Why you should take a break from Facebook marketing

Marketing is at times a bit like finance. You place a bet on what you think will perform, and then you wait and see. Luck plays a part in the result, but often experience adds to the success rate.

But just like an investor is not placing bets by guessing, neither are marketers. A variety of information and some intuition goes into every bet. We often base our bets on a combination of historical data, the calculated risk and the potential return.

Both marketers and investors are looking for the best return on their investments. For every dollar we spend, we want as much back as possible. For an investor, the gain is often in dollars, but for a marketer, it might be in sales growth or recognition. And while it might be easier to calculate ROI for a financial investment, the underlying mechanism is very similar.

Betting on the right thing

Marketers, just like investors, need to know how their surroundings are changing to make sure they place their bet strategically. Today, the surroundings for marketers primarily consists of algorithms: Facebook’s news feed, Google search, YouTube’s video recommendations, and some others.

When algorithms change, a bet that once was lucrative won’t pay off anymore. And while investors are quickly moving from an investment that is no longer paying off, marketers seem to keep going for a long time before they realise they should change something.

Why is that? Why do so many marketers continue with efforts that do not pay off? Is it because we are stupid? Or lazy? Is it because marketers don’t follow what’s happening in their field as closely as investors read the financial press? I don’t have an answer, but it fascinates me greatly.

Facebook marketing and ROI

Last summer the debate intensified around “fake news” and Facebook’s part in the outcome of the US presidential election. Quickly after this, the referral traffic from Facebook started to dive. The Facebook algorithm changes from time to time, but they didn’t say anything about changes at this time.

Facebook has made changes before as well. The platform has moved from being a source of free engagement and referral traffic, to become an advertising platform where you buy your impressions. These changes make a lot of sense; Facebook is a for-profit company.

As you see, betting on Facebook for marketing results comes with a high risk. If they change things on their platform, you will lose. Even if you’ve invested all your money on the Facebook platform for years now, you still might end up with nothing at the end of the day. Since you only “borrow” the relationship with your fans from Facebook, there is no real value in a Facebook page even if it has hundred thousand followers.

During 2018 we have continued to see multiple indicators suggesting the ROI on Facebook marketing is declining. When you, as a brand, publish content on Facebook today almost nothing happens. Some features, like groups and live streams, are ways for brands to still be relevant for users on the platform, but it’s very hard to get a good return on your investments.

Maybe, it’s time to take a break? At least, if you don’t have an advertising budget and if you spend a lot of time on your Facebook content and still get small results.

What to do instead of Facebook marketing

A lot of people think that digital marketing equals Facebook marketing. Some might throw Instagram and Twitter into the mix too. And while Social Media marketing is an essential part of digital marketing, one skill you should have in your toolbox, it’s not everything.

You need to move over to platforms where you have more control of your success. Brands doing content marketing should most likely focus on other platforms, and increase their focus on SEO and e-mail.

E-mail marketing and blogging

E-mail is an alternative to Facebook because your success is very closely correlated with factors you can impact. Search is another (now) stable platform, although not entirely without risk since it’s still an algorithm behind it.

Business blogging have for long been a bit uncool, “why should anyone wanna read a business blog”, but I think it is will soon have a revival. It’s safer than Facebook if you want to get back what you invest. A bonus is that more formats and topics will work in a blog setting than on Facebook. To be successful, you don’t have to do 45-second videos with text in the frame.

Podcasts and YouTube

Podcasts and YouTube will also become more popular when Facebook investments are no longer creating great returns. YouTube is apparently the second largest search-engine in the world, after Google, so there’s potentially an even more significant return to make from this platform. Podcasts are unique because users can use multiple technical solutions to consume your content, something that almost makes it similar to e-mail and reduces the investment risk.

Not that many marketers are very good at working with podcasts or YouTube today unless it is one-offs or sponsorships. Much of the best material is instead generated by “Podcasters” or “Youtubers”, with the content as their core product. But both these platforms now feels more stable than many other content platforms (as long as you are not trying to use it to make a living and don’t have to care about the compensations models), and the potential audiences are large.

How I try to place my bets

Personally, I began to refocus my Facebook marketing initiatives for most clients or projects last summer. More than a year ago (maybe earlier) I moved away from Twitter and Snapchat and recommended others to do it too. The platforms weren’t delivering enough results, and the risk of them just disappearing felt too big to for the small ROI they produced.

