I recently set up some alerts in Adobe Analytics Workspace and what I learned is that you can monitor a lot without much effort. The low hanging fruit is to track all your events in one alert. The set up is so fast. You just open a new alert and drag in all your metrics that you want to track. This includes the instances of evars, which will at least tell you that your evars are firing as often as they have in the past.
Now, it won’t the values being passed in your evars or props. You will need to set up more complicated alerts for that. But you can get really far just using metrics.

What I did was set up one for my commerce site and then applied segments for the different regions, since each region has their own code base. Then I set up one for my content sites and applied relevant segments there as well. The benefit of adding segments is that the calculations will occur within those respective data sets, giving you even more confidence in your data.

I chose the “anomaly exceeds 95%” test. This will check to see if your metric counts are outside the normal range for the lookback window (which is dependent on your granularity). You can select the confidence interval you would like. If you have a few metrics that are super important you may want to select those ranges to be tighter.

I’ve had these set up for about a week and it’s working well. When I get an alert email, there’s enough information in the email itself that I can decide whether I want to drill deeper. If I do, I can click straight into the Workspace alert results and see the data in graphical form.

There’s so much more you can do with Adobe Alerts, but this will get you off to a running start with minimal efforts.

Apps data isn’t always realtime. If you enable offline app tracking, you will inevitably get late hits, which means yesterday’s data isn’t yet complete. But how long should you wait to assume that data is “complete” enough? In other words, you want to know a distribution of when late hits come in.

A quick primer on how offline hits work. If a user is offline using an app, the app will store the hit data in memory until the user comes back online. Often that is in a subsequent session. But is that 2 days, 1 week or longer? It depends on the type of app you have. Apps with high repeat frequency will see late hits come in sooner than apps with lower use. It also depends on how much functionality a user gets while being offline. Users can use a fitness or game app offline a lot more readily than an ecommerce or news app.

One way to track this curve is to pick one day to track, let’s say September 1. Then each day after and including September 1, you need to look at your traffic numbers until you feel the traffic has “settled out”. Then assume that September 1 is representative of all your days.

Or you can look at Adobe raw data to measure the time between two key time stamps: 1) the time the hit was collected by the app (while online or offline) and 2) the time the hit arrived at Adobe’s servers (note, only when the app was online).

In order to pull Adobe raw data, you have to be an Admin and you will see the “Data Feeds” link under the Admin menu. I won’t go into details here but a key thing to note is that if you want to open the data in Excel, you will want to try and keep the data under 15MB.

Here’s what you want to pull in:

1. post_cust_hit_time_gmt (the time the device collected the hit)
2. hit_time_gmt (this is the time Adobe received the hit)

Then convert the timestamps from unix to Excel using this formula:

= (((COLUMN_ID_HERE/60)/60)/24)+DATE(1970,1,1)

Then use this formula to subtract the two:

=DATEDIF(column1,column2,"d")

and get the number of days lapsed. Make sure you put the earlier of the two dates in the column1 place and the later date in column2.

Now you can create a pretty Excel chart that shows you the distribution of hit latency.

One thing that can be difficult to detect is whether your campaign tracking codes are “falling off”. There are a variety of reasons they can break or fall off: in the query string syntax or publishing process, or if your IT team adds a redirect to the landing page URL.

One way to detect if your campaign codes might be falling off is to look at the % of your traffic that comes from the Typed/Bookmark bucket (aka, "No Referral" Traffic). Here’s my theory.

You can’t artificially deflate Typed/Bookmark traffic, but it can be artificially inflated when campaign codes fall off. You should be able to look at a historical trend of the % of your traffic that is from Typed/Bookmark. As a % of total traffic, it shouldn’t increase drastically. If it there’s a big PR or social media campaigns, referrals from PR and social media sites should still rise more than Typed/Bookmark.

Note, I’m assuming that all PR events nowadays generate referral traffic from news/social media sites.

How often, anymore, do you have a purely offline PR event that drives online traffic directly to your site. If you are using any social media listening tools, you should be able to detect this PR somewhere. Therefore, you can reasonable assume that only paid campaigns with a query string can artificially inflate your Typed/Bookmark as a % of total traffic. (Again, normalizing it against total traffic is key to this theory).

So, my conclusion is this: Typed/Bookmark traffic should not spike as a % of total traffic. If it does, then I would start looking into whether your campaign codes are dropping off somewhere. Test your campaign landing pages and make sure they don’t redirect. Check with your marketing team to see examples of campaigns “in the wild” and test the campaign links to make sure they are working correctly.

This is a great approach when you need to look for many values of the same dimensions in a quick-and-dirty sort of a way. For example, if I wanted to segment for visits that saw a specific set of 300 products and all I have is the list of products. Just concatenate all of these values together, delimited by a space (Link 1), and use that as the value with the "contains any of" criteria (Link 2).

Keep in mind that this uses a space as a delimiter so if the values you are wanting to look for have spances then see next note.

This is good for a very temporary but short list. If this is something you are doing regularly or you are getting into thousands of values it really is better to coordinate with your AA admin to set up a classification.

