5 New Year Resolutions Your Company Should Make For 2018

It’s all about to start again. You’ve probably made up your strategic plan for 2018. Decided in which period you will target your different segments of potential customers, when you’ll launch your newest product or service, what your KPI’s will be.

I’m hoping these are on the list. If not, we need to talk…

1. Know what’s going through people’s minds and act on it.

This year, the biggest breakthroughs I had when working with clients were in Empathy Mapping sessions and starting every plan from the perspective of the potential customer.

empathy mapping and customer journeys

Customer Journey following Empathy Mapping
Picture ¬© Geert Troch ūüėČ  

In most companies’ day-to-day setting, it’s very hard not to think in terms of your existing business and not turn a blind eye to the universe it is existing in. Having everyone in this customer-mindset makes the decisions much more easy to take. It becomes clear as daylight which difference you should be making in the lives of your potential customer.

So, if you really want to make 2018 about your customer, start to implement Service Design-principles in your process. It’s the most efficient thing to do considering (when done will) this ensures you’re spending your time on the right stuff. And that’s priceless.

2. Study Serotonin, Oxytocin & Dopamine.

Attention kept on shifting in 2017. It’s more distributed, it’s aimed at value, meaning and engagement. People shift away from any negative experiences with media, products, services and others. Ad blocking, second screen usage, paid subscriptions are signs of people increasingly looking away from your banner ads, TV & radio commercials, etc. I’m not saying you should stop using them, but:

‘Loss aversion’ has been one of the most interesting human behaviours I’ve encountered and studied in the last few years. You have to make up for the time and money you take from people. As the market for attention is getting bigger, you’ll have to step up your game.

neurotransmitters and marketing

Study neurotransmitters and understand their functions.
Image from https://appsychtextbk.wikispaces.com/Neurotransmitters

The Manifesto of Greater Than X really sums this up for me. You have to design for Serotonin, Oxytocin and Dopamine if you want people to use your product or service. Sounds very scientific, but it’s the most human thing to do.

Ask yourself how your product, service or marketing can be altered to incorporate this knowledge. Are you generating pain or pleasure? Are you doing this for the right reasons? Pain can lead to customers if you’re the one who solves it, an absence of pleasure will lead to a loss of customers.

3. Embrace platforms, apps, VPA’s… You’ll need them.

The ‘Hub & Spokes’-model is rumbling. Stop using the ‘spokes’ as just a pathway to your website. Website traffic is not a goal, creating valuable touch-points is.

More than just adding links to your website, be ready to carry out your brand’s most suitable message for the platforms you’re on. Know why people use which apps and draw analogies (Hint: you’ll probably end up somewhere in my previous resolution).

Understand what it is that people are asking VPA’s for and develop in function of this need. Have a team working on the statement ‘What should we build if all internet searches are replaced with conversational voice search?’.

At Wijs, we have the amazing Koen Vinken en Stijn Spanhove working on Alexa and voice search.

4. Get your company ready for conversational platforms, AI and immersive UX.

In my best guess, these are the three technologies that will influence your job most within the next 5 years. These are very much aligned with and influenced by Gartner’s findings on Digital Marketing and Advertising technology.

For any one of these technologies will probably be ‘somewhat useful’ by 2019, this year should be about getting to know them and try out what value they could bring for single-purpose goals. Don’t expect to have a general purpose conversational platform in 2018 if this is not the only focus for the year. It will not happen.

  • Conversational Platforms: Your focus should be on NLP and intention detection. Build single-purpose chatbots to solve one of the most common problems in the customer journey. Doesn’t matter if it’s in onboarding, informing, entertaining or getting a cab.
  • Artificial Intelligence: There is a lot of artificial intelligence in place right now. Mostly its in the hands of second parties and put in a black box. Get someone in your team educated on the subject and have him/her both challenge your partners as well as finding opportunities for your business flows.
  • Immersive UX: Play around. The world of gaming will probably be some years ahead in the use of technologies like AR and VR. Look at what they do. 2018 will probably see relatively cheap head-mounted displays for gaming and thus advance this field.  Imagine a world with smartphones and HMD’s being the main carriers of Augmented Reality. Find your place in that world.

5. Build your own datasets.

More specifically when it comes to marketing technology, 2018 should be the year that companies get smarter about the data they acquire and use. (All within GDPR-boundaries of course)

The importance of useful and clean data will increase. Having this data at your disposal, combined with the know-how to use it, will give you a better position when talking to MarTech partners and publishers.

