Data Science and AI Predictions: Why You Need it

According to Forbes, over 50% of businesses that were somewhat affected by COVID-19 reacted by investing more in innovative technology, including predictive analytics. Predictive analytics is the use of statistical algorithms to make accurate predictions and business decisions. Statistical algorithms help businesses to make better inventory decisions, fulfill orders quicker, grow revenue, and control costs more efficiently. AI predictions can then be paired with statistical models to make even more accurate predictions regarding businesses.

AI models are closely related to predictive analytics because they heavily rely on data. The main difference is that AI models are able to make assumptions, test them, and learn autonomously. They get better with time. Machine learning is a subset of AI that works through iterations thus helping to learn and uncover patterns from data.

Historical Data Is Not Enough

The business environment today, taking into account globalization, is such that historical data is not sufficient to accurately predict the future. Events such as supply chain disruptions resulting from pandemics or conflict in certain regions are impossible to predict from historical data.

Consumer behavior is not always predictable, especially when big events come into play. For instance, producers of toilet paper were caught unawares by the sudden surge in demand in early 2020. There will always be these unforeseen occurrences in the business environment. Therefore, it’s best to rely on models that take into account as many data points as possible to make the most reliable business decisions. This is why businesses now combine predictive analytics with AI to make improvements in the following areas:

Inventory Management

Big companies such as Amazon have applied AI to make better decisions on inventory levels. Even before COVID, there was already a case for businesses to better monitor inventory levels and make decisions on when and how much to order for each item. AI models are able to make sense of numerous factors that affect inventory levels including customer locations, seasonality, and demand trends, to make recommendations on the appropriate levels of stock to maintain. 

IoT devices help to monitor stock levels in real-time. This information acts as a data source for predictive models.

AI-powered robots are also being deployed in large warehouses, and with good reason. Besides working tirelessly, they can monitor and record data on the conditions of items. By keeping track of faulty items, they can help reduce the likelihood of shipping items that will end up being returned. 

Optimizing Delivery through AI predictions

Predictive analytics help determine the best routes for delivery trucks to follow in order to reduce order fulfillment time. However, predictive analytics fall short when unexpected events such as accidents and other obstructions arise on the road. When deployed, artificial intelligence models can make real-time decisions as events occur. This results in real-time rerouting to avoid delays. AI algorithms can also make decisions such as when the best time is to make deliveries to warehouses to minimize the time in transit. 

IoT technology now enables companies to track the condition of goods while in transit. They monitor not only safety but also the condition of items. For instance, the driver gets notified if delicate goods shift around while in transit. Companies are able to monitor the condition of fresh goods throughout the journey. If items go bad while on the way, the company decides not to deliver them to customers in that state. 

Risk Mitigation through Behavior Analysis

Organizations can benefit immensely by being able to carry out risk mitigation efforts that one would consider impossible a decade ago. Consider a situation where an AI risk mitigation system monitors employees’ interaction with the organization’s mission-critical systems. The risk mitigation system notices a sudden rise in the number of times a certain employee logs in and that these logins seem to happen during unusual hours. The person also seems to be downloading a huge number of documents. This may launch an internal investigation that uncovers that the employee is unhappy and was perhaps plotting resignation, taking with them crucial company information.

Behavior analysis through AI predictions can come good in other situations, including reaching out to unhappy customers, judging from a sudden reduction in purchase frequency or volume. Ever seen offers of a free one-month subscription from a service you stopped renewing? 

Fraud Detection through AI prediction

AI predictions are changing how e-commerce businesses and banks are dealing with fraud. According to Fintech News, more than $200billion annually goes to deploying AI-based applications in Fintech to prevent and detect fraud. These applications analyze transactions and come up with a way to categorize customers into different risk profiles. 

Machine learning systems help to build profiles of what normal customer behavior looks like. They make predictions on what a customer is likely to do based on past transactions. This is why unusual transactions are flagged immediately. For instance, a person who travels often will use their card in multiple locations. However, if someone who doesn’t travel regularly uses their card in a new location, their bank may get in touch with them regarding a transaction. 

Improving Accuracy in Research and Marketing

AI is helping deliver efficiency in many other areas of business operations. For instance, continually building customer profiles helps uncover new revenue opportunities. This means that a business can build new products with an idea of how many of their existing customers will want or need them. This helps in crafting the marketing message and positioning of the new product in terms of price, distribution, packaging, and so forth. This is applicable in almost every industry- automotive, beverage, banking, and more. 

Another application of predictive analytics is in manufacturing industries, where investment in heavy equipment is necessary. By collecting the right data and feeding it into models, they can make predictions on what equipment is about to break down.  This reduces disruptions in operations.

Building AI Solutions with AI

The range of areas where AI and predictive analytics can drive value is virtually limitless. The challenge is picking the right areas to focus on and delivering value. Transcendent Software is an IT-services firm helping business clients get the best out of the available technology. We can help build and deploy predictive analytics and AI-powered applications. Reach out to us today for a free consultation session.