Instead, I’ve started this blog, and I have an e-mail newsletter (you can subscribe in the sidebar to the right). I’m also looking at starting a pod or a YouTube channel when I have the time. Sure, this tiny blog is not Spotify, but I would probably recommend them too to move away from Facebook at the moment.

My focus is on consistency and not quantity. It’s often wise not to hurry when it comes to scaling content initiatives; it takes time to build an audience.

My tools for Digital Marketing – Data Processing and Data Analysis

I often get questions about tools for digital marketing. Great topic for a blog post I thought, but when I started to write it became so massive that I realised I need to make a blog series out of this topic. In this post, I’ll walk you through the tools I use for data processing and data analysis.

Do you need technology to do marketing? Well, not long ago you could get by pretty well without it, but marketing is becoming more and more about technology. Sure, the underlying idea of understanding people is mostly the same, but you will soon be left behind if you don’t add technology to your toolkit.

Finding tools for digital marketing

With a background as a tech journalist, I have this weird interest in new technology. Whenever I find something new I can try out, I get excited, and I love spending time on ProductHunt to see what’s new. Maybe not the most normal thing to do on a Sunday night.

I’m always on a hunt for two types of tools: 1. technology that can simplify or even automate part of my current job, or 2. technology that can give me possibilities that I don’t have today.

But I started out like most people: writing reports, making presentations and sometimes using spreadsheets to do calculations. I guess most of my time is still spent putting thoughts on paper, and my every day “martech stack” is still pretty basic.

Everyone can learn data analysis

Since I’m going to talk about tools for data, I just want to say that my background is not in statistics or engineering. I do have some university credits in basic statistics, and I once knew how to perform a significance test in SPSS. But I’ve learned most of my marketing data skills by doing. I started testing with small side projects, spending time with both MOOCs and tutorials online has been a pretty good way for me to learn.

But I would also say that part of why I learned it all was that no one else around me knew how to do data analysis, so if I didn’t try to figure it out on my own, I wouldn’t have any quantitative insight at all. Not using the data I had access to in some way felt more stupid than to try to do some data analysis on my own.

My tools for marketing analytics and data analysis

I regularly walk clients over slides with data analysis nowadays. And I do everything from the first export to the visualisation on my own. I think I have three types of reoccurring projects where I need my data skill set:

  1. Auditing – Looking at historical data, in a delimited and pre-defined context, to find how something performed
  2. Monitoring and Measuring – Visualising data in real-time, creating opportunities for better decision-making
  3. Research – Looking at trends and decoding information in an unknown context

The tools I use are either for processing, analysis, or visualising and presenting data. And once again, it wasn’t long ago I didn’t know how to split up a CSV-file without help from Google; Hence, you shouldn’t be intimidated or feel like it’s something you cannot do yourself.

Tools for Data Processing

Google Sheets

Google Sheets Screen Capture

Google Sheets is a great way to work with certain types of data, especially medium-sized data structured in rows and columns. My first real relationship with spreadsheets started here, and its interface made me feel safe(r than other spreadsheet tools). I also realised I could find the answers to most of my Google Spreadsheet related questions online.

The first time I used Google’s spreadsheets was for personal budgets and other types of simple calculations. But I like tracking stuff, so most of my courses at Uni had a spreadsheet with all the tasks and deadlines and suggested readings, and I kept track of my progress using colours and conditional formatting. I used it more to create well-structured files than to process data or calculate anything. I later started to do “Content Calendars” for clients in Google Spreadsheets, since it was easy to get an overview.

My introduction to using Google Sheets

But my first real use of Google Sheets for work was to create monthly performance reports. I exported standard data files from Facebook, Twitter, Pinterest and the other sources I needed. In my Spreadsheet I had one “master sheet” calculating all my KPIs, referring to data from sheets named per data source. When I wanted to update my KPIs, I just overwrote the data in my import sheets, and all calculations in my master sheet refreshed automatically – as long as all the columns in my export were in the same place, but usually, they were.

I later started to write Google Script to make more automatic data imports, not having to download and upload files. Using scripts to get access to data from an API makes it possible to have automatically updated data in your sheets. This method makes it possible to use Google Sheets as a simple dashboard solution if you need to. I also use tools like Supermetrics to automatically import data from different APIs through a Google Sheets plugin (more on that further down).

Microsoft Excel

Microsoft Excel Screen Capture

While more advanced users might disagree, I would say that Google Sheets and Excel are very similar. You can do most things on a fundamental level in both tools. Some things are more comfortable to do in one than the other, and I guess that’s why I continue to use them both simultaneously.

