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Managing business development of an ad platform

Here’s a great example of a business development program of an ad platform:

Google provides similar service through its AdWords Partner program. Facebook and Google are offering the free 1-on-1 help for one simple reason:

It improves the quality of ads.

Because of this, two positive effects take place:

a) the users are happier. As two-sided markets, FB and Google need to constantly monitor and improve the experience for both sides, users and advertisers. Particularly, they need to curb the potential negative indirect network effect resulting from bad ads.

b) the results are better. Most of FB’s +2M advertisers are small businesses and lack expertise – with expert guidance, they will use the funtionalities of the ad platform better and will see better results. This prompts an increased investment in the ads, which increases the platform’s revenues.

Thus, this program is an example of a win-win-win business development program of a platform. The users are shown better ads, the advertiser gets better results and the platform increases its revenue. Given that FB and Google conduct some “lead scoring” to choose the advertisers with the most growth potential, the ROI of these efforts is almost certainly positive.


With these programs, FB and Google are once again beating the traditional media industry that has very weak support in managing online advertising. Basically, no interest in the client after getting the money. To do better in competition, traditional publishers need to help their clients optimize and increase the quality of their ads, as well as improve their core technology to close the gap between them and FB and Google.

Affinity analysis in political social media marketing – the missing link

Introduction. Hm… I’ve figured out how to execute successful political marketing campaign on social media [1], but one link is missing still. Namely, applying affinity analysis (cf. market basket analysis).

Discounting conversions. Now, you are supposed to measure “conversions” by some proxy – e.g., time spent on site, number of pages visited, email subscription. Determining which measurable action is the best proxy for likelihood of voting is a crucial sub-problem, which you can approach with several tactics. For example, you can use the closest action to final conversion (vote), i.e. micro-conversion. This requires you have an understanding of the sequence of actions leading to final conversion. You could also use a relative cut-off point; e.g. the nth percentile with the highest degree of engagement is considered as converted.

Anyhow, this is very important because once you have secured a vote, you don’t want to waste your marketing budget by showing ads to people who already have decided to vote for your candidate. Otherwise, you risk “preaching to the choir”. Instead, you want to convert as many uncertain voters to voters as possible, by using different persuasion tactics.

Affinity analysis. The affinity analysis can be used to accomplish this. In ecommerce, you would use it as a basis for recommendation engine for cross-selling or up-selling (“customers who bought this item also bought…” à la Amazon). First you detemine which sets of products are most popular, and then show those combinations to buyers interested in any item belonging to that set.

In political marketing, affinity analysis means that because a voter is interested in topic A, he’s also interested in topic B. Therefore, we will show him information on topic B, given our extant knowledge his interests, in order to increase likelihood of conversion. This is a form of associative

Operationalization. But operationalizing this is where I’m still in doubt. One solution could be building an association matrix based on website behavior, and then form corresponding retargeting audiences (e.g., website custom audiences on Facebook). The following picture illustrates the idea.

Figure 1 Example of affinity analysis (1=Visited page, 0=Did not visit page)

For example, we can see that themes C&D and A&F commonly occur together, i.e. people visit those sub-pages in the campaign site. You can validate this by calculating correlations between all pairs. When you set your data in binary format (0/1), you can use Pearson correlation for the calculations.

Facebook targeting. Knowing this information, we can build target audiences on Facebook, e.g. “Visited /Theme_A; NOT /Theme_F; NOT /confirmation”, where confirmation indicates conversion. Then, we would show ads on Theme F to that particular audience. In practice, we could facilitate the process by first identifying the most popular themes, and then finding the associated themes. Once the user has been exposed to a given theme, and did not convert, he needs to be exposed to another theme (with the highest association score). The process is continued until themes run out, or the user converts, which ever comes first. Applying the earlier logic of determining proxy for conversion, visiting all theme sub-pages can also be used as a measure for conversion.

Finally, it is possible to use more advanced methods of associative learning. That is, we could determine that {Theme A, Theme F} => {Theme C}, so that themes A and B predict interest in theme C. However, it is more appropriate to predict conversion rather than interest in other themes, because ultimately we’re interested in persuading more voters.


[1] Posts in Finnish:

Total remarketing – the concept

Here’s a definition:

Total remarketing is remarketing in all possible channels with all possible list combinations.


  • Programmatic display networks (e.g., Adroll)
  • Google (GDN, RLSA)
  • Facebook (Website Custom Audience)
  • Facebook (Video viewers / Engaged with ads)
  • etc.

