Predictive Analytics

Assisting medium and large businesses make informed decisions through data analysis using machine learning technologies.

Understanding Predictive Analytics

Predictive analytics, in its broadest sense, involves the analysis of accumulated data to uncover patterns and predict the occurrence of certain events. By scrutinizing available data, we can identify patterns that ultimately lead to specific outcomes.

By integrating machine learning technologies into the data analysis process, we can construct sophisticated analytical models, thus obtaining more precise predictive insights. This enables companies to optimize their business strategies and make informed decisions for the future.

Exploring the Applications

Predictive analytics finds application across nearly all business domains. Let's highlight some of its most popular use cases.

Retail

Predictive analytics is widely used in retail. It helps answer questions such as how many and what type of products should be stocked in a specific retail location, what the demand for products will be on different days of the week and seasons, what prices to set for various items, and many other questions.

Manufacturing

Another in-demand area is manufacturing. For instance, by processing information about equipment malfunctions, it's possible to predict future breakdowns, thereby avoiding them or minimizing their impact on production processes. This ultimately has a positive effect on the company's revenue.

Financial Sector

Perhaps the most popular application of predictive analytics in finance is fraud detection. Another important area is risk management and scoring. The use of predictive analytics helps identify unreliable clients even at the stage of application processing.

Logistics

Building analytical models allows for a qualitative improvement in logistic chains, directly impacting delivery speed and the company's overall profitability. Many factors are taken into account, such as weather conditions, the most optimal routes, predicting vehicle breakdowns, seasonality, road congestion, and others.

How Accurate is Predictive Analytics?

Predictive analytics is not a service or software that, with the click of a button, will magically create the perfect analytical picture for you. In other words, to obtain high-quality analytics, it's essential not only for the developer to put in effort but also to gather all the necessary information available to the client company. Let's outline the main factors influencing accuracy.

In the realm of analytics, historical data stands out as a cornerstone. Forecasting models rely heavily on this data, and the depth of our analysis directly impacts the quality of our insights.

Another significant factor is the number of parameters. The more parameters used in the analysis, the higher the accuracy of the final result.

The forecasting horizon indicates the time period (hour, day, week, month, year, etc.) for which the analysis is conducted. The longer this period, the more challenging it is to perform analytical calculations.

External factors such as climate, natural conditions, political or economic situations, and the level of competition also play a vital role.

The forecasting model is also a crucial factor in the accuracy of predictive analytics. We tailor a unique model for each client, enabling us to achieve maximum precision.

How It Works

Before diving into predictive analytics implementation, it's helpful to understand the key stages of its design.

Step 1

Definition

It's essential to clearly define the goal you want to achieve.

Step 2

Collection of data

Gather historical company data or data from open sources.

Step 3

Exploration

Analyze the collected datasets and identify patterns.

Step 4

Modelling

Develop a custom predictive model based on the client's data.