Archive | WAW Recaps

March 2016 Recap, Jim Sterne

March was an energizing talk from digital analytics legend Jim Sterne on how to become indispensable to your organization, complete with authentic #lobby-bar experience at local beer hall Hofbräuhaus.

Maybe we aren’t all indispensable yet (though we’re hoping not to be “a danger to ourselves and
a menace to society”), but we learned some great tips. Like how to think about where we are now and where our organization might be, as well as important thoughts on where the future may be headed. The future is either killer robots forcing us to do taxonomy for them (it’s worse than being a human battery, but as far as the robots are concerned while we are slow we are smart at that kind of stuff) and/or machine learning becoming more plugged into everything we do.

The cbuswaw “tweet-of-the-night” award, which is an award we are creating this very moment in lieu of a more lengthy and meandering recap is from Dr. Levin who summed up Jim’s points on communication very well as:

February 2016 Recap – Data Collection with Jason Packer

Our February WAW was a presentation by Jason Packer on the details of data collection, when that runs into trouble, and what we can do to clean up our data. A significantly below-average beers per attendee ratio confirms that indeed this is a very sobering topic.

Here are the slides (reference links on the final slide):




 

WAW regulars may recall in our January meeting that one of our goals for 2016 was to try and get a better understanding of how data collection works and where it’s headed. During said meeting Jason (who is totally not the one writing this recap, because that would be weird, right?) had such a good time that the next morning he woke up to realize he had volunteered to give a talk the following month on that very topic.

Next month, Jim Sterne on becoming indespensible.

January 2016 Recap – We Came, We Shared

Our January WAW was a group discussion. Attendees filled in the blank in the statement:

By the end of 2016, when it comes to digital analytics, I’d like to be able to say ______________.

We spent 45 minutes discussing the thoughts shared by various attendees, which are summarized below.

Build R Knowledge and Apply It

The attendee felt like he had nearly universally heard, “It’s a steep learning curve…but totally worth it once it all clicks,” so he’s going to try to make it over that learning curve and see if he can get the tool clicking for him (and, he hopes, in a way that he can use some of the same scripts across multiple clients. This led to an “R vs. Python” discussion, as well as a minor diversion into “Tableau vs. Domo.”

“Solve” Referral Spam

Everyone working with Google Analytics is fighting referral spam, or, really, data quality issues writ large. This led to some very-near-to-cbuswaw references:

Despite the temptation to do so (because it was discussed), none of the links above have monkied-with campaign tracking parameters included.

Convert All Clients to Use a TMS

Rather than 2016 being the fifth consecutive “Year of Mobile,” how about making it “The Year of the TMS?” This was the third thought that was in the technical weeds, but we chatted about the value that a tag management system can deliver (and the lingering ill effects of TMS vendors initially leading with “no need to work with IT!”). We also discussed that a TMS is still just a tool. It can be a mechanism to think about and develop better data governance, but it doesn’t automatically handle data governance without some process development and on-going rigor!

Get Stakeholders to Use the Data They Get

The Analyst’s Dilemma: I built it…but they didn’t come. The two points attendees made here were:

  • Lead with “insights” rather than “the data” (full disclosure: the scribe/author of this post has a gag reflex he has to fight when platitudes use the “i” word…)
  • Include stakeholders in the measurement planning process (throwback WAW! February 2011 presentation was on campaign measurement planning)

“Figure Out” Facebook/Social Media Analytics

Oy. We were getting pretty ambitious by this point.

Figure Out the “Magic Number” for Each Stakeholder

What’s a magic number? In baseball, it’s the combined number of games a team needs to win or another team needs to lose in order for a team to win their division.

That is not what this attendee was referring to. Rather, it was a very neat little way of saying, “KPI:” figuring out what one metric is most critical for any given stakeholder, and then making sure it’s being measured and reported accurately.

Get a Better Understanding of Data Collection

Things used to be “simple” — a log file. Then page tagging (client side JavaScript) data collection came along. And then social media. And mobile. And now a drift back to server-side data collection. It behooves the analyst to have a good handle of where the bits and bytes are flowing and how they’re being grabbed and recorded.

It was a lively discussion overall!