For most organizations, descriptive and predictive analytics form the core of data-driven marketing.

Descriptive analytics look at historical data to provide a high-level view of performance and trends relevant to the business.

They rely on metrics of interest like sales volumes, conversion rates, or average purchase value.

When you look at these metrics together, you get a clear overview of business performance.

For example, a product's sales performance can be described through total sales over time and average spend per consumer segment.

These insights are typically presented through visual and interactive dashboards that display information by week or month and by consumer segment characteristics.

Although insights from descriptive statistics are a basic form of analysis, AI-driven models can enhance some dashboards and the insights they display.

For example, machine learning models can be used to create smarter consumer segments, which are then tracked via descriptive statistics dashboards.

Predictive analytics rely on advanced modelling techniques like machine learning and forecasting to predict future outcomes from historical data.

Marketers typically rely on these types of methods to forecast sales, anticipate consumer behavior, or estimate churn risk and project campaign performance.

This supports planning and budget allocation, as well as identifying opportunities.

AI-driven techniques are central at this stage, powering these models and increasingly embedding them directly into marketing platforms.

Here, things like forecasts, lead scoring, and audience insights can all be generated in real time.

Prescriptive analytics represents a more advanced stage, relying on higher data maturity, stronger infrastructure, and the ability to operationalize insights quickly.

The prescriptive analytics build on both descriptive and predictive analytics to determine the best course of action.

This kind of analysis gives you recommendations on what to do next by combining predictions with business rules or optimization techniques.

In marketing, this includes use cases like personalized product recommendations, next best action suggestions, or dynamic budget optimization across channels.

AI-driven techniques play a key role by enabling continuous learning from new data, refining these recommendations, and in some cases automating decisions at scale.

In practice, AI is embedded in the tools marketers already use.

For example, e-commerce platforms, which are just product displays in real time, ad platforms that automatically reallocate budgets, and CRM systems that trigger actions such as retention offers when risk thresholds are met.

Importantly, you should always think of AI as an enabler.

It enhances your efforts, but it's not a separate type of analytics in its own right, and good quality data should always be your first priority for sound decision making.

Techniques for data analysis

About