#029: (Reflections on) The History of Digital Analytics with Jim Sterne

The following is a straight-up machine translation. It has not been human-reviewed or human-corrected. However, we did replace the original transcription, produced in 2017, with an updated one produced using OpenAI’s WhisperX in 2025, which, trust us, is much, much better than the original. Still, we apologize on behalf of the machines for any text that winds up being incorrect, nonsensical, or offensive. We have asked the machine to do better, but it simply responds with, “I’m sorry, Dave. I’m afraid I can’t do that.”

00:00:00.82 [Jim Sterne]: Hi, I’m Jim Stern. You’ll hear the E-metric summit mentioned multiple times in this episode. So the guys asked me to just plug it outright at the beginning. So here’s what you need to know. The E-metric summit brings together the best and the brightest and the most willing to share what they know about online marketing analytics. Learn about how to optimize awareness and persuasion and retention. You’re going to learn from practitioners and consultants and vendors. and it’s going to be in San Francisco in April, Chicago in June, New York and London in October and Berlin in November. It’s the E-Metrics Summit. It is the birthplace of the Digital Analytics Association and you can find it at e-metrics.org.

00:00:45.37 [Announcer]: Welcome to the Digital Analytics Power Hour. Three analytics pros and the occasional guest discussing digital analytics issues of the day. Find them on Facebook at facebook.com forward slash analytics hour. And now, the Digital Analytics Power Hour.

00:01:05.58 [Michael Helbling]: Hi everyone. Welcome to the Digital Analytics Power Hour. This is Episode 29. You know, every nation has founding fathers. And digital analytics nation, that’s a term I just made up, is no exception. Who are these brave men and women from our past? And how can we find out more about the short but rich history of this most amazing of disciplines? It sounds like a job for our guest tonight, Jim Stern. You probably know him best from his cameo appearance in episode 26, but actually, Jim has led the charge for digital analytics and digital marketing for almost 25 years. He started the most recognizable digital analytics conference, E-Metrics. He co-founded the Digital Analytics Association and has written three books on the topic of digital analytics. And he got Chris the Sidon to start drinking scotch. So sit down, pour a drink as smooth as his voice, and relax. This episode of the Digital Analytics Power Hour is about the history of digital analytics with none other than the godfather himself. Welcome, Jim.

00:02:17.17 [Jim Sterne]: I’m a little overwhelmed and a bit for Clempt. Thank you so much for that introduction.

00:02:22.93 [Michael Helbling]: Speaking of overwhelmed and for Clempt, let me also bring in our other two co-hosts. Tim Wilson, senior partner in analytics demystified.

00:02:34.69 [Tim Wilson]: I am keeping a running list of vocabulary words I need to Google during this episode.

00:02:39.83 [Michael Helbling]: And of course, our neighbor to the north, Jim Cain, CEO of Napkyn and Babbage Systems. Hello. And most of you know that I am Michael Helbling, and I lead analytics at a small company in Georgia called Search Discovery.

00:02:56.45 [Tim Wilson]: So I had to go to my, the first dictionary I looked in did not have a definition for Verklimp, because that was the Devil’s Data Dictionary. But I did, I did, I did find it on the interwebs. So I’m all caught up now. Carry on.

00:03:09.94 [Michael Helbling]: So I think most people would want to start off right away, Jim Stern, with the question, what were you thinking when you invented Web Analytics?

00:03:23.84 [Jim Sterne]: So here’s where the name Godfather really comes in handy, because I was not there at the conception I had nothing to do with the birth, but I am devoted to making sure that this industry grows up and lives a long, healthy life. So Godfather is pretty appropriate. Just like the internet, this is something I tripped over rather than had anything to do with inventing.

00:03:51.95 [Tim Wilson]: This does kind of make me feel like we should have a dramatic reading from the Devil’s Data Dictionary for the definition of algorithm. Could you deliver that from your book? A selection.

00:04:01.71 [Jim Sterne]: Are you kidding? I travel with the book in hand constantly. Algorithm is defined as regularly recurring remarks from the former U.S. Vice President who invented the Internet.

00:04:12.86 [Tim Wilson]: See how I brought that together? I feel like I studied up for a guest.

00:04:17.09 [Jim Sterne]: This is also one definition in the book that anybody over 40 years old gets, even though they don’t get any of the others. My dad liked that one, and he’s 89.

00:04:27.12 [Tim Wilson]: Well, and since we’re kind of regularly anti-millennial, it’s okay if the other people who are under 40 don’t get it, right?

00:04:35.85 [Jim Sterne]: If somebody comes along who gets all of the definitions and understands all the jokes, it will freak me out.

00:04:43.53 [Tim Wilson]: Did I tell you that actually my mother was in town and I made a comment and said, oh, you’ve heard me talk about this guy. He wrote this book because she’s an English major, so she certainly gets the Ambrose Beers reference. However, I had to leave the room after about the fourth grilling she was trying to give me to explain the humor or explain one of the definitions because she not only had never heard of the word, And then she, any of the jokes behind it, I was like, I’m so sorry that this was a poor choice on my part.

