Marketing Analytics Foundations


Marketing analytics is the tracking and analysis of data derived from marketing efforts, aimed at achieving quantitative goals. Through this process, units gain insights to enhance marketing channels, boost the ROI of marketing initiatives and shape future strategies. Utilizing technology and analytical methods on marketing data, units understand audience behaviors, resource allocation and make business decisions to help your unit understand an audience’s priorities.

Target Audience

  • Marketing and communications staff who work in digital media.


The following are the most common types of analytics and what they help you understand, with examples.

Descriptive Analytics: Painting a Picture of the Past

Focuses on summarizing historical data to understand what has happened in the past.

  • What: How many students applied last year? Which events had the highest attendance? What’s the average open rate for alumni emails?
  • Who: Which demographics are most engaged with social media? Where are prospective students coming from geographically? Which programs attract the most interest?

Diagnostic Analytics: Unraveling the “Why”

Delves deeper into data to identify why certain events occurred.

  • Why: Investigating a decline in website traffic to determine if it’s related to a specific campaign, website update, or external factors. Why did application numbers dip in a specific region?

Predictive Analytics: Looking to the future

Uses historical data and statistical algorithms to forecast future outcomes.

  • Predicting potential enrollment numbers for the upcoming semester based on historical enrollment data, application trends, and other relevant factors.
  • If your website data shows increased audience visits to your website during the fall to sign up for your email newsletter, you can work ahead on a fall newsletter to address the new signups and share information with them.

Prescriptive Analytics: Building successful future outcomes

Provides recommendations on possible actions to optimize outcomes.

  • Recommending personalized communication strategies for different segments of prospective students based on predictive analytics, aiming to increase enrollment.
  • If your website data analysis shows you that students navigate four pages deep to sign up to get more information or attend an event, you can shorten their user journey and offer them a shortcut to the information on your home page.

Marketing analytics phases:

  1. Collecting data
  2. Analyzing data
  3. Using data to make decisions.

Collecting data – a wide variety of digital channels collect data on your behalf. You can review your:

Analyzing data – to analyze data, you need to establish business goals for your unit. Your digital marketing channels serve as the front page of your unit for most of your audiences. What do you want to know about them? Some examples are:

Using data to make decisions – Data-driven decisions empower you to optimize your marketing efforts. Some examples:

  • Low website conversion rates? Analyze user behavior to identify bottlenecks and redesign the page for a smoother experience.
  • Underperforming email campaigns? Test different subject lines, content formats, and sending times to improve open and click-through rates.
  • Unsure where to invest marketing budget? Compare the ROI of different channels based on data insights to allocate resources effectively.

Remember to contextualize your data by considering external factors (e.g., seasonality, industry trends) when making decisions. Visualizations such as charts and graphs can help communicate insights clearly and persuasively to stakeholders.

Tip and Tricks

  • Data should be collected for at least 4-6 weeks before being deemed as a good source of information.
  • Data is context dependent, be mindful of adding context to make sense of the data. For example, your web traffic might be lower than usual, but it might be summer or over winter break. An ad channel might have fewer impressions but higher clicks if the audience is tightly knit and specific. Always present data with context to help make sure that decision makers are aware of the “why.”
  • Setting Key Performance Indicators (KPIs) will help you evaluate your data consistently and point out any changes in the data.

Additional Resources


Rashmi Tenneti, Director of Analytics and Alignment,
Maggie Evenson, Analytics Specialist,