9 Practical Use-Cases of Predictive Analytics

Data is put to its most valuable utilization when it is used to identify patterns of future change based on a study of historical and Live data. Every set of data that is available can be used as inputs in a Predictive Analytics platform. When all data is unified and continuously put through machine learning, trends and actions that are going to be occur in the future are identified in the present. Looking at use cases of Predictive Analytics

Monitoring manufacturing operations: With sensors deployed in a manufacturing unit, every component is monitored in real time. Any impending failure of a part or a process is raised much in advance. With advance predictions, the issue of downtime can be eliminated. Quality is ensured with analysis of all patterns detected in the manufacturing process leading to fail-proof manufacturing outputs.

Predicting credit default: Predictive analytics can be used to develop a model on the possibility of a credit default in the future by individuals, institutions or countries. Using Logical Regression methods, the probability of default can be predicted by the system.

Analysing symptoms to arrive at diagnosis conclusions: A predictive system to help doctors has been in the making for many decades now. With the taking off of big-data analytics, systems that use symptoms and diagnose diseases can be ramped up to predict diseases. Some symptoms can be common to many ailments making diagnosis a long-drawn issue. With predictive analytics, diagnosis can be automated leading to time and cost savings for the medical practice as well as the patient. The model will bring up risk patterns in the symptoms that the doctor might miss out.

Using country-wide or worldwide data to predict epidemics: Any outbreak of a virus in a location around the world can be used with all the surrounding data to predict whether the outbreak has the potential of becoming an epidemic. Social media is also brought into the system as a reporting platform. Using current data and data from past outbreaks, a predictive analytics platform will be able to announce the next onset of a disease with high accuracy. The use of mobile phones and data on travel are all used to predict the spread of an epidemic.

Prediction of revenue by retailers: Seasonal variations in sales in different years are the norm in times of economic boom or stagnation. When retailers are able to predict demand based on economic indicators and social trends, they can have stocks available in response to a pattern that is seen on the horizon. Predictions are also very useful when retailers look to expand stores. All available relevant data can be used to guide a decision on selection of the ideal location for the next store.

Prediction of guests in the hotel industry: In this application, hotels can undertake planning on the basis of predictions of demand. In a weaker season or in a sudden drop in a normally steady period, prices can be optimized to attract guests. At the other end, from the perspective of travellers, they can make use of price predictions when making bookings. Overall, hotels will be able to prepare better to serve guests by predicting their preferences. This can be used to sell the right products and services to the right customers at the right time.

Prediction of customer behavior in the online industry: Behavior of customers can be predicted by looking at past trends and predicting when a customer/customer segment who made a purchase in the past will do so again in the future. This can be followed up with appropriate advertising which can be a high-value investment. Using Decision Tree statistical models, one project used click streams and mouse movements to predict whether customers would buy products they had added to their shopping cart. Another study observed that customers who look to purchase over the long term click through more content and to save the content. This study discovered that signals of a purchase were available weeks before the purchase.

Predicting social trends for governmental decision making: Every government agency uses data to make decisions. Most of the time, the analysis of the data does not generate useful observations when they could have been applied in an useful fashion. At present, predictions are available in the form of results from studies that are used to make calculated assumptions. With a powerful platform, all future expectations can be double-checked or even based totally on the capability of machine learning. The most important aspect in such an implementation will be the speed with which the platform can deliver its findings. This can help remove totally and completely the slow pace of decision making and implementation that governments are burdened with.

Using analytics to retrieve agriculture: Prediction of weather patterns and market demand can transform Agriculture into an altogether new profession. At present, weather forecasts are the only prediction that are available but they are presented in an overall manner and not with precision. And there are unseasonal occurrences that can destroy crops. When predictions of these unseasonal events are available, farmers can adopt the necessary safeguards. Market research and forecast is also available at present but more at the level of theoreticians. With Predictive Analytics, the information can be made available to farmers when they are making the decision to select the crop for the next sowing season. This can lead to agriculture ridding itself of the tag of an unproductive activity and it can join the league of other businesses which are also dependent on predictions as we have seen in the previous applications.

With predictive analytics, we have a platform that brings together diverse sets of data and diverse statistical techniques. Decision making improves to a level where the most likely possibilities are clearly available as inputs. At the most fundamental level, it is about reducing risks while in its most advanced utilization, it delivers the capabilities of reducing wasteful expenditure of time, resources and effort.

Looking to implement Big Data Analytics related use cases at your organization? Do reach out to us at contact@dataone.io. We’ll be happy to discuss and collaborate further.