As we spend more and more money with video tracking we need to see the payoff and if it’s leading to extra revenue. All the big platforms have analytics built in but we still want to capture events in Google Analytics or Adobe so we can apply see how they affect conversion rate in this visit or future ones. To do this you need to tap into their JavaScript player APIs and use the API events to push data to your analytics platform. This is a common thing for developers but isn’t always used by technical analysts for implementation.

Here are the following four biggest player APIs:

· Youtube [detailed walk through]
· Wistia(detailed walk through)
· Vimeo
· JW Player

Here is an example of using the youtube API, note sometimes you have to create your own player events for specific metrics based on logic. For example, there is no start video metric but there is a play event and you can check the time when a play event is triggered. So check if the time is 0 when the play is triggered and boom! you got your start event.

To backfill your goal data, simply create a segment with the same criteria was your goal. For example, let’s say your goal completion location was “thank-you.html” - create a session based segment for page=thank-you.html. The numbers for this segment should be identical to your goal (for the time period since the goal was created.) This is because goals are de-duplicated to 1x/session, so they essentially function the same as a segment.

“But can’t I just use unique pageviews?” Maybe. Unique Pageviews are actually unique based on just on the URL, but also the Page Title. So, let’s say you have multiple regions with a thank-you.hmtl page, but their Page Title is different (“BizWorld USA Confirmation”, “BizWorld DL Confirmation” etc.) These will be considered unique in your Unique Pageviews report, but not in your goal or your segment, since those only care about the URL. So, the most accurate way to backfill goal data is going to be a session-based segment, and using the sessions metric.

Analytics tools like Google Analytics can automatically detecting some things about your incoming traffic (for example, that they came from a search engine, or a previous website.) However, this information is fairly rudimentary. Therefore, analytics solutions provide a way for you to TELL them how the traffic is getting there. For GA, this is utm (aka campaign) tracking. You can read about GA's solution here: http://bit.ly/ga-url-builder (Link 1)

The most important thing is that your marketers are consistent. (For example, that they all use "social" as the Medium - not "socialmedia" or "sm" or "social" - these will lead to multiple Mediums that all mean the same thing!) So whatever you choose to name things, keep them used consistently.

A shared spreadsheet like this one can be a helpful start. You can add drop downs and data validation to force even more consistency! Just make a copy, and customise to your heart's content. http://bit.ly/ga-url-builder-sheet (Link 1)

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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. In many cases, the way Google Analytics segments get created is such 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 filter condition 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 entered the site on the home page."

This is a 4-step process (all four steps are shown in the first figure, Link 1):

1. Open the original segment and click "Copy" to make a copy of it.

2. Update the name of the new segment 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 filter type to be "Exclude" rather than "Include."

4. Click "Save" to save the segment.

The example shown is a very simple one. This technique works any time the segment is a "Condition" segment with a single filter. More advanced segments require more care to create the "everything else" version, but it is still often a worthwhile exercise to do that.

The second image (Link 2) shows the two segments applied side-by-side within Google Analytics. It's generally a good idea to also initially include the "All Users" segment to ensure that, as intended, the sum of the main two segments equal the total.

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Some channels we can quickly and directly influence (paid search, paid social, display, email, etc.), while others are more difficult to quickly impact (organic search, organic social, 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 landing page then enables you to explore that traffic through any set of reports: flow navigation, 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 Default Channel Grouping (or a Custom Channel Grouping or Medium) and Landing Page as shown in the figure (Link 1)

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This is a web-based app that works with Google Analytics data to explore site search usage on a site. There are three main components of what it does:

* "Stemming" of site search terms -- Sébastien Brodeur did a demo at Superweek 2017 of how he collapsed the variations of search terms into a single "stemmed" term. This makes for more meaninful frequency counts.

* Selective removal of terms -- many sites have some "dominant" search terms that are valid...but that dwarf the ability to get to the really interesting stuff. This app allows the user to simply type in words to remove them from the frequency counts and word cloud.

* Questions in search -- this was something Nancy Koons presented a few years ago -- filter down to just the searches that start with a "question word." These are searches well out on the long tail of searches, but they can be very insightful

Link 1 included here shows the first two items -- a word cloud and how "dominant but uninteresting" terms can be removed to make a more meaningful word cloud.

Link 2 shows the second item -- how "questions" get surfaced by filtering for specific terms.

A more complete description of this approach is available at: http://analyticsdemystified.com/google-analytics/exploring-site-search-help-r/ (Link 3)

If you have access to a Google Analytics account that is configured for site search tracking, you can try this tool out without doing any coding at: https://gilligan.shinyapps.io/ga-site-search/.

If you would like to download the R code to run it locally or make modifications, it is available on Github: https://github.com/gilliganondata/site-search-wordcloud.

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Useful context for evaluating one segment of visitors is often "all the other visitors (or visits)." This is very straightforward to do with Adobe Analytics:

1. Create the initial segment and save it.
2. Click on the arrow at the top right of that segment and select "Copy."
2. Name the segment and change the dropdown from "Include" to "Exclude."
3. Click "Save" and save the new segment.

This process is shown in the first image included here. What is shown is a simple example of "visits that entered on the home page." More often, there is a more involved segment .

I like to start names with "Include:" and "Exclude" to make it clear in the segment's name what type of segment it is.

The two segments can then be added to a report in Google Analytics or Data Studio. It can be reassuring to include the "All Users" segment, too, initially to confirm that the two segments, when combined, actually do equal "the whole." The second image here shows an example of that.

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This segment looks for the names of major social networks. It can be customized for your business, to add any ones that might be missing (for example, to add more obscure social networks used in other countries or industries.) It is up to date as of mid-2017.

To download this to your Google Analytics account, use this link: https://analytics.google.com/analytics/web/template?uid=KPisySh9RiKvJd7WDiNFWw (Link 1)

You'll be prompted to choose which view you would like to apply it to. Once it's included in your account, you can optionally set it to share with other collaborators in that view, or share with all the views you have access to.

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