Predictive and forecasting analytics is another field where artificial intelligence seems to dominate in all of its splendour. Predictive analytics refers to the use of statistical techniques and models for:
For predicting future outcomes.
For determining future performance.
Forecasting and predictive analytics have been used for similar terms also. But there is a fundamental difference between these two. Forecasting analytics makes logical and defendable predictions based on historical and present data and gives an estimated calculation by carefully analyzing the trends over time.
Forecasting and predictive analytics are employed in several industries and disciplines, such as retail analytics use cases, business industries, insurance companies, marketing firms, weather forecasting channels, customer service decisions, and much more. The different predictive models used as a basis in these domains are decision trees, regression, and neural networks.
Before going into the details, it is essential to know how predictive and forecasting analytics work. Predictive analytics is based on the models that use concluded results to train a system for predicting values for days yet.
The historical data (data from the past) is used along with the current data. The AI-operated systems then help forecast potential scenarios and future trends and events. This helps us in making strategic decisions.
The predictions made by these AI forecasting systems could be for the near future (as in predicting any malfunctioning of any system or machine), or it can be for the distant future (as indicating the cash flow of a company for the upcoming year). Either way, the historical data is being used to make an assumption about the future and coming days.
Predictive analytics tools:
Predictive analytics work differently from the classical descriptive and diagnostic models. Some of the predictive analytical techniques that are most commonly used in retail analytics use cases are discussed in the next section of the article:
- Decision trees are a classification model that divides the data into different subgroups and subsets based on the categories of input variables. It has been associated with a tree where each branch represents a choice between different alternative paths, and each leaf represents the ultimate decision or classification. Decision trees are more popular than others because they are comparatively easy to understand
- Regression analytical models can determine the relationship between two more than two variables that help predict the outcome just as one of the variables changes. IT gives a direct insight into the base of that relationship and measures how well the data fit that relationship.
- Neural networks are another technique that models very complex relationships between different variables. It is a highly flexible technology that can handle nonlinear associations in a given data. They are primarily based on pattern recognition and other AI techniques representing model parameters.
How is predictive analytics unlocking new opportunities for us?
Predictive and forecasting analytics are opening doors for new awaiting opportunities. Although this idea has been around for many decades, recent times have proved its significance. More and more disciplines are using predictive and forecasting analytics for various reasons:
- It can be applied to large volumes and almost all types of data.
- Easy to use soft wares.
- Efficiency and accuracy.
With all of its features, predictive and forecasting analytics are used in retail analytics use cases and other fields. Artificial intelligence has strengthened predictive analytics by:
- Aiding in decision-making.
- In-depth analysis of businesses and companies.
- Handling huge volumes of data efficiently.
- Improving data governance as in proofreading and integrating.
- Detecting anomalies in systems and securing the future.
Predictive analytics is helping us solve problems and opening new opportunities. A brief overview of the significance of predictive and forecasting analytics is discussed in the next section of the article:
Enhancing efficiency of systems:
Many companies use predictive models to improve their operations and enhance efficiency and productivity. The predictive models are employed to forecast inventory and manage the company’s resources. This enables organizations to function much more efficiently. For example, airlines use predictive analytics to set the prices of tickets. Predictions can also be used to increase the revenue of companies.
- Detection of any fraud:
Multiples analytical methods are combined to improve pattern detection. This is then used as a basis to prevent any criminal activity. Cybersecurity has recently emerged as a growing concern; AI analytical tools are used to spot any suspicious activity, including fraud and other threats.
- Optimizing marketing:
One of the retail analytics use cases is the optimization of marketing campaigns. Current data and trends are studied, and predictions are made regarding the selling customer’s response and purchase. Predictive and forecasting analytics aid businesses in attracting potential customers by pointing out their weak points, and optimizing the marketing campaigns, thus helping the companies to grow.
- In-depth analysis and decision-making:
Another retail analytic use case example is the use of it in deeper analysis and making apt and timely decisions. Modern AI algorithms use predictive models, especially neural networks, to help leverage huge businesses and companies. These analytics also allow for withdrawing conclusions that will shape specific patterns for the future. While decision making, the input data, and all the influencing factors are kept forth. Another feature that makes it highly distinguishable is handling large volumes of data very efficiently.
