Showing posts with label metrics. Show all posts
Showing posts with label metrics. Show all posts

Thursday, October 27, 2016

2 Key Lessons From Facebook’s Video Views Metrics Fiasco

People have short term memory (or selective memory), when they can’t remember things they will resort to how they think something should be. Recently Facebook was in the hot seat because of this very reason. 

Facebook metrics definition issue

Facebook has a metrics called “Video View” for video ads.  In this metric they only counted the video as viewed if it was watched more than 3 seconds by the viewer.  In other words, if someone watches a video for 2 seconds then that video view won’t be counted as a view in this metric.

Facebook also has another metrics, called “Average duration of Video views”, the “standard” definition of it should be Total Time spent watching video divided by Total Viewers. However, that’s not how Facebook defined it.  In Sept Wall Street Journal reported that Facebook "vastly overestimated average viewing time for video ads on its platform for two years.”  This lead to an apology from Facebook

About a month ago, we found an error in the way we calculate one of the video metrics on our dashboard – average duration of video viewed. The metric should have reflected the total time spent watching a video divided by the total number of people who played the video. But it didn't – it reflected the total time spent watching a video divided by only the number of “views” of a video (that is, when the video was watched for three or more seconds). And so the miscalculation overstated this metric. While this is only one of the many metrics marketers look at, we take any mistake seriously.


As per DM News article, Facebook did state the definition when it rolled out this metric two years ago.  So it was not actually doing anything wrong.  It was a case of short term memory issue.

“The problem, as critics put it, is a problem of omission. While Facebook very clearly states that it's only counting views as any video-play event that lasts longer than three seconds, it does not go out of its way to explicitly beat readers over the head with the fact that this definition of a "video view" applies equally to the calculation of average duration of video views.”


If Facebook product team had read my posts from 2012 on “Creating a culture of analytics” then they might have likely avoided this “scandal”. The two issues that Facebook dealt with were the exact same ones I talked about in my posts. To recap, here are the gist of those two posts:
  • Make sure everybody understands clearly how metrics are defined.  I talked about this in my post in 2012, Standard Definitions of Metrics: Creating a Culture of Analytics. In that post I said 

  • Lack of standard definitions for the metrics causes people to report different numbers for supposedly same metrics, leading to confusion and total lack of trust in data.  No trust in data means that nobody is going to use the data to make strategic decisions and there goes all your efforts to create a culture of Analytics.

    Having standard definitions is not as easy as it sounds.  It starts from you and your team having a clear understanding on how to calculate various metrics.   Some seemingly simple metrics can be calculated in various different ways and all of those ways might be right but getting one standard way of calculating those removes any confusion and gets everybody on the same page.
  • People have short term memory.  In my 2012 post, titled  Dealing with Short-Term Memory: Creating a Culture of Analytics,  I wrote:

    We all make assumptions from time to time; sometime we state them clearly and sometimes we just assume in our own head. We then operate under those assumptions.  In context of Analytics, one such assumption is that everybody knows what the goals and KPIs are.  We might have defined them on the onset of the program, campaign, beginning of month, quarter, year etc., but once those are defined we start to assume that everybody knows about them and is operating keeping those goals in mind.

    Well the truth is that people have short term memory. They do forget and then start to interpret the KPIs, defined based on those goals, in their own way.  As the Analytics head/analyst/manager, it is your job to constantly remind stakeholders of the goals and KPIs. 

Two Lessons

This fiasco provides two great lesson for all the Digital Analytics teams.

1.       Clearly define your metrics and make sure the underlying metrics and calculations are clear in your definition.
2.       Don’t make any assumptions, people have short term memory. Just because you stated a definition of a KPI in past does not mean everybody will remember it and know how tit was calculated. It is your job to make sure anybody using your metrics/KPI can get to the definition and calculations right away. 


Questions? Comments?

