Why predictive AI is key to a company's success

The integration of predictive analytics tools is already common in large companies, but thanks to the evolution and, above all, the democratization of technology, their application is already spreading to many SMEs, where they are being used to improve the user experience, improve their products or offer a good after-sales service.

Thanks to artificial intelligence applied to predictive analytics, companies will gain a number of competitive advantages and optimize their sales strategies to better face market changes and competitive challenges. Using predictive AI, patterns and trends can be identified from historical and current company data. In other words, to detect patterns in customers' purchasing behavior that will allow them to assess which are the most favorable procedures to achieve their objectives.

AI-driven algorithms can be used to identify patterns in consumer behavior and develop personalized marketing strategies and campaigns. This provides valuable insights (evidence) that allow much more accurate and strategic decisions to take advantage of opportunities and avoid or minimize risks, managing the company's resources more efficiently. 

Predictive analytics solutions enable companies to accurately estimate what event is likely to happen in a given situation.In this way, companies can detect, eliminate or mitigate potential conflicts that may arise. It is therefore a matter of staying ahead of both customers and competitors, taking advantage of the new opportunities suggested by predictive analytics.

In this sense, AI applied to predictive analytics offers businesses

a powerful set of tools to gain valuable insights into your customers' behavior and trends, improve performance and maximize profitability, as well as identify business opportunities and predict future returns.

Quality data, a suitable management model and appropriate interpretation and visualization techniques are essential for the successful application of predictive analytics. By analyzing large volumes of data, organizations can identify trends, forecast future demands and make informed decisions to improve efficiency. 

Basically, predictive analytics uses statistical techniques, machine learning models and algorithms to extract valuable information (insights) from large volumes of structured and unstructured data, in order to identify business opportunities and avoid risks. In short, it is a matter of taking advantage of commercial opportunities, bearing in mind that commercial development must involve controlling risks, i.e. selling more and getting paid for what is marketed.

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Predictive AI is therefore a further step in the evolution of predictive analytics. In marketing, it is common to use data analysis techniques to predict the behavior of a brand's potential customers based on their preferences. The AI applied to predictive marketing gives the advantage of analyzing in real time this data. This makes it possible to generate behavioral patterns segmented by user typology in order to offer personalized products and services.

Types of predictive models

Depending on the type of learning with which AI algorithms are trained, we can distinguish three predictive models for solving specific problems.

1. Predictive model based on supervised learning. In this case, the algorithm is trained to learn from the historical data analyzed. Knowledge production is generated by labeled examples or for which the correct answer is known; this allows the AI to learn by trial and error: comparing at each iteration how right or wrong its answer is with respect to the correct answer.
   
2. Predictive model based on unsupervised learning. In this case, the algorithm is trained to find similarities or patterns in the data, so that it associates known variables from the information collected, but without specifying what to look for. The model collects information without the need to know a priori any correct output, since it explores even data that are not yet labeled.
   
3. Predictive model based on reinforced learning. Also known as semi-supervised learning. In this case, the goal is decision making in changing environments, where the AI learns as it interacts with the environment and tries to maximize the rewards it obtains when the decisions made are correct. , 

Types of algorithms used for predictive analysis

We can distinguish three basic classes of machine learning algorithms used in the development of predictive models.

1. Classification algorithms. They engage in supervised learning in order to predict a "category" or "class label" based on the collected historical data. Classification algorithms are used in cases where the result is a finite set of results. The spam detector of an email account is a good example of such an algorithm; in this case each email is automatically assigned a label with two possible values: "Spam" or "Not Spam".
   
2. Regression algorithms. They participate in supervised learning in order to predict a "value" or "number" based on the historical data collected. This allows to establish a methodology to link a certain number of characteristics and a target variable that has continuous values (prices, product demand, stocks, temperatures, ...). This kind of algorithms can estimate the time it takes a person to reach a destination or calculate the value of a house based on its location, orientation or size.
   