Implement Artificial Intelligence at work to meet the changing expectations of teams around the world
Top uses and applications of predictive analytics:
Predictive analytics has shaped many domains and disciplines. It turned out to be one of the revolutionizing features of artificial intelligence. Its practical applications have brought ease in many ways. Predictive analytics encompasses a broad range of statistical techniques and complex algorithms. With the advancements in every other field, we are looking for ways to take advantage of modern technology as much as possible. And predictive analytics is serving us well in this regard.
Business companies, management firms, insurance companies, communication organizations, and many other firms employ predictive analytics to generate increased revenues. Inventories are managed more efficiently, keeping complete control over the stored items and warehouse. Retailers are able to predict store-level demand for inventory management. Decision-making is now much easier than it has ever been, be it in the field of medicine, business or any other discipline. Apart from all this, predictive analytics help underwriting by accurately forecasting trends or any other activity. Future risks can be mitigated to a greater extent by using predictive analytical techniques. Following are some of the applications of predictive and forecasting analytics:
Manufacturing industries:
Predictive analytics has a significant role in manufacturing industries. Predictive analytical tools are employed at different stages of the manufacturing process where it overlooks the entire process and ensures it running smoothly. Analytical forecasting tools optimize the design, purchasing, quality control, inventory, and delivery processes.
- Another feature that makes it useful in manufacturing industries is its ability to track machine anomalies and performance. Predictive analytics is usually combined with machine data for this purpose and helps with equipment maintenance.
- Predictive analytics has also observed a decrease in shipping and delivery costs as it overlooks all the systems accurately and efficiently.
- Predictions regarding the purchase of raw materials can also be made with predictive analytics. These tools predict the purchase requirements using the sale data and supply chain system. The entire manufacturing process is aligned with consumers’ demands in this way.
Retail
Retail analytics use cases are also based on the same principle of machine learning. The most crucial thing for every retailer is managing inventory and logistics, and this is the part where predictive and forecasting analytics has settled in. The predictive analytical tools extract information from historical sales data, products purchase trends, and geographical influences over the sale and correlate all these factors to optimize the retailer’s operations.
- The sales data provides valuable information regarding personalized promotional products for individual customers. Predictive analytical tools target these customers for planning personalized campaigns, promotional content, and ads. More the optimization of marketing, more buyers will be attracted.
- Sales data is also used in retail analytics cases to control inventory efficiently. It helps the retailers ensure the availability of products in warehouses whenever needed.
- Sales data can also provide suitable timing for increasing or lowering prices. Predictive analytics analyzes the trends and gives a conclusion accordingly. This way, retailers can know when to gear up to raise the prices and when to lower them.
Apart from the above-mentioned retail analytics use cases, there are some other ways by which predictive analytics is being used in the retail business to augment its growth. Predictive analytics is now able to transform your business by:
- It is understanding the customer’s behaviors and minimizing the acquisition costs.
- The internet and optimized marketing campaigns improve the customer’s service and experience.
- Targeting the potential customers.
- Better and well-managed control of inventory.
- Optimization of trade promotions aids the retailers in predicting the possible output results.
- Forecasting revenues- Retailers can change the variables and get to know the future outcomes through predictive analytics.
- By giving personalized recommendations, we can buy the attraction of more customers.
Healthcare settings:
Apart from retail analytics use cases, the industry most benefitted from predictive analytics in the healthcare industry. Now how is it being done? Here Ii an explanation of this question:
- Health data is used to get a deeper insight into any patient’s history and current illness. Predictive and forecasting analytics are employed to understand the patient’s disease and form an accurate diagnosis based on the data provided.
- Various factors are kept forth, and doctors are helped by these modern techniques to find the root cause of the disease. Once the root cause is identified, then the patient has managed accordingly. Even the treatment plans are based on the diagnosis made by it. We can minimize the chances of any adverse health effects this way.
- Health insurance companies are also using predictive analytics to determine the patients at more risk of chronic or incurable diseases. Suitable interventions can be done this way accordingly.
