Is Predictive Analytics Artificial Intelligence?

Analyzing data is as essential to modern business as making money, and technology increasingly allows companies to extract more value from data analytics.

Two of the more powerful data tools at a business’s disposal are predictive analytics and artificial intelligence. Let’s explore how these are both similar and different.

What is Predictive Analytics?

Based on complex mathematics, predictive analytics models operate on collected data to generate predictions for potential events and measure unknown characteristics. Put very simply, predictive analytics uses known information to make projections that inform business decisions.

Artificial intelligence also uses collected data to generate new information. However, AI may use a variety of other methods to analyze data, build models, and refine its own prediction methods.

As a sub-domain of artificial intelligence, predictive analytics platforms are focused on projecting outcomes and supporting business decisions, whereas the broader scope of artificial intelligence also encompasses a set of techniques meant to resemble how our brain learns from experiences.

Predictive Analytics in Business

Predictive analytics is particularly well-suited to a variety of business applications. Companies are becoming more efficient and more innovative by making use of this powerful technology.

One specific area ripe for improvement is the executive decision-making process. When a direct-to-consumer company has a large amount of customer data, for example, it can better identify purchasing patterns and adjust production accordingly.

Predictive analytics software can also be leveraged to boost efficiency. Based on customer, marketing campaign, and point-of-sale data, predictive analytics can be used in consumer packaged goods businesses to create personalized offers and optimize omnichannel marketing strategies.

Companies are best positioned to get the most out of their predictive analytics tools when they have clear goals and objectives in mind, including specific ways they intend to use the predictions they receive from their data. For example, a subscription customer predicted to have a high likelihood of churning could receive a promotional offer. Internal stakeholders should also be strongly aligned around these objectives.

Businesses that are using predictive analytics to make a difference understand that all data is not created equal. A key factor to getting the most out of the technology is determining which data is relevant and which data is not. With good data collection and powerful analytic tools, though, the options are virtually limitless, regardless of industry.

Industry Use Cases for Predictive Analytics

Predictive analytics tools may seem like the exclusive domain of academia and the highest levels of the technology industry. However, organizations across many different industries are increasingly using this technology to improve their operations.

  • In the retail industry, companies are using predictive analytics to nudge customers toward making a purchase. Customers’ purchase history can be analyzed and used to offer them discounts or position products strategically in an online store. The technology can also be used to optimize inventory and mitigate supply chain issues.
  • In the mobile gaming industry, predictive analytics is used to project how potential players will act, from the time they install the app to their reasoning for permanently leaving the app. These predictive models are used to reduce player churn and optimize lifetime value.
  • In the direct-to-consumer industry, businesses are using predictive analytics to minimize subscriber churn and optimize customer lifetime value. Data from marketing campaigns, distribution, promotions, and other activities can be used to detect potential churn, allowing companies to apply solutions like offering promotional deals or alternative subscription packages.
  • In the insurance industry, companies have used predictive analytics to more effectively cross-sell insurance products and optimize customer service. Using existing data on customers and insurance claims, companies can more effectively target existing customers for cross-selling and staff service teams.

Organizations that have strong technology leadership and good structure in place are well-positioned to get the most out of their investment, regardless of industry. That begs the question — how can you leverage predictive analytics?

Join the Predictive Analytics Revolution with Technology from Pecan

Predictive analytics is quickly shifting from cutting-edge to standard technology in many industries. Companies that don’t move now to leverage predictive analytics risk falling behind the curve.

At Pecan, our team is well-versed in helping companies adopt and extract the most value from our advanced analytics tools, including for eCommerce and mobile applications. Contact us today to learn more about how we can support your company’s continued success.

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