Correctly evaluating Google Analytics Traffic by hour and day for conversion optimization

Google Analytics provide more than dozen dimensions to evaluate website traffic by time:

Majority of these dimensions are useful for analysis only when your target market is confined to one timezone and your timezone matches with your users’ timezone.

In other words, you and your target audience live in the same timezone.

This is because Google Analytics report on ‘time’ in the ‘timezone’ configured for your GA property.

It does not report on your users’ local time:

Let us suppose you are a retailer from ‘New York’ but you sell all over US.

Since you are from ‘New York’, most likely the timezone configured in your GA property is EST (Eastern Time Zone):


Now if your customer (say from US west coast) made a purchase at 11 pm PST but the timezone configured in your GA property is EST then Google will report that your customer made a purchase on your website the next day at 2 am (there is 3 hours time difference between PST and EST time zones).

US alone has got 9 standard timezones and corresponding daylight saving time.

And if you also happen to sell in other countries then you have even more timezones to consider for your analysis and time sensitive campaigns (like email newsletters).

So if you rely on the ‘time’ reported by Google Analytics, you will not get accurate picture of your customers purchase journey.

You will most likely optimize all of your time sensitive campaigns in your local time and lose money.

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Majority of your customers can not make a purchase on your website in the ‘afternoon’, if they live in different timezones.

Your afternoon is not necessarily there afternoon. When we talk about ‘afternoon’, we should talk about the ‘afternoon’ in a particular timezone.

Only then it makes sense.

For example ‘EST afternoon’ or ‘GMT afternoon’.


Majority of your customers can not make a purchase on ‘Wednesday’, if they live in different timezones.

Your ‘Wednesday’ is not necessarily there ‘Wednesday’.

It could be their ‘Tuesday’ or ‘Thursday’.


Majority of your customers can not make a purchase around 12pm, if they live in different timezones.

Your ‘12 pm’ is not necessarily there ‘12pm’.

It could be any other time, depending upon the timezone they live in.

When we refer to a particular hour, we add a timezone to it.

So there is no 12 pm.

You need to be specific.

Is it 12 pm EST, 12 pm PST, 12 pm CST or 12 pm GMT?


It is not just Google Analytics but almost all analytics tools (Facebook insights, twitter analytics, followerwonk, GetResponse etc) report users engagement and activity in your local time.

So if you do not take into account your users’ local time, you may end up optimizing your time sensitive campaigns for wrong day and time and could lose lot of sales and conversions.

6 data drilldowns to optimize time sensitive campaigns for conversions and sales

Following are the 6 data drilldowns for optimizing time sensitive campaigns for conversions:

  1. Traffic Source and medium
  2. Country
  3. Users’ local timezone
  4. Users’ local time of the day (morning, afternoon, evening or night)
  5. Users’ local hour of website visit
  6. Exact Users’ local time (down to minutes and seconds)

You can create this data drilldowns in Google Analytics by creating a custom report with following dimensions drilldown:

and then using another custom dimension (in my case ‘Visit Date and Time’) as secondary dimension:

#1 Time Zone – this custom dimension report on ‘users’ local timezone’

#2 Time of the Day – this custom dimension report on ‘Users’ local time of the day’ (morning, afternoon, evening or night)

#3 Visit Hour – this custom dimension report on Users’ local hour of website visit

#4 Visit Date and Time – this custom dimension report on exact users’ local time (down to minutes and seconds)

To learn more about creating these custom dimensions, read this article: How to correctly measure Conversion Date & Time in Google Analytics

Once you have created these custom dimensions, download and use the custom report for creating 6 data drilldowns from here

Finding optimal time for sending out email newsletters via Google Analytics

Newsletter campaigns are time sensitive campaigns.

In order to find out the optimal time for sending out your email newsletters, you first need to create different cohorts (group of users who showed common characteristics, attributes or experience in a particular time frame).

Once you have created different cohorts then target them individually depending upon the time of day they are most likely to visit your website and convert (via your email campaign).

What that means, you may have to choose different day and time for sending out newsletters to cohort from a particular timezone.

You never send out newsletters to all cohorts (from different timezones) at the same time (your local time).

That result in poor open rate, poor CTR, low conversions and sales.

In order to find the optimal time for sending out email newsletters to a particular cohort, you first need to know their timezone and their local time, when they are most likely to visit your website and convert.

Let me give you an example.

Say I want to know, when my newsletter subscribers from the US (located in EST timezone) are most likely to visit my website via my email campaigns.

I can do the following data drill down via my custom report:

So if I have to send out newsletter to the cohort located in the EST timezone then the best time is their morning (or my afternoon) between 10 and 11 am EST (or 3-4 pm GMT my local time).

This is the kind of insight you can get by correctly evaluating your website traffic by timezone, hour and day for conversion optimization.

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Himanshu Sharma

Certified web analyst and founder of

My name is Himanshu Sharma and I help businesses find and fix their Google Analytics and conversion issues. If you have any questions or comments please contact me.

  • Over twelve years' experience in SEO, PPC and web analytics
  • Google Analytics certified
  • Google AdWords certified
  • Nominated for Digital Analytics Association Award for Excellence
  • Bachelors degree in Internet Science
  • Founder of and

I am also the author of four books:

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