I tried to stay as far away as possible from Excel for very long. To be honest, it felt like Excel was something only dull people needed, and my biggest fear in life was to seem boring. So, I stayed far away. For a very long time, I opened my .xlsx files in Google Sheets.

When is Excel better than Google Sheets?

The more I work with data, the more time I spend cleaning and prepping data files for data analysis. Additionally, my data files got heavier (and still do). So, the first feature I needed in Excel for was to use it without an internet connection. I know you can work with Google Sheets in offline mode too, but it doesn’t feel as safe.

Quite quickly I also learned that Excel was a bit more stable than Google Sheets when I was working with larger files. (It’s not like Excel is perfectly durable though, I’m very often looking at the spinning rainbow ball when I work with Excel). One elegant Excel feature is that you can turn off automatic calculation and make all your changes to the document before it calculates what’s in your cell. This feature might give you some extra processor power when you need it.

Today I often use more advanced functions in Excel than I did in the beginning, and I could probably do in Google Sheets with a plugin, but it’s neat that they are already part of Excel. The type of data imports I do in Google Sheets, using Google Script is not something I do in Excel, even though it’s possible. And as soon as I need to share my documents with someone else, working in Google Sheets is often much more accessible.


Atom Text Editor Screen Capture

Atom is a text editor (like TextEdit on a Mac or Notes on a PC). But why do you need a text editor to work with data you might think, it doesn’t make any sense? Well, at times when I work with data processing, I get across data stored in JSON or XML-files. When you try to read what’s in there, it looks like gibberish. A good text editor is very helpful when decoding these files.

But any text editor won’t help you; you need a good enough one (like Atom or Sublime) so that it formats the text in the file based on its language to make it more readable. This process is often called prettifying, unfolding code that is hard to read into a structure that is friendly and easy to understand.

For example, Facebook Ads API display the targeting data per ad as a JSON-object. If you try to read it in a spreadsheet column, you will struggle. But Atom formats JSON beautifully if you activate one of the “prettyfiers” that comes with the tool. You create a JSON-file, paste your FB-targeting data into it, and save it to your hard drive. On the save it will magically become formatted and (almost) readable. And the same is true for many other file formats that you might need to decode.

Tools for Data Analysis


Tableau as a tool for digital marketing

I felt intimidated when I first found Tableau. Partly because I didn’t already know about the tool or that there’s a whole field called Business Intelligence (where people are doing data analysis for a living). But also because it felt like I needed an exam to have the right to use it.

Business Intelligence software makes data analysis much more manageable, so I decided not to care about my lack of previous knowledge and downloaded a demo version. But I had quickly found myself with two problems: 1. It’s hard to get started with Tableau as a beginner, 2. Tableau is super expensive.

When should you use Tableau?

I use Tableau to extract information from my data, to look for learnings or insights that  I could never see without help from software. For instance, are we spending our advertising money on the content our clients engage with or the material we think is best? How are different segments of our users behaving when they interact with our product? I ask questions to my data through Tableau, and most of the time it shows me that my first gut feeling is incorrect. But at the same time, Tableau often shows me things that I had no clue about and would have never found through empirical studies.

Tableau is hard, but this is just because it’s different from most other software you’ve used before, so we cannot translate much earlier knowledge into this tool.  Your data are categorised in dimensions and metrics, and in the beginning, nothing makes sense.

Teaching myself Tableau is probably the best thing I’ve done – it made me understand data on a deeper level. I’ve always known about the difference between boolean variables, strings and integers from my background in programming, but it became much more tangible when I started to look at different data sets, trying to extract information from them.

Tableau has excellent tutorials online, so you are not alone in this process of not having a clue. There is also a great forum where you can read and post. Users always help each other out which is nice. But Tableau is not (yet) as well documented online as Google Sheets and Excel. When you have a problem, you might not find the answer on your first search. Stack Overflow is another place if you need help.


Last but not least, R is a tool that I don’t have to use that often. It is a useful backup tool for analysing data, and it can solve some problems that are not possible with the earlier ones I’ve talked about in this post. If you have a coding background, you will have fun learning R. But if you don’t R will seem pretty hardcore since you are interacting with the program through writing code and not through nice visual interfaces. It is similar to the terminal on your computer, in many ways.

I only use R when I have extensive data files, or if I need to calculate relationships between data points or data sets. Neither of these needs appears very often in my everyday life in marketing. But I did a network analysis once, that wasn’t possible to do in any of the other tools. One key feature is that you can run your calculations on a server in the cloud if your computer cannot handle the size of your data sets or calculations. I’m not saying you should start doing that, but it’s good to know that it’s possible.