2017 has been the year in which trust in Third Party-data decreased even further. Mainly due to the lack of transparency in gathering of the data and thus the quality of it.

Define a data-approach on how to create as much first party data as possible. Get your partners in on it. The use of their data should always have your first-party data as a starting point and should mainly involve similar audiences (clustering) and exclusion of existing audiences.

Signing off for 2017. Please do let me know how your year has been.
Want to have chat on some of these subjects? Contact me.

Have a great 2018.

Structuring XML Sitemaps for SEO

XML Sitemap Structure SEO

There’s a lot of information all around the internet considering the use of XML Sitemaps for SEO purposes. In this post I’ll explain why you should have them, how you should structure them and how you can use XML Sitemaps to increase crawling and indexing of your website. I’ll answer some frequently asked questions too.

What is the use of an XML Sitemap?

The XML Sitemap is a list of pages on your website that you would like to have crawled and indexed by search engines. You can send this information to search engines to give them a clearer view of which content is on your website.

What does it consist of?

Although commonly mistakes are made here, the XML-file doesn’t need to contain a lot of info.
Take for example this excerpt from Moz.com’s XML Sitemap:

moz xml sitemap excerpt

These are the elements that should be in your XML Sitemap:

  • Document info: At the start you say that this is a sitemap that has been put together as defined by sitemaps.org protocol. This is being done by using the <urlset xlmns=”http://www.sitemaps.org/schemas/sitemap/0.9″> line and closing it at the end of the sitemap with </urlset>. This is referred to as the xml’s ‘namespace’.
  • URL loc: For every URL you want to include, you open up a <url> and within a <loc> you put the URL of the page. So far, this is the only thing that’s required for it to be considered a XML Sitemap.
  • URL lastmod: Within the same <url>, you can include the timestamp that indicates when the page has last been modified. So if you have updated a blog article, this timestamp should change.

These are the only 3 things you really need in your XML Sitemap for SEO reasons. If you followed through the link above, you might have noticed the <changefreq> and <priority>.

  • <changefreq>Indicates how frequently this page is updated. This ranges from ‘always’ (indicating the content on the page changes every time the page is being served) to ‘never’ (indicating this page is always the same).
  • <priority>: A metric between 0.0 and 1.0 that you can use to indicate how this page compares to other sites in importance for crawlers.

So why not use these in your XML Sitemap? Because Google doesn’t care about them that much. After all, you could say that every page is 1.0 in priority and gets changed daily to increase the crawling of your website. So they aren’t counting on your honesty…. There are other things you can do though:

  • Priority: Googlebot reads XML Sitemaps top to bottom. Make sure that the pages you really need to have crawled are at the top.
  • Lastmod: Googlebot checks the <lastmod> and compares it with the last time it visited the page. So make sure this is functioning correctly and that your XML Sitemap is updated automatically.

Should every page be in your XML Sitemap?

No, just the ones that have great content that is useful for searchers. After all, what you are doing is pointing at URL’s that might be useful for indexing.

This is were a lot of mistakes happen. Especially if your CMS is not really SEO-friendly. Often these CMS’s will generate an XML Sitemap including all pages that are published. This will also include utility-pages that offer no value when indexed. This kind of trumps the use of an XML Sitemap and, if these pages are not properly marked with a ‘noindex’, could hurt your overall site quality metrics.

Synchronise your data!

Further elaborating my previous point: make sure your Robots.txt, Meta robots and XML Sitemap are in sync. You don’t want URL’s in your XML Sitemap that are blocked by robots.txt or have a ‘noindex’. Seeing this from Googlebot’s perspective, this would make no sense.

When to use an ‘index sitemap’?

If you have a lot of pages or want to structure your pages into different XML Sitemaps (as I will suggest later on), make sure you have an index sitemap in place. This is a sitemap that links to all of your divided sitemaps.

This might be useful for:

  • Different language versions
  • Different page types (product / category / blog / …)
  • Different content topics

How will the XML Sitemap influence crawling and indexing?

What will happen when you submit your XML Sitemap to Google Search Console? These are two frequently asked questions. If you have other questions, please let me know.

Will Google only crawl / index the pages in my XML Sitemap?