RELATED: Link 1 · Link 2

Another easy-but-so-good-it-makes-me-queasy segment (was that a stretch? nah!) is to look for visits that performed some key action on your site that you have tracked through your event variables. All you do is add the segment to your definition and change the criteria to "exists". This will then include any visit that triggered 1 or more of these events. See Link 1 for details.

RELATED: Link 1

Do you want to segment for something simple such as visits that saw the home page? Link 1 shows you how it's done. No problem, right? Well don't get too comfortable. Doing the inverse (visits that didn't see the home page) causes problems all the time. Whatever you do, don't use the "does not equal" criteria in this sort of scenario (Link 2). The reason for this is that as soon as the user goes to another page they will see a page that isn't the home page. This will result in their whole visit being included when you really wanted it excluded. The safest way to do this is using a positive criteria within a exclusion container (Link 3)

RELATED: Link 1 · Link 2 · Link 3

In Adobe Analytics, you can create a segment based upon any path that users take on your site. For example, in Link 1, you can see a typical page fallout report. From there you can click on the link to create segment from this path to see Link 2 which is a sequential segment that models that path. From there you can customize the segment any way you'd like and even make it span across multiple visits by switching it to a Visitor segment.

RELATED: Link 1 · Link 2

Using the IP-based City, State or Country dimensions in Adobe Analytics, you can create geographic segments. The linked segment here (Link 1) shows an example of segmenting traffic from the city of Chicago.  Adobe analytics captures country, state, city by default using the end-users' IP address. This information can be helpful when building segments since it allows you to narrow down your audience by country, state or city. This can be used to see where customers are searching for products or where training might be needed.

RELATED: Link 1

This segment looks for visits that are greater than one to isolate return visits. Return visits are useful for times that you want to only look at visits that have been to your site/app in the past. This can be used to see:

· How paths differ between first-time and returning visitors
· Which products first-time vs. return visitors view
· What content is viewed differently by first-time and returning visitors
· How landing page clicks differ between first-time and returning visitors

In addition, in Adobe Analytics, you can add segments to calculated metrics, so the Return Visits segment can be used to see any metric trended over time for returning visits.

To see an example of how this segment can be created, click on Link 1.

RELATED: Link 1

This segment looks for visits that are first-time visits. First-time visits are useful for times that you want to only look at visits that are new to your site/app in the past. This can be used to see:

· How paths differ between first-time and returning visitors
· Which products first-time vs. return visitors view
· What content is viewed differently by first-time and returning visitors
· How landing page clicks differ between first-time and returning visitors

In addition, in Adobe Analytics, you can add segments to calculated metrics, so the First-Time Visits segment can be used to see any metric trended over time for first-time visits.

To see an example of how this segment can be created, click on Link 1.

RELATED: Link 1

To better understand Cart Abandonment, you may want to do some analysis of those who Abandon Cart, vs. those who Abandon Cart but return to it later. You can use a segment like this:

Visitor

Cart Additions exists AND Orders does not exist*

THEN

Cart Views exists

To dive deeper, you can:

· Apply specific timeframes around the “THEN” (for example, “Then within 1 day”, “Within 1 week”, etc.
· Compare those who return to view the cart vs. those who return, view the cart and actually buy

* Note: Sometimes this acts a little bit funky - if so, you can always tweak it to something like “Orders is less than 1”

The core application of segmentation is to isolate a subset of traffic to the site. The risk, though, is that the traffic that is *not* included is often useful context for the main segment. One nice thing about the way Adobe Analytics segments work is that it is very easy to use one segment to make a segment that is "everything else." This is done by simply switching the overall container for the segment from its default of being an "Include" segment to being an "Exclude" segment as shown in the first figure, which is a simple example of "visits that used site search."

This is a 3-step process (all three steps are shown in the first figure --- Link 1):

1. Open the original segment.

2. Change the name to make it clear that it is an "Exclude" segment (I like to pre-pend most of my segments with "Include:" or "Exclude:" if their nature is such that that makes sense.

3. Change the overarching container type to be an "Exclude" container rather than an "Include" container (or vice versa if the original segment was an "Exclude" segment").

4. Click "Save As" and save the segment.

The example shown is a very simple one, but this technique works on much more advanced segments -- even if the segment has a mix of include and exclude containers within it, simply "flipping" the overarching container type will make the segment "everything else."

The second image (Link 2) shows the two segments applied side-by-side in Analysis Workspace. It's generally a good idea to also view the first few reports with *no* segment applied as well to ensure that, as intended, the sum of the main two segments equal the total.

RELATED: Link 1 · Link 2

Some channels we can quickly and directly influence (paid search, paid social, display, email, etc.), while others are more difficult to quickly impact (natural search, direct, etc.).

At the same time, updating content on a page is one of the most easily modified (internal politics and systems notwithstanding) aspects of a site.

So, once you've identified the top (influence-able) channels and the top landing pages for those channels, a few quick segments to isolate the traffic from a specific channel and to a specific entry page then enables you to explore that traffic through any set of reports: pathing, devices, exit pages, site search terms, etc.

This segment typically makes the most sense as a visit-based segment with a simple "And" condition using Last Touch Channel (or First Touch Channel or something else) and Entry Page as shown in the figure (Link 1)

RELATED: Link 1