00:05:13.92 [Jim Sterne]: Tim, I am so sorry. I apologize for putting a rift between you and your mother, but for your listeners benefit, I would like to introduce Ambrose Beers to those who do not know who he is. This is the guy who wrote the devil’s dictionary in 1906 and it is full of stuff. It’s a big book and it contains really choice gems, including bore, a person who talks when you wish him to listen, love, a temporary insanity curable by marriage, politeness, the most acceptable hypocrisy, and success, the one unpardonable sin against one’s fellows. I take no credit for those, but I am inspired.

00:05:56.79 [Tim Wilson]: Did he have a definition for web analytics? Or did that predate the history just a little bit, 1996?

00:06:03.68 [Jim Sterne]: I didn’t get that far in the book.

00:06:06.48 [Tim Wilson]: So when exactly did I this I should know this what do we date the start of web analytics to 95 and that was 9093 was the browser was mosaic.

00:06:21.17 [Jim Sterne]: And by 95, we had commercial available stuff and open source, although they didn’t know to call it that. So there was sawmill to do log file analysis. Web trends came along in 1995. Net Genesis came along in 1995. And that’s, that’s where it began.

00:06:39.32 [Michael Helbling]: Analog, I think came out that year.

00:06:41.93 [Tim Wilson]: Fun fact, Mike, my cousin was part of the original Moesaic team.

00:06:45.03 [Jim Cain]: That is fun. Fun fact. Web analytics is 10 years younger than back to the future.

00:06:52.59 [Tim Wilson]: I thought we’d move beyond back to the future.

00:06:54.95 [Jim Cain]: At least more than Facebook.

00:06:56.94 [Jim Sterne]: So that’s all that matter.

00:06:58.14 [Jim Cain]: I was standing on my toilet trying to hang a clock and I fell and hit my head. And that’s when I came up with the idea for the web trends. That was a Doc Brown reference.

00:07:08.71 [Michael Helbling]: Well done.

00:07:09.57 [Jim Cain]: Thank you.

00:07:10.31 [Michael Helbling]: Yep. So there is a really interesting thing to talk about really briefly, and it gets on to a couple of different tangents, but we’ll see where it takes us. Web analytics did come from, first and foremost, the log files coming out of websites, and people started looking at those. And so that was primarily a technical activity. And so the first tools were being used by technologists and IT organizations. And when did people start to really make that corner of, hey, we could use this in the context of our business, right? Cause that’s certainly where we are today. But like, let’s talk about that. And what were some of those first uses that, you know, you’ve seen Jim Stern and others.

00:07:55.11 [Jim Sterne]: Well, so that would be 2002 because that’s when the first e-metric summit was 15 years ago. There was a shouting argument in 2002 at E-metrics in Santa Barbara between two guys wearing Birkenstock sandals and ponytails about whether log file analysis or JavaScript was better.

00:08:18.38 [Tim Wilson]: Do tell who was on which side or Birkenstock.

00:08:21.62 [Jim Sterne]: Rufus Evans was one of the technical founders of a company called Clickstream in the UK that did packet sniffing. And the other ponytail wearer was Eric Peterson. And they got into a heated argument that extended all the way into the lobby bar, hence the beginning of the lobby bar. And the result was they ended up agreeing completely that both of them were necessary.

00:08:48.97 [Jim Cain]: So we’re just the inheritors of the ponytail bar debate.

00:08:53.11 [Tim Wilson]: That’s right. But it does seem like that transition and I count myself back to about 2001 when I inherited a net genesis implementation. And so in a sense, I count myself lucky and that between migrating from net genesis to web trends, I got a a deep technical education on what goes into a log file and what’s in the HTTP header and what can be recorded. But there’s part of me that while the data was always messy, like with the net genesis stuff, it took forever to run because we were basically once a month cranking out a ton of reports that took increasingly long to crank out. And then they were the static report and then they put micro strategy on front of it and said, oh, now you can can slice and dice and you really couldn’t not in an effective way. So we were battling it. And on the one hand, I feel like back then I was battling the shoot. I don’t have that data or I don’t trust that data or. Gomez was doing a sales pitch and they pinged this page and they fucked up the data so fighting kind of data quality ever since then but on the flip side I feel like the old guy who says yeah but when it was just when digital was really just our site. And we were just discovering AdWords as a way to drive, you know, pay to drive traffic to the site. Things did seem simpler, although I’d say it was still incredibly guilty of, you know, just counting visits and page views. I feel like the data has gotten more complex and we haven’t kept up with getting the data consistently clean. Like that’s a never ending battle. But the behavior of the consumer and the number of digital channels that all fall under when we, you know, transition from web site analysis to digital analytics, that feels like just such a much more involved and complex scope. And I don’t know that those, I kind of lived through that transition and was oblivious to it through the entire change stuff just kept getting more complicated.

00:10:57.18 [Jim Cain]: I remember when the WAA changed its name to the DAA and I was like, come on guys, you’re just trying to get cute. And then I started to think about it more and more and it was like, That’s a good step towards not marginalizing the web analysts.

00:11:10.72 [Michael Helbling]: Yeah, it’s almost like we had a brief period there. What it must have felt like for advertisers when there was only three television stations, you know, it’s like we just have our website to measure. That’s all we’ve got to worry about.