3. Segmentation grouping. It participates in unsupervised learning in order to group data into clusters based on similar patterns and characteristics. These types of algorithms are often used in e-commerce to classify customers based on their browsing and/or purchase history.

6 steps to perform predictive analytics

Predictive analytics is performed in companies to resolve, mitigate or suppress a problem or conflict before it occurs.These include: customer churn, inefficient processes, falling conversion rates, reputational crisis, etc. Although we should not overlook the fact that with predictive AI commercial opportunities can also be seized if it is predicted that a customer may purchase or use a brand's products and/or services..

So, in order for the predictive analysis to yield valid results, it is necessary to follow a series of steps that we will see below.

1. Obtain all historical data necessary to help predict future results in order to make informed decisions and take actions to improve the visibility, perception or profitability of a business.
2. Filtering, combining and integrating only those data that provide value for the use of analytical techniques.
3. Elaborate the predictive model. In this step it is key to select only the specific parameters with which the selected type of algorithm will work. 
4. For validate the predictive model it is important to keep adjusting the parameters and training data in successive revisions. It is a matter of compare predictions with what actually happened to ensure that the model is well-trained and that its performance does not decline over time.
5. Implement the predictive model. This is the moment to give controlled access to the model so that the company can provide the data with which the predictions will be made. The data provided and the predictive results will be monitored to ensure that the model's performance matches that measured during its creation and validation. In successive iterations, the model is retrained to achieve increasingly accurate predictions.
6. The last step consists of integrating predictive modeling into the company's systems and processes. 

Predictive analytics models

There are several predictive analytics algorithms that can be applied depending on the specific needs and objectives of a company. Below, we will list the five most common algorithms, whose objectives are very similar, namely: to learn from historical data, to predict future results from current data....

1. Neural networks. This model simulates how the brain works to catalog huge amounts of data and generate predictions. Using AI and machine learning they are able to model extremely complex connections to ascertain the exact nature of the relationship between input and output values.
   
2. Logistic regression. This model is used to predict the outcome of a categorical variable as a function of the independent or predictive variables. It is commonly used to make predictions about credit risk, since it is capable of determining the probability of an event occurring in relation to other factors.
   
3. Linear regression. One of the best known algorithms is the "least squares method" because it calculates the sum of the squared distances between the points representing the data and the points of the hyperplane (in the simplest case: a straight line) that generates the model. Thus, the smaller the distance, the more favorable or accurate the estimate will be.
   
4. Decision trees. In this case, it is used to represent hypothetical decisions and the possible consequences they generate. They use a nonparametric supervised learning algorithm for both classification and regression tasks. Each tree is divided into different branches and leaves that represent each classification according to the conditions that are selected until the problem is solved.
   
5. Support Vector Machines (SVM). This prediction model employs machine learning techniques to find the best possible separation between classes. Instead of detecting the optimal line, the SVM finds the optimal hyperplane that maximizes the margin of separation between classes by classifying repetitive models.

But, why use predictive AI in your company? If you are not yet convinced of the great benefits of AI applied to predictive analytics, here are some of its advantages.

Advantages of predictive AI

Today's companies are increasingly looking for better and better ways to improve their sales processes. Artificial intelligence applied to predictive analytics is becoming an invaluable tool to achieve these goals.

Predictive AI enables companies to automate the entire sales process, from lead generation and qualification to customer segmentation and after-sales service. This allows you to significantly reduce the time spent on repetitive tasks and focus on improving profitability and customer satisfaction.

Let's look at some of the advantages predictive AI provides.

1. Facilitates data-driven decision making

Companies work with large amounts of data from which they can Predictive AI can provide vital information for business decision making and take advantage of business development opportunities.. This reduces the chances of failure of the actions to be implemented, reduces costs and increases profitability.

