Recently, one of our fantastic clients challenged us to think about how we’re spending their money and how lucrative different times of the day were for them. This is a big deal to them considering that the leads that our ads generate direct their customers to a call center that isn’t open 24/7. After doing some research, I found this really useful tool created by Benjamin Vigneron on SearchEngineLand that made me want to take a deeper dive into how performance varies by hour of the day and what we could do to get the most out of peak times.

The logic behind the hourly/daily bid multiplier suggestions was that based off of a historical CPA and a minimum click threshold (set or based on day of week and/or hourly performance), you could determine what you should boost your bids by… but that’s the exact problem – a historical CPA might not be the goal that you want your CPAs to be at and your spend levels aren’t determined by a CPA; They’re determined by CPCs.

Using Benjamin’s Hourly Bid Multiplier template as a framework, we broke down the formula and mixed in additional logic. Now, using the historic data, specifically the conversion rate and a CPA goal, we were able to determine a goal CPC which is then compared to the historic average CPC to obtain an hourly bid multiplier. The same method can be used to determine boosts or reductions by day of week as well.

It’s important to keep in mind that depending on how your account(s) are set up, your campaigns will act differently based on the assets in them, so filter out the campaign results as you see fit – i.e. Brand terms vs Non-brand terms by region because these core segments may have wildly varying performance and allowables with huge shifts throughout the day.

Bid Multiplier by DayPart – Results

After running this experiment for 4 weeks, we took a look at some preliminary results. Naturally, since we are applying bid boosts, we expected to see a more aggressive CPC and CPA. But check out the 18% increase in total conversions and the 16% increase in conversion rate! We were also able to rank higher on the page, with ranking increasing by 4%. The conversion data itself is unbiased as we made no changes to ad copy or landing pages while the experiment was running.

Improving Non-Brand Performance

A main goal of this experiment was to boost Non-brand performance.These bid boosts helped to increase conversion rate by a whopping 57%!

And as a bonus in such a competitive landscape we were able to increase impression share by 24% to drive even more potential quality traffic.

I invite you to check out the sheet, and let us know your thoughts! Happy dayparting!

Special thanks to Omri Levin and Lindsay Blankenship for helping me push the boundaries of this experiment!