How Data Helps Optimize Campaigns — Five Model Scenarios Illustrating How to Use Data in Practice
Data is the starting point, but it is the decisions we make based on them that lead to real change. This part of the series shows how educational institutions — from universities to e-learning startups — use data to optimize their marketing activities. Instead of theory: five concrete cases.
Marketing data has a real impact on decisions — but only when it is well-collected, interpreted, and placed in the context of a strategy.
1. Variable Effectiveness of Recruitment Campaigns – The Problem with Attribution of Sources
Context: A public university ran a recruitment campaign for postgraduate studies. The main channels included Google Ads (search and display), Facebook Ads, influencers on YouTube, newsletters, and emails to their own database. Initially, most of the budget was allocated to Google Ads.
Problem:
In the “position-based” attribution model:
40% of the credit is given to the first contact with the brand (e.g., clicking on a Google ad),
40% to the last contact (e.g., clicking a link in an email),
20% is distributed among all intermediate interactions.
In practice: The user first saw the Google ad and clicked — this was the first contact. Then, they returned multiple times via different channels, until they ultimately clicked on the email link and signed up — this was the last contact.
Thanks to this model change, the team realized that Google Ads was not ineffective, but it wasn’t valued properly in the analysis because it served as the first step in the decision-making process. If we only looked at the “last click,” it would seem that the email “did all the work.” In reality, however, the Google ad initiated the whole process.
After three weeks of the campaign, the conversion rate from Google Ads seemed low, which led to pressure to cut the budget. However, data from Google Analytics showed that many users returned after a period of time — usually after opening the email or directly entering via a bookmark.
Action: The team implemented a multi-channel conversion path analysis and changed the attribution model from “last click” to “position-based.” It turned out that Google Ads played a key role in initiating conversions.
Conclusion: The data indicated that reducing the budget could weaken the entire campaign. Analyzing the user journey changed the approach to evaluating effectiveness and allowed for better allocation of resources.
2. High Bounce Rate – Errors in Landing Page Content
Context: A B2C educational platform promoted a new online course in psychology. The campaign directed users to a dedicated landing page with a sign-up form.
Problem: Google Analytics and Hotjar showed a very high bounce rate — over 80%. Despite good traffic (mainly from Meta Ads), conversions were minimal.
Action: The team analyzed heatmaps (showing which elements of the page attracted the most attention — e.g., where users click, where they hover, how they scroll) and session recordings (allowing the observation of actual user behavior — whether they try to click something non-clickable, where they stop, where they drop off) to understand how users interact with the course sign-up page.
It turned out that key information (course dates, format, certification) was hidden under a dropdown. Users couldn’t see important details before clicking “Sign up.”
Conclusion: A minor error in the content structure of the page had a huge impact on the effectiveness of the entire campaign. After redesigning the landing page, the conversion rate increased from 0.7% to 3.2%.
3. Demographic Differences in Conversion – Segmentation Changes Communication
Context: An institution offering online courses in law and management targeted ads at two groups: ages 25–35 and 45–60+. The ads were the same for both segments.
Problem: Data from Meta showed that younger users clicked more often but didn’t sign up. Older users generated fewer clicks but had a much higher sign-up rate.
Action: The campaign was segmented, and separate versions of the message were created: for the younger group — dynamic formats and social proof; for the older group — emphasizing the practical value of the course and certification.
Conclusion: Without segmenting the messages, it is impossible to realize the full potential of the campaign. Demographic data allows for the creation of more targeted messages for different audiences.
4. Warning Signals in the Funnel – Abandonment at the Payment Stage
Context: An educational platform sells subscriptions to technical courses. Users go through a classic funnel: course page → cart → payment.
Problem: Data from Google Tag Manager showed that 42% of users abandoned the process at the last step — after adding the course to the cart.
Action: UX analysis and A/B tests revealed that the payment form had too many fields (e.g., physical address, despite the product being digital). After simplifying the process and adding Google Pay/Apple Pay, the abandonment rate dropped to 18%.
Conclusion: Data helped identify a “bottleneck” in the conversion funnel that wasn’t visible before. Even small UX improvements can have a significant impact on revenue.
5. Low Retention of Participants – The Need for Engagement Analysis
Context: Online courses lasting several months (e.g., programming, data analytics) have high sales conversion but very low attendance in later modules.
Problem: Data from the LMS system showed that 50% of participants stopped logging in after the first month. The problem was especially prevalent among users who signed up through heavily promoted price offers.
Action: The team created user segments based on the sign-up source, login timing, and activity. Additional onboarding emails with reminders were introduced, along with in-app alerts and webinars to maintain engagement.
Conclusion: Simply purchasing the course isn’t enough. Data from analytical tools can help predict participant dropout and implement mechanisms to sustain motivation.
Data without action is just numbers. The key to effectiveness is interpretation, implementing changes, and continuous testing. In education — where user decisions are complex and the purchasing cycle is long — an analytical approach yields particularly high returns.
It should be added here that data is just the beginning. The key to success is combining analysis with the ability to understand users and build relationships. Once we know what interests them, the next step is to create space for authentic dialogue. How can we do this in practice? I will explain this in the next part, where we will dive into the topic that forms the foundation of effective content marketing in education – communication. Find out why it is crucial in building valuable relationships with your audience and how you can use it to strengthen your educational strategy.