How to apply:

  1. Test 2-3 different value propositions per group
  2. Prefer up-selling and cross-selling over discounts (the goal is to increase AOV, not reduce it; e.g. you can include an $20 gift voucher when basket size exceeds $100)
  3. Configure well; exclude those who bought; use information you have to improve remarketing focus (e.g. time of site, products or categories visited — the same remarketing for all groups is like the same marketing for all groups)
  4. Consider automation options (dynamic retargeting; behavior based campaign suggestions for the target)

In 2016, Facebook bypassed Google in ads. Here’s why.


The gone 2016 was the first year I thought Facebook ends up beating Google in the ad race, despite the fact Google still dominates in revenue ($67Bn vs. $17Bn in 2015). I’ll explain why.

First, consider that Google’s growth is restricted by three things:

  1. natural demand
  2. keyword volumes, and
  3. approach of perfect market.

More demand than supply

First, at any given time there is a limited number of people interested in a product/service. The interest can be of purchase intent or just general interest, but either way it translates into searches. Each search is an impression that Google can sell to advertisers through its AdWords bidding. The major problem is this: even when I’d like to spend more money on AdWords, I cannot. There is simply not enough search volume to satisfy my budget (in many cases there is, but in highly targeted and profitable campaigns many times there isn’t). So, the excess budget I will spend elsewhere where the profitable ad inventory is not limited (that is, Facebook at the moment).

Limited growth

According to estimates, search volume is growing by 10-15% annually [1]. Yet, Google’s revenue is expected to grow even by 26% [2]. Over the year, Google’s growth rate in terms of search volume has substantially decreased, although this is perceived as a natural phenomenon (after trillion searches it’s hard to keep growing double digits). In any case, the aforementioned dynamics reflect to search volumes – when the volumes don’t grow much and new advertisers keep entering the ad auction, there is more competition over the same searches. In other words, supply stays stable but demand increases, resulting in more intense bid wars.

Approaching perfect market

For a long time now, I’ve added +15% increase in internal budgeting for AdWords, and last year that was hard to maintain. Google is still a profitable channel, but the advertisers’ surplus is decreasing year by year, incentivizing them to look for alternative channels. While Google is restrained by its natural search volumes, Facebook’s ad inventory (=impressions) are practically limitless. The closer AdWords gets to a perfect market (=no economic rents), the less attractive it is for savvy marketers. Facebook is less exploited, and allows rents.

What will Google do?

Finally, I don’t like the Alphabet business. Already in the beginning it signals to investors that Google is in “whatever comes to mind” business instead of strategic focus on search. Most likely Alphabet ends up draining resources from the mother company, producing loss and taking human capital off from succeeding in online ads business (which is where their money comes from). In contrast, Facebook is very focused on social; it buys off competitors and improves fast. That said, I do have to recognize that Google’s advertising system is still much better than that of Facebook, and in fact still the best in the world. But momentum seems to be shifting to Facebook’s side.


The maximum number of impressions (=ad inventory) of Facebook is much higher than that of Google, because Google is limited by natural demand and Facebook is not. In the marketplace, there is always more supply than demand which is why advertisers want to spend more than what Google enables. These factors combined with Facebook’s continously increasing ability to match interested people with the right type of ads, makes Facebook’s revenue potential much bigger than Google’s.

From advertiser’s perspective, Facebook and Google both are and are not competitors. They are competitors for ad revenue, but they are not competitors in the online channel mix. Because Google is for demand capture and Facebook for demand creation, most marketers want to include both in their channel mix. This means Google’s share of online ad revenue might decrease, but a rational online advertisers will not drop its use so it will remain as a (less important) channel into foreseeable future.




Facebook Ads: remember data breakdowns

Here’s a small case study.

We observed irrational behavior from Facebook ads. We have two ad versions running; but the one with lower CTR gets a better relevance score and lower CPC.

This seems like an irrational outcome, because in my understanding, CTR as a measure of relevance should be largest impact factor to CPC and Relevance Score.

Figure 1  Aggregate data

So, we dug a little bit futher and did a breakdown of the data. It turns out, the ad version with lower aggregate CTR performs better on mobile. Apparently this adds emphasis to the algorithm’s calculation.

Figure 2  Breakdown data

Lesson learned: Always dig in deeper to understand aggregate numbers. (If you’re interested in learning more about aggregate data problems, do a lookup on “Simpson’s paradox”.)

Miten tuottaa lisäarvoa dynaamisella uudellenkohdennuksella?

Juttelin tänään erään Facebookin edustajan kanssa, jonka tehtävänä on auttaa Elämyslahjoja tekemään parempaa Facebook-mainontaa.

Keskustelu pyöri aloittelijatasolla, kunnes sanoin että olemme tehneet Facebook-mainontaa jo monta vuotta ja tiedämme nämä perusjutut.

