Why Predictive Modeling is Used in Business Analytics

Predictive analytics is a computational process that uses historical and current data to identify patterns, apply predictive modeling, and accurately forecast future events.

Focused on the future, predictive modeling differs from other areas of analytics. For example, descriptive analytics is used to draw insights from past events, and diagnostic analytics is used to identify key trends and relationships among variables in a dataset.

Using a combination of statistics, mathematics, and computation, predictive modeling in business identifies industry-specific patterns and uncovers critical insights that inform various business decisions. Business analytics tools are currently used in multiple functions, including increasing sales, improving customer experiences, and increasing productivity.

Predictive analysis tools are becoming more popular in business because they provide decision-makers with highly informed projections about future events. As a result, predictive modeling is becoming the foundation on which companies seize opportunities, grow, and refine existing practices.

There are different types of predictive models, including:

  • Classification models. These models describe customers by predicting types or “classes” of outcomes based on historical data. For example, customers could be classified by their potential to churn or respond to special offers. Decision trees are an algorithm often used to build classification models that describe customer decisions, allowing them to be divided into groups based on specific variables.
  • Regression models. This type of prediction model projects a specific number, such as the lifetime value (LTV) of a customer. This is done by estimating relationships among variables.

So how does this technology work in the real world? There are many use cases.

Business Use Cases for Predictive Modeling

Predictive modeling is currently applied in a wide range of industries. Uses for this technology include refining marketing campaigns, predicting the needs of insurance customers, increasing sales in e-commerce, and enhancing subscriber retention in direct-to-consumer businesses.

In marketing departments and companies, predictive modeling reveals campaign insights based on demographics, product types, and product brands. The technology is used to analyze both first-party and third-party data. For example, predictions of consumer preferences and customers’ likelihood to engage with messaging can provide a competitive advantage. These insights also help companies increase their return on marketing investments while avoiding costly missteps.

Predictive modeling can provide exceptional value for established insurance and insurance tech companies. The technology can predict customer needs and increase the share of wallet by predicting customer behavior. Insurers can use their data to develop proactive strategies that optimize revenue and facilitate growth.

E-commerce sites can use predictive modeling to increase their conversion rate through upselling. Using raw data on sales and customer actions, a predictive model can allow sales teams to focus more on leads with the greatest purchasing likelihood. These more focused efforts lead to higher sales volume and value per transaction.

In the mobile app industry, predictive analytics is used to identify potential customers and increase profits. Predictive analytics in mobile apps analyzes user data to inform marketing campaigns and minimize user churn. Mobile app companies can keep customers engaged by providing offers and incentives based on usage patterns. This engagement results in longer subscriptions and increased in-app purchases.

Direct-to-consumer companies are increasingly turning to subscriptions to generate revenue, and predictive analytics can help retain these subscribers, optimizing LTV. Predictive modeling can use historical subscriber data to identify users most likely to churn for many different types of products and direct-to-consumer industries. These models can also optimize upselling efforts, generating more revenue from existing subscribers.

Take Your Business Operations to the Next Level with Pecan

Predictive modeling from Pecan can provide impactful, scalable business value to companies in a wide range of industries. Using the power of prediction, business outcomes can be significantly enhanced by Pecan’s proprietary algorithms within a very short time.

Schedule a demo with us today if you’d like to know more about our innovative technology.

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