No, they will still crawl and possibly index other content. Consider blocking by Robots.txt if you don’t want it to be crawled and/or no-indexing it when you don’t want it to be indexed.

Will Google crawl / index all the pages in my XML Sitemap?

No, the XML Sitemap is just an indication of which pages you want to have crawled or indexed. If you include more URL’s than Googlebot is willing to crawl, it will not crawl every URL. Consider moving important ones to the top and make sure your <lastmod> is set correctly.

How can I use XML Sitemaps to optimise crawling and indexing?

As I have stated a couple times in the article above, the XML Sitemaps are only used as an indication of what pages you want crawled and indexed by search engines. By cleverly using some other tools, you might get important information on how search engines are handling your website…

Using Google Search Console

If you have added your XML Sitemap to Google Search Console, it will (in time) give you feedback on how many pages have been indexed by Google. By splitting up your XML Sitemaps into different parts, you can easily trace indexing issues.

structuring xml sitemaps for seo

You might want to split it up by language, page type, subject, … Whatever makes sense for your website.

It will also give you information on the progress:

xml sitemap crawling

This way you can track if changes to your website / sitemap / … have had any influence on the indexing of your website.

The more you split up your XML Sitemaps, the better the view on indexed or non-indexed pages.

Using your own crawl data

By using tools like Screaming Frog SEO Spider, you can both crawl your website and your XML Sitemap. By comparing these two crawls, you can solve issues in crawling and indexing by Google:

A simple Screaming Frog site crawl.

You just put Screaming Frog into List-Mode.

Insert your (Index) Sitemap URL.

And Screaming Frog will crawl all URL’s in there. Which in my case shows why my images are ‘not being indexed’ according to Google Search Console…

  • Page both in Site Crawl & Sitemap Crawl: Good!
  • Page in Site Crawl but not in Sitemap Crawl: Should it be? Add it! This list should only consist of pages that have no value for search. Probably, most of them should be ‘noindex’.
  • Page in Sitemap Crawl but not in Site Crawl: Possibly there’s no internal linking to this page or these pages are wrong.

Using Server Logs

If you want to take this a step further, you could also add your Server Logs by using something like Screaming Frog Log File Analyser. This will show you which pages were visited by Googlebot and how frequently this has happened.

If you combine this information with the Site Crawl and the XML Sitemap crawl, any crawling or indexing issues should become clear as daylight. It requires some technical skills to do this analysis, but it definitely pays off!

Still need help? Let me know in the comments below.


Machine Learning: How does it impact SEO?

machine learning seo

So, in the previous post we discussed what Machine Learning is. In this post we’ll go over how machine learning is impacting the way search engines (more precisely Google) work. How are they using machine learning (e.g. RankBrain) to deliver the best search results to their audience.

Without saying this is also the way Google is treating this, I want to split the impact into two subdomains:

  • How Google processes your search query and tries to understand intent.
  • How Google designs¬†SERP’s¬†that are¬†relevant to the search query.

There’s a lot more to talk about than just these two subjects, but it’s the main deal. I’ll be explaining both by giving you a brief overview on how Google performed both actions before and after RankBrain. Let’s-a-go!

Processing Search Queries

With DMOZ¬†closing recently, we’ve got a throwback to the early internet browsing behavior before search engines were a thing. The more pages were being made and thrown on the internet, the harder it became to just find what you were looking for. So people tried to solve this problem. People tried to ‘organise the internet’.

Categorising pages

Just like people were used to organise everything in these days, they started to gather the most important websites and put them into folders. You have¬†a website about your local soccer team? We’ll put that here:

ALL -> Sports -> Soccer -> Teams -> Europe -> Belgium -> KMSK Deinze

This way DMOZ, at its closing time, categorized a stunning 3.861.210 websites into 1.031.722 categories over 90 languages. To do this, they had a team of 91.929 editors.

DMOZ Sports

This became an increasingly hard task to do, considering the enormous volume of websites going live on the internet each hour of the day. We needed a new, easier way to find the website page you were looking for.

Search engines based on query/document matching

google processing search queries

Go ahead, type in anything you want.

Why not let people type in the thing they’re looking for and return all the pages that contain the exact search term?

That’s where search engines started. Matching exact search queries to documents. If I had a document online that has the title ‘Coffee Machine’ and I used the phrase¬†‘coffee machine’ a lot in the document, it would be a very relevant result for the search query ‘coffee machine’.

