Hopefully someone is able to shed some insight here as I'm running out of ideas. When I look at a single day's transaction count in the Ecommerce Overview section vs. the Sales Performance section, I am seeing different numbers.
Initially, I wanted to chalk it up to sampled data but both reports claim to be based on 100% of sessions. The thing I find odd is that the shield next to the report name is green (unsampled) for the Ecommerce Overview and it is yellow (sampled) for the Sales Performance.
Perhaps it is a bug in GA where the shield is yellow but claims to be based on 100% of sessions? I've attached screenshots of the two reports below.
Thanks in advance for any insight offered.
This could be due to duplicate transactions (2 transaction being sent with same ID).
One way to check for that is creating custom report with ‘transactions id‘ as a dimension and transactions as a metric and then sort by transactions metric.
Related
Our main challenge in Google Analytics at the time is to measure the success of our magazine articles.
The problem is that views grow over time so in any timeframe we always have the older articles overshadowing the newer ones. Sidenote: The same problem occurs for measuring social media post success.
My idea of a solution is to measure the rate by which views on articles grow. An article that has a higher growth of views is much more successful than an older article with more views, but with a lower growth rate.
Alternatively something like "views within the first week(s) of publishing this individual article" would also be a good metric.
Unfortunately to some extent also the growth rates rely on this publishing period of individual articles if we are interested in an eternal high score of articles. But since we are mainly interested in recent articles, growth rate would still give us the desired result of showing the most successful recent articles.
Has anyone dealt with the same challenges and found any solution to this, in best case with Google Analytics?
These examples may help, of which I have direct experience.
In the data layer we included a date of publication for the article and then used this to determine growth. This was taken from the CRM and was relatively straightforwards for the dev team. This was stored as a custom dimension in Google Analytics.
We had nothing in the data layer but instead a I just used the date on which page views started appearing as a proxy for date of publication. Not entirely reliable, and you may want to filter by views >5, or whatever is appropriate, to avoid any hits from editors or staff before a page is visible in the site navigation.
In both cases I was exporting data either to Google Sheets (using for example the Google Analytics API addon for sheets) or BigQuery, where it was relatively straight forwards to identify the first date and then calculate, for example, views per day. In your case it would be having a function which looks at the date of publication + 7 days. You may also be able to achieve this with Google Data Studio or similar dashboarding platform.
This seems really basic but i am struggling with it
We have a client who runs a travel website.
They have a few different search bars eg Flights, Hotels, Carhire.
I am trying to track the performance of each... "What % of people completed a sale that ran a Flight search." Same for Hotel, and for Car hire
Any ideas for the best way to get this info in GA?
Many thanks
There are a few ways to get this information, each with their pros and cons. The options that I see immediately available are segments and goals.
Segments are great because they are retrospective and generally more flexible, with the ability to be changed if you find your criteria isn't quite right. You create here, and specify sessions that go through search results pages etc:
Then you can create another segment for booking confirmation page, and any other intermediary steps that you'd like to report on. The main con of segments is that you can only pull in 4 at a time, but if you have more you can pull them 4 at a time and copy+paste the data into an excel sheet or google sheet. Segments can also be pulled via the Core Reporting Api and DataStudio which makes them great for automating into dashboards.
Goals are cool because they pull into the default reports, and basically track sessions through a particular page, event or sequence. The main con I see and the reason is that I don't use them is that they only start tracking fro mthe time you create them , and if you change the configuration it does not impact historical data, so your data can get messed up quickly if you don't have sandbox GA views or sandbox goals for your testing before putting it into a dedicated goal slot. You can also only have 10 or 20 goals depending on your plan, so once data is tracked against that goal you can't remove or clear it.
I own multiple affiliate websites and I am currently working on a piece of software that matches clicks and sales to visitors in GA. This allows me to see my earnings, the number of transactions and best advertisers directly in GA rather than having to sum up the different commissions from various networks.
However, sometimes a sale is rejected by an advertiser, for instance if someone returns (some of) the products sold. In these cases, I need to update the sales in GA. I currently use the normal (not enhanced) GA E-commerce plugin where I can easily submit a transaction or (partial) refund with this payload via the measurement protocol:
{v: 1, t: transaction, tid: something, cid: something, ti: xxx, tr: (-)xx, ta: advertiser}
However, every time I issue a refund, GA increases the transaction count and conversion rate, which skews my data. How can I solve this? I've had a look at the enhanced E-commerce plugin, but it seems partial refunds only work when using products and their respective prices, which is information that I simply do not have for every sale.
Thanks in advance.
You can't. Even though Google supports negative transactions via their documentation this is not a refund, this is just another transaction (which e.g. means if you select your timeframe so that is encompasses only the original or only the negative transaction your data is just as skewed as before, this works only if both transactions are within the timeframe. Also make sure your negative transaction is attributed to the same channel as your original transaction, or channel based reporting will be off).
Even EEC does not reverse transactions, it stores the refund in a separate field.
Since a proper reversal or removal of transactions would require a massive recalculation of data I do not think this will come anytime soon.
I run a monthly report which tracks session views by region, most popular knowledge articles, deflection rates, most popular product pages, software download stats, etc.
We have a new ELT member who is keen to get into the numbers around our contact centre. As I only look after the support site I need only concern myself with putting together a report which outlines what I feel will be useful information around web traffic. I want the report to be brief, and to highlight 4-5 key metrics.
Please can I have some suggestions for data you think would be useful given the target audience?
So far I am considering:
Deflection rates
Bounce rates.
Time on page
Most popular software downloads.
Global session views year to date.
Any help would be really appreciated. Thanks!
I think those metrics are great. Ideally, the value in the data comes from slicing your metrics with a dimension, ie pivoting. For example, bounce rate as an average means little whereas bounce rate by Content Group or Device Category would be more interesting.
Speaking of Device Category, consider completely isolating the metrics for Mobile vs Desktop+Tablet. Those experiences are so drastically different you'd be doing a disservice to average those metrics together.
Lastly, I'd say this new ETL member should get their own access to GA and learn how to pull the data need. GA now offers machine learning insights that quickly surface relevant drivers in metrics; a static approach to KPI reporting is becoming increasingly obsolete.
Hello I just want to ask if how long will the shopping behavior analysis updates it's data and how am i going to check if im tracking the correct amount of data. Thanks
Maybe 30mins lag for me. it might be different depending on traffic volume. If you're not seeing data, I've found there are a number of quirks in setting the tracking up, depending on your approach.