00:11:23.98 [Jim Sterne]: And when the WAA started, the first meeting of what would become the board of directors was what do we call this thing? And I was adamant that it should be customer analytics. And I was shouted down because no, no, no, we want to draw attention to the fact that this is a new data set that nobody’s ever seen before. It’s the website, it’s web data, it’s behavioral data, it’s stuff you’ve never been able to analyze before. It’s not CRM, it’s not database management systems and doing direct mail analysis. This is unique. So it’s the web. analytics associations so I lost that battle then six years later the board had the discussion again shouldn’t we change our name because web data is such a narrow thing and we’re measuring email and search and campaigns and social media and and oh by the way when they’re running out on TV they ask us to measure the impact that it’s having On twitter so clearly we’re beyond web analytics we should be the digital analytics association and i was adamant that we really needed to be. The customer data analytics kind of the customer relationship with the company online the digital experience. No, no, no, that’s too narrow. We might want to take over the world someday. So that’s twice that I’ve lost in the battle of what we should call this thing.

00:12:49.62 [Tim Wilson]: It surprised me as I’ve run into it, the more companies that I work with, that when we talk about customer analytics, and I know that I absolutely get the need to be truly customer centric, and there’s been some great writing on what does customer centric even mean, But regularly run into organizations that have, you know, customer analytics lives in the BI organization, and they are the ones who are cranking through the customer data warehouse, the CRM, wherever the customer data lives. And it’s not even occurring to anyone that that world should be blending and merging with the traditional web analytics world. The funny thing is in my, when I got into web analytics, I was very quickly in an organization where all of that was in one BI department. And I just didn’t, we didn’t know any better. It just seemed like that was all the data. We should have our data warehouse, our customer data, our ERP, all that should be kind of in one, our market research should be in one centralized group. Wait, we know we’re talking about the history. Maybe this is more of the future. And I don’t know what you guys see as well. I certainly still seem to run into places where they say, we have no digital analytics. Oh yeah, we’ve got a whole customer analytics team. We got 20 customer analysts. And it’s not, the light bulb’s not going on that, hey, maybe that group should also be the experts on the digital data.

00:14:22.71 [Jim Sterne]: I have maintained my sanity by dividing all of this up into four really clear areas. BI has traditionally been about internal business processes. Let’s manage shop floor control. Let’s manage supply chain. Let’s manage efficiency of our employees. Then we’ve got customer analysis, which has been the mailing database, direct mail database, which then sort of became customer relationship management and Salesforce automation. And then there was market research. Let’s go out and interview and survey a gazillion people and produce reports that are general across the whole thing. And then came along this weird little thing that was behavioral data. Nobody had ever seen mouse clicks before determining intent based on what you type into a search engine. This was so unique and so new. BI people didn’t know what to do with it. CRM people said, it won’t fit in my database. I can’t handle time series data that flows that big. So a unique group, a unique industry was created. Digital analytics. How do we measure the ongoing touch point relationship between customer and company? so that we can make mobile better, we can make the website better, we can measure whether or not our campaigns are working and whether or not our search keywords are correct. And this is so different from the structured data mainframe mindset that it had to be a unique different group of people.

00:15:57.70 [Tim Wilson]: Yeah, this is funny. I mean, I told this little story before. The way I got into what was a BI role, although it probably fit more under the customer analytics definition, was almost exactly market research lived in the advertising. Web analytics lived in Marcom. It kind of moved with me through a couple of things from our community to Marcom. Be I what we call be I maybe was more customer analysis although we sort of touch all sorts of stuff and I was having to logically it just made sense to several of us to move the behavior or the web analytics. into the BI or in this case the customer analysis group and after having that conversation of saying hey does this make sense and they said sure you know if you’ll help kind of hold our hands and help us understand what the data is we’d love to get our hands on that data it totally makes sense to bring this behavioral stuff together with the customer stuff and then As I was leaving that meeting, I said, well, this kind of sucks because this is like the one part of my job. I was the Markov manager, basically a web Markov manager. This is the most fun I have with my job. It just doesn’t fit. I don’t have a bandwidth for it. And they said, oh, let’s keep talking. So I went up going with it. And it was inside of a year after that that we said, why is the market research department off in a totally different group? And they were kind of orphaned in the group they were in. So it wasn’t like we were doing any sort of a land grab. It was just a little two person department. And we found ourselves talking to them all the time and said, Hey, wouldn’t it make sense for you guys to be with us as well? And. I continue to be amazed that, and this is, it sounds almost like a humble brag. I mean, we literally were just oblivious. It made so much logical sense. And now with as many companies as I’ve seen how they’re organized, one, the ones that don’t have a behavioral digital analytics staff, or they don’t have market research, or they don’t have any of those groups, or when they do, there’s not a clear direction that these groups should come together. And maybe just a little bit with, what Gary Angel talked about on our last episode, that that’s just kind of organizational evolution that we just kind of gravitate to silos. And that seems like for something that’s now been around since 1995, that we should, you’d think that light bulb would have gone on by now.

00:18:27.08 [Jim Cain]: But the light bulb’s gone off before for people or gone on rather. It’s to go back to Jim’s statements earlier about why was it the DAA or why was it the WAA? If you call it web analytics or digital analytics, you’re building a very valuable house, but you’re building it on land nobody else wanted. There are, at the enterprise level, pre-existing teams of people that do customer analytics. Then just like you said in that role, Tim, there was a team of people who do behavioral analytics, and they happen to get along with you. But if you go and you rename the DAA, the Customer Analytics Association, you’re shooting a flare in the air. You’re trying to eat someone’s lunch. That becomes a thing.