Sunday, May 18, 2014

21 Metrics for Measuring Online Display Advertising

In this post I am listing the 21 metrics to measure the success of your display advertising.  Most of these are also applicable, with some variation, to other forms of advertising such as Paid Search, Social Media Ads, Print and email. I will cover these other channels and mediums in the future posts.
  1. Impressions – It is the number of times your ad is displayed. The number by itself does not hold much value but it is a metric used to calculate other metrics and KPIs. Keep in mind that an impression does not mean that someone actually saw the ad, it just that the ad was shown on a web page/app.
  2. Reach –This is the number of unique people (generally identified by cookies) that were reached by your ad. This number is always lower than the impressions because your ad is generally shown to same person (cookie) multiple times.
  3. Cost – The total cost of running the ad campaigns.  This is calculated differently by different tools and organizations. Some use actual media cost while other use a fully load number that includes the agency cost, creative cost etc. Whichever number you use, be consistent in your approach. If you are going to do comparisons with CPC models such as Paid Search then I suggest using the actual media cost. Most of the publicly available benchmarks are based on actual media cost and are expressed in CPM (explained later in this list).
  4. Engagement Rate or Interaction Rate– This applies to the Rich Media Ads, where a user can interact with the ad without leaving the Ad unit/widget.  Engagement Rate is the percentage of interactions per impression of the ad unit and is calculated as (Number of Interactions/Total Impressions)*100%.
  5. CPM – This is the cost for 1000 Impressions of the ad unit. Display advertising is generally sold on CPM basis. (For more information on CPM, see  Cost of Advertising: CPM, CPC and eCPM Demystified).
  6. Clicks – Number of clicks on an ad unit that lead to a person leaving the ad unit.  Keep in mind that a click does not mean that a person landed on the intended destination of the banner ad click. There are multiple factors that could lead to a click but not a visit to the destination (I won’t cover those here but am happy to discuss over email or a call).
  7. CTR (Click though rate) – It is the number of Clicks generated per impression of a banner ad. This number is expressed as a percentage. CTR = (click/impressions)*100%
  8. CPC – Cost per Clicks is the cost that you pay for each click.  Generally, display advertising is sold by CMP (see above), you can easily convert the cost in to Cost Per Click to compare it against other channels such as paid search. Cost per click is the effective amount you paid to get a click.  It is calculated by dividing the cost with number of clicks.  CPC = Cost/Clicks. Sometime this number is also referred as eCPC (effective Cost per Click).
  9. Visits – As stated above in the definition of clicks, not every click turns into a person landing on your destination (generally your website). Visits measures the clicks that did end up on your site.  (For more definition of visits, please see Page Views, Visitors, Visits and Hits Demystified)
  10. Visitors – Visitors metric goes one step ahead of the visits and calculates the number of people (as identified by cookies) who ended up on your site as a results of the clicks on the banner ads.
  11. Bounce Rate – Is the percentage of visits that left without taking any actions on your site. It is calculated as Number of Visits with one page view /Total number of visits resulting from the display ads. (Bounce Rate Demystified for further explanation).
  12. Engaged Visit Rate – Generally this is opposite of bounce rate (though you can have your own definitions of engagement).  It measure the quality of the visits arriving from your display advertising. You can calculate Engaged Visits as  (100 – Bounce Rate expressed as percentage).
  13. Cost/Engaged Visit – This is effective cost of each engaged visits. It is calculated as total Cost divided by number of engaged visits.
  14. Page Views/Visit – Page views the number of pages on your site viewed by each visit. With a lot interactions happening on one single page, this metrics is losing its value. However, for now, it is still a valuable metric for ad supported sites.
  15. Cost/Page View – As above, this is valuable metrics for ad supported site to figure out the cost of generating on extra page view.
  16. Conversions – Conversion is defined as the count of action that you want the visitors to take when they arrive from you display ads. Some examples of conversions are – purchase, signup for newsletter, download a whitepaper, sign up for an event, Lead from completions etc.
  17. Conversion Rate  – This is the percentage of visits that resulted in the desired conversion actions.  Conversion Rate = Total conversions/visits*100. If you have more than one conversion actions then you should do this calculation for each one of the action as well for all the actions combined.  In case of Leads, you can take it one step further and calculate not only the “Leads Generation Rate” (Online Conversion Rate) but also Lead Conversion Rate, which is, Leads that convert to a customer divided by total leads generated.
  18. Cost per Conversion – This is the Total Cost divided by the number of conversions achieved from visits coming via display ads.
  19. Revenue – This is total revenue that is directly attributed to the visits coming from display advertising. It is pretty straightforward to calculate in eCommerce but gets a little tricky when you have offline conversions.
  20. Revenue per Visit   – Shows the direct revenue achieved per visit originating from the display advertising. It is calculated as Revenue Generated from Display Ads divided by the total Visits.
  21. Revenue per Page – This is useful for ad supported business models. This is sometimes expressed as RPM (Revenue per thousand impressions of ads) = (Total Ad Revenue/Number of page views) * 1000
Note: In addition to Clicks, you can also looks at View Through and calculate your other related metrics by view through.  View Through is sum of all the cookies that visited a page that showed your ad on it, and then landed on your site. The assumption, in this calculation, is that you landed on the brands site because of that ad exposure.
 Where can you get these metrics from?
  • Impressions, Reach, Cost, Engagement Rate, Clicks, CTR and CPC data is available from your agency or ad server tool.
  • Visits, Visitors, Page Views, Bounce Rate, Engaged Visit Rate, Conversion, and Conversion Rate are available in your Web Analytics tool.
  • Revenue is available in either your Web Analytics tool or other offline sales database.
  • Cost/Conversion, Cost/Engaged Visits, Cost/Page view and Revenue/page are calculated using data from multiple tools.
Questions/Comments?