Silloin henkilö ehdotti meille Facebookin dynaamista uudellenkohdennusta (eng. dynamic retargeting). Ko. mainonnan muoto siis toimii niin, että tietyllä tuotesivulla käyneelle henkilölle näytetään Facebookissa samasta tuotteesta mainoksia. Katsot siis verkkokaupasta kenkiä, ja näet samat kengät Facebook-mainoksessa.

Hän suositteli sitä meille, koska se kuulemma toimii. Kysyin että miksi se toimii? No, hän sanoi, että ensinnäkin monet isot verkkokauppa-asiakkaat käyttävät sitä ja toiseksi se tuottaa hyvin konversioita.

Tarkastellaan näitä argumentteja:

A) “Muut käyttävät” –> pätevyys: huono, koska se että muut tekevät jotain ei tarkoita että se olisi järkevää; nettimarkkinoinnissa on paljon harhaisia markkinointikäsityksiä, jotka ajavat tehottomuutta, isoissakin firmoissa.

B) “Tuottaa konversioita” –> pätevyys: luultavasti huono, koska attribuutio ja ostoprosessi osuvat yksiin, jolloin Facebook-mainos saa sille “kuulumatonta” kunniaa myyneistä. Kirjoitin tästä ilmiöstä täällä.

Selitin siis nämä perustelut ja kerroin että tarkoitin kysymyksellä sitä, että mitä lisäarvoa kyseinen ominaisuus tuottaa asiakkaalle. Henkilö selkeästi häkeltyi, eikä osannut vastata. Hän sitten toisti ensin sanomansa argumentit.

Rupesin miettimään tätä asiaa.

Oikeasti – mitä hyötyä on siitä, että henkilö A näkee tuotteen X vielä uudelleen mahdollisesti viikkojen ajan uudelleenmarkkinoinnissa? Kun hän selailee Facebookia tai uutissivustoja. Se sama tuote, jonka jo katsoin läpi enkä ostanut.

Omassa päätelmässäni ei mitään hyötyä. Päinvastoin, se on hukattu mahdollisuus. Miksi “hukattu”?

Koska itse tieto siitä mistä tuotteesta henkilö oli kiinnostunut on erittäin arvokas, jos sitä käytetään oikein.

On väärin näyttää samaa tuotetta uudelleen ja uudelleen ja kuvitella, että ihminen yhtäkkiä muuttaisi mielensä. Tämä on sama kuin inttäminen perinteisessä myynnissä. Toimiiko sellainen taktiikka? Ei toimi. Inttämisen sijaan pitää tarjota vaihtoehtoja, ja kun tiedetään mistä asiakas on ollut kiinnostunut, voidaan sitä tietoa käyttää suosittelun pohjana.

Toisin sanoen Facebook kehottaa käyttämään uudelleenkohdennusta näin:

1) Kerro lisää samasta tuotteesta –> uskottavuus: matala, koska tämä on inttämistä (ts. tarjotaan sama asia tuhat kertaa, ja odotetaan eri tulosta = Einsteinin määritelmä typeryydelle)

2) Muistuta ostamisesta –> uskottavuus: matala, koska ihmisen muisti on pidempi kuin kultakalan (ts. olen jo nähnyt tuotteen ja päättänyt että ei)

Molempien taustalla on väärä ihmiskuva: “ihmiset ovat tyhmiä ja manipuloitavissa, joten heitä tulee koko ajan muistuttaa ja he kuin taikaiskusta päättävätkin ostaa tuotteen.” Ajatellaan, että ihmiset ovat aivottomia robotteja. Todellisuudessa retargeting toimii suureksi osaksi mainitsemani attribuutioharhan vuoksi, ei sen takia että se tuottaisi aitoa lisäarvoa.

Kuinka sitten tehdä asia oikein? Mahdollisuuksia tuottaa aitoa lisäarvoa dynaamisella uudelleenkohdennuksella ovat ainakin:

1) Ylösmyynti (eng. upselling, en löytänyt hyvää suomenkielistä käännöstä) – suositellaan asiakkaalle kalliimpaa (tai edullisempaa) vaihtoehtoa ja mahdollisesti lisäosia taikka lisäpalveluja. Lue lisää.

2) Ristiinmyynti (eng. cross-selling) – suositellaan asiakkaalle täydentäviä tuotteita (komplementteja); esim. jos ostit kengät, osta sukat, jne. Lue lisää ristiinmyynnistä.

Molemmissa taktiikoissa ajatuksena on, että suositellaan asiakkaalle tuotteita, joita hän ei ole vielä nähnyt, mutta joista hän datamme perusteella voisi olla kiinnostunut. Tilastollisten mallien avulla pystytään tunnistamaan paitsi tuotteiden suhdetta toisiinsa, myös tekemään suosituksia aikaisempien asiakkaiden ostoskorien sisällön perusteella. Ao. kuva havainnollistaa asiaa.