00:19:06.58 [Tim Wilson]: What is that? Is the DMA, would they say that they are the Customer Analytics Association now?

00:19:11.51 [Jim Sterne]: Understand that the DMA started out as the Direct Mail Association. Then they changed the direct marketing. Now they want to change the digital marketing. It’s humorous.

00:19:23.64 [Tim Wilson]: Yeah, they’re just fortunate with their first letters of their evolution.

00:19:29.85 [Jim Cain]: Well, and I’m actually, I’m not lobbying for keeping any name what it is. I actually think it’s important that if web analysts don’t start to say, actually, I influence or direct key pieces of the business that aren’t just web analytics data. We’re going to be marginalized to the point where someone eats our lunch. And then the next thing, you know, we all have to show up to a bullshit IAB meeting.

00:19:55.13 [Michael Helbling]: Well, I mean, it’s so interesting that these silos happen in organizations, but then as you get good, you break these silos back down again. And I wonder if, let’s say the silos didn’t exist, would we master this data set? Or will we still be approaching it kind of the wrong way and doing it in the ways that the other groups would try to do it?

00:20:20.07 [Jim Sterne]: I’m going to throw an orange flag on the field. This is where I have a problem with data democratization, and that is the only way you can put all the data together in one place and have it work is if you sprinkle machine learning on top of it, because every data set requires a subject matter expert. And if you’re an expert in market research and doing surveys, if you’re an expert in television ratings, if you’re an expert in Salesforce automation tools, each of those are really unique realms and you can’t just throw all the data together and expect it to make sense to an individual. So I don’t think that if there were one place in the company where all the data feeds came that it would magically how value, I think every data stream needs a steward who really understands the deep darkness of how trustworthy the data is, how reliable it is, how fresh it is.

00:21:19.61 [Jim Cain]: We use the word curator a lot more talking about that. Totally agree.

00:21:24.78 [Michael Helbling]: I agree with that as well. I think maybe the non sequitur for me there was the necessity for machine learning. But maybe I just didn’t understand that. Do you want to expand on that a little bit?

00:21:38.25 [Jim Sterne]: So the thing that machine learning is really good at is finding correlations. So there is a correlation between the sales of ice cream and the number of people who drown in a month. And for the machine to go, oh, look, there’s a correlation is useful to the human. The human can look at that and say, well, of course, because the temperature goes up, more ice cream is sold and more people go swimming, but there’s no causality here. So thank you, but no, thank you. And then the machine says, hey, there’s a correlation between when it rains and people buy umbrellas. And the human says, thank you, Captain Obvious. That’s true, but it’s not useful. Try again. And the machine says, you know, when this particular kind of person who exhibits this particular kind of behavior, who came to us from this particular source and responded to that particular email is 37% more likely to purchase it, we send him a message that has this benefit statement. I say, thank you.

00:22:37.11 [Michael Helbling]: So that yeah, I’m with all of that.

00:22:39.80 [Tim Wilson]: Well, but I’m skeptical to one further degree on that because there’s still a need to have variation in activity. I think that’s that’s the other place where things start to fall down. What does that mean? Well, so let me give a very, very simple, simple example of social media. Facebook, back in the, what’s the best time for us to post on Facebook? And this is kind of a gross simplified example of where if the machine isn’t fed, it can’t do anything with it. When it’s like, well, we want to know what the best time of day to post on Facebook is. So you say, well, okay, we’re going to look at all the posts you’ve done for the last six months. Well, it turns out that you’ve only posted between 8 and 10 a.m. Every single Facebook post because that’s the process that the agency or the company set up that somebody thought this is the best time to post. And so that’s when they post. So you can’t find a correlation between, oh, if we post after 10 p.m., we get higher engagement, because there’s no data there. So if you take to your example, if we send this type of email, well, that means I have to be sending a range of types of emails to a range of types of people. And all too often, we get in the, what’s the simplest path? What’s the one email we’re gonna send at the one time? Maybe we’ll do some subject line testing. But we’re going to be in this rhythm and we say, we’ve got all this data. It’s like, yes, you have all this data, but it’s all largely the same because you haven’t tried anything different. So you need to kind of back up and have a little bit of a design of experiments approach and saying, well, how do we want to mix things up so that we’re generating some data so the machine has something to play with?

00:24:24.20 [Jim Sterne]: And this is why Skynet will never eat your lunch and take your job. Because Skynet’s really good at stuff, but it’s really dumb at other stuff. You know, computers are really fast, but really dumb. And humans are really smart, but really slow. And you got to put them both together.

00:24:39.97 [Michael Helbling]: Well, next time I’m eating some ice cream by a pool and a robot slaps it out of my hand. Be careful.

00:24:46.16 [Tim Wilson]: I’ll know what happened.

00:24:46.98 [Michael Helbling]: It just saved your life.

00:24:49.08 [Tim Wilson]: It just saved your life. Wow. So that took a… Anyways.

00:24:53.74 [Jim Cain]: All right. Where were we? So question for me, you’ve met a lot of companies that do measurement. I’m sure a lot of them have brought you in. And everybody on this call today is making money off companies that know what winning looks like in measurement, but they need someone to help them do it. And they’re all missing different pieces of that equation. Can you give an example of a company where you walked in and you were preparing? Because it’s not your first rodeo. I’m sure when you go into a company, you go, when I ask this question, they will say no, and I will help them fix it. You know the dance steps. Can you kind of share an anecdote about an organization you walked into where you were like, whoa, shit, that you don’t need me. Let me write this down.

