Location Performance Analysis

Overview: The Location Performance Analysis is a comprehensive document designed to evaluate campaign performance across different regions. By breaking down key performance indicators (KPIs) by location, this analysis provides a clear view of how various areas are contributing to overall campaign results. This allows us to make data-driven decisions on which regions to target or exclude, optimising ad spend and improving overall campaign performance.

Objective: The primary objective of the Location Performance Analysis was to create user-friendly tables with clear visual indicators, such as conditional formatting, to highlight priority KPIs for each region. The focus was particularly on conversion-related metrics, including cost-per-lead (CPL) and conversion rate, enabling a straightforward comparison of performance across different locations. This approach was intended to help identify regions with the best and worst performance, so we could refine our targeting strategy for better results.

Role & Responsibilities: This project was entirely my own initiative, and I developed it from the ground up. I was responsible for creating custom reports in Google Ads, which were regularly exported into the Google Location Report document. Within Google Sheets, I processed the data into structured tables and used pivot tables to generate insightful visual reports. I applied conditional formatting and added KPI metrics to make the analysis intuitive and actionable for users, allowing them to quickly assess performance by region.

Approach & Tools Used: I utilised Google Sheets for this project, taking advantage of its robust data manipulation and visualisation features. The workflow began with importing raw location data from Google Ads, which was then processed into a data table format. From there, I used pivot tables to create dynamic reports that displayed key metrics for each region. To enhance the readability and usability of the data, I incorporated conditional formatting and slicers, allowing for easy filtering by campaign, ad group, and other variables.

Challenges & Solutions: One of the key challenges was managing the large volume of data across multiple regions and ensuring that the analysis remained clear and actionable. To address this, I implemented slicers that allowed for quick filtering of the data by various criteria, such as campaign or ad group. This made the pivot tables more manageable and enabled a more focused analysis without overwhelming the user with too much information at once.

Results & Impact: The Location Performance Analysis proved invaluable in optimising campaign strategies. By providing clear insights into which regions were underperforming, we were able to make informed decisions about where to cut spending and where to concentrate efforts. This led to improved conversion rates and a more efficient allocation of budget, ultimately enhancing the overall performance of our campaigns. The document became a crucial tool for regularly monitoring and refining our regional targeting, driving better results for our clients.