Identify and reduce risks

There is no doubt that managing the risks of each operation is fundamental to ensure a company's success in the market. With the support of predictive AI, potential risks are identified before they occur. In this way, companies can anticipate and take preventive action. to avoid or minimize the impact, reduce recovery time and costs in the event of a crisis. For example, in the case of insurers, which are permanently exposed to fraud risks, such as false claims, predictive AI can anticipate fraudsters by analyzing behavioral patterns. To do so, it collects and analyzes cases of deceptive activities and thus executes preventive measures.

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Risk assessment helps reduce industry losses and improve performance. To predict the probability of claims or accidents, predictive AI analyzes customers' policy documents. It can also predict which policyholders are unlikely to renew their contracts and devise a strategy to increase retention.

3. Efficiently manage existing resources

Through predictive AI, companies can forecast market behavior and future demand for products and/or services. It allows companies to determine optimal inventory levels to meet demand and minimize inventory, control prices seasonally, or design marketing strategies that are more responsive to customer needs and in line with demand. It also makes it possible to determine detailed inventory requirements by region, location and usage, resulting in better planning and reduced costs.

4. Reduce costs by optimizing processes 

Closely related to the previous section, predictive AI helps reduce costs and increase the efficiency of operations by optimizing inventory management. This is essential for ensuring the flow of product supplies, optimizing warehouse management and improving resource planning.

5. Improve customer segmentation and sales forecasting.

Predictive AI allows to achieve a higher accuracy in segmenting customers and customizing the way sales departments interact with them for each customer. In this way, they can identify the best time to communicate with potential customers and tailor their messages, products, services and promotions.

Understanding customer preferences and behavior gives companies an undeniable advantage because it allows them to identify business opportunities and trends before their competitors do. In this way, the company can anticipate and respond more precisely to customer demands, providing products and services that meet their wishes. This also helps to build customer loyalty and generate higher sales conversion rates.

AI also makes it possible to identify emerging segments and even design subgroups within existing ones. More detailed segmentation can increase the chances of conversion. 

Amazon uses predictive analytics to provide recommendations based on analysis of past purchase data and user preferences, improving the customer experience and increasing the likelihood of a sale.

6. Increases customer loyalty 

By identifying patterns and trends in the behaviors, preferences and needs of your customers companies can better understand exactly what their customers want and what their desires are and thus segment the audience, personalize your messages and offers, as well as select the right channels to reach them, optimizing marketing campaigns.

In addition, predictive analytics can also help to measure the performance of these campaigns, identifying which tactics work best and which do not.. AI analyzes customer data to detect anomalies in sales performance and provides information on how to improve it. 

Loyal customers generate a steady and predictable flow of revenue and contribute to the maintenance and sustainable growth of the business. A satisfied customer is potentially loyal because they have had their expectations met by a brand. In this sense, improving customer retention goes far beyond giving customers what they want. It is necessary to identify causes of abandonment based on purchase history, activity on the website or opinions expressed on social media or reviews. It is clear that the digital customer service tools, such as chatbots, are becoming more and more precise in order to facilitate a more direct and personal relationship with customers. 

7. Optimization of routes, delivery times and logistics operations.

Logistics management is a vital part of any organization dealing with product/stock materials and its efficiency is key to achieving objectives. For example, Predictive AI greatly facilitates the task of identifying in real time the best available routes to improve delivery times.The AI can also be used to track which vehicles will need maintenance or replacement in a given period. In addition, AI allows tracking of vehicles that will need maintenance or replacement in a specific period.

8. Predictive maintenance of machinery

Predicting the wear and tear of machinery used in industry allows companies to perform predictive maintenance. This reduces downtime and costs associated with repairs and equipment replacement.

Conclusion

Predictive artificial intelligence has established itself as a cornerstone of business success. By leveraging historical and current data, it provides accurate insight into future trends, enabling informed decisions. This is essential for anticipating demand, optimizing resources and improving customer experience.

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The ability to anticipate problems and needs also leads to efficient preventive maintenance and the identification of market opportunities. In addition, predictive AI powers innovation, improves marketing and sales efficiency, and provides real-time analytics.This gives companies a competitive advantage by addressing challenges in a proactive and agile manner in an ever-changing business environment.