00:25:33.35 [Jim Sterne]: Yeah, there were two of those. The first one was Hewlett Packard. So when I wrote customer service on the internet in 1997, I interviewed a guy at Compact Computer and he was getting it. I mean, he just totally had it wired. But I was talking to them the day that it was announced that Hewlett Packard was going to hire them. So a year later, he calls me up and says, hey, we need to workshop at Hewlett Packard because the compact computer team became the head of all analytics for HP and I’m running it and we need your help. I went, yeah, right. So I go to HP, we spend the first half of the day where they’re telling me what they’re doing and I am just blown away. They are doing conversion analysis and source analysis and how is my dollar being spent and where is it being wasted? Perfectly. It’s just beautiful. I’m ready to write another book and we go out to lunch and I turn to Seth Romano one of the founding board members of the WAA and head of analytics for HP. And I say, Seth, I’m out, I’m beyond, you guys are just like totally in the groove. I can’t, there’s nothing I can do for you. He says, no, it’s fine, you’ll be fine. I said, no, really, I can’t, I cannot send you an invoice. I shouldn’t even be here. You guys are awesome. I’m writing another book about you. He said, well, just, you know, come on back and like, let’s reconvene and we’ll talk some more. And I got nothing. So I pull out of the bottom of my bag, the last question that proves to the audience that I have no idea what I’m talking about. It is the consultants last ditch effort. I say, so guys, tell me, what’s the hard part? And it was like they picked up the garbage can and dumped it on the table. Well, this doesn’t work. And this technology is broken. And this executive won’t give us budget. And these people don’t believe what we’re saying. And these people won’t do this. And it was like, oh, guys, guys, settle down. Yes, I can help you. The second time was in Boston at the last Demetric Summit. I went out to dinner with Jun Lee and Stefan Hamal and Josh Aberrant, who is the postmaster at Twitter, who had given a keynote speech in Boston. And we said, so, you know, here are all the problems that we’re all experienced with our clients. And you know, they’ve got data silos and we’ve got legacy systems and we’ve got people who don’t get it and we’re trying to convince upper management and we’re trying to get more budget. And he’s looking at us like we’re from Mars. He says, I’m sorry, I cannot relate. At Twitter, we have one data set and we have one goal and we all work off the same data And we all believe in data. And a year ago, the CEO got up at a company, all hands meeting and said, if anybody wants to run a 1% test, they don’t need permission. They just go ahead and run the test. And we all went, we can just like hypothesize out the wazoo and test everything we want. And then we realized that if you ran a 1% test on Twitter and it didn’t work, You just pissed off 6 million people for a whole day. Your job is in trouble. So be careful what you’re testing. And then they ran all these tests and they would go, wow, look, we found this and we found this and we found this. And then they discovered that they were up against the wall of, I found this really cool correlation. And the first question was always, well, really what experiments did you run? What does it mean? So June and Stefan and I are looking at this guy going, Well, this is Nirvana. This is a giant corporation that has one data set. There’s one source of truth. They believe in data. They love testing. They’re doing it right. And it’s still really hard.

00:29:30.79 [Tim Wilson]: And then Josh left two months later and went to Spark post, right?

00:29:35.11 [Jim Sterne]: He went to Spark post where he has been doing some further research and then doing research about what Google and Microsoft are up to. And he will be keynoting again in San Francisco at the metric summit in April. So we’ll get a bigger story.

00:29:51.24 [Tim Wilson]: It sounds like in both cases you walked in and there was a good story, or there was a story of, we’ve got it all figured out, but it sounds like as you probed farther, there really wasn’t really Nirvana.

00:30:08.74 [Jim Sterne]: There is no end game. There is, there is not a solution to this problem. It is data, which is messy and difficult to understand and requires a curator for each dataset. And there is politics and there is legacy. And there are people who are not very bright, who have too much control over the process in every company at every level.

00:30:35.23 [Jim Cain]: I like how the two companies, like one of them was an old school enterprise that had a winning team, but kind of organizational entropy. And the other one had organization at the speed of light and people were just getting like ice cream headaches. You know what I mean? Like opposite problems on both sides. Yeah.

00:30:54.34 [Michael Helbling]: Yeah, I’d like to turn our attention toward the venerable hippo for a minute, because they entered our digital analytics world and caused a lot of turmoil. And yet the three of us find ourselves defending them quite often. When did that really start entering the lingua franca, if you will, of the analytics community? I’m not sure of the timing.

00:31:23.06 [Jim Sterne]: As soon as it became about money, I understand the culture of this stuff goes way back. 1993 is when Moesaic came out. And the only people that had websites were called webmasters. It was Dilbert in the bowels of the IT department. And he built a website out of a server that he downloaded somewhere, put it on an old 386 machine under his desk and hooked it up to an ISDN line. And eventually, somebody in the company realized that they had a website and that the brand was being represented by Dilbert. And the marketing department took it over and said, well, Dilbert, we’re going to now name you Webmaster. And Dilbert had never been in marketing and marketing had never seen technology before. And it was Amasha. of people who had never talked to each other before and the webmaster was responsible for all of the creative and display because people said we want this picture so I can put up a pdf but if you want something with buttons I’ve got to do all this stuff same thing happened with analytics it was the webmaster who discovered there were log files and so he said oh there’s you know look at all this data there’s got to be a pony in here somewhere Without any clue that there was a BI department without any clue had never had statistics classes. He’s just looking at these static reports from net Genesis and going, wow, I got money for this product and now I’m getting these reports and I don’t know what they mean. And that’s the way it has always been until eventually. The website was actually making enough money that somebody upstairs said, oh, this is important. It shouldn’t just be something we’re doing for fun. We should invest in this and therefore we should manage it and control it. And my wife likes blue, so all the buttons should be blue. And that’s how the hippo came to be.

