3 Types of Marketing Analytics: A Comprehensive Guide

By: Published: February 24, 2023

Types of marketing analytics FI

Marketing analytics is essential to understand the marketing impact and predict marketing trends to drive sales. Various marketing analytics processes are used by businesses to improve marketing strategies and the overall performance of an organization. Marketing analytics include statistics and predictive modeling to gain valuable insights from the data and take necessary actions for their business. There are various types of marketing analytics that companies use for analyzing their marketing strategies. This article provides an overview of types of marketing analytics and their pros and cons.

Types of Marketing Analytics

Descriptive Analytics

Types of marketing analytics: descriptive analysis
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Descriptive analytics is the simplest form of marketing analytics. In descriptive analytics, businesses use historical data to identify the changes occurring in an organization over time. With historical data, decision-makers can have an overview of the company’s performance and benchmarks used for progress. As a result, they can understand the current performance and implement a new business strategy by using the data from the past. 

Descriptive analytics usually answers the questions like what is happening with the marketing efforts. It leverages two primary techniques called data aggregation and data mining. In descriptive analytics, data is collected and then segregated into chunks to obtain meaningful insights. With descriptive analytics, you can identify the number of leads generated across marketing channels, no of unique users, and more.


  • Descriptive analytics is simple to perform for any non-technical person. As a result, businesses do not need to hire technicians for descriptive analytics.
  • Unlike other analytics methods, descriptive analytics focuses on what happened in the past rather than predicting what will happen in your organization in the future. Marketers perform descriptive analytics as a first step since it sets up the base for advanced analytics.


  • Descriptive analytics is a simple analytics process that can determine fundamental insights. As a result, it provides a limited view of the marketing data for businesses.
  • Descriptive analytics doesn’t help you find correlations among variables that are essential to understand the performance of your marketing efforts. For instance, descriptive analytics cannot identify the reason behind the boost in traffic even after reducing advertisement spending. In this case, the increase in traffic could be due to brand building through digital marketing efforts over the months. 

Predictive Analytics

Types of marketing analytics: predictive analysis
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Businesses use machine learning algorithms for predictive analytics to make accurate projections based on their marketing data. Three standard techniques used in predictive analytics are decision trees, regression, and classification. With predictive analytics in marketing, you can understand the long-term impact of the marketing campaign. This allows you to optimize your efforts to obtain better results from marketing campaigns. Some of the use cases of predictive analytics also include churn prediction and sentiment analysis. For instance, you can perform sentiment analysis of customer reviews through NLP techniques. It will enable you to understand how your customers feel about your product/service and whether the customer experience is degrading or improving.

Predictive analytics is often carried out after descriptive analytics as it helps in understanding what could be the future outcome. As a result, predictive analytics is used in businesses to improve operational efficiencies and reduce risks. 


  • With predictive analytics, businesses can determine the outputs of their marketing efforts. This provides them with clarity on future business growth. 
  • Predictive analytics reduces operational risk in marketing. Every business is associated with risks while performing its day-to-day operations. But it’s crucial how companies manage such risks and achieve success. With predictive analytics, companies can identify potential risks of customer churn. Necessary steps can be implemented to avoid the risk of losing customers.


  • Although predictive analytics helps identify the marketing risks of the company in the future, the performance of predictive analytics depends on different factors that cannot always be quantified in data. As a result, the outcomes could be misleading at times. For instance, a sudden rise in traffic could be due to unusual trends like the COVID-19 lockdown. As people stay at home, people will likely engage more on digital platforms. 
  • In predictive marketing analytics, marketers mostly rely on data collected from advertisements, websites, surveys in ads, and more. Companies need to spend additional effort in setting up the infrastructure for obtaining frequent updates to ensure the accuracy of predictions. 

Prescriptive Analytics

Types of marketing analytics: prescriptive analytics
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Prescriptive analytics is used to make the best and most optimized decisions for the future of businesses. It answers questions like what you should do next if certain things happen in the future. Prescriptive analytics uses machine learning algorithms, business rules, and artificial intelligence to optimize business practices.

In predictive analytics, businesses use historical data to identify marketing trends in the future. In comparison, prescriptive analytics helps companies understand how and which trends are essential in achieving marketing goals.

For example, if your website has new visitors, then prescriptive analytics will help you make the best of it. Prescriptive analytics will allow you to offer personalized ads, deals, and more for customers to generate more revenue.

At times, you might also have fewer visitors to your website. In such cases, you can use prescriptive analytics to push promotions, increase targeted marketing, and more to bring visitors to your website.


  • The insights from the predictive analytics act as input to prescriptive analytics. Businesses can make effective decisions with prescriptive analytics, as it considers future marketing trends as well as from the past. When the company’s marketing strategies are not generating positive results, prescriptive analytics can help them work on their weaknesses.
  • It has the potential to identify better marketing opportunities through different optimizations and help companies make subsequent decisions for their marketing strategies.


  • Although prescriptive analytics allows marketers to make optimized business decisions, it requires complex data modeling and specialization in machine learning algorithms. 
  • It can include bias and may have legal and ethical concerns that can restrict the feasibility of the recommendation. 

Marketing analytics is an essential part of businesses trying to accomplish their marketing objectives. Descriptive analytics help enterprises start with their marketing analysis and identify trends in the past. At the same time, predictive analytics leverages the organization’s historical data to identify future marketing trends. Lastly, prescriptive analytics uses the past performance of predictive analytics to determine what needs to be done to achieve marketing goals. All three types of analytics allow marketers to improve their marketing campaigns and attract more customers.


In conclusion, marketing analytics is a crucial aspect of modern marketing strategies, as it provides valuable insights into consumer behavior, marketing effectiveness, and ROI. By using data-driven approaches, businesses can make informed decisions, track the success of their marketing efforts, and continually improve their marketing strategies. Investing in marketing analytics tools and techniques can result in better decision-making, increased efficiency, and improved overall marketing performance.

Marketing Analytics gives a 360-degree view of a business that governs all the business operations. It optimizes and prioritizes time and resources so that an organization can make effective decisions to boost its performance.

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Manjiri Gaikwad
Freelance Technical Content Writer, Hevo Data

Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.

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