Archives for October 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

fb-video-metricsFacebook 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:

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?

Personalization Is Not Optional – Don’t Leave Money On The Table

I started writing about personalization ever since I started this blog, back in 2006. I wrote extensively about privacy and how marketers should address it to engage in personalization. Consumers are now more at ease with online purchases, they have moved past initial privacy concerns of online tracking and now expect personalization.

According to an eConsultancy report, 94% of the companies realize that personalization is critical to current and future success.
However many can’t move forward with personalization because of many barriers they face in implementing personalization.  The biggest being IT, Technology and Budget. (see below).
Charts from eConsultancy Reports – The Realities of Online Personalization
Many marketers don’t realize that personalization does not have to be complex. A recent study be Accenture shows that consumer are likely to buy from a retailer who provides some level of personalization (see below):
As you see in the chart above, simply recognizing the customers by name will get them to buy more. So start there if you are already not doing it, that won’t require big IT infrastructure or budgets or data. Don’t be bogged down by the hype created by press releases or marketing presentation about the sophisticated personalization few companies are doing. Many of the personalization techniques don’t require big budgets or IT infrastructures.
Start simple, understand the value personalization brings, show case the value to your internal stakeholders and make a case for getting more funding for more sophisticated personalization tools and technologies. I will be writing more about personalization so subscribe to my blog if you are interested. If you need help feel free to reach out to me. I would also love to hear from those who are currently involved with personalization or have a good story to share.
Article referenced in this post

Bootcamps and Courses for Developing Digital Analysts

main-image-optizent2I have been working in Digital Marketing and Analytics fields for more than 10 years now. Started my career in the field by playing with HTML, developing website and looking at server log files to parse the data to calculate metrics such as sessions, users, page views and many other metrics and KPIs that you see in your tools like Google Analytics, Adobe Analytics etc.

All through my life I have been passionate about teaching.  As a kid I used to teach other kids as a tutor and then I taught as a teaching assistance in graduate school.  For past 10 years or so I have been involved in speaking, teaching and tutoring in Digital Marketing and Analytics at schools, colleges and events. Some of the places I have taught and spoken at are, University of British Columbia, University of Washington, Bellevue College and eMetrics.

Recently I have started developing courses that can be delivered on-demand online as well as immersive bootcamps to develop new Digital Analysts.

The purpose of this blog post is to connect with individuals and organization (for profit or non-profit) who are interested in helping shape these programs.  Specifically, I am looking for:

  1. Input on what course should be developed? I have a long list of courses but would like to see which ones should be developed first, where are the gaps in what current programs teach. The idea is to augment what current university programs teach and not replace them.
  2. Find likeminded folks who have a passion for teaching and training.
  3. Connect with organization (for profit and non-profit) that are interested in partnering with me in providing real business problems and data for students to work on.
  4. Connect with schools and educational institutions that want to enhance their Digital Marketing and Analytics programs.
  5. Connect with organizations that are willing to provide internships and job opportunities for the students.

So if you are interested in helping develop new Digital Analysts then please contact me at batraonline at gmail.