00:33:17.19 [Jim Cain]: So can I throw one more idea in because I know we’re getting close to time. I’m getting close to drunk.

00:33:23.53 [Michael Helbling]: Hey, let’s throw another idea.

00:33:25.63 [Jim Cain]: Every other big marketing group has a lobby, like beyond the lobby bar, the lobby group. There it is. That’s a podcast title.

00:33:37.18 [Jim Sterne]: There you go.

00:33:38.08 [Jim Cain]: The IAB spends a lot of money in Washington.

00:33:40.75 [Jim Sterne]: That is their raison.

00:33:42.23 [Jim Cain]: The Web Analytics Association, we spend all our money literally at the lobby bar. So when people look at like our community, they confuse us with people that do dirtbag things with online data. I was reading something about Mr. Sanders and Ms. Clinton and they have access to this democratic, you know, member database and weird things happened and it immediately starts turning into analytics and analysis. We get painted with some pretty shitty brushes. And analysts tend to be some of the more conservative people about what you should capture and how you should maintain and do things with it. Are we at the point where, and it could be, in your opinion, a name change, it could be everybody using the game has to put a few dollars in the hat so we can start to lobby or position ourselves differently than some of these advertising organizations? Is there a big next step for us as a community? Good answer. Thank you.

00:34:50.02 [Jim Sterne]: You’re welcome.

00:34:50.66 [Jim Cain]: There we’re done.

00:34:51.55 [Jim Sterne]: Glad we’re here. So when the WAA started, it was a considered decision to be inclusive of practitioners, us, consultants, us, and vendors. Now, if you can figure out a lobby platform that would benefit all three of those, You are a better person than I am. So we just said, look, if we’re going to be a lobby organization, we need to go to each corporation and ask for $50,000 a year to represent them in Washington DC and have a very clear platform. The IAB, they have a clear platform. They have a purpose. They represent a segment. We don’t. We represent an area of inquiry that is inclusive. So from the very get-go, we said, we’re not going to do lobbying. It’s not who we are.

00:35:54.23 [Michael Helbling]: That’s interesting. And I love that you just said that because that really helps position the DAA as the organization that it is. But it does make one wonder in the future Is there a separate organization that will need to come up to, you know, contend for the interests of actual measurement and doing it responsibly and in ways that businesses can use it to do the things to make the customer experience ideal?

00:36:22.98 [Jim Sterne]: I don’t see that as really possible because you’re advocating for and against collecting data. So we did have Eric Peterson did come up with and posted the standards of ethics. Do you agree to this code of conduct?

00:36:43.31 [Michael Helbling]: Yeah, the code of ethics.

00:36:44.73 [Jim Cain]: I really like that. I signed it, I think, the first week. Absolutely. I did, too.

00:36:49.42 [Michael Helbling]: I never signed it. I’m telling you. Because I am. Well, I just told. Yeah, I don’t know why I didn’t sign it, because I certainly agree with it.

00:37:01.76 [Jim Cain]: Michael can either confirm nor deny that he has a thumb drive of credit card numbers in his pocket right now. Oh, man.

00:37:09.89 [Michael Helbling]: Yeah, exactly. Confirm nor deny.

00:37:13.79 [Jim Sterne]: I have such a schizophrenic relationship with privacy that I really have trouble discussing all of this stuff. because on the one hand, I do not trust corporations and I do not trust governments at all with my data. On the other hand, I will gladly tattoo a barcode on my forehead if it will get me on the airplane faster.

00:37:42.96 [Michael Helbling]: Yeah, or get me the products I want to my house.

00:37:46.81 [Jim Sterne]: Or tell me what I need before I need it. You know, be the artificial intelligence for all led to believe could happen. Where’s my flying car?

00:37:55.24 [Michael Helbling]: Yeah, or, you know, Google on your phone predicting what you want to know next, which is totally happening.

00:38:01.66 [Jim Sterne]: Every time I get in my car, Google says how many minutes it will take me to get to the office. I’m not going to the office right now.

00:38:10.81 [Michael Helbling]: How can people be so stupid? They’re trying.

00:38:14.25 [Jim Sterne]: God bless them.

00:38:15.19 [Michael Helbling]: Yeah. Wait till they start driving your car. You get in your car and you just show up at the office and be like, this is not where I was going, guys.

00:38:22.02 [Jim Sterne]: Self-driving cars cannot happen soon enough.

00:38:28.25 [Michael Helbling]: Tell me about it. I’m excited about it. I can’t wait till I can just sort of be reading the Devil’s Data Dictionary while commuting to the office.

00:38:34.96 [Jim Cain]: I will never need to get my license. I can’t skip that whole phase of adulthood entirely and still buy a car.