There are a lot of different ways to determine the relevance of a document considering a search term. Consider just the following possibilities:

  • Keyword Usage:¬†Is the document using this query? How many times does it use it (in absolute / relative terms)?
  • Term Frequency x Inverse Document Frequency (TF*IDF):¬†This method takes into account the commonality of a word used in the query. If we’re looking for ‘great guitars’, the word ‘great’ will be more common, so the word ‘guitars’ will be more important to determine the relevance.
  • Co-occurence:¬†Assuming you have a lot of data, you could check which words frequently co-occur with the search query. For example: If a document is about ‘guitar lessons’, it will probably mention ‘chords’, ‘frets’, ‘notes’ and other relevant words. A document containing these co-occuring words (measured across documents) will be considered more relevant.
  • Topic Modeling (e.g. LDA):¬†This is were it gets though. Notice that co-occurence doesn’t imply the words are relevant. Topic modeling is a bunch of ways to determine which words are related to each other. For example the word ‘up’ and ‘down’ are related to each other. They are both related to ‘elevators’ but they are also related in a total different way to ‘manic depression’. Topic modeling uses vectors to determine how words are related. There is an awesome post from 2010 on the Moz blog about LDA and how it’s correlated to rankings. It also visually explains the previous topics.

This works great but has two downsides:

  • Exact search query usage:¬†Matching documents to search queries doesn’t take search intent into account. This means that two different search queries, having the same intent, will have two different results. Also: misspellings are a big issue.
  • Manual topic modeling:¬†The topic modeling used is mostly based on human, non-automated work. This means an enormous amount of work and editors needed. (DMOZ, anyone? ūüėČ )

Search engines using machine learning

What is needed is a machine learning system that learns how words, topics and concepts relate to each other. We need Artificial Intelligence to make search engines understand the questions we are asking so they can give us the correct answer.

I’ve found this great talk from Hang Li (Huawei Technologies), who presented his view on how to use machine learning for advanced query / document matching. The main problem being: how to adapt to natural language (synonyms, misspellings, similar¬†queries <-> same intent,…)?

If you don’t want to watch the full video, the main aspects are here:

Hang speaks about matching the keywords and concepts on different levels:

  • Term:¬†Comparable to the query/document matching. If a document uses the term ‘NY’ a lot, it’s probably relevant for the search term ‘NY’.
  • Phrase:¬†Just like before but on the level of phrases. Term-level matching ‘hot’ and ‘dog’ will not necessarily give you the documents that are relevant to the phrase ‘hot dog’.
  • Word Sense:¬†This is where it starts to get interesting. On this level of matching, we need to be connecting similar word senses. The system should know that ‘NY’ is actually ‘New York’, and that someone searching for ‘utube’ probably is looking for ‘YouTube’.
  • Topic:¬†Even further we should be able to match the topics of the queries being used. If we can link ‘microsoft office’ to ‘powerpoint’, ‘excel’, … and other relevant terms, this gives us an extra layer to determine relevancy of a document.
  • Structure: On this level, we should be able to get the intent of the search, no matter how it is formulated. So the structure of the language should be understood. The system should ask ‘What is/are the most defining part/s of this search?’

So the way this works from a ‘Query Understanding’-standpoint:

search query understanding machine learning

  1. The searcher enters the query ‘michael jordan berkele‘, which contains a typo.
  2. On a¬†term level, the spelling error is corrected. So ‘berkele’ is interpreted as ‘berkeley’.
  3. On a¬†phrase level¬†‘michael jordan’ is identified as being a phrase.
  4. On the¬†sense level there are similar queries like ‘michael l. jordan’ or just ‘michael jordan’.
  5. Importantly, on a¬†topic level, the system recognizes the topic as being ‘machine learning’. If ‘Berkeley’ wasn’t in the query, there would have been confusion on the topic as ‘Michael Jordan’ is obviously also a very famous former basketball player.
  6. On a¬†structure level it becomes clear that Michael Jordan is the main phrase of importance. It’s not Berkeley.

Looking at it from the other side, we have a similar process:

So when both the query and document can be understood on these levels, the system can start matching the search query intent to the most relevant documents. Hang goes further into this process, but this first part explains a lot about the task that’s been given to machine learning.

