Predictive Analytics

Predictive analytics refers to the use of data and statistics to predict future outcomes. It can depend on various techniques to create predictions, such as artificial intelligence, machine learning, and data mining. It involves looking at historical data and identifying patterns that emerge over time. This allows businesses to manage their resources, reduce risk, develop marketing strategies, and forecast growth.

Predictive Analytics at Work

Predictive analytics relies on different methods and techniques, including artificial intelligence, machine learning, data mining, and statistical modeling. To create predictions of future outcomes and identify trends that can develop at a particular time, predictive analytics takes into account both current and historical data.

Brands and retailers can spot patterns and identify connections, managing risks and maximizing positive results. Depending on the model utilized, predictive analytics can also establish relationships between certain behaviors and results. The outcomes can then guide the decision-making of businesses, from the supply chain to price optimization.

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Predictive Analytical Models

There are various predictive analytical models, but the following three techniques are the most commonly used:

  • Decision Trees help understand the motivations behind a decision. This model distributes data into different sections, just as fruits are spread on various parts of a tree. The branches and the leaves represent choices and decisions, respectively. Decision trees are preferred because they are simple to make and easy to follow.
  • Regression is preferable if you want to identify patterns and determine relationships in large sets of data. It investigates the nature of the link between a dependent and independent variable. Different models fall under regression techniques, among them linear regression, logistic regression, and probit regression.
  • Neural networks are akin to the way the brain works, as they can deal with a large set of data and complex relationships. Similar to regression techniques, neural networks are preferred for their ability to manage big data. They are also favored for creating predictions rather than explanations, and when no formula encapsulates the connection between the different variables.

Importance to eCommerce

There are many benefits to using predictive analytics in online retail. Among them are the following:

  • Brands and retailers can have a better understanding of customer interactions and preferences. This allows them to think ahead and provide just the right products that customers will be looking for based on previous behavior.
  • Predictive analytics can guide businesses in setting optimum prices for their products. They can gain insight into just how much shoppers are willing to pay for the items they offer and the pricing set by the competition.
  • Predictive analytics can enhance personalization, product recommendations, and promotions for brands and retailers.
  • The different models for predictive analytics can help determine the popularity of brand products and consequently manage demand and supply.

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