Overlaps between data sets are also hard for the other tools to handle, but R nicely calculates differences and draws Venn diagrams. R can do a lot of graphs and visualisation, but they are not the most visually appealing, so I don’t recommend to use it only for that.

How to learn R

If you think you’d like to learn R, multiple MOOCs can help you get started. I’d recommend taking one of them. But if I were you, I’d start with one of the other tools; I only use a fraction of the functionalities in R. But sure, I plan on getting better at it, I just have some other things in life that I might prioritize before that…

The difference between an offline and online audience

The one thing I get most requests about these days is helping out with online audiences that are not working. And I realise I see the same problems over and over. Naturally, that’s when a blog post is born.

Most people working with marketing audiences today started long before online marketing was the norm. They still create audiences for the online world like they did (or still do) for the offline world. But the two are very different, and naturally, audiences don’t translate very well between the two contexts.

The difference between online and offline audiences is how you decide if someone is part of your audience or not.

Your audience hypothesis

Before you create an audience for your ads, you usually have an idea of who you want the reach with your product. If you’re going to market a contraception app, spending your marketing budget on women between the age of 23-45 seems fair, but if you try to sell fancy cheese, your audience is somewhat different and probably should consist of cheese lovers with enough income to spend on cheese.

Sidenote: Some people work with personas to get to know their audiences. I try to avoid that since I find it limiting. I will save my take on personas for a separate post. But its safe to say that personas create a lot of trouble when people are trying to reach their “personas” with online ads.

Let’s get back on track. Often you have to translate your business audience into an advertising audience. Maybe because you want to personalise your ads based on preferences, or age; Or, because you have a small budget and want to make sure you spend it on those who are most likely to consume it.

The limit with offline audiences

In the offline world, you have very little information about people. You often know the average income level in a zip code or a magazines rate of female readers, but you don’t have rich profiles or detailed information about a single person.

So, when you advertise offline, you do it in a zip code where the average income level is similar to what you think your audience earn. Or you choose a magazine with mostly female readers in a relevant age span. But you will never know if they like cheese or are trying to get pregnant.

What is a “proxy”?

In statistics (yes, building audiences is statistics), a proxy variable is a variable that is not in itself directly relevant, but that serves in place of an unobservable or immeasurable variable (Wikipedia).

The zip code is a proxy for income, and you can use a women’s magazine as a proxy for gender and maybe also specific interests if the magazine focuses on a particular type of content. Marketers try to find good proxies to make their advertising do better – but it is hard for some products and services.

Say you run a house cleaning service. You believe you should try to reach women because they feel more responsible for house cleaning (bleh). But you also want them to make enough money to afford your service. But how can you narrow it down further? Say you want to reach women with demanding and high paying jobs. Then you can advertise in magazines that people in this category are likely to read.

This magazine sure seems like a good proxy for your audience, but you won’t know how many of the magazine readers who are relevant to you. Some women who read the magazine might have demanding and high-paying jobs, but they have husbands that do all the housework. And others might not have a high paying job yet but wish to have it one day, so they are reading the magazine as inspiration.

Offline audience spill-over

So among those you target with an offline audience, only some people are the ones you’re trying to reach. You will have “spill over” to other groups that you’re not looking for. It’s the same with “out of home” ads, you can put them up on the bus stops in an area where the income level is high, but that doesn’t mean a significant share of those walking by, looking at your ads, won’t have that high income.

The benefit of online audiences

When you create audiences online, you have much more data about people. Either, you have your own customer data (“first-party data”), or you use data from Facebook or Google (“secondary data”), or you buy data from someone else.

Facebook and Google collect data not only on their platforms but all around the web and in apps through different scripts such as Facebook like buttons and analytics scripts. This data collection is why they know so much about their users, and this is why their advertising solutions are thriving online.

When you have data directly about each person, you don’t need proxies. You can target users on Facebook directly on income level. Or cheese interest, or their interest in contraceptive apps. These targeting possibilities are why Facebook ads can become so relevant. We don’t need to guess if those interested in cleaning services are also reading a specific type of magazine, or are more likely to have a gym membership. We can target directly towards people interested in cleaning services.

Why your Facebook Audiences don’t work

Almost everyone that contacts me, about poorly performing online audiences, have built them incorrectly. Hence, they are using proxies instead of pinpointing the behaviours or interests they are trying to target. This set-up makes their ads taking a massive detour in deciding who is relevant and who isn’t.