Lähde: Liukkonen, 2016

Yritysten pitäisi siis rakentaa samanlaisia suosittelukeinoja (eng. recommendation engine) mainontaan kuin mitä verkkosivuilla käytetään (ks. esim. Amazon, suomalaisia palveluntarjoajia ovat ainakin Nosto ja Custobar). Näillä voidaan parantaa mainonnan relevanssia ja ennen kaikkea tuottaa lisäarvoa. Perinteisessä myynnissä tehokkaiksi havaitut taktiikat tulisi jalkauttaa sopivalla tavalla verkkoon, koska niiden takana on pitkä tutkimus ja käytäntö ja niiden toimivuus voidaan täten perustella.

Johtopäätös: Dynaamisessa uudelleenmarkkinoinnissa kannattaa 1) näyttää mitä asiakas ei ole nähnyt, ja 2) tuottaa lisäarvoa (ei spämmiä).

Olin häkeltynyt kuinka pinnallisia ja yksioikoisia suosituksia sain Facebookin edustajalta (ihmiskuva? robotti; lisäarvo? ei mietitä). Mutta olen opettaessani huomannut tämän ennenkin: markkinoinnin opiskelijoille syötetään väärää mielikuvaa yksinkertaisista asiakkaista, joiden manipulaatioon (konversioon) riittää tietty määrä toistoja. Tungetaan pullaa kurkusta alas, jos ei hyvällä niin pahalla.

Tällainen vanhanaikainen ihmiskäsitys — ns. spämmääjän mentaliteetti — on markkinoijalle haitallista. Spämmäävä markkinointi johtaa reaktanssiin ja mainosten yleiseen vastustukseen. Lisäksi ohjelmallisessa ostamisessa on muitakin laatuongelmia, joten markkinoijien tulisi kaikin keinoin pyrkiä toteuttamaan lisäarvon periaatetta.

Toivottavasti tämä artikkeli herätti ajattelemaan asioista toisella tavalla, ainakin dynaamisen uudelleenkohdistuksen kontekstissa.

Algorithm Neutrality and Bias: How Much Control?

The Facebook algorithm is a global super power.

So, I read this article: Facebook is prioritizing my family and friends – but am I?

The point of the article — that you should focus on your friends & family in real life instead of Facebook — is poignant and topical. So much of our lives is spent on social media, without the “social” part, and even when it is there, something is missing in comparison to physical presence (without smart phones!).

Anyway, this post is not about that. I got to think about the from the algorithm neutrality perspective. So what does that mean?

Algorithm neutrality takes place when social networks allow content spread freely based on its merits (e.g., CTR, engagement rate); so that the most popular content gets the most dissemination. In other words, the network imposes no media bias. Although the content spreading might have a media bias, the social network is objective and only accounting its quantifiable merits.

Why does this matter? Well, a neutral algorithm guarantees manipulation-free dissemination of information. As soon as human judgment intervenes, there is a bias. That bias may lead to censorship and favoring of certain political party, for example. The effect can be clearly seen in the so-called media bias. Anyone following either the political coverage of the US elections or the Brexit coverage has noticed the immense media bias which is omnipresent in even the esteemed publications, like the Economist and Washington Post. Indeed, they take a stance and report based on their stance, instead of covering objectively. A politically biased media like the one in the US is not much better than the politically biased media in Russia.

It is clear that free channels of expression enable the proliferation of alternative views, whereupon an individual is (theoretically) better off, since there are more data points to base his/her opinion on. Thus, social networks (again, theoretically) mitigate media bias.

There are many issues though. First is the one that I call neutrality dilemma.

The neutrality dilemma arises from what I already mentioned: the information bias can be embedded in the content people share. If the network restricts the information dissemination, it moves from neutrality to control. If it doesn’t restrict information dissemination, there is a risk of propagation of harmful misinformation, or propaganda. Therefore, in this continuum of control and freedom there is a trade-off that the social networks constantly need to address in their algorithms and community policies. For example, Facebook is banning some content, such as violent extremism. They are also collaborating with local governments which can ask for removal of certain content. This can be viewed in their transparency report.

The dilemma has multiple dimensions.

First of all, there are ethical issues. From the perspective of “what is right”, shouldn’t the network prohibit diffusion of information when it is counter-factual? Otherwise, peopled can be mislead by false stories. But also, from perspective of what is right, shouldn’t there be free expression, even if a piece of information is not validated?

Second, there are some technical challenges:

A. How to identify “truthfulness” of content? In many cases, it is seemingly impossible because the issues are complex and not factual to begin with. Consider e.g. the Brexit: it is not a fact that the leave vote would lead into a worse situation than the stay vote, and vice versa. In a similar vein, it is not a fact that the EU should be kept together. These are questions of assumptions which make them hard: people freely choose the assumptions they want to believe, but there can be no objective validation of this sort of complex social problem.