This process of including machine learning into understanding language and search intent has come a long way. Google uses TensorFlow¬†to have machines learning language. Through a massive input of language data, it can make it’s own knowledge by understanding vectorial correlations between words or phrases. There’s little doubt that this technology is part of RankBrain.

So from a query-processing standpoint, machine learning is helping query/document matching by developing its own understanding of language.

Ranking search results

As said earlier, search engines have two main objectives: First, understanding the search intent to match the right pages. Then, rank all the matched pages so the most useful will be highest in the list.

When we finally decided which pages are probably relevant to the searcher’s intent, we’ll have to make a guess on what page will be the best to rank first. And there are a lot of factors being used to do that. But as you might have learned from the previous blog in this series, all these possibilities become too hard to handle right for every search. And that’s where machine learning and stuff like RankBrain come into play.

So let’s see how we could rank pages.

Pages ranked based on query / document matching

Plain and simple. We let the matching-algorithm run and define scores based on on-page relevance of the document. The document with the highest score, gets ranked first.

Although simple, this is not the best way to do this as it is an easy-to-trick system. Once you know how the query / document matching is done, you’ll be able to design a document that is very relevant according to the algorithm, but not for the user.

Pages ranked based on a set of manually weighted factors

Second thing to do is add in extra factors which can define if a page will be relevant or not. Then manually setting the weight these different factors should have to rank the search results. There are a lot of factors:

  • Page level:¬†query / document matching score, links to the page, linking C-blocks to the page, …
  • Domain level:¬†overall topical relevance, links to the domain, quality of content, …
  • Search level:¬†branded search on this topic, …
  • User level:¬†has visited this website before, visits video content regularly, …
  • Device level:¬†what device is used, how’s the internet connection, …

Problem is, different searches will need different weighting in factors. And that’s more than any man can do…

Pages ranked based on machine learning

Not only does Google have the necessary information on query / document matching, incoming links to the domain and the page, overall relevance and power of the domain… It also gathers information on how well the search results are working. It measures click-through rate, bounce rate, etc…

For example, if you perform a search and get a search results page, there are a couple of things that can happen. Suppose you don’t click the first result.¬†Why in hell, would you not click the first result? The possible list of answers is endless.

  • You’ve already visited this domain in the past and didn’t like it.
  • The search result is not relevant to your particular situation.
  • You think this website is for older people.
  • You don’t like the way the meta description is written.

Everything from user profile (demographics, interests, …) to on- or off-page factors (domain, meta title, …) can be in play. It is too much for a manually updated algorithm to get al these factors right. But given you have enough data (// enough searches), a self-learning algo could do the job.

It can work its way back from the results (‘What is the page that people clicked and¬†probably had a good user experience?’) to define how the different algorithm factors should be weighted.


Machine Learning & Digital Marketing: What is Machine Learning?

RankBrain, Programmatic Buying, Artificial Intelligence, Real Time Bidding, Algorithm Updates… Digital marketing these days is all about big words and the math behind them. How is machine learning actually impacting digital marketing?

That’s what I’m exploring in this series on ‘Machine Learning & Digital Marketing’. Although I’m not a machine learning expert, I’m trying to give you an insight on how the practice itself is changing the way we do (digital) marketing today and how we will do it in the future. In the next episodes, we’ll be covering SEO, SEA, Media Buying and Analytics. But first, in this intro, let’s take a look at machine learning.

What is machine learning?

First things first! You’ve probably already heard about these 3 terms:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

It’s good to know that there’s a difference between those 3 terms. In fact, Nvidia wrote a great blog about this subject. In short:

Artificial Intelligence is human intelligence exhibited by machines. Machine Learning is an approach to achieve artificial intelligence. Deep Learning is a technique for implementing machine learning.

For example with what is called “narrow AI” we can ask a machine to do a very specific task, like ‘beating a human at chess’ or ‘given a certain word, returning the most relevant page of a website’. Notice how the AI doesn’t need to understand Alexis de Tocqueville’s view on democracy. It doesn’t need to mimic the human brain, just do what is needed to perform te task at hand.

Artificial Intelligence: The art of beating a human at chess

There are lots of ways to make a computer beat a human at chess:

Source: Maarten van den Heuvel @ unsplash.com

  • Ask expert chess players for their strategy and implement it as a combination of ‘If this then that’-rules.
  • Gather data on every chess game between two humans. For every situation, plan out the possible actions and the probability of winning the game for each action. Let¬†the system always choose the action that gives¬†the highest¬†probability of winning the game.

