The era of meaningful web analytics has only just begun. More and more companies understand that PageViews are not a suitable KPI to check the success of content investments. And yet the end of what we are just growing fond of is approaching before it can become too beautiful.
This is not another click-bait article about how machines will take our jobs. Of course, machine learning will eliminate the simple analyst jobs. We can already see today that Google Analytics detects anomalies independently in the free version. Questions about the data can be asked by spoken language. And since the challenge is usually to ask the right questions of the data, this will also be covered by independent analysis. In the 360 version, Analytics offers the new Analyze mode. It will be possible to create analyses more and more automatically. And that’s a good thing. Because even if we have a lot of experience and know which segments are worth investigating, a machine can simply calculate all the combinations and come up with segments that we as humans would never have found. The poking in the haystack has thus come to an end. And it is more efficient when machines undertake this search. It is no longer the case that information is only generated from data – actions are already being derived. What is most difficult for most users to learn from the data, what should actually be done, will also be able to be interpreted and articulated by the machine.
It’s not that simple, the coachmen of web analysis will say: You can only draw something meaningful from the data if it has been collected sensibly. And in most cases, this is not the case that meaningful data is collected. As long as a standard installation of a web tracking tool is used in many cases, there is still a lot to do. But if we take a closer look at today’s Google Tag Manager, it becomes clear that many user interactions can already be tracked automatically. Clicks on links. Scroll depth. Element Visibility. What still has to be set up today could be done automatically tomorrow. And it would be the logical next step. So let’s assume that at some point in the near future, setting up a tag management will be eliminated. Depending on how much you pay, events are measured more or less granularly. And only those from which the machine has learned that they are important for a defined goal.
The complexity of data acquisition will be eliminated, and analysis will be eliminated. What will be left then? To work out a digital strategy that is designed on the basis of data? I don’t see that in the brand essence of web analysts. Manual web analysis is a bridging qualification, the gas station attendant of analytics solutions. Because in 5 years at the latest, 2023, the big customers will already be working with web analytics AI and no longer with an expensive, vain web analyst.
What should we do, we “web analytics heroes”? Either we qualify further, from petrol station attendant to module manufacturer. Or we sell the sweets around it. The McJobs of web analytics. And they can also be torched in India. The only option left for us is to use data science to develop solutions that are too niche for Google Analytics and Co to be the first to prove them. We will be able to earn money with implementations and training for a few more years, but by 2023 at the latest, this will be over.
Comments (since February 2020 the comment function has been removed from my blog):
Maik says
- May 2018 at 13:22 Hallo Tom,
thank you for your contribution, which offers a new perspective for many. From our conversation during our podcast recording, I already know that you have a very data-driven approach and also subordinate the future of web analytics to machine learning.
Like so many disciplines that currently exist in (online) marketing, web analytics is one of the – in my opinion – still rising trends. Think of SEA, SEO, affiliate marketing, email marketing, … All of this is booming. Still. Although, for example, SEO was constantly talked to death for a long time – and Google was increasingly credited with more competence to be able to do things “on its own”. Certainly, Google HAS gotten better. But the people/companies with their websites are not. And that’s how I see it with web analytics.
As long as a discipline has not yet reached the breadth (especially medium-sized companies and small companies), five years is, in my opinion, too short a period of time to achieve changes in breadth. At the top (e.g. corporations) I see it somewhat differently, but the masses have to notice it – otherwise the change is more of a local maxima. But maybe that also means that we will all work for Amazon.
I agree with you when you say that machines can detect anomalies or clusters in the data much better than any human ever could. But the question is: What do we, the people, do with it in the end? And there is still a need for people – probably much longer than in 2023 – to translate this into “doing” after they have previously compared it with their strategy.
The catch is that machine learning needs data – as much as possible. And IMHO we took a decent step backwards in this regard on May 25th. Perhaps we – or those who passed the GDPR – have even put Europe on the sidelines. And the regulations of the regulation and people’s fears of misuse of the data ensure that it remains that way for the time being. This is quite a brake for many companies.
In addition, even if this can and certainly will change in the next few years, AI or ML is not yet a “mass phenomenon”. Everyone talks about it and maybe guesses the possibilities, but in fact “Hurray” is not heard everywhere out there. There is a lack of systems that collect more data, (currently) people who are familiar with it and, ultimately, often also a lack of knowledge of what you can do with it.
You said quite rightly: You need the right questions. Without them, AI will be helpless for much longer than in 2023. But in order to ask the right questions, you need people with an understanding of numbers, business, strategies and implementation knowledge. OK, maybe at some point they won’t be called web analysts anymore (just like there may be no more pure SEOs and the SEA’s people, whose jobs have all been taken over by Google AI), but web analytics itself will be part of a perhaps new job description that ALSO requires an understanding of web analytics – and for that you still need people who don’t do anything else all day. Training may then be more focused on strategies.
I see web analysts and data scientists on more or less separate tracks. For me, the web analyst is often closer to the business and what follows from numbers. The pure data preparation and the like – I’m sure – will certainly simply fall away at some point.
In the course of this, perhaps a reference to a great article that I devoured the other day and will only let us chill in 50 years anyway. Here is the link: https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
Well, and above everything you and I say, there’s one more thing: Maybe in 5-10 years we won’t have internet anymore, but something even cooler.