00:38:41.48 [Jim Sterne]: I can’t wait until I can finish this bottle of Jameson’s without fearing for my life.

00:38:46.55 [Jim Cain]: You get pulled over. Have you been drinking? Hell yes, I’ve been drinking.

00:38:50.48 [Michael Helbling]: And texting, thank you very much.

00:38:54.49 [Tim Wilson]: The cars had nothing but 220 volts of the pure, the good stuff, the good stuff.

00:39:00.96 [Michael Helbling]: That’s right.

00:39:01.88 [Tim Wilson]: And yeah, if it’s high octane electricity. All right.

00:39:05.32 [Jim Sterne]: So just just because I it’s time for a break in the rhythm, I’m going to read all of the A’s from my book and they’re not that not that many. So the first one, the first entry in the Devil’s Data Dictionary you have already heard, it’s algorithm, regularly recurring remarks from a former US vice president who invented the internet. This is followed by analysis, when a calculation requires more than 10 fingers. Analytics, same as analysis, but garnished with red pepper rings, microgreens, and chicory. Analysis paralysis Thursday API API is a gateway drug that leads to pulling more data than rational from systems gathering data faster than sensible for reasons more aspirational than comprehensible with feeling.

00:40:04.14 [Michael Helbling]: I like that one.

00:40:05.66 [Jim Sterne]: Assumption is the part that’s always wrong and the A’s finish up with average, obscuring the interesting or valuable through homogenization. In other words, on average, Switzerland is flat.

00:40:23.33 [Tim Wilson]: Now, you skipped 1A and it is the most appropriate for this entire podcast. Oh, Mr. Stern, would you please define artificial intelligence?

00:40:36.83 [Jim Sterne]: Artificial intelligence was included. You must have already been artificially intelligent because I did not read that out loud. Artificial intelligence is the fourth cocktail.

00:40:48.10 [Tim Wilson]: I swear, maybe I had the fourth cocktail. I think you went straight from API to assumption.

00:40:53.47 [Jim Sterne]: Now that’s an assumption.

00:40:54.63 [Jim Cain]: We’ll have to listen to you.

00:40:56.73 [Jim Cain]: That could be the title of this podcast. Just call it the fourth cocktail.

00:41:00.37 [Tim Wilson]: It’s intimate and interactive. I’m like, hey, guys, I’m keynoting in the morning. Maybe it’s time to close out the tab.

00:41:08.22 [Jim Cain]: That’s when I say, hey, Tim, I got invited, too. I’m going to be speaking after lunch on the last day of the show. How’d you get a keynote? All right.

00:41:19.82 [Michael Helbling]: Well, it’s that time of the show where we go around and we reflect on maybe what we’ve learned or share an observation. Who wants to go first?

00:41:31.78 [Tim Wilson]: Oh, go first. Yeah. I gotta say this has been a little depressing.

00:41:37.55 [Jim Sterne]: You’re welcome.

00:41:39.80 [Tim Wilson]: I feel like we talked about the history that could not help but morph into a discussion of the present, and that then also led to a little bit of the future. There’s a lot of stuff that maybe stops just short of intractable from challenges. Organizational, the complexity of the data, the aspirational beliefs in what automation can and will do.

00:42:14.97 [Jim Sterne]: Tim, I’d just like you to change the word intractable to job security.

00:42:19.50 [Michael Helbling]: Job security.

00:42:20.80 [Tim Wilson]: Exactly what I was thinking. Job security is looking up. I can continue to be marginally above mediocre and still be gainfully employed.

00:42:32.10 [Jim Sterne]: You too are from Lake Wobegon.

00:42:33.96 [Tim Wilson]: Yes. So I think that’s kind of my, I mean, I’ve enjoyed the discussion. It wasn’t, I picked up a few kind of historical anecdotes, but I would say one of the last things we covered, the why the DAA is unlikely to ever be an effective lobbying organization. I don’t think I’ve heard it put that clearly as to why You don’t have common interests. By design, the DAA has diverse interests represented, and it would be hard for them to unify by design. So that’s kind of fascinating, but also mildly depressing. So thank you. I’m going to go and shoot myself now. No, not job security. I’m going to not do that because I’m going to remind myself that I have job security.

00:43:23.04 [Jim Cain]: There you go. All right.

00:43:24.20 [Michael Helbling]: I’m going to do this thing where I jump right in front of you, Jim Cain.

00:43:27.64 [Jim Cain]: I was having fun until Wilson got all dark and shit. I know.

00:43:32.70 [Michael Helbling]: I did not take that from this conversation. I was delighted. I want to share a brief anecdote about my first e-metrics conference. I will share a couple of details in the hopes that some of our listeners can use some of these tips to themselves come to an e-metrics someday. I went to my boss and I said, there are two conferences I want to attend this year. E-metrics in Santa Barbara and this search conference over here. And my boss looked at me and he’s like, I don’t know, I don’t know if we can do two conferences in a year. And I said, okay, I’ll just do the one then, E-metrics Santa Barbara. So I was fortunate enough to attend the last e-metrics in Santa Barbara in 2006, and it was a life-changing experience. And I owe you, Jim Stern, a debt of gratitude for fostering a community so open and friendly and willing to share their knowledge and experience in a context like the lobby bar. It has enriched me as a professional for years and years and years.

00:44:47.66 [Jim Cain]: So, this is the very… No, I mean… You just brought tears to my eyes.

