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The key predictive techniques in business analytics

The key predictive techniques in business analytics

Decision-making is a constant for any business or organization, large or small. Although it is impossible to tell the future, predictive analytics will enable you to make the best possible guesses. This data analytical tool uses past and ongoing events to forecast what will happen in the future. It implements data, statistical algorithms, and machine learning to assess the likelihood of future outcomes.

The end goal of any business is to get returns on investment and gain a competitive advantage. Nowadays, businesses need more than just promotional marketing to grow. They need to uncover new opportunities and solve problems that may arise. Luckily, predictive analytics helps to do that, and more. It can be used to detect fraud, improve operations, reduce risks, and achieve optimal marketing strategies.

For these reasons, business analytics is becoming increasingly essential, and this article highlights the key predictive techniques used.

  • Data mining

Data mining is a technique that involves sorting through large sets of unstructured data to discover patterns and relationships that reveal relevant insights. It is a standard data analytics approach that combines statistics and machine learning. This technique allows businesses to convert data to business intelligence that supports informed decisions.

Common aspects of data mining are:

Data warehousing — most data mining efforts stem from a data warehouse. The latter is a data management system, consolidating multiple data sources like application log files and transactional data from POS (point of sale) systems.

Clustering — this involves grouping data into smaller subsets based on their similarities. For example, clustering customers based on purchase habits for personalized promotional campaigns.

Classification — this involves calculating the probability that a data item belongs to a category. A common application is fraud detection techniques that flag certain activities based on predefined criteria.

  • Predictive modeling

This technique combines probability and data mining to predict future outcomes. After collecting data, data scientists use predictive models to find correlations between data elements within a data set. Once a model is formulated, it can be modified as required to produce accurate results.

Analysts can run the model against selected data to generate predictions. For instance, determining how much inventory to stock up on in different shop locations, or how many calls a customer support agent should expect in a day. Predictive models can be revised regularly to include new data.

Predictive modeling is therefore a technical approach that requires an academic background in data analysis. With an online MBA degree with a digital focus or other related scientific disciplines, you will acquire the relevant skills to apply this technique for the best results. Aston University offers one such online course which provides you with four concentrations that will put you ahead in the race.

  • Regression techniques

Regression analysis is a primary statistical tool for predictive modeling. It uses structural data to find correlations between variables, presenting them in a graphical linear expression that describes the strength of the relationship. A regression line is constructed using a mathematical formula that involves dependent and independent variables. The output can indicate a positive relationship, a negative relationship, or no relationship at all.

For instance, regression techniques can show the relationship between the age of customers and interest in a particular product or group of products. It can also show the correlation between house prices and interest rates. With a given interest rate, it is possible to calculate future house prices.

Two types of linear regression exist:

Simple linear regression — involving one dependent and one independent variable.

Multiple linear regression — involving one dependent and multiple independent variables.

  • Machine learning

The ability to learn and mimic human behavior is the basis of machine learning. These processes are carried out through a combination of artificial intelligence, algorithms and models. Machine learning is a type of artificial intelligence that uses algorithms and statistical models to analyze and detect patterns in data in order to predict outcomes.

It enables computer systems to adapt without following explicit instructions. These computers can generate models that fit the data set under analysis, even data it encounters for the first time.

Machine learning uses pattern recognition to train itself and derive patterns from large-scale observations. A typical example involves using historical data to predict whether a transaction is fraudulent or legitimate. Customer recommendation engines are also powered by machine learning to deliver the best customer experience. Image, voice, and text recognition are some of the most popular applications of machine learning.

However, the effectiveness of this method depends on the quality of the training data. Obsolete or biased data will affect the accuracy of predictions.

  • Decision trees

A common way to visualize analytical models is with a decision tree. This technique is a supervised learning method for classification and regression. A decision tree provides a tree-like model of decisions and their possible consequences. It often starts with a single node and branches out into various alternatives, which can further have multiple possibilities. In this way, it forms a tree-like shape.

It helps management decide the best course of action for any situation involving multiple variables.

Here is the five-step process for decision tree analysis in business:

  1. Determine the problem that requires decision making.
  2. Create a decision tree with possible solutions and their consequences.
  3. Enter the relevant variables and their probability rates.
  4. Determine the payoffs for each outcome.
  5. Determine and compare the expected monetary values to determine which solution is most valuable.

When an organization is considering two options to increase revenue, this model helps identify the best one. Decision trees also provide greater ease and clarity when choosing to downsize a business.

Decision trees are easy to learn and can be combined with other decision-making tools such as predictive modeling and machine learning. In these decision trees, the nodes represent the data classes being analyzed.


How do businesses determine which products to market to specific customers and on which platforms? Or how does a company set prices in advance, or determine prospective store locations? It is all through predictive analytics. This decision-making tool empowers businesses to make decisions with the highest chances of generating revenue.

The use of business analytics is now a standard for any modern enterprise, and several techniques are available. Many of these techniques are related, and they can often be combined for greater accuracy.