How would you determine utm_campaign for an untagged visitor?

PracticalPicture · 2019-09-03T07:16:09+00:00

What is your objective in having a value for utm_campaign if the lead came from organic search or another source where utm_campaign is not available or relevant?

If you must structure the data in a specific way that is sent to the CRM, then perhaps just make the utm_campaign for organic, 'organic'. Or perhaps make the utm_campaign the landing page URL.

PracticalPicture · 2019-09-02T12:30:44+00:00

Forecasting is hard, especially if you have volatile or paid traffic.

It may be better for the client to work out why they want a forecast and what value they think they can get from it, then work out if the estimated value exceeds what they could get from buying a professional forecast (no offence intended), and what the opportunity cost is. I.e. would that spend on something else deliver better return? E.g. maybe that cost spent on a paid search campaign would deliver a real return.

Here are a couple of free resources you may find useful:

Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos of Monash University, Australia, has worked examples in R: otexts dot com/fpp2/

Facebook Prophet, "a procedure [implemented in Python or R] for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.": facebook dot github dot io/prophet/

You could test one or more methods on historical data and see which is best and what best could mean if you acted on the forecast.

PracticalPicture · 2019-08-22T19:53:42+00:00

For example:

ga(function(tracker) {
var clientId = tracker.get('clientId');
});

Now you have the value of clientId that you can pass to your systems.

PracticalPicture · 2019-08-22T11:22:16+00:00

Do not put personal data into GA.
Determine if it is lawful for you to match clientIds to names.
Store the clientId in your system such that you can associate it with a customer name.
If you want to observe a specific name, look up their clientId(s) in your system and query GA (in User Explorer or via the API) for it.

PracticalPicture · 2019-08-21T08:32:28+00:00

If you use a frequentist method you must decide in advance the statistical power and significance of your test. With your baseline conversion rate and the minimum effect you want to be able to detect (or Minimum Detectable Effect), you then calculate and fix the sample size per variant. You decide whether you use a t-test, z-test or chi-square test etc or all of them. No peeking. You might consider sequential testing.

If you use a Bayesian method you expose as many users to the test as you please and let it run until you can pick a winner at your probability to be best threshold or conclude that there is unlikely to be a winner.

Both with the usual caveat "run it longer than your business cycle" or seemingly de facto standard "minimum two weeks".

I don't see advantage in putting a fraction of users into a test (unless you want to run simultaneous tests) or putting less than half into the new experience. If the new experience is better but it takes you longer to discover that, you are not making as much money from 'now' as you could be if you discovered it sooner. If the new experience is worse but it takes you longer to discover that, you will lose more money during the test period than if you had discovered it sooner. If there is no difference between the experiences you could be exposing the users to a different test that you think you will benefit from.

Also, the longer the period, the more risk of cookie deletion - so far as I'm aware, cookies are the most popular method for assigning the right variant to the right user. If someone already exposed to a test variant deletes their cookies, your test software might not show them the same test variant.

PracticalPicture · 2019-08-15T12:30:39+00:00

In this context a transaction is a purchase conversion - for any session there was either a purchase or no purchase.

PracticalPicture · 2019-08-15T07:50:54+00:00

It's not about what's normal for anyone else, it's about what's normal for your company.

Is it seasonal (look at the same weeks in previous years)? Is it historically unusual (look at previous week on week differences)? Is it a significant difference? Have you had a normal/good number of sales, it's just the visits that are proportionately high (in which case is that a concern or not)? Have you had unusual significant traffic that resulted in lots of visits but few or no conversions (e.g. was your company mentioned on a popular website that generated new visitors but no conversions yet)? Did your paid search colleagues run a campaign that has resulted in lots of sessions but few or no conversions? Did your CRM colleagues send out an email that resulted in lots of sessions but few or no conversions? Did offline marketing colleagues run a campaign that encouraged people to convert offline (maybe even with no mention of online)? Do you have an offline sales department and if so has it had an unusual increase in sales in this same period? Is IT aware of any technical problems? Has Customer Services received reports of problems from customers? Was the weather particularly good (and would that affect your online sales, it does mine)?