- Hospital-acquired infections can also be prevented by predicting the likelihood of patients susceptible to central line-associated bloodstream infections. Medical practitioners tend to be more cautious and act faster according to the predictions provided by predictive analytical tools and systems.
- The possible chances of a patient not showing up for a scheduled appointment can also be accessed by applying one of the predictive analytical models.
- An example of an application of predictive analytics in healthcare is the development of a sensor called “AbbieSense.” This sensor can detect early physiological signs of an allergic reaction or anaphylaxis. The most distinguishing feature of this sensor is that it can see this reaction just as fast as the human body is capable of doing so. Whenever the sensor predicts a reply, an algorithm-operated response is triggered. The algorithm predicts the intensity of the reaction to occur considering the individual’s immune system, alerts the person, and injects a dose of epinephrine whenever necessary. This ability of the sensor to detect the reaction even before it has occurred has proved to be life-saving for many people.
Healthcare practices are improved significantly as the health data gives sufficient information to the medical practitioners.
Banking and finances:
The financial industry has always been at stake with the enormous volumes of data and money. Predictive and forecasting analytics is used in this industry to measure credit risk, retain valuable customers, ensure customer satisfaction and maximize cross-selling and up-selling opportunities. Any fraudulent activity can be detected in milliseconds now. Commonwealth Bank uses analytics to forecast any likelihood of criminal activity for any transaction before it is authorized, within 4o milliseconds of the marketing initiating process.
-
Predictive analytics is helpful in banking and finances in:
- Credit card scoring- this directly helps in approving or denying loan applications.
- Credit risks are measured using predictive analytics.
- Most importantly, predictive analytics analyzes the customer’s churn data. This allows the banks to target potential customers and approach them before switching to other companies.
Marketing industry:
The key to diverting the customer’s traffic and gaining their attention is the skill of advertising and marketing. No matter what you sell, you fail at it if you do not have the proper technique to attract customers. Therefore, marketing and advertisement are emphasized again and again when talking about selling. And this is the reason why it is discussed along with predictive and forecasting analytics and retail analytics use cases.
- The marketing team needs to look at how customers react to a particular marketing campaign, how much audience the campaign attracted, and the influencing factors.
- Advertisements are displayed on various websites and social media platforms by segmenting marketing leads. Predictive analytics help segment information, so the consumer’s behaviour and interest is always the top priority.
- The “Expect to purchase” idea can be explored by analyzing the consumer’s behaviour in the past and present times. This way, we can find more people whose data closely resembles the ideal consumers.
- Retail analytics use cases have been employed in lead scoring as well.
Virtual assistance:
Combined with deep learning, predictive analytics is exerted in several AI-operated systems and primarily virtual assistants. The virtual assistants use the machine and deep learning techniques to learn from the consumer’s behavior and deliver the results accordingly.
- Examples of predictive analytics-based virtual assistants are used by Siri, Google, and Alexa companies.
- Companies can also use these virtual assistants as chatbots. These bots learn from the customer’s interactions and reactions to different circumstances. They generate autonomous answers from the system without any human intervention. They improve the customer’s experience as they do not have to wait too long for their query to be answered. In this way, it enables the working organization to manage customers without hiring many staff members.
Maintenance of equipment:
This particular aspect of predictive analytics is employed in every other domain, from manufacturing companies to healthcare setting equipment and retail analytics use cases. In every industry, equipment must be kept in shape to support the operations running smoothly. Predictive analytics models are employed to ensure the maintenance of equipment. Any malfunction or accidental equipment breakdown can result in a complete halt of functions and even risk human lives, resulting in loss to the company.
- For example, IoT is integrated with equipment to understand when the equipment requires maintenance. This way, the personnel is informed beforehand, and maintenance can be done to avoid accidental breakdowns.
In A Nutshell
Predictive and forecasting analytics use advanced algorithms and modern analytical techniques that use historical data to make assumptions about the future. Forecasting and predictive analytics have been employed in every other domain because of their accuracy and efficiency. Thus we can say that predictive analytics is the ultimate game changer as astonishing results can be attained by using this. As we are never sure what will happen in the future, it is essential to employ predictive analytics in this competing environment. And we need it more than ever before.