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Saturday, February 17, 2007

Search for a Mertics to Compare Web Sites

Steve Rubel wrote:
"The page view is on life support. It fails to capture all of the myriad of ways consumers engage in online activities without ever leaving a web page. To get a feel for this, spend some time playing with Yourminis. So what will replace it and when will that happen? Let's handicap the field." On his blog What Will Replace the Almighty Page View and he thinks it willbe events or time spent.

Eric Peterson voiced his opinions on this subject on his blog
Worried about page views dying? Don't be.

I however, have a different opinion than Steve and Eric. I think Unique Users make the most sense of all the different metrics that are discussed in these two articles. I also think that maybe we should not just rely on a single metrics such as page views, unique users, no. of events, sessions etc? Maybe it is time to find a new metrics combining some (or all) of these above metrics to compare web sites?

I am going to list my reasons why I think, time spent, event, pages views and session by themselves don’t make sense as measure to rank one site against another.

Time Spent on Site: I am not going to go into detail on this one, you can read my blog article that explains why I am not a big fan of "Time Spent on Site".

Page Views:

Page views were not the right metrics to compare web properties to begin with. Why? Because they can be manipulated very easily. Say it t takes 2+ pages on site A to do anything compared to 1 page on site B, is site A really doing better than site B? Additionally you can split your content in as many pages as you want, there is no min standard page size, thus inflating page views.

Events: I think events will have the same issues as page views, plus everything in flash or AJAX interaction could be an event, where do you draw the line? What count’s as a valid event?

Session: I agree with Eric that this is a relatively stable metrics and agree with all the things he listed out for session. However, I don’t think sessions (alone) make sense as measure of measuring relative value of web properties.
I agree that Unique users have issues but those issues affect every web property, most likely in similar fashion. For example, if I delete my cookies, most likely I will delete for both myspace and yahoo.

Here is an example to make my point:
I go to myspace and read 2 pages in 1 min, wait 31 mins and then go back and read 2 more pages in 1 min. So here is what the web analytics reports will look like

2 sessions (visits)
1 unique users
4 page views
2 mins.

Now I go to yahoo spend 2 mins reading 4 pages in 1 session. Here is what the web analytics report will look like
1 session
1 unique users
4 page views
2 mins

What about the following scenario

Which property is number 1? Aren’t they both the same? If you use Session myspace appear to be number 1. But if you look at Unique Users and rest everything too, they both are equal.

What about the following scenario

I go to myspace site and read 2 pages in 1 min, wait 31 mins and then go back and read 2 more pages in 1 min then come back after 2 hours and read 2 more pages for 1 min. So here is what the web analytics report will look like

3 sessions
1 unique users
6 page views
3 mins.

Now I go to Yahoo spend 2 mins reading 4 pages in 1 session. My friend goes and reads 2 pages in 1 mins in one session. Here is what report will look like

2 sessions
2 unique users
6 page views
3 mins

Which property is number 1? Session will say myspace, even tough yahoo is getting more users?.

Will an advertiser be happy by showing same ad 10 times to one user in (more session but only 1 user) or they will be happier by showing the 5 times to 2 users (fewer sessions but more users)? So shouldn’t yahoo be number one in this scenario?

Let’s face it, it is about unique users. But other metrics do play a role in determining the value of a website

So do you agree that it is time to find a new metrics combining some (or all) of these above metrics? Comments/Thoughts?