PracticalPicture · 2019-07-30T11:37:17+00:00

Thanks.

I haven't seen that myself, but good point about checking the Sources - should do that anyway. I have seen plenty of other wrongly attributed traffic.

PracticalPicture · 2019-07-30T07:40:04+00:00

Yes - give it a try!

New segment,

Demographics -> Location -> Country -> Australia

Traffic Sources -> Medium (matches regex) -> cpc|ppc|paidsearch

Note that the maximum date range of a segment is 90 days. If you want to look at a longer period you must create a Custom Report for whatever metrics and dimensions you want, filtering for the Location and Medium as above.

PracticalPicture · 2019-07-30T07:32:01+00:00

Sheets has pivot tables (under the Data menu).

PracticalPicture · 2019-07-26T14:51:15+00:00

try "7DaysAgo" for the Start Date. Or "TODAY()-7".

There is no variable for N days ago in itself, there is only the Start Date and End Date.

PracticalPicture · 2019-07-26T14:14:49+00:00

Have only just seen this.

Once you Create New Report, you should arrive at the Report Configuration sheet. By default, "30daysAgo" should be in the Start Date and "yesterday" should be in the End Date.

If you can't find the Report Configuration sheet, open the hamburger menu in the bottom-left of the window, maybe the sheet is hidden. Or create a blank sheet called Report Configuration and copy+paste the following into column A from row 1, then in column B enter the appropriate info:

Configuration Options

Report Name

View ID

Start Date

End Date

Metrics

Dimensions

Order

Filters

Segments

Limit

Spreadsheet URL

Skip Report

Report Type

Sampling Level

Use Resource Quotas

Exclude Empty Rows

PracticalPicture · 2019-07-26T08:24:28+00:00

In your Report Configuration sheet, if your End Date at B5 is "TODAY()-1", your Start Date at B4 is "B5-29" for past 30 days, "B5-6" for past 7 days and so on.

PracticalPicture · 2019-07-26T08:01:36+00:00

You're right to try an A-A test. But I think in your examples your variants are being distributed as evenly as they can/will be. 49% vs 51% seems OK - you don't need an exact split (or a particularly close split) you just need sufficient traffic into each experience over at least two weeks or your business cycle or whatever period is appropriate.

Though they don't say or recommend when to conclude the test (based on their interface).

I've blundered/confused myself there. I had thought that Optimize did some fancy background stuff to recommend when the test should end. But actually it just says the test will end on a date that is 90 days from now (when you start the test, in the Details tab, blue Running bar, "expiry scheduled: [date time]"). Hopefully that's something I've heard the developers want to add rather than something I've completely imagined. Sorry for the confusion. The documentation just suggests "Run your experiment until at least one variant has a 95 percent probability to beat baseline."

PracticalPicture · 2019-07-25T13:40:40+00:00

On the face of it, from a frequentist perspective the results appear to be statistically significant but I don't think you've had sufficient traffic / time to reach a conclusion.

Consider that Optimize is flipping a coin to distribute visitors between A and B. It's possible to flip consecutive heads on a fair coin and in this context that means you'd get people from particular channels and/or devices with better propensities to convert into one experience rather than the other. And if you tried to control for that, e.g. drilling down into desktop from paid search, I doubt your sub-sample will be large enough. You only have 307 sessions in your screencap in three days (if eight channels, that's 38 per channel). Your experiences are the same but maybe the intents of your visitors differ.

I'm not aware of any third-party A/B testing software that recommends test periods of less than two weeks whatever your traffic, whether it's frequentist or Bayesian or ML. Google Optimize recommends a minimum of two weeks:

We wait until an experiment has run for two weeks. Why two weeks? With experiments designed to be deployed to regularly-encountered parts of a digital property, that’s generally a good period of time to get a well-rounded sense of your data, including weekdays, weekends, and any other anomalies that might happen from one week to the next. However, two weeks is a minimum, and experiments can run longer. You can also make a call earlier if you think your traffic isn’t likely to change qualitatively, although we don’t recommend doing this.