B. How to classify political/argumentative views and relate them to one another? There are different point of views, like “pro-Brexit” and “anti-Brexit”. The social network algorithm should detect based on an individual’s behavior their membership in a given group: the behavior consists of messages posted, content liked, shared and commented. It should be fairly easy to form a view of a person’s stance on a given topic with the help of these parameters. Then, it is crucial to map the stances in relation to one another, so that the extremes can be identified.

As it currently stands, one is being shown the content he/she prefers which confirms the already established opinion. This does not support learning or getting an objective view of the matter: instead, if reinforces a biased worldview and indeed exacerbates the problems. It is crucial to remember that opinions do not remain only opinions but reflect into behavior: what is socially established becomes physically established through people’s actions in the real world. Therefore, the power of social networks needs to be taken with precaution.

C. How to identify the quality of argumentation? Quality of argumentation is important if applying the rotation of alternative views intended to mitigate reinforcement of bias. This is because the counter-arguments need to be solid: in fact, when making a decision, the pro and contra-sides need both be well-argued for an objective decision to emerge. Machine learning could be the solution — assuming we have training data on the “proper” structure of solid argumentation, we can compare this archetype to any kind of text material and assign it a score based on how good the argumentation is. Such a method does not consider the content of the argument, only its logical value. It would include a way to detect known argumentation errors based on syntax used. In fact, such a system is not unimaginably hard to achieve — common argumentation errors or logical fallacies are well documented.

Another form of detecting quality of argumentation is user-based reporting: individuals report the posts they don’t like, and these get discounted by the algorithm. However, Even when allowing users to report “low-quality” content, there is a risk they report content they disagree with, not which is poorly argued. In reporting, there is relativism or subjectivism that cannot be avoided.

Perhaps the most problematic of all are the socio-psychological challenges associated with human nature. The neutral algorithm enforces group polarization by connecting people who agree on a topic. This is natural outcome of a neutral algorithm, since people by their behavior confirm their liking of a content they agree with. This leads to reinforcement whereupon they are shown more of that type of content. The social effect is known as group polarization – an individual’s original opinion is enforced through observing other individuals sharing that opinion. That is why so much discussion in social media is polarized: there is this well known tendency of human nature not to remain objective but to take a stance in one group against another.

How can we curb this effect? A couple of solutions readily come to mind.

1. Rotating opposing views. If in a neutral system you are shown 90% of content that confirms your beliefs, rotation should force you to see more than 10% percent of alternative (say, 25%). Technically, this would require that “opinion archetypes” can be classified and contrasted to one another. Machine learning to the rescue?

The power of rotation comes from the idea it simulates social behavior: the more a person is exposed to subjects that initially seem strange and unlikeable (i.e., xenophobia), the more likely they are to be understood. A greater degree of awareness and understanding leads into higher acceptance of those things. In real world, people who frequently meet people from other cultures are more likely to accept other cultures in general.

Therefore, the same logic could by applied by Facebook in forcing us to see well-argumented counter-evidence to our beliefs. It is crucial that the counter-evidence is well-argued, or else there is a strong risk of reactance — people rejecting the opposing view even more. Unfortunately, this is a feature of the uneducated mind – not to be able to change one’s opinions but remain fixated on one’s beliefs. So the method is not full-proof, but it is better than what we now have.

2. Automatic fact-checking. Imagine a social network telling you “This content might contain false information”. Caution signals may curb the willingness to accept any information. In fact, it may be more efficient to show misinformation tagged as unreliable rather than hide it — in the latter case, there is possibility for individuals to correct their false beliefs.

3. Research in sociology. I am not educated to know enough about the general solutions of group polarization, groupthink and other associated social problems. But I know sociologists have worked on them – this research should be put to use in collaboration with engineers who design the algorithms.

However, the root causes for dissemination of misinformation, either purposefully harmful or due to ignorance, lie not on technology. The are human-based problems and must have a human-based solution.

What are these root causes? Lack of education. Poor quality of educational system. Lack of willingness to study a topic before forming an opinion (i.e., lazy mind). Lack of source/media criticism. Confirmation bias. Groupthink. Group polarization.

Ultimately, these are the root causes of why some content that should not spread, spreads. They are social and psychological traits of human beings, which cannot be altered via algorithmic solutions. However, algorithms can direct behavior into more positive outcomes, or at least avoid the most harmful extremes – if the aforementioned classification problems can be solved.

The other part of the equation is education — kids need to be taught from early on about media and source criticism, logical argumentation, argumentation skills and respect to another party in a debate. Indeed, respect and sympathy go a long way — in the current atmosphere of online debating it seems like many have forgotten basic manners.