Thank you for bringing the topic here to your blog. And double thanks for the analogy with the gas station attendant. Love it. Fill up the tank, please. Maik
Tom Alby says
- May 2018 at 23:33 Dear Maik,
every exchange with you helps me to sort and question my thoughts, and as always, I am very grateful to you for that.
The reason why I predict the end of the Web Analyst within a few years is not pure sensationalism. I believe in it. Here’s why:
- If we assumed a linear development, then I would not formulate such a prophecy. But we don’t have a linear development. We have a non-linear development. An exponential development. And it is a characteristic of humans to underestimate exponential developments. An old legend shows this in the person of Sissa ibn Dahir (https://de.wikipedia.org/wiki/Sissa_ibn_Dahir). In 1996, my father was still scolding me for buying something as crazy as a cell phone, 10 years later we had Internet on our cell phones, another 10 years later we have voice assistants at home, and an Internet of less than 10 MBit is unthinkable, at least in the cities. Your article describes exactly this exponential development, doesn’t it?
- We don’t just create technology, technology changes us. Technology is changing the way we think, and that always leads to technology being criticized. Scripture, Ong argues, was criticized by Plato for externalizing thought. When calculators came along, the same criticism was voiced that people were unlearning how to calculate. And now we hear that artificial intelligence does the thinking for us and that this is not good. In fact, however, every technology has brought us further. Whether that was always a good thing is another matter. But even writing as a technology has changed our thinking. And so machine learning will also change our thinking. In a few years, we will be thinking in terms of machine learning. And if you say that this is still very far away at the moment, I don’t think so because of the exponential development.
- You say that web analysts are closer to the business problem than computer scientists. I couldn’t disagree more. Of course, I believe you that you focus primarily on business goals. As a data scientist, on the other hand, the first question for me is always what problem I am actually solving. That’s what I teach my students (http://wordpress-95500-642800.cloudwaysapps.com/lehrveranstaltungen/data-science-analytics/understanding-the-business-problem/).
- SEO is dead. For me, most SEOs are the alternative practitioners of online marketing. We believe them because it is difficult for us to assume that we are not in control. But I think I’ve already published enough data to prove that we are dealing with snake oil in particular. I have never seen an SEO publish his numbers like I do.
Yes, maybe 2023 will be sporty. But if Google does anything, it’s something scalable. And even though AdWords Express didn’t work great in the beginning, more data will make it work.
Would you like a Snickers to fill up?
Tom
Maik says
- May 2018 at 10:12 Hey Tom,
the legend of Sissa ibn Dahir is a great example. On the other hand, especially with exponential gradients, especially at the beginning, the gradients are not yet so high that they provoke such overturns. But you’re right: If scalable, then Google.
And indeed, the post I linked describes, among other things, exactly this growth – in sometimes frightening, because incredibly huge, dimensions. And of course, anyone who tries to understand this has trouble. What will it be like if there are machines that are 1 million times more intelligent than us humans before the end of this century? I cannot imagine …
The good thing is that the two of us don’t really talk about IF it (the replacement of certain jobs) will happen, but actually only WHEN. In this respect, I am with you on many points. Ultimately, it doesn’t matter whether web analyst or data scientist jobs are more likely to disappear than others, but rather what our future task will be. Whether in the future there will still be a need for people who think “analytically” and approach solutions with “creativity”.
And yes, I also see that SEO has become “overthinkable” (I would call it benevolent) in many places. At least in the way it is operated by many SEOs. (Your term “alternative practitioner” fits it quite well, I think).
In this special discipline, too, it is increasingly true for me: People are needed who deal with a sensible information architecture (technically and content-wise) with regard to its users and make improvements. This requires (for the time being) an understanding of people, values – and solid data to measure improvements. But maybe in 5 years websites will be built completely by machines. Maybe in 10 years there will be no more websites, but only VR. Who knows?
One more thing: I hope that developments in the next few years/decades will only progress so quickly that we humans have a chance to find our role in the “new world” – and not be completely overwhelmed. Because if machines can one day do everything that we humans can do, and do it much better, then we will certainly be able to solve a lot of problems here in the world – but we will also have many new ones as far as our tasks are concerned. And especially the component “exponential” is somehow – as funny as it may sound – unpredictable.
Snickers? Yes, of course. Takeaway, please. Maik
Christian Hansch says
- June 2018 at 16:09 Hallo Tom,
what exactly do you mean by the qualification as a module manufacturer at the end of your article? I also believe that we web analysts will be replaced in 5-10 years, but actually I still have to work for almost 20-25 years. Hypothesis: In the future, it will be even more important to ask the right questions with the right constraints if necessary, so that the machine can “spit out” the right answer.
I think the GA app with the point out of the anomalies is also great. But I wonder how the machine is supposed to favor the various goals and not always only the monetary output is maximized, sometimes also the communication, which is difficult to measure.
Best regards Christian
P.S.: It’s a pity that I didn’t see your presentation at the Campixx, then I could better understand or discuss why SEO should be dead.
Christian Hansch says
- June 2018 at 10:54 .. Here is an answer from the Google team to your question: https://www.youtube.com/watch?v=BBZh_O0MeeE&list=PLI5YfMzCfRtaaQpilSJf9jqrP7BVfjBWI&index=2&t=0s at approx. 13:30 …