We live in an era where even the mixer grinder in the kitchen produces considerable data and shares it over the internet to a host of apps and platforms. More than 2.5 quintillion bytes of data are generated every single day according to estimates, which translates into an average of 1.7MB of data created every second by every human on the planet!
So, what do we do with all this data? The answer is simple – use it to improve our lives in multiple areas. Now comes the question of how to achieve this feat? The solution lies in predictive analytics, which deals with predicting outcomes of scenarios using statistical modeling of historic data. This is one of the key pillars of artificial intelligence and machine learning.
Globally the market for predictive analytics was around USD 7.32 Billion in 2019, and it is expected that this figure will reach a staggering USD 35.45 Billion by 2027. In the past, predictive analytics has been perceived as an experimental concept or one that had limited practical usage within a business environment. However, it has now transitioned into a mainstream business enabling capability that is sought out by firms of all sizes. On this note let’s have a detailed look at some of the top real-world use cases of predictive analytics witnessed today:
Being heavily dependent on machines, the manufacturing sector constantly invests in keeping their hardware at optimum health to ensure their production commitments are always met. This is precisely the area where predictive analytics has witnessed its most crucial real-time application in the manufacturing sector. By continuously monitoring data from factory components, manufacturers can predict maintenance routines needed.
For example, General Motors runs AI-based predictive analytics on images from cameras mounted on factory robots to estimate their failure rates based on signs of wear and tear of components. These may be difficult to be spotted by the human eye without a thorough inspection, but an AI-based system can analyze and predict the chances of failure instantaneously. By identifying such problems at an earlier stage, they will be able to replace the components in due time before it leads to unplanned stoppages or disruptions in the assembly line.
Nearly 60% of healthcare executives have said that their organization uses predictive analytics to improve patient care and streamline their operations.
One of the biggest use cases for predictive analytics in recent times came in the wake of COVID-19 when several hospitals used predictive analytics to track the deterioration of patients in general wards and predicted when they would require ICU support. There have been instances where a hospital reduced serious events by over 35% with proactive monitoring and predictive warning systems. Patient deterioration is a key factor that needs constant attention and in times like these when the healthcare facilities are overwhelmed with patients requiring care, events, where a patient undergoing serious deterioration goes unnoticed by staff, is common. Autonomous monitoring and prediction of criticality ill thus become vital use cases for predictive analytics in healthcare.
In the retail sector, the more you know about your customer, the better will you be able to sell products faster to them. Predictive analytics can be the game-changer in deriving preferences of your customers from the vast amounts of data that they have already shared with brands in their past interactions.
One of the best examples of how predictive analytics enabled a retail brand to improve in-store sales was how a Harley Davidson dealership in New York increased its sales leads by 2930% using an AI-based marketing platform. By analyzing customer information from across their CRM, point of sales, and website inquiries, the platform was able to identify the most relevant audience to target with marketing campaigns. This target base was found to have more interest in talking to an in-store salesperson, and the dealership was soon flooded with customer inquiries.
The eCommerce boom witnessed ever since the early 2000s has created a large-scale impact on how product availability is managed across buyer geographies. Customers expect their shipments to arrive in the fastest possible time and without any damages or misplaced order items. This has created the need for a massive supply chain system for eCommerce companies who rely on different stakeholders in their supply chain to ensure that the product is made available to the customer in the fastest and most economical timeframe.
Predictive analytics is bringing about a revolutionary shift in the eCommerce supply chain market by helping businesses anticipate demand and optimize their supplies accordingly to ensure customer satisfaction for every order.
Take a look at Amazon which has rolled out a new predictive analytics-based supply chain initiative called anticipatory shipping. In this, wherein data from past orders is used to determine where customers are likely to have more demand for certain products soon and then shipping the products to warehouses or storage destinations closer to the demand areas even before the order is placed. In this way, when the actual order is placed, customers can get hold of their desired product in a matter of hours rather than waiting for days for delivery. This mode of optimization of the supply chain is extremely useful for eCommerce businesses that handle perishable goods like groceries, meat, dairy, and daily-use vegetables and fruits.
The digital transformation wave has hit the shores of civic administration as well. Across the world, smart cities are being envisaged by governments to help create a sustainable living ecosystem for residents while at the same time preserving resources through optimal utilization. Predictive analytics can set the stage for propelling smart cities into the next level and make lives easier for its residents.
For example, the city of Beijing uses data from around 35 air quality monitoring stations in its boundaries to forecast the chances of smog. Using predictive analytics, the city officials get an idea of the situations that will eventually lead to smog conditions like the presence of pollutants, humidity levels, etc. This forecasting technology can help issue traffic warnings to commuters who can plan their travel accordingly to avoid the smog scenario.
The real-world use cases we have covered for predictive analytics are a testimonial to the fact that is today a mainstream digital capability that can impact organizations of all sizes and across industries.
Leveraging the right insights from data captured across your business will help in forecasting and predicting a better outcome for your operations, which will ultimately result in better services and experiences for your target consumers.