And the use of proxy-based audiences for social media is widespread. I’m continually meeting both media agencies and social media marketers that are doing it daily. But it is just MUCH MORE WORK that will give you a WORSE RESULT. So it’s pretty easy for me to recommend you to stop.

Can marketing personalisation be unethical?

Personalisation is an ongoing marketing trend: far from new, far from over. Being specific and relevant to every single customer is a powerful marketing tactic, often appreciated by those receiving your message. But it has some downsides worth knowing.

I don’t know how many times a week I talk about the magic triad of marketing. Providing customers with 1. the right message, at 2. the right time, in 3. the right place. Personalised experiences give you tools to nail the first part, creating more relevant messaging.

We can personalise experiences in many ways. Personalisation online includes everything from adding customer names to welcome phrases in emails, to changing website content and design for every single visitor with help from artificial intelligence. The goal is often to increase customer engagement and conversions through improved relevance.

In this post, I talk about both targeting and personalisation. And why it’s not the same thing, doing one without the other is hard. If you have personalised content, you need to target the right person. If you target a specific group, you often (but not always) do it to become more relevant to this group. This will turn into personalisation if you narrow your audience enough and change the content to suit them.

The current state of personalisation

Few companies use full-scale personalisation today, but some display content on websites or in emails based on a customer’s previous behaviour. Others are creating content for different segments of customers, although there might be more than one person in each. This marketing tactic is underlying when customers with a birthday in November will get the same email, or those who bought notebooks recently will get the same ad.

Netflix or Amazon are both using advanced personalisation. They select every item on display specifically for you, and it sometimes feels like they know you better than you know yourself. Looking at my Netflix recommendations is like looking directly into my brain, it reveals every little quirk I’m not talking about in public.

Why personalisation needs careful thought

While personalisation is a powerful marketing tactic, it is sometimes perceived as “creepy” by customers. In the best of worlds, people like their personalised experiences. At times they might “only” get a bit uncomfortable. But personalised content can also be very unsuitable or even unethical.

There will be a constant battle between personalisation and privacy, and it is essential for marketers to know a bit about the risks. I will discuss two types of content personalisation and online targeting situations that are more problematic than we might think at first – but there are of course many more.

Many (most?) digital marketers use these methods today without knowing it is problematic. In this world of continuous consumer data collection, we need to discuss marketing tactics and marketing ethics – but we don’t. This blog post is far from a complete guide, but it might get your thoughts started.

Targeting based on health data

We give away a lot of health information online. Googling symptoms, looking for home cures, worrying about constant headaches or trying to break our bad habits. Our online behaviour additionally gives away many cues about our mental health – how we interact with social media, for instance.

But just because we can target an obese person with diet tips or a depressed person with advertisements for self-help books or therapists, doesn’t necessarily mean we should. And it just becomes even worse if you start targeting cancer patients or their relatives (for instance, people who have visited the cancer wing at your local hospital) with ads about funeral services.

Health issues are one of these things people don’t want others to know about, and we will find that targeted ads violate our integrity or are intrusive. While it is probably okay to communicate with parents around the pains of having sick kids in February, we should carefully make sure we don’t fall over.

Retargeting users with health-related content

Another issue that often appears is retargeting based on earlier shopping behaviours. But what if we spend some time looking at health advice, comparing medicine or googling back problems? If you have browsed around for self-help books, and all of a sudden get suggestions for all the other self-help books you should buy too. Or if you look for specific medical treatments or drugs and it follows you around the web for weeks, that is not very good marketing.

Personalised or retargeted content based on someone’s health, is often a terrible idea. It will give people the feeling that you know things should not know about them, and the ads likely will not perform since customers find this intrusive. Pick another marketing tactic.

Political opinion as targeting

Using someone’s opinions for targeting is an ancient (well, sure) marketing tactic. It is not only about how someone is voting; political views include so much more.

Many niche opinions that might seem harmless to you is controversial or even illegal in parts of the world. People engaged in gay rights, pro-drugs or anti-abortion movements – might end up in unpleasant or even dangerous situations if their opinions get out. Maybe not in your context, but in theirs.

With Facebook’s interest targeting, it was previously possible to target people with interest “Jew hater” or “How to Burn Jews”. And while Facebook took away these particular targeting options, many others are still there. You can target people based on ideology, such as liberal or conservative, or fans of a specific political party.

There are cases where targeted ads seem to have put people at risk, or in uncomfortable situations, both in online and offline contexts. For example, people do lose their jobs when their employers find their opinions inappropriate.

