This page brings together client feedback already published by Pixel Pulse Media and a reporting demonstration. Client statements remain attributed to the people who made them. The demonstration uses sample data.
Eduvoyant: paid advertising and social media
The published scope was paid advertising and social media management. Eduvoyant’s managing director described the result in this testimonial:
Paid advertising and social media management helped us grow faster and increased our revenue by 20%.
Sameera Malawige, Managing Director, Eduvoyant
The 20% figure is the client’s reported revenue change. A measurement period, baseline revenue, media spend and attribution breakdown are not published with the testimonial. It should not be read as an independently verified experiment, a ROAS figure or a forecast for another business.
Related services: Google Ads management and Meta Ads management.
SULECO: client feedback on awareness and growth
They helped us a lot with awareness and growth.
Rajitha Jayasinghe, Managing Director, SULECO
This is qualitative feedback. No percentage improvement, campaign period or financial result is attached to it. It describes the client’s experience without implying a quantified outcome that has not been published.
See the reporting approach
The interactive dashboard demonstration uses 90 days of demonstration data. It shows how spend, conversions, acquisition cost and return on ad spend can be presented together. It is not a live client account.
A real report starts by defining what counts as a conversion and where its value comes from. Platform figures should be checked against actual enquiries or orders before the dashboard is used to set budgets. Data freshness depends on the source and connector.
Read how to connect Google Ads to Looker Studio or view the reporting service.
What a useful case study should show
A detailed performance case study needs the starting position, date range, agreed goal, spend, changes made, measurement source and commercial result. It also needs client permission to publish those details. Those records help distinguish improvement after a change from improvement caused by it.
For a clearly labelled example of that reasoning, read the AI visibility audit worked example. To discuss the evidence relevant to your own project, get in touch.