In digital marketing, traffic is only half the story. The real goal is to align your budget with the moments your audience is most likely to take action. Through the Google Digital Marketing & eCommerce certificate, I completed a two-part project for L’Acier, a fictional online retailer. This project demonstrates how I use spreadsheet data to turn raw numbers into a profitable ad strategy.

The Challenge: High Traffic, Low Conversion

L’Acier sells restaurant-quality kitchen tools to an international customer base. While their website traffic was high, the number of new account creations—a key metric for Customer Lifetime Value—was lower than expected. My task was to analyze a month of performance data to determine the best times to run a new discount ad campaign designed to drive sign-ups.

Phase 1: Precision Filtering and Sorting

I was tasked with answering specific business questions to narrow down the performance peaks:

• Identifying Peaks: By sorting the “Conversions” column, I pinpointed the exact day and hour with the highest performance.
• Volume Analysis: I needed to find how many time blocks resulted in over 1,000 conversions. To do this efficiently, I filtered the data and used the COUNTA formula. While the exercise suggested simply counting rows, I used formulas to ensure accuracy and scalability.
• Weekend Opportunities: I filtered specifically for Saturday and Sunday and sorted by conversion rate to find the times when traffic might be lower, but the intent to buy remains high.

The Lesson: Even basic functions are powerful when you know what questions to ask. Sorting tells you the “who,” but filtering tells you the “why.”

Phase 2: Deep Dive with Pivot Tables

To see the “big picture,” I created three Pivot Tables to visualize the sum of sessions, sum of conversions, and average conversion rate. I then applied Conditional Formatting to create a heat map of the week.

Key Findings:

• The Midweek Spike: Wednesday and Thursday lead in total traffic and conversions.
• The Conversion Disconnect: While Wednesday has the most traffic, Tuesday boasts the highest average conversion rate at 14.62%. This indicates that Tuesday visitors are highly motivated to take action.
• The “Dead Zones”: Early mornings (2:00 AM – 5:00 AM) and Saturdays consistently show the lowest volume and conversion rates across all metrics.

Phase 3: Strategic Recommendations

Data is only valuable if it leads to action. Based on my analysis, I drafted a strategy for the marketing team to maximize their budget.

1. Where to “Pulse” (Increase) Ad Spend

I identified blocks with high conversion rates and at least moderate session volume:

• Tuesdays 1:00 PM – 5:59 PM (5 hours): A consistent window of high intent.
• Mondays 8:00 PM – 9:59 PM (2 hours): Strong evening performance.

2. Where to “Trim” (Decrease) Ad Spend

To avoid wasting budget, I recommended running fewer ads during high-volume/low-conversion times:

• 3:00 AM – 4:59 AM Daily: The lowest conversion rates of the day.
• Saturday Mornings: Despite the time of day, Saturday overall is the weakest day for account creation.

Reflections: Lessons Learned

Comparing my analysis to the course examples taught me a valuable lesson in marketing: be bold with the data. While my initial recommendations were conservative, the examples suggested a more aggressive increase to truly capture the afternoon and evening momentum.

I learned that even a single hour of “dead air” is a waste of money. Excluding underperforming hours from an ad schedule is just as important as finding the peaks. Although I cannot apply this specific retail dataset to my own website just yet, this project allowed me to prove that my existing Excel skills are directly transferable to driving ROI in a digital marketing role.

To read the behind-the-scenes story & the personal challenges I faced during this project, check out the full [Blog Post here].