Sure, if you live in a democracy and everyone is free to speak their mind openly, the risk might be small. But it was not long ago that people had to hide their political views from friends and family in parts of Europe – and people still get into trouble because of what they think politically, all over the world.

Calculating the risk

Giving someone’s political views away by showing them personalised content can lead to consequences we might not consider, or even understand when we do the targeting. These risks vary from market to market, but you are responsible for deciding what is okay and what is not.

Often, when you do use someones political opinion for targeting, you are using it as a proxy to categorise people with a specific set of values, behaviours or traits. Using these traits directly is often more ethical, and it will make your marketing much more relevant to your customers.

(A side note: Using consumers political residence for targeting or personalised content does not mean you cannot promote political content. But who gets your political content and why needs to be an active and careful decision based on fair grounds.)

The need for marketing ethics will increase

Marketing ethics will become a big deal over the next few years. All marketers will need to know more about ethics than we do today, or we will get in trouble. Many companies will get lost, and they can end up in some real trouble putting their brands at risk.

Technology, both specific to marketing and not, will continue to give us marketing opportunities that are inappropriate and unethical. New tools help us target and personalise messaging, and they are continually getting better and more popular. But most marketers using these new tools have never had to think about what is okay and what is not. Instead, we do things just because we can, because that’s what we’ve previously been doing.

To follow the law will never be enough

Customers don’t mind to barter with their data if they get access to free valuable services in exchange. But they are picky about how brands use their data. As soon as ads feel intrusive or inappropriate, customers will not engage. This behaviour creates a paradox where marketers will have to balance between extremely relevant to each customer, and not to make customers feel uncomfortable with why they get what they get.

Policy and legislation will soon create more boundaries for digital marketers. But it won’t ever catch up with technology. Law development being slow is a problem for computer scientists and programmers as well. So, we all need to brush up our ethics.

I’m not saying that business has ever been entirely ethical. Too much money is often at stake. But marketing is easy to review since it’s on display, so I think we’ll have to get in line pretty quickly – or we will get in trouble.


Do you have any thoughts on this? Please leave a comment!

Dark Posts in Social Media, what is it?

One buzzword currently circling marketing departments is “Dark Post”. It sounds like something dangerous and illegal, probably because you have heard about the “darknet” or the “dark web”. However, the two have nothing to do with each other.

Dark posts are nothing unlawful or dangerous; they show up daily in our Facebook feeds. Moreover, the only negative thing is that they are sometimes hard to track.

If you are active on Facebook now and then, you know that what you see isn’t only posts from other personal users. Pages – representing everything from your local flower shop, an extremist political party, or an initiative to teach kids to code – can also create posts that show up in your feed.

However, a lot of the posts that show up in your feed are ads. When a page creates a post on Facebook, it appears both on the page wall and in some followers feeds. The page can if it wants to, decide to boost the post to reach more people. Then they pay Facebook to show the post for Facebook users they want to reach. When this happens, the post technically becomes an ad*. These posts are often called boosted posts.

*If you do not boost your post it is referred to as an organic post.

You see dark posts all day long

Facebook advertisers want all their ads in your feed to look like any Facebook post. But the also want to send different messages to different users based on what they think they’ll like. To become relevant, or spend their money as effectively as possible, advertisers often create multiple versions of a message. They have different versions of copy, links and images, to find the one combination that performs best. This is a common advertising method called A/B testing.

But brands on Facebook usually don’t want their Facebook page wall to show the same post over and over with slightly different combinations of text, links and images. So creating an organic post and boosting it is not a very good option. Instead, they create a large variety of ad posts in Facebook Ads Manager and Facebook shows you the ad that it is most likely that you act on.

There’s a difference between a boosted post and an ad post created with Ads Manager. Boosted posts show up on the page wall, but ads don’t show up on the wall (if you don’t want them to). Dark posts are ad posts that don’t show up on a brands page wall. Dark posts live “undercover” or “in the dark” and no one except the ads targeted audience knows about the post. Another name for dark posts is “unpublished posts”.

You can create dark posts on both Facebook, Instagram, Pinterest, Snapchat and Twitter. On Snapchat and Instagram, all promoted posts are dark posts. The basic idea is the same to all these platforms, but this text will continue to talk about Facebook.

What a dark post looks like


On Facebook, boosted posts and post ads created in Ads manager look identical. You can see the  “Sponsored”-mark directly under the page name when they show up in your feed.

You can only see a dark post if you are in the target audience for the particular ad and it happens to show up in your feed or if you know its direct URL. (You find the URL for a Facebook post by clicking on its timestamp).