In the online environment, provocations are easy and escalate more easily than in face-to-face encounters. It is “fun” to make fun of the ignorant people – a habit of the so-called intellectuals – nor it is correct to ignore science and facts – a habit of the so-called ignorants.

It is also unfortunate that many of the topics people debate on can be traced down to values and worldviews instead of more objective topics. When values and worldviews are fundamentally different among participants, it is truly hard to find a middle-way. It takes a lot of effort and character to be able to put yourself on the opposing party’s shoes, much more so than just point blank rejecting their view. It takes even more strength to change your opinion once you discover it was the wrong one.

Conclusion and discussion. Avoiding media bias is an essential advantage of social networks in information dissemination. I repeat: it’s a tremendous advantage. People are able to disseminate information and opinions without being controlled by mass-media outlets. At the same time, neutrality imposes new challenges. The most prominent question is to which extent should the network govern its content.

One one hand, user behavior is driving Facebook towards information sharing network – people are seemingly sharing more and more news content and less about their own lives – but Facebook wants to remain as social network, and therefore reduces neutrality in favor of personal content. What are the strategic implications? Will users be happier? Is it right to deviate from algorithm neutrality when you have dominant power over information flow?

Facebook is approaching a sort of an information monopoly when it comes to discovery (Google is the monopoly in information search), and I’d say it’s the most powerful global information dissemination medium today. That power comes with responsibility and ethical question, and hence the algorithm neutrality discussion. The strategic question for Facebook is that does it make sense for them to manipulate the natural information flow based on user behavior in a neutral system. The question for the society is should Facebook news feeds be regulated.

I am not advocating more regulation, since regulation is never a creative solution to any problem, nor does it tends to be informed by science. I advocate collaboration of sociologists and social networks in order to identify the best means to filter harmful misinformation and curb the generally known negative social tendencies that we humans possess. For sure, this can be done without endangering the free flow of information – the best part of social networks.

Why social advertising beats display advertising


I’ve long been skeptical of display advertising. At least my students know this, since ever year I start the digital marketing course by giving a lecture on why display sucks (and why inbound / search-engine marketing performs much better).

But this post is not about the many pitfalls of display. Rather, it’s outlining three arguments as to why I nowadays prefer social advertising, epitomized by Facebook Ads, over display advertising. Without further ado, here are the reasons why social rocks at the moment.

1. Quality of contacts

It’s commonly known Facebook advertising is cheap in comparison to many advertising channels, when measured by CPM or cost per individual reached. Display can be even cheaper, so isn’t that better? No, absolutely not. Reach or impressions are completely fallacious metrics — their business value approaches zero. Orders of magnitude more important is the quality of contacts.

The quality of Facebook traffic, when looking at post-click behavior, tends to be better than the quality of display traffic. Even when media companies speak of “premium inventory”, the results are weak. People just don’t like banner ads. The people who click them, if they are people and not bots to begin with, often exit the site instantly without clicking further.

2. Social interaction

People actually interact with social ads. They pose questions, like them and even share them to their friends. Share advertisements? OMG, but they really do. That represents an overkill opportunity for a brand to interact with its customer base, and systematically gather feedback and customer insight. This is simply not possible with any other form of advertising, display including.

Display ads, albeit using rich media executions, are completely static and dead when it comes to social interaction. Whereas social advertising creates an opportunity to gather social proof and actual word-of-mouth, even viral diffusion, in the one and same advertising platform, display advertising is completely lacking the social dimension.

3. Better ad formats

Social advertising, specifically Facebook gives a great flexibility in combining text, images and video. Typically, a banner ad can only fit a brief slogan (“Just do it.”), whereas a social advertisement can include many sentences of text, a compelling picture and even link description that together give the advertisers the ability to communicate the whole story of the company or its offering in one advertisement.

But isn’t that boring? No, you can craft it in a compelling way – the huge advantage is that people don’t even need to click to learn the most essential. If the goal of advertising is to inform about offerings, social advertising is among the most efficient ways to actually do it.


That’s it. I don’t see a way for display advertising to overcome these advantages of social advertising. Notice that I didn’t mention the superior targeting criteria — this is because display is quickly catching up to Facebook in that respect. It just won’t be enough.

5 questions to ask your Facebook marketing agency

Facebook marketing is not magic, although it might seem like it if you have no clue how to do it. Therefore, before anything else, the first piece of advice is: get to know the basics. is a good resource for that, as well as Facebook’s free training modules.

Now, to the actual point. A company may run Facebook marketing in-house or via an agency. For small companies, it often makes sense to do it yourself, but larger budgets require a deeper know-how and more time to get the best results. For these reasons, outsourcing is often chosen by many medium and large companies. When outsourcing, an agency can take care of organic Facebook marketing, paid advertising, or both.

