Nowadays, companies worldwide are automating their operations, which also means collecting a lot of data about these processes. The combination of artificial intelligence, big data analysis, and data science technology seems to be the growth trend in many industries. Predictive analysis is one of the most famous techniques that various businesses use to advance their development processes. To discuss the real-world examples of Predictive Analytics, let us first understand what it actually means:
What is Predictive Analytics?
Predictive analytics refers to the use of historical data, artificial intelligence, and machine learning to forecast future problems, solutions, or opportunities. This historical data is processed through a mathematical model that examines key trends and patterns in the data. The model is then implanted in the current data for predicting future trends. The information collected by this process provides value to the companies by suggesting lucrative actions that might bring positive operational developments.
In short, Predictive analytics is used by analysts to envision the results of any business decision. They can foresee whether a change will improve operations, reduce market risks, or increase profits, etc. It answers the question, “What can happen based on current data, and what can be done to change the results?”
Real-World Examples of Predictive Analytics
Commercial Audio/Visual Sector
Predictive Analysis in the AV sector has lately been quite popular. Businesses empower their marketing strategies with Predictive Analysis, providing insights into customer’s preferences. It helps in designing a more personalized and targeted campaign that businesses can leverage to save more money and gain extra value. As the Predictive Analysis models are based on the most updated and relevant data, they allow businesses to target potential customers with the right product.
Industry use-case
One of the examples of predictive analytics in the audiovisual sector is Netflix. With more than 100 million subscribers, the company collects vast amounts of data to determine customer preferences through predictive analytics. That is how Netflix sends personalized recommendations to its viewers, suggesting movies/shows tailored to their interests. Moreover, this method helps Netflix trigger 80% of viewers’ engagement without any extra marketing effort.
Finance sector
Predictive analytics in Finance helps in enhancing various processes by providing insights into business barriers and opportunities. Finance leaders use predictive analytics for estimating future revenue and consumer demand. It further helps them to improve their supply chain, detect frauds and analyze business losses.
Industry use-case
In 2011, PayPal collaborated with Rapidminer, a data science software platform, to measure the customers’ data, monitor their complaints, and enhance the product experience. They used predictive analytics to categorize the top problems faced by customers and provide actionable insights on their potential solutions. As a result, in just a few weeks after integration, Paypal’s customer satisfaction increased up to 50%.
Customer Service and Marketing sector
Predictive Analytics is a useful tool for any consumer-oriented business. It helps marketers to know when to pitch a customer by analyzing their purchase history. By gathering data on buyers’ preferences and forecasting trends, they can improve their services and gain more positive feedback. Predictive Analytics models also provide brands with prior knowledge of market fluctuations, giving them room to strategize accordingly.
Industry use-case
Starbucks uses Predictive Analytics to deliver a more personalized experience to its customers and increase its sales. By analyzing their location and purchase history Starbucks’s recommendation system sends personalized offers to customers approaching their outlet. This not only improves its customer experience but also increases the ACS (average customer spend).
Food Delivery Sector
The food delivery industry is a growing field for data-based development. The data collected from online food delivery services can be employed to improve their operations. This also increases profitability while cutting down unnecessary costs. Predictive Analytics can help the food delivery industry to make rational predictions about the customer’s interest. It can also enable them to estimate its time to deliver the food to the customer. Some companies use predictive analytics to schedule the staff needed for a specific date, shift intervals, and reimbursement to the drivers.
Industry use-case:
Zomato, an Indian multinational restaurant aggregator and food delivery company, uses Predictive Analytics models to personalize its services. By collecting data regarding customer’s affinity, location, price range, brand preference, etc., Zomato recommends restaurants and cuisines that they can try. It also helps them estimate the delivery time and notify the customer. The models further allow them to monitor customer satisfaction with particular items to predict the demand for the food items in the near future.
Healthcare Sector
The application of data in the healthcare sector is growing lately, and putting Predictive analytics in use can further boost the efficiency of the treatments. The predictive models allow hospitals to catalog data about the patient’s medical history and suggest tailored treatment plans to the doctor. Moreover, predictive models also help hospitals detect the likelihood of patients canceling or dropping appointments, allowing administrators to organize patient call lists to fill those slots.
Industry use-case:
For example, a hospital might use Predictive Analytics to foretell which of its patients is most likely to develop a central line-associated bloodstream infection (CLABSI) so that healthcare professionals can act accordingly.
Conclusion
Applications of Predictive Analytics are widespread and varied among various industries. It is emerging as an important tool for businesses to solve the existing problems and identify those that may occur in the future. Predictive modeling enables companies to understand their business thoroughly to achieve greater success in the future.
Because of the increasing popularity of analytics in various sectors, a career in Data Analytics is becoming an aspiration for many people. If these examples of predictive analytics kindle your interest in the subject, consider a course to advance a lucrative career in the field. At SkilloVilla we have created career tracks on Data Analytics and individual courses on Data Analytics with the help of top industry masters to foster your career in this Data-driven world. Moreover, you learn directly from the industry masters via interactive LIVE classes and concept videos. By focusing on industry use cases, you can clearly understand the implementation of the theory in real life.
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