Who sees a dark post?

Sponsored posts do not show up randomly. We all have different ads in our feeds, and the same ad does not show up for everyone. When a page creates an ad, they decide whom they want to show it to, and then it only shows up in their feeds. Targeting is the advertising term for choosing who to reach.

An ad shows up in your feed because you fall into a group the advertiser wants to reach. Either because of your gender, age, or where you live. However, it can also be because you have behaved a certain way online. Facebook use user data for targeting ads on its platform. Sometimes the pages you like or the pages your friends follow is motive for ad placement. But it is also possible to base targeting on different interests Facebook believe you have, most likely because you have clicked links or viewed videos about a subject.

However, even if the ads are showing up on Facebook they collect your online behaviours all around the internet. Facebook collects data from an enormous amount of sites online. Pages can re-target their web page visitors on Facebook if they have installed a Facebook script called a “Facebook Pixel”.

Dark post as a tool to reach niche audiences

Dark posts target particular niche audiences. The main idea is to create an ad and promote content towards someone likely to enjoy the content. The targeted user is often a potential customer, but dark posts can also be used to persuade voters in a presidential election or increase streams on a specific Netflix show among existing Netflix users.

For a brand publishing content on Facebook, it is sometimes hard to create content that talks to all your potential customers or fans at once. People are much more likely to engage with your brand if they feel like you are relevant to them, but how can you be of interest to a varied group of potential customers? This dilemma is why dark posts are often part of a successful content strategy.

Since an advertiser is in almost complete control over who sees an unpublished post, they can talk to multiple audiences at once, with different voices and propositions to each of them. The people who get the content in their feed do not know they are part of a bucket; they are just happy their Facebook ads are somewhat relevant**.

**Facebook also wants to show people relevant ads. They assign all ads on their platform a score from 1-10 based on their relevance to the target audience. If your ad shows a low relevance score, you can tweak your ad, or it’s targeting, to improve the effectiveness of the ad.

Dark posts in Social Media are not good or bad

Dark posts are in some circuits starting to become somewhat mythical, a tool used to manipulate people without their knowledge. The name for it is probably not helping though, especially not when it is few that know what it is.

The thing that makes dark posts somewhat criticised, or at least met with scepticism in the debate, is mainly two things: 1. Fake (junk) news sites, companies and political campaigns have used dark posts unethically in different ways, 2. It is very hard to track dark posts from an ad account that you do not own yourself.

First, the ethical aspects of dark posts

The fact that we can target people in extreme detail while being somewhat non-transparent makes it easy for an advertiser to enter into semi-legal or at least unethical activities either by mistake or knowingly. The regulations are often vague, and no custom exist around these issues.

Facebook is a profit-driven platform and seems happy as long as they get paid by advertisers. They have changed some policies related to fake news and ad posts after the storm of criticism that followed both the US presidential election and Brexit. With the new GDPR regulation from EU, arriving in 2018, this will probably change.

Second, transparency

Keeping track of what others are saying might seem like a vague argument against dark posts. Targeting ads have always been part of marketing and communication. But the vast amount of ads and the difficulty to track them makes it complicated. This grey zone is not a big deal when it comes to shoe sales, but if it is false political arguments getting spread around in the dark, it is somewhat problematic when they cannot be met or debunked.

It is also likely that we will see more tools for tracking dark posts popping up due to increasing demand from marketers to keep track of competitors. We can probably also expect greater transparency on Facebook, as well as on other digital platforms, both when it comes to both dark posts, and different types of online targeting. Facebook is trying to become an infrastructure more than just a social platform, but for that to happen, society needs essential insight into the platforms citizens use daily.

Should we be worried?

As with all tools, dark posts in the wrong hands can create some damage. However, it is not a dangerous tool in itself. If we keep spreading the knowledge about how dark posts work and how we can check if the messages we get online are really true, we don’t need to be afraid.

Data-driven marketing for the anxious marketer

During a lunch with the CEO of a small start-up, he said: “People tell us that we must do data-driven marketing, but I’m not sure that that’s the most important focus for us at this time”. I tried to explain to him that there’s no way to do marketing without basing it on some data input.

When people talk about data-driven marketing, I find it problematic in several ways. It’s like saying that we should do information-driven marketing, or people-driven marketing, or behaviour based marketing. But I understood his concern since data-driven marketing is what many people are talking about now.