But how to test the quality of your agency?

Well, remember the first advice – learn the basics of Facebook marketing. If you don’t know something, you cannot manage it. Second, you can ask these questions, before engaging an agency or during your relationship with them.

  1. What goals would you set for our Facebook marketing?
  2. How would you measure the achievement of those goals?
  3. Describe your strategy in achieving the goals.
  4. Describe your optimization process for Facebook marketing.
  5. Based on our Facebook posts, tell me something that I don’t know about my business

The first question reveals how well the agency grasps your business, and how they would fit your business goals to the Facebook environment. The goals don’t have to be exactly what you had thought of — it’s more important that they show innovativeness and general understanding of your business.

The second question reveals the metrics they would choose to measure performance – the more they are aligned with your general business goals, the better. In addition, if they are able to argue efficiently for both ROI- and non-ROI-oriented metrics, it’s a good sign as it shows an understanding of the general complexity of multichannel consumer behavior.

The third question tells how they would go about creating a Facebook marketing strategy — here you can pay attention to their proposed split between organic and paid, frequency of posting/optimization, target group definition, ad creation process, etc. You can ask specifying questions, e.g. about the suggested size of budget. That shows how they approach campaign planning on the fly – the better they know the environment, the better answers they can give.

Fourth, it is important to know how they would run the accounts in practice. For example, how much time are they willing to invest? Facebook marketing is a time-consuming activity, which is actually a major reason the optimization workflow has to be efficient to achieve the best results. For an agency it’s easy to spend money precariously because Facebook takes all the money you can throw at it — but optimization is a different ballgame.

The fifth question tells how well they have analyzed your accounts and prior Facebook marketing activities. Not all agencies bother to analyze the status quo in your Facebook marketing this before meeting you — or even when they are doing marketing for you — but obviously doing so communicates a genuine interest in closing/keeping you as a client, as well as attention to detail. If they are able to tell you something about your customers, for instance, that you didn’t know, it’s a very good sign.

There. Asking these questions and going through the associated discussion is, in my opinion, an excellent way to vet a Facebook marketing agency.

In addition, one of the by far most neglected aspect of managing digital marketing agencies is auditing. You should frequently have a 3rd party, such as another agency, audit your campaigns. Never be “forever happy” with an agency but instead always push for more. You want to show commitment so they see value in investing in the relationship, but you also want to keep them a little bit on their toes so they actually bother doing their best for you, as oppose to only chasing new clients.

Miten sovittaa Facebook-mainonta yrityksen muuhun markkinointiin?


Käsittelen tässä artikkelissa lyhyesti tapoja suhteuttaa Facebook-mainonta yrityksen muuhun markkinointiin.

Miten hoitaa homma?

Seitsemän vaiheen kautta.

1. Määritä Facebook-mainonnalle oma budjetti, mieluiten vuositasolla. Facebook-markkinointi ei ole ilmaista orgaanisellakaan puolella, mutta etenkin mainonta tarvitsee budjetin. Mieluusti ison sellaisen, jotta on varaa skaalata tulosten mukaan. Facebook-mainontahan skaalautuu täysin halutun budjetin mukaisesti – päivässä voi käyttää viisi euroa tai miljoonan. Vuositaso on hyvä, sillä se mahdollistaa pitkäjänteisen kampanjastrategian laadinnan. Kuukausitasolla budjetti jalkautetaan tulosten perusteella, mikä eroaa yleensä perinteisen mainonnan budjetoinnista.

2. Varmista itsenäisyys. Haluat itsenäisesti päättää budjetin käytöstä tulosten perusteella. Et halua missään nimessä hyväksyttää kampanjoita jollain esimiehillä – tämä tuhoaa edellytykset tehokkaalle optimointirutiinille. Itsenäisyyden puolesta taistelu on jäykissä orgaanisaatioissa hankalaa – jos toimit sellaisessa päättäjänä, tiedä että parhaat osaajat kaipaavat itsenäisyyttä ja ovat sen arvoisessa. Jos toimit sellaisessa digimarkkinoinnin tekijänä, tiedä että on parempia vaihtoehtoja ja harkitse työpaikan vaihtoehtoa.

3. Hyödynnä yrityksen visuaalista ilmettä, mutta älä alistu sille. Visuaalisen identiteetin säilyttäminen on ok, koska se rakentaa “brändiä”, sillä tavalla mitä brändi tässä yhteydessä tarkoittaa eli tunnistettavaa yritysilmettä. Kun peruselementit, eli fontit, värimaailma ja graafiset elementit on määritelty, se riittää. Itse viestejä ja sisältöjä tulee testata ahkerasti. Näin voidaan yhdistää integroidun markkinointiviestinnän teoreettiset edut ja online-mainonnan ketteryys. Mainosten ylenpalttinen hinkkaaminen sen sijaan kääntyy itseään vastaan – et tarvitse sadan henkilön mielipidettä konseptin testaamiseen. Testaa se yhdessä hyväksyttyjen puitteiden rajoissa ja tee päätökset tulosten perusteella.

