What are the key differences between descriptive, predictive, and prescriptive analytics in marketing?

1 Descriptive analytics

Descriptive analytics is the most basic and common type of marketing analytics. It answers the question: what happened? Descriptive analytics summarizes and visualizes past data to show the trends, patterns, and performance of marketing activities and campaigns. For example, descriptive analytics can tell you how many visitors came to your website, how many leads you generated, how many conversions you achieved, and how much revenue you generated. Descriptive analytics is useful for reporting and monitoring your marketing goals and KPIs, as well as for identifying strengths and weaknesses in your marketing performance. However, descriptive analytics does not explain why something happened or what will happen next.

2 Predictive analytics

Predictive analytics is the next level of marketing analytics. It answers the question: what will happen? Predictive analytics uses statistical models and machine learning algorithms to analyze past and present data and forecast future outcomes and scenarios. For example, predictive analytics can tell you how likely a lead is to convert, how much revenue a customer will generate, how a marketing campaign will perform, and what factors influence customer behavior and satisfaction. Predictive analytics is useful for planning and optimizing your marketing strategy and tactics, as well as for anticipating and mitigating risks and opportunities. However, predictive analytics does not prescribe what actions to take or how to achieve your marketing objectives.

3 Prescriptive analytics

Prescriptive analytics is the most advanced and complex type of marketing analytics. It answers the question: what should we do? Prescriptive analytics uses optimization techniques and simulation models to analyze data and recommend the best course of action for a specific goal or situation. For example, prescriptive analytics can tell you what marketing channels, content, offers, and messages to use, how to allocate your budget and resources, how to personalize your customer experience, and how to improve your customer retention and loyalty. Prescriptive analytics is useful for executing and improving your marketing decisions and actions, as well as for maximizing your marketing ROI and value. However, prescriptive analytics requires a lot of data, expertise, and technology to implement and maintain.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?