You might have noticed that there’s a problem with these¬†two¬†solutions. If the data the AI is based on, is static, the AI becomes very predictable. Even though it might beat humans a few times, once the human gets the decision-making performed by the AI, it should never win a game again considering the human is then able to a develop a counter-tactic against the ‘highest probability’-choices. The AI will not be changing its strategy. So we need a new way.

Machine Learning: The art of beating a human at chess again and again

The new way would be something like this:

  • Let the computer play millions of games and gather data on winning probabilities for every action in every possible situations. Make it constantly learn¬†to adjust its own choices, including as many hand-written parameters you can imagine.

This last part, machine learning, ensures that the AI will be able to keep beating humans in chess in the future. Keep in mind that it will need to make mistakes and lose games to be able to learn how to win them. It will not go on a winning streak of 100% starting at its first win. (There is a very good life lesson in this paragraph. ūüėČ )

To get back to our example, the chess computer might¬†probably learn that the hand-written parameter of ‘randomness’ is important. If it doesn’t want to be perfectly predictable, the AI might want to sometimes pick ‘the second highest probability move’ to challenge the human’s processing capacity. But excellence will be in the balance. It should not lower its chances of succes by too much.

Another example:

Artificial Intelligence: Simulating a game of football

The one thing that started¬†my interest in AI is gaming. Most of all sim(ulation) gaming. For example (and I’m sorry non-football lovers) the game Football Manager.

Football Manager AI

The amount of hours I played this game…

It essentially mimics the game of football being played in the real world, with excellent precision. The game seems simple:

  • You have a club with a group of players, each of them having their own set of abilities. For example: Scott Davidson, a central defender at the Scottish League Two club Stirling Albion:
    Scott Davidson Stirling
  • When playing a match, Scott is put in a line-up, combined with some high level strategy decisions that will guide his decision making:
  • In-game these players are constantly making decisions. For example, Henderson in this case gets the ball and should decide what to do:

    Artificial Intelligence Football Manager

    Run down the flank? Pass the ball? To whom?

And this is when it becomes interesting: Henderson has to make a decision, which is based on his parameters (Vision, Anticipation, Decisions…), tactical¬†guidelines (‘Pass Shorter’, ‘Take No Risk’) and many more factors. Once he has made his decision, the execution of his action is also based on factors like his parameters (Passing, Technique, Dribbling, …), the pitch quality, his fitness level…

Machine Learning: Keeping the game interesting to play

This would¬†be (and for most people: is ūüėÄ ) a very boring game if there was one tactic that would win every game. The thing with the game is that, given certain limitations, the ‘other coaches’ are adapting their tactic to what you are doing.

This ensures that you’ll have to keep changing your tactics to keep winning games. Makes it a very frustrating game at times, but in essence, makes this game endlessly playable (and some of us do…).

So, now we know what Artificial Intelligence and Machine Learning are, what’s this deep learning thing?

Deep Learning: Mimicking neural networks

Then for the absolute abstract part of this. What deep learning is actually doing is very close to how the think our brains work: through neural networks:

Artificial Neural Network

A very simple Artificial Neural Network – Source: Wikipedia.com

There is a certain amount of input that is being divided over different nodes. This input is getting transformed in different hidden layers of nodes. The amazing thing is that the nodes are connected give their ‘transformed input’ and a¬†weighting of their own input (considering the output) to the next layer.

Given the rising processing capacities and math innovations that science has created in the latest years, we are capable of doing ‘sort of what the brain does’ on a smaller scale.

Dr. Pete Meyers actually explained this brilliantly simple on MozCon 2016:

The way a neural network works is: We have these [layers of]¬†inputs and we have these [layers of] outputs we want to achieve. […] So we’re trying to put something in between that can model that [input to output]. […] We put in this data to train it, but then the machine itself can handle new inputs that’s never seen before.

So actually, by letting the machine learn backwards from the output to the input, we create Artificial Intelligence that processes new input into the desired output. This allows us (bearing in mind the quality of the training data, processor capacity…) to build better data processing tools then our mind is consciously facilitating us to do by hand. That’s crazy.

And this is and will be impacting the world in general and digital marketing in specific. In the next episodes we will be discussing the impact this has on SEO, SEA, Media Buying and Analytics. If you have any other ideas on this, be sure to let me know!