00:44:54.00 [Michael Helbling]: But it’s true. If you think about the work of this industry and the people of this industry, where they have intersected over the years in the most meaningful ways has been in the context of the e-metrics conference. So it’s something we’re always trying to recreate together. And so anyway, I’m going on too long. I’m very glad that you were able to be our guest and join us on the Analytics Power Hour. Even if it’s mushy, Jim Cain is, you know, nervous about his feelings. Just let him out, Jim. Just let him out.

00:45:37.65 [Jim Cain]: I’m a good Irish boy. I packed him into a ball and buried him so deep he’ll never find out. But kidding aside, when I started, so 10 years ago when I hadn’t started napkin, I was still selling, but started to do some analysis, and I sat on the standards committee. And so I very clearly now understand why it can’t be a lobby organization. That was not a fun club.

00:45:59.69 [Jim Sterne]: No.

00:46:00.13 [Jim Cain]: That was tough. But separately, when I, and Tim and I in particular have talked about imposter syndrome before, when I started getting going, one of the first things I got to do when I started napkin, and it was me and a bulldog in a living room with a Skype phone. Smart dog. It was a smart dog. But his little pauses couldn’t work the Excel, so I had him put down. But I don’t want to talk about that story. But Jim, let me speak at E-metrics Toronto. And that gave me a lot of confidence. Let me meet some people. It helped make some of that imposter syndrome go away. There certainly wouldn’t be this podcast if there wasn’t any metrics. I’ll tell you that. Because this is literally what we’re trying to do. So it’s pretty cool to have you on the show.

00:46:41.24 [Jim Sterne]: Really appreciate your comments. It’s kind of why I do this for a living and will continue to do it because people end up saying stuff like that. So thank you. Tim, please don’t shoot yourself. Jim, please don’t shoot your dog.

00:46:57.71 [Michael Helbling]: Well, it has certainly been a pleasure. And of course, if you are listening out there and you want to get in on this conversation, I think you may have noticed one thing as we’ve talked on this show today. And that is Jim Stern is a really approachable guy. And so if you ever see him at an E-metrics conference lobby bar, you could probably sit down and have a chat and buy him a drink. And you may be surprised. Some person from Europe, probably named Renee, will come over and give you a chocolate. You never know. And if you’re listening and you’ve got comments, we would love to hear them. You can always read just on our Facebook page or the measure Slack, which you can also find from our Facebook page. And of course, we’re on Twitter as well. I don’t know whose bright idea that was, but apparently we might be running 1% tests on you on that. So just be careful. What a great nostalgic show. My spirits are lifted. Apparently Tim’s are like it has opposite effects.

00:48:07.08 [Jim Sterne]: Zero sum game.

00:48:08.54 [Michael Helbling]: Yeah, there you go. But it’s it’s so great to get a chance to sort of peel back the layers and think about where we’ve come from and how that points us to where we might be going. As you’re listening, how you can play a part in that. Obviously, there’s a couple of things that we talked about tonight that probably should be of interest to everyone. The Metrics Conference, certainly worth attending. They have multiple locations in the US and abroad, so definitely look for that near you this year. The Devil’s Data Dictionary, I think aptly displayed on this show, is noticeable in that it should be a part of every good analyst desk reference. You should have that easy to hand. So definitely go and get a copy of that. And of course, the DAA, which is not a lobbying organization, but it’s an organization for you, the practitioner, consultant, or vendor. So become a member of that as well. So Jim Stern, thank you once again for being our guest. We had a great time. And as always, for my co-hosts, Tim Wilson and Jim Cain, keep analyzing.

00:49:26.85 [Announcer]: Thanks for listening. And don’t forget to join the conversation on Facebook or Twitter. We welcome your comments and questions, facebook.com forward slash analytics hour or at analytics hour on Twitter.

00:49:40.81 [Jim Cain]: With your indulgence, I’d like to read a little poem I call Insight.

00:49:55.50 [Jim Sterne]: Insight from the Devil’s Data Dictionary. It’s not having the answer. It’s dreaming up the question. It’s pivoting like a dancer than testing with regression. It’s not about the facts. It’s making the connection. It’s that which then attracts you to create a new projection. It’s not about the figures. It’s about the correlation and big data just gets bigger with more data integration. It’s not the data warehouse. It’s the cross association. It’s not the beans that count, but considering causation. It’s asking why and asking how and looking for the info that will open doors and disavow hip shooting from the hippo. Big data isn’t magic. Big data is not divine. Big data becomes tragic when art is left behind. The human head contains much more than a hundred billion neurons. There are thoughts and schemes and stuff that dreams are built of and are built on. Human beings can look at a cloud and see a face resolving and fantasize how that relates to the problem they’re solving. Feed your head, the dormouse said. Don’t die from dried-up facts. Imagination speeds creation, and for that you must relax. Let your brain recuperate and give it room to breathe. Let the sweat of your brow evaporate. Stop the grinding of your teeth. go for a walk, have a beer, go swimming in the ocean, light a fire, indulge desire, wallow in emotion. As one grows older, Einstein said, one sees the great futility, of imposing your will on the chaos, with brute force and hostility. But if you can be patient, there may come that moment when. Your mind is on vacation. The answer bows and says, Here I am.

Podcast: Download | Embed

Subscribe: RSS