Time & Day Performance Analysis

Overview: The Time & Day Performance Analysis was an ad-hoc project aimed at gaining deeper insights into the performance of advertising campaigns based on the time of day and day of the week. The purpose was to identify when campaigns were most and least effective according to key performance indicators (KPIs), such as clicks, conversions, and cost-per-lead (CPL). This analysis provided a granular view of campaign performance, allowing us to make data-driven decisions about optimal times for ad placements and budget allocation.

Objective: The primary objective of this project was to create a detailed performance analysis document that showcased key metrics such as cost, impressions, clicks, conversions, CPL, conversion rate, CPC, and CTR across different times of the day and days of the week. The document was designed to be highly flexible, with filtering capabilities that allowed users to view performance data by campaign, business area, and business unit. This enabled us to pinpoint the most and least effective times for ad performance, helping to optimise budget spend and improve overall campaign efficiency.

Role & Responsibilities: I was solely responsible for the creation and execution of this project, which involved importing performance data directly from Google Ads and structuring it into an organised data table within Google Sheets. I then transformed this raw data into pivot tables and interactive dashboards that provided clear and actionable insights. My role encompassed everything from initial data import and processing to the design and refinement of the final visualisation tools used by the team.

Approach & Tools Used: The project utilised Google Sheets for its robust data handling and visualisation capabilities. I began by importing raw data from Google Ads into a dedicated data tab, using formulas to calculate additional fields such as day of the week and hour of the day. This data was then fed into pivot tables, which allowed for dynamic analysis of performance metrics across different times and days. To avoid redundancy and maintain simplicity, I used slicers to filter data within the pivot tables rather than creating multiple tabs for each campaign, making the analysis more user-friendly and efficient.

Challenges & Solutions: One of the main challenges was managing the extensive data set, which cross-referenced hourly and daily metrics for each campaign. This resulted in large tables that could become unwieldy and difficult to navigate. To address this, I implemented slicers, which allowed users to filter the data by specific criteria, such as campaign or business unit, directly within the pivot tables. This approach streamlined the analysis process, reducing the need for multiple repetitive tabs and making the document easier to use.

Results & Impact: The implementation of the Time & Day Performance Analysis had a significant impact on campaign optimisation efforts. By identifying the specific times and days when campaigns were underperforming, we were able to exclude less efficient hours and reallocate budgets to more promising time slots. This resulted in better allocation of resources and improved overall performance, as we were able to focus our efforts on the most productive periods for ad delivery. The insights gained from this analysis helped enhance the efficiency of our campaigns, maximising conversions and improving cost-effectiveness.