Archive for the ‘Yahoo Web Analytics’ Category
Online Video Analytics – An Introduction
In recent years the Internet has seen an explosion in the use of streaming video. This has simplified the process of getting video to consumers as well as giving content providers the opportunity to measure the consumer’s engagement. Online Video Analytics allows for content providers to quantify user engagement in new ways that should help them measure returns on their video investments.
Essential Online Video Metrics
If you host videos using your own flash player (or stream through Windows Media Player) then many different user activities can be recorded for analytics purposes, such as pressing the pause button or sliding the video position slider bar forwards or backwards.
Some of the key video metrics (or KPIs) are:
- Online video started
- Online video core content started
- Online video positive consumption action
- Online negative consumption action
- Online video paused
- Online video ended
- Online video played, percentage of total
- Online video played, total seconds
- Online video play position slider user action
- Online video section played at 1st, 2nd through to last tenth section
- Online video time section played: 1st, 2nd 10 and last 10 seconds, etc…
These can be measured using a combination of media player events, methods, timer events and tracking code written in JavaScript or Adobe ActionScript.
Video Segmentation
For the purpose of making measurements we can choose to conceptually split the video into a fixed number of sections, say 10 segments of 10% length. These segments can be used to measure which sections of a video have particular high end user engagement activity.
We can also define another way of splitting the video into slots, where in this second method, we can choose a fixed lot length in time of say, 10 seconds “time sections” or “time slots”. These can be used to measure engagement in terms of fixed time slots of 10 seconds length. This type of measurement can also show the total playing time of groups or individual videos.
We can further group the videos into categories such as chapters, channels (yahoo, msn, etc..), types (sports, sci-fi, etc..) or any other categorization the publisher may wish to use for analytics purposes.
Video Engagement Characteristics
A video engagement characteristic is a representation of the audience’s interactions with the video. Content providers can use this characteristic to rapidly see which of their videos are most compelling to their audience. It can also be used to analyze the strengths and weaknesses of a particular video.
For the engagement characteristics we will define two types of audience video consumption:
Positive Consumption Actions
Negative Consumption Actions
Positive consumption actions are ones that associated with a positive user experience. One positive consumption action could be the act of rewinding the video to re-watch a specific section. This indicates that the user saw something that they want to see again. Therefore each time an end user rewinds the video a positive consumption action is recorded for the section that is rewinded to.
We can also decide that certain activities constitute a negative consumption action. One such example can be the act of fast-forwarding the video (or moving the slider bar) to a position further forward in a video. This can indicate that the audience is bored with the current section they are fast-forwarding from, and so for this case of fast forward, we use the section of video moved from, and not the video section moved to, as the grouping for negative consumption action.
The positive consumption action reported by the analytics system then be used indicate which sections of a video are particularly compelling. Conversely the negative consumption action sections can be removed, reworked or edited to make them more positive.
Videos and Links
Another type of video analytics could be the correlation between video sections and clicks to links for product information.
It could be deemed useful to measure at which stages in a video your audience was compelled to actively seek extra textual information sales or specification information about a product. This may also be considered as positive consumption as it shows active interest in the product, and such data can be gathered about audience behavior.
Conclusion
We’ve just scratched the surface of the types of data content providers can mine from their online videos and why it can be useful. In our next post we will show the nuts and bolts of how to implement online video analytics using Yahoo! Web Analytics.
Hello World, Hello Internal Campaigns – measuring the effectiveness of on-site promotions
Note: The following is a post by Dennis R. Mortensen, who is the Director of Data Insights at Yahoo! In New York.
Hi everybody. Welcome to the Yahoo! Web Analytics blog, which is, well, a lot of good things, but also an internal campaign – and what better way to start the blog, than to debate how one (us included) measures internal campaigns.
Traditional campaign tracking provides you with the opportunity to relate cause and effect in your marketing efforts [1]. This standard web analytics tool feature and web analyst activity, tends to be where most people start. And that is not a bad choice at all. However, on-site promotions, such as a simple banner on your front page, promoting that Easter free shipping offer, is rarely measured at the beginning of the funnel. Such promotions ends up being measured mid-funnel or actually by the end of the funnel, thus losing out on getting a full understanding of the actual effectiveness of the internal campaign. I certainly see a lot of people measuring the outcome, such as whether we increased revenue over Easter, now that we did that free shipping thing.
You shouldn’t set up on-site promotions as traditional campaigns, as that will damage your off-site campaign attributions, meaning that, if you paid for a visit through a Search Campaign on Bing, that might get “overwritten” (should you use e.g. last-click attribution) by the visitor clicking that free shipping offer. You don’t want that. You want to be able to measure the two campaign activities, on- and off-site disjointedly (or at least have the opportunity to do so).
Specifically, internal campaigns in Yahoo! Web Analytics are on-site activities designed to elicit a certain response from your visitors, not that far from any other external campaign. Remember that on-site campaigns do not have to be a banner looking like an advertisement; it might be a prominent navigational link. With internal campaigns, you get an “easy” way to test the effectiveness of these activities.
Any out-of-the-box tool that claims to do this should be viewed with suspicion (YWA included), simply because most web analytics tools determine the cause based on the effect, such as a campaign banner click. Relating cause and effect is doable, but quite a difficult task in external campaigns, and a very shaky proposition with internal campaigns. This does not mean you cannot and should not use the feature – I highly recommend you do, as it is a good indicator as to what to look for – but just do not see it as the final truth. However, it is a whole lot better than just treating on-site inventory as free-of-charge inventory, because it is definitely not.
You experience the same challenge when using our internal search reports and you’re trying to work out a cause-and-effect analysis – such as whether an internal search caused a sale, for example. But that’s for another blog post.
Imagine a scenario where you have a visitor arrive from a bookmark and he lands on your Easter-specific home page. From that home page, he clicks on a banner announcing the free shipping offer; he then moves on and searches for a given product in your internal search box; and finally uses the directory and navigates to the product flavor of his choice and ends up buying the specific product. Did he buy the product because of the banner click, because of the internal search, or just because of the general positive site usability? Determining cause and effect is difficult!
To set up internal campaigns, you work in an environment similar to that for external campaigns, but it’s simplified. You find the internal campaigns option by choosing Settings > Manage Internal Campaigns (see below Figure).
The biggest difference between external campaigns and internal campaigns is the way you identify them. You identify internal campaigns with a unique URL string containing a variable called _s_icmp, which is used for internal campaigns only. So when you set the target (let’s call it landing page) for your internal campaign, the link looks something like this:
http://visualrevenue.com/example/easter-shipping-offer.html?_s_icmp=dennis1
Please note that the _s_icmp variable is case sensitive. When you create a new internal campaign, Yahoo! Web Analytics will automatically provide you with a URL pattern such as _s_icmp=c6pw5X2E, which you can edit if there is a conflict or if you just prefer to follow your own naming conventions.
Once you decide what URL string to use, click the Add button and your internal campaign will be saved. Make sure that you add the same URL string to your website in order to identify the campaign.
Finally, and to confirm: Internal campaigns do not conflict with regular external campaigns, neither in the setup and recognition nor in the attribution of the actions taken. That said, I would like to remind you that categorization is indeed possible for internal campaigns but that it is also separate from regular external campaigns.
You can read more about Internal Campaign and Campaign tracking at the Help Center (How do I track internal campaigns?) or turn silly with your money and buy the Yahoo Web Analytics book.
Cheers 
/ Dennis (@dennismortensen)
[1] Causality indicates a necessary relationship between one event (cause) and another event
(effect) that is the direct consequence (result) of the first.
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