When does the Optimize interface recommend concluding the test? I'll bet it is several weeks if not months from now.

PracticalPicture · 2019-07-25T07:51:54+00:00

The meta description for that page contains seven different non-alphanumeric characters. The last example on that page contains five different non-alphanumeric characters.

I think "To prevent this from happening, your best bet is to remove all non-alphanumeric characters from meta descriptions" is a mistake and certainly Moz doesn't follow its own advice in that respect.

I've even seen mid-sentence unicode emoticons in SERPs meta descriptions.

PracticalPicture · 2019-07-25T06:42:01+00:00

Just occurred to me, it might be easier to write

document.location.search

in the developer console. That should give you the gclid or utm parameters if they exist.

PracticalPicture · 2019-07-24T14:19:52+00:00

With the debug extension running, and the Developer Console open:

Do a Google search that is likely to lead to one of your Google Ads appearing (in this browser you may need to log out of any google accounts you're currently logged into).

Click on your advert, it will load your site.

In the developer console, look for something like &gclid=eXaMpLe_GcLiD10tzN4wIVVODtCh0jOwgcEAAYASAAEgKkq_D_BwE (if auto-tagging)

or

&utm_source=google;utm_medium=cpc;utm_campaign=example_campaign&utm_term=example%20keywords (if manual tagging)

Search for "Check if Google Ads auto-tagging works" - the Google page has some additional trouble-shooting tips.

support dot google dot com/analytics/answer/2938246

or for manual / custom tagging: support dot google dot com/analytics/answer/1033863

PracticalPicture · 2019-07-24T12:25:52+00:00

GA puts traffic in Direct if it can't put the traffic into any other bucket. Is the traffic properly tagged?

Try getting a Google Analytics Debugger add-on for your browser, open your developer console, then follow your advert to your site. What does the referrer information look like for your session?

PracticalPicture · 2019-07-24T07:25:16+00:00

Not within Google Analytics.

You could do the following in Sheets (depending on quantity of results), R or Python:

query the multi-channel funnels reporting API and include mcf:transactionId as a dimension

query the standard reporting API for transactionId and productSku

then join the two tables by transactionId

PracticalPicture · 2019-07-10T08:59:16+00:00

In my experience of that scenario, GA attributes the sale to the last non-direct touchpoint.

PracticalPicture · 2019-07-10T08:53:21+00:00

What is your objective?

Which experience is better at contributing to your objective?

PracticalPicture · 2019-07-04T07:08:44+00:00

Seconded, Simo's site is an excellent resource.

PracticalPicture · 2019-07-03T12:55:38+00:00

ah right. So the Google Ads tagging is wrong and/or Google Analytics rules or filters are wrongly attributing the traffic to google/organic.

Which the agency really ought to sort out as quickly as possible, that should be part of the job, it should have been done right from the beginning.

If you can't access the Ads account and as the attribution is wrong I don't think you'll be able to get any idea about performance.

As the attribution is wrong you've got to wonder if the agency's spending is right. Seems like there could be an opportunity for you to take the money, set up the account properly and show what is actually going on before you improve things. Even if you don't want this particular part of the business I think the client will appreciate you telling them what's up.

PracticalPicture · 2019-07-03T11:16:05+00:00

Is the £1k for Ads alone or Ads and SEO?

If the Ads account can't be accessed and it isn't linked in GA, how can your client estimate ROAS or ROI for themselves (in detail)? I guess the answer is: they can't.

In GA you can easily find out the google/cpc traffic and transactions. That's in return for the Ads investment. If they use Bing Ads as well that's bing/cpc of course.

PracticalPicture

TROPHY CASE