Exploring the Power of Predictive Analytics in AI

In this four-part series, we're exploring four categories of artificial intelligence (AI) and how they can meaningfully impact marketers and their customers. In this second article, we'll look at predictive analytics — tools using data such as user behaviour (aggregated and per-customer basis) and other factors to provide marketers with predictions of future behaviour and other trends.

Predictive analytics builds on the wealth of data companies have on their customers' behaviour and actions, and other trends and information that might be available to them. It is AI that makes predictions about future outcomes using historical data combined with statistical modelling, machine learning and other forms of analytical tools. While generative AI tools are getting the most press these days, as many as 95% of companies are currently incorporating some form of predictive analytics into their marketing.

Predictive analytics has a variety of applications, including segmenting customers through machine learning, prioritising leads, calculating churn or at-risk customers, determining the propensity of a customer to convert, and calculating the optimal advertising spend. This information can be used to entice an individual to stay or to determine if they're worth a large investment of advertising dollars to convert. Predictive analytics is a powerful tool for marketers to help make better decisions, target the best potential customers and be more efficient in their use of marketing and advertising dollars.

There are a few reasons to pay particular attention to predictive analytics when considering further adoption of AI in your marketing approach. It can help identify customer trends, find new and valuable audience segments, determine when customers are most likely to purchase, uncover other opportunities that can translate into a tangible return, optimise advertising spend, and even prevent undesirable outcomes. Predictive analytics can also be used in combination with generative AI to identify timely marketing opportunities and create content to meet the moment.

However, it's important to note that while predictive analytics is an exciting area of AI, humans are still needed in a strategic role. People must be the curators and interpreters of AI predictions and be careful to substantiate why decisions are made. Additionally, bias can creep into the system, so it's essential to be able to see how predictions are being made.

In conclusion, predictive analytics is an area of AI that has been around long enough to mature in several areas. It can provide marketers with powerful tools to make better decisions, target the best potential customers and be more efficient in their use of marketing and advertising dollars. However, people must still be in a strategic role and vigilance should be maintained to ensure AI models are making predictions without introducing bias.

Originally reported by Martech: https://martech.org/4-ai-categories-impacting-marketing-predictive-analytics/
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