4. Hyödynnä organisaatiossasi jo tehtyä markkinointityötä. Tähän kuuluu vaikkapa markkinointitutkimus, kristallisoidut arvolupaukset (ts. asiakashyödyt), tuotteistaminen, määritellyt viestit, luodut markkinointipersoonat ja kohderyhmät, kuvamaailmat ja muu sellainen suunnittelutyö, jota jo hyödynnätte printissä mutta ette välttämättä digissä. Kaiken tästä voi siirtää Facebook-mainonnan maailmaan tavalla tai toisella. Mutta ole valmis kyseenalaistamaan tehdyn työn hyvyys – esimerkiksi kuvittelemasi markkinointipersoonat voivatkin olla aivan muita henkilöitä kuin oikeat asiakkaasi. Data paljastaa, se jolla on silmät katsoo.

5. Opettele klassista copywriting-taitoa. Lue Ogilvyn, Parantaisen ja Hopkinsin mainiot teokset – copy on se osa-alue, jossa voit parhaiten erottautua: kohdentamisen ja hinnoittelun osaa kone tehdä sinua paremmin. Tekniset taidot eivät jatkossa tuo markkinoijille etua näissä alustoissa, sillä automatiikka ottaa yhä suuremman roolin kampanjoiden optimoinnissa. Luovuus on se osa-alue, jolla ihminen pärjää. Facebook-mainonta ei ole tekninen suoritus, vaan luova taidonnäyte. Ja hyvä copy on hyvää myös Facebookissa.

6. Käytä jo tehtyjä luovia konsepteja. Esimerkiksi printtiä tai tv:tä varten luodut konseptit soveltuvat suoraan Facebookiin – vaihda vain resoluutio ja määritä kohderyhmä, niin voilà! Olet valmis räjäyttämään pankin. Et tarvitse mitään ”Facebook-konsepteja”, kilpailuita tai muuta mukamas asiakkaita kiinnostavaa ”vuorovaikutusta”, vaan voit hyödyntää tekemääsi korkeatasoista markkinoinnin luovaa työtä suoraan Facebookissa. Ja se toimii siellä paremmin, koska kohdennusalgoritmi löytää automaattisesti konseptiisi parhaiten reagoivat kohderyhmät. Huomio myös, että vuorovaikutus tulee luoksesi myös tätä kautta – ihmiset kommentoivat eli kehuvat hyviä mainoksia ja haukkuvat huonoja. Ja jakavat niitä eteenpäin. Haluat paljon enemmän, että he jakavat eteenpäin mainoksesi kuin kilpailun, jossa annat pois jotain ilmaista roinaa.

Tämä toimii kahteen suuntaan, siis:

7. Generalisoi Facebook-mainonnan tulokset muuhun markkinointiin. Kun löydät testaamalla, että arvolupaus X toimii kohderyhmässä Y, vie se myös printtiin tai radioon. Ehkä pikkuhiljaa pääsemme pois massamedia-ajan ajatuksesta, että kaikille pitää olla yksi ja sama viesti. Ehkä alat oppia, että eri ihmisille pitää samasta asiasta puhua eri lailla. Kyllä, se on itsestään selvää, mutta kuinka monessa organisaatiossa näin tehdään? Ei monessa, koska ”meillä on tuo IMC.” Tulevaisuuden mainontaa ei ole IMC, vaan hajauttaminen, diversifikaatio. Sillä saadaan optimaalinen tuotto, koska viesti ja kohderyhmä vastaavat toisiaan mahdollisimman hyvin. Mitä tarkempi kohdennus, sen paremmat tulokset.


Johtopäätöksenä Facebook-mainonnan ja muun markkinoinnin suhde on kaksisuuntainen:  muun markkinoinnin eteen tehty tausta- ja suunnittelutyö hyödyttää Facebook-mainontaa, mutta Facebook-mainonta toimii samalla reaalitestinä esimerkiksi hypoteettisille markkinointipersoonille ja mahdollistaa konseptien testaamisen pienilläkin panostuksilla. Paljon pienemmillä kuin mitä televisiomainoksen testaaminen televisiossa maksaisi.

Facebook-mainonta ei tarvitse omia konsepteja – nyt on aika unohtaa kilpailut ja ”asiakkaiden osallistaminen” ja siirtää printti digiin.

Kirjoittaja toimii Postdoc-tutkijana ja opettaa digitaalista markkinointia Turun kauppakorkeakoulussa.