Tips To Ensure The Validity Of Your Day Parting

One of the most classic and beneficial analyses conducted for paid traffic acquisition channels is day parting. Not all visits are equal, and the time of day/day of week of the visit usually has a strong correlation with user qualification and likelihood to convert.

That said, day parting is one of the most commonly misconstrued analyses as it’s very easy to manipulate and misread the results when segmenting data into very small buckets such as hour of week.

This post is intended to walk through some basic recommendations for ensuring the validity of a day part analysis, showcasing some classic misinterpretations, and finally taking a spin on simple day parting to identify real pockets of opportunity.

As with any analysis, the single, most important consideration is volume of data. Without enough data, it’s almost certain that your analysis will be misleading.

Consider the situation where your average conversion rate is 1%. At 10:00 am on Sundays, you observe 10 visits and 1 conversion (a 10% conversion rate). Is it safe to conclude that visits at 10:00 am on Sunday are worth more than the average visit? Probably not.

With an average conversion rate of 1%, you would need at least 350 visits to conclude with 95% confidence that visits at 10:00 AM on Sunday have a 10% conversion rate. While directionally, we may believe 10:00 am on Sunday shows promise, we need more volume to draw any reasonable conclusion.

The amount of data available is not the only means for manipulating results. Even if you have enough data, what data should you take into account? Consider an advertiser managing to a CPA goal trying to maximize conversions. First, let’s look at conversion rate:



The color formatting highlights that Sunday, Wednesday, and Friday AM, as well as all day Tuesday, drive the most efficient visits. But this doesn’t tell the whole story because these visits aren’t necessarily free. Let’s take cost into account and look at CPA:

CPA Day Parts

In this chart, Thursday and Friday drive the most efficient visits. Because CPA considers cost and conversions, we take cost per visit into account. Tuesdays, despite having very high conversion rates, also have high CPCs — making those visits, albeit qualified, less profitable than the cheaper visits on Thursday and Friday.

Making Day Parting Work For Your Business

Your goals for today are not necessarily the same as your goals for tomorrow. Your business might be in a period of growth where the primary concern is driving conversions without as much weight on efficiency. Or maybe you just launched a new product and you just need traffic.

Changing the metric used for day parting can quickly open up opportunity for more qualified volume (conversion rate), more efficient volume (CPA or ROI), or just more volume (CTR) depending on the key performance indicator your business is interested in pursuing.

If single KPI day parting is already in place, consider taking day parting to the next level by creating composite metrics considering multiple KPIs, such as average position and ROI/CPA for paid search or impressions and ROI/CPA for display.

Such a multivariate analysis requires weighted metrics, so determining the proper coefficients is tricky — but if you operate an enterprise-scale paid marketing program, any edge can make or break your quarter.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: Analytics | Search & Analytics


About The Author: is the Vice President of Performance Marketing and Analytics at SellPoints and is based in the San Francisco Bay Area.

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  • John Jones

    Nice introduction to the problem Benny. One suggestion that helps improve the data quality is to calculate User Hour of Week. In the US we have 6-7 time zones and log files record Server Hour of Week. Many (IP address, time zone) databases are available. More subtle is a second problem of conditional dependencies. For example sex and age have a big impact on hour of week conversion rates. And lastly, grouping hours of the week into segments is a good idea. Consider grouping the segments so that your conversion rate changes are greatest across segments while keeping the number of segments small.


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