Data-driven marketing is non-sense

Data-driven marketing is just one of the marketing buzzwords that pop up every year. At the beginning of the digital marketing era, everyone was social media experts. After a while, they decided to become content marketing experts instead. Lately, I hear that more and more people claim that they’re experts on data-driven marketing.

At Retune in Berlin this fall, the British programmer Karsten Smith said: “If you focus on a tool just because it’s new, you might become a victim of the rhetorics of newness”. This quote stuck with me. In marketing, we are always hungry for trends, and we need to embrace changes and opportunities to stay ahead. But at times must remind me, and others: nothing is smart just because it’s new. There’s no causal relationship between the two attributes.

What is data-driven marketing?

Definition: Data — “facts and statistics collected together for reference or analysis.”

Definition: Marketing — “the action or business of promoting and selling products or services, including market research and advertising.”

If we start at the beginning: data-driven marketing should be promoting and selling products or services based on facts and statistics. But what if I tell you that facts and statistics is the base for all marketing? To do marketing successfully, you usually consider everything you know about your customer and based on that knowledge you act in ways that serve the business well. This method usually means that you use knowledge extracted from data in one way or another.

New technology gives us more data

New digital technologies have created new ways for marketers to get to know their customers. Most things we do online is trackable and possible to measure. These new technical possibilities make digital data cheap and pretty easy to collect in large volumes. But it is important to remember that data in itself, without proper analysis, is worth close to nothing. And it’s the people who interpret data that offer the value and stands for the most of the cost.

But digital data is not superior to other data types in marketing. Marketing is a complex task, so we need all sorts of information to do it successfully. Digital data can only count as part of this information. Digital data is often lacking more qualitative information, such as the customers’ thoughts, feelings or body language. And while the number of clicks before conversion, or the average time on an individual page, might say something about more qualitative aspects, we can never be sure.

To do effective marketing, you need a variety of information, including several types of digital data. If you blindly trust your digitally tracked data points, you won’t have all information, and you won’t fully understand your customers and their needs. Data analysis is about more than the slope of a curve. It’s about combining the curve with other things you know, and draw conclusions based on all the info you’ve got at hand.

Data-driven marketing is not for everyone

Few marketers know how to read the massive amounts of digital data that they collect. If you’re not familiar with statistics and data analysis, it will be tough to understand it fully at first. But the most important thing is that you should combine your (new) digital marketing skills with the rest of your “classic” marketing knowledge, such as customer interviews, focus groups and gut feeling.

Doing data-driven marketing without enough knowledge can probably be worse than not doing any data-driven marketing at all. If you don’t fully understand the information you’re basing your conclusions on, how can you trust your decisions? How can you create a growth strategy if you’re not sure what you’re tracking and why it’s important?

I studied both statistics, research methods and data analysis at Uni. I did quantitative data analysis for my psychology bachelor thesis and qualitative analysis for my business one. And sure, I believe I remember most of it, but it only makes me smart enough to know when to get help. I don’t mind doing fundamental analysis in Google Analytics; it gives me a lot of information that I need. But I never trust my interpretation when it comes to details.

Whenever I have clients in need of more advanced analysis, I partner with someone who knows more than me. Implementing tracking is, for instance, something I try never to do. But the good thing is how the stage when I need help keeps moving forward.

Don’t forget your qualitative data skills

With a background in behavioural psychology and consumer behaviour, two fields that are heavy on qualitative data, I often prefer qualitative data analysis. I still do a lot of customer interviews and empirical observations as an essential part of my work.

I also talk to salespeople and others who meet the customers. Salespeople talk to “real” customers every day. They often have a closer relationship with the customers than I can ever get from behind my screen. To me, this little chat is also data collection. And although digital data is essential, it is not a good idea for an organisation to trust only this data.

The data you get from a script in a browser is just half the truth. Complement what you see with more (qualitative) information. Try to merge different perspectives to make smarter decisions.

Update your knowledge

We will continue to need well-educated marketers and marketing teams with cross competences. If you don’t have any data analysis knowledge yet, make sure you update your skill set. It will become essential in a few years. A good start is to talk to people who know more than you. (You can often persuade data scientists and programmers with your real interest in their field).

But don’t believe people who tell you that qualitative data analysis is the only skill a modern marketer need. Or that data-driven marketing in the meaning of “big data” is the only thing you should do from now on. That’s just not true.

My three 2015 takeaways for the anxious marketer

  1. Base all marketing on reliable data and analysis that gives relevant information
  2. Don’t focus on what’s new, focus on what’s smart
  3. If you try to follow the buzz, you’ll always be behind everyone else instead of ahead