments, businesses can identify opportunities to improve marketing tactics among each segment. As a result, customer facing teams can tailor their interactions to meet each segment’s unique needs and address their most pertinent challenges, bolstering customer satisfaction, brand reputation, and profitability.
Example
One global advertising agency turned to data science to gain a deeper understanding of their buyers. While advertising used to center around having the “winning” set of traits to prompt consumers to buy, it now focuses on memory—linking the good traits for the consumer to recall.
Using machine learning to identify hidden insights and patterns within their customer base, allowed them to segment buyers into meaningful groups and identify the best traits for consumer recall. The agency was able to implement machine learning within pre-established marketing strategies for an easy, yet impactful, transition.
2. Avoid operations shutdown
Full operations shutdowns may be infrequent, but when they happen, the implications for a business are disastrous. Out-of-service time directly leads to lost revenue and unnecessary service crew travel costs. However, accounting for the various risk factors for these shutdowns—the lifetime of machines and equipment, anticipated machine failures, need for replacement components, and so on—is often overwhelming and difficult, especially amidst other business operations.
Utilizing data science for predictive maintenance, businesses can drastically reduce the risk of shutdown stemming from critical equipment failure and parts for repair being unavailable. By training machine learning models on a variety of data sources, including sensor data and log entries, businesses can anticipate which machines and equipment are at risk of causing shutdowns. Then, they can provide service needs and tend to them before larger problems and associated expenses arise.
Example
One multinational auto parts manufacturer aimed to reduce the likelihood of full operations shutdowns by lowering plant out-of-service times, as well as predicting machine failures, the lifetime of factory components and machines, and the need for replacement components.
By successfully implementing data science, they were able to unify their data in the end-to-end tire lifecycle and anticipate shutdown risks across their operations. As a result, this manufacturer avoided one to two shutdowns on average per year, with each avoidance saving over $20 million per day cost.
3. Identify & prevent churn
Maintaining a solid customer base is crucial for any business, as the cost of acquiring new customers is high—hence why identifying and preventing customer churn is necessary. However, it can be difficult, especially when the warning signs of many lost customers are concealed amidst a multitude of records. Fortunately, data science can help.
Based on customer data like product usage, purchase history and other risk factors that are much more subtle than complaints, businesses can identify which customers are likely to churn and take preventative action by reaching out to these customers, listening to critical feedback, and resolving problems. Taking such prompt action not only avoids customer attrition, but also reduces associated long-term costs and improves ROI.
Example
Verizon Wireless was having trouble with customer retention, and with prepaid churn rates higher than postpaid rates, they needed to predict churn customers. Considering 32 million prepaid transactions daily with over 10 transaction types and 40 different channels, it was difficult. Leveraging the RapidMiner platform, they were able to capture real-time data and determine the leading indicators of churn for both prepaid and postpaid customers.
4. Boost customer loyalty
As we have mentioned, the cost of acquiring new customers is high, which is one big reason loyalty is so important. Obtaining it means treating each customer like the unique individual they are, rather than inundating them with broad marketing messages meant for thousands of people.
Fostering a one-to-one relationship with each buyer entails determining the next best action, whether you’re making an offer, suggesting relevant content, or tailoring customer service. By accessing data from life event patterns, buying behavior, social media interactions and much more, businesses can anticipate and deliver what customers need next to improve conversion rates and purchase volumes.
Example
One of the most common examples of how companies put this to work is through email marketing. By storing data on customers’ purchase dates and calculating how long the product will last, they are able to send customer emails reminding them to restock, offering discounts and providing information about other product they may also be interested in purchasing.
5. Eliminate safety & health issues
Every business should aim to foster a secure work environment for employees, and that requires minimizing safety and health issues. Failure to do so will result in lost time, money, and even lives. However, traditional preventive methods—annual trainings, posters, and protective equipment—cannot guarantee the safety of employees. Especially when there may be hundreds of people on the shop floor, it may seem impossible to monitor everything.
Fortunately, data science provides a more efficient solution. Employing a combination of IoT connectivity, audio-visual processing, and a collaborative software platform, businesses can save equipment, time, money, and ensure the safety of valued employees. Organizations can continuously monitor the shop floor for potential hazards and make sure that pre-defined safety conditions and procedures are being followed before larger problems arise.
Example
One industrial company was looking to implement data science to remain competitive in the chemical industry. While a high degree of automation comes with positive economic outcomes, it also reduces operators’ experience regarding process dynamics. This can lead to information overload, especially in critical situations, endangering the environment and human lives.
With help from the RapidMiner platform, this company was able to design and build a big data infrastructure to manage the production plant’s data, develop data-driven methods to detect critical situations early and help users during these situations, and craft ad-hoc analysis functions for intervention strategies. In doing so, they were able to reap the benefits of industrial automation, while also minimizing health and safety hazards.
6. Reduce time-to-market
From product designs to workflow optimizations, a lot of time resources undoubtedly go into the process of releasing a new product. But, spending too much time on the back and forth between design teams and employees on the factory floor can waste time, hinder profitability, and leave buyers unsatisfied. With data science, businesses can reduce time-to-market and bolster collaboration, all while improving efficiency and customer satisfaction.
Example
Managers on the shop floor have to make decisions quickly using their expertise and experience, but it can get tricky when they oversee many processes and flows, often impairing time-to-market.
Virtual optimizers—real-time prescriptive dashboards that display the status of various systems on the shop floor—can help make better informed, more efficient decisions. They also allow experimentation with potential changes to implement the best solution, prescribe, and even automate adjustments to improve output. Other tools, like 3D simulations, capture a 360-degree view of the product development process, enabling users to visualize new offerings, advance design productivity, and drive overall time-to-market.
7. Forecast supply chain demand
Considering the volatility of the supply chain, businesses need to evaluate as many factors as they can to best plan for production. Yet, it is common to solely rely on historical data. This simply is not enough when there are various other factors impacting the supply chain, like changing consumer preferences and new competitors entering the market. Without considering these externalities, these businesses are likely to be operationally inefficient.
Utilizing data science, businesses can forecast demand beyond historical purchase data. Through building machine learning models that account for more than just past trends and patterns, they gain a more comprehensive view of market demand. By avoiding over or underproduction, organizations can increase profits, while staying clear of any brand damage and associated inventory costs.
Example
With over 4,000 locations, Domino’s Pizza was looking to optimize their supply chain forecasting. But they were challenged by scalability— each location required an individual data science model, and too many unique variables were causing food spoilage, idle labor and overtime, and strained supplier relations.
Combining native operators and open source libraries in RapidMiner, they were able to implement predictive models based on demand history, previous and upcoming promotions, and holidays across all locations. As a result, Domino’s increased profit margins, while reducing wasted service and retention efforts.
8. Predict & improve product quality
It’s no secret that product quality is integral to profitability and maintaining customer relationships. But it is important to remember that in today’s digital age, your customer base has access to an unprecedented amount of information about your competitors and alternatives.
Buyers have high product quality expectations, and if left unmet, your business is at risk of dissatisfaction, churn, and brand reputation damage. By integrating data science into the quality assurance process, businesses can address quality issues on the production floor before they result in waste and damaged customer relationships.
Utilizing IoT and a collaborative software platform, businesses can identify early indicators of production problems that may affect quality, and then proactively address them before any negative impacts hit. As a result, they reduce warranty and liability costs, waste, and energy usage, while simultaneously improving product quality and increasing customer satisfaction.
Example
A steel manufacturer employed RapidMiner for this purpose. Each step when processing steel requires proficiency in terms of process conditions—temperature, casting speed, cooling flow rate, and so on—which can vary across different facilities. When this process requires such precision, it is easy for quality issues, like cracks and surface defects, to arise.
Using the RapidMiner platform, they created a NoSQL architecture that split their sensory data into three planes: a data layer, analytics layer, and knowledge layer. This architecture made it fast and simple to transform the data and store it back in the NoSQL database. The manufacturer was able to perform predictive algorithms in RapidMiner to detect anomalous behavior on casting process variables, monitor results, and then accordingly improve model construction and management.
9. Detect risks of fraud & take action
Fraud detection is difficult, and it’s nearly impossible to sift through every transaction to identify those that are fraudulent. Selecting random samples is still a time-consuming method that only provides a narrow scope of widespread issues.
While traditional fraud detection methods may be expensive and ineffective, the right data science platform will help businesses monitor large volumes of transactional data in real-time, and automatically flag and isolate high-risk cases.
Through detecting complex consumer behavioral patterns associated with fraud—many of which are often latent—these businesses can act before serious harm is done. As a result, they avoid fraud-induced losses, while also wasting less time on manual methods and better serving legitimate buyers.
Example
One U.S. state auditor was using a manual method to detect healthcare fraud, but this technique was not effective. When they inspected random samples, it proved to be time consuming while only less than 5% of all transactions were actually evaluated. Once they deployed supervised learning models, they were able to scan and flag high-risk fraud cases in high volume and in real-time.
Through this new end-to-end detection and prevention process, this auditor was able to identify and prioritize high-risk cases while detecting new fraud patterns, discovering new categories of fraud, and reducing time wasted with manual methods and random inspections. In total, they were able to identify $20M in fraud.
10. Optimize marketing strategies
To execute a successful marketing campaign, businesses need comprehensive knowledge of their customer base. But this knowledge alone is insufficient to a proper campaign—this data needs to be transformed into actionable insights as well.
There are many ways that data science can be used to optimize marketing campaigns, including predicting customer behavior, providing personalized recommendations and messaging, forecasting customer lifetime value, analyzing trends and seasonality, and more. Let’s look at one specific example.
Example
By using customer data like average purchase value, purchase frequency, and likelihood of churn to predict customer lifetime value, businesses can optimize marketing strategies and improve the effectiveness of campaigns.
Quantifying customers’ potential value allows businesses to communicate and interact with each customer and prospect based on their long-term impact, rather than just surface level observations. By differentiating those who are likely to drive significant revenue, you can then use their profiles to identify and acquire prospects with similar characteristics. This data-driven, target acquisition marketing strategy bolsters revenue and margins, while reducing time on customers that do not merit the retention efforts.
Wrapping Up
We hope that that these applications—and the potential results of each—have motivated you to get started on your own projects. These examples are without a doubt impactful, but they are only the start. If you are looking for more inspiration, here are 50 ways to impact your business with AI.
