March 30, 2017
Earlier today, I had a brief exchange of tweets with @jonathanstray about algorithms.
It started from his tweet:
Perhaps the biggest technical problem in making fair algorithms is this: if they are designed to learn what humans do, they will.
To which I replied:
Yes, and that’s why learning is not the way to go. “Fair” should not be goal, is inherently subjective. “Objective” is better
Then he wrote:
lots of things that are really important to society are in no way objective, though. Really the only exception is prediction.
And I wrote:
True, but I think algorithms should be as neutral (objective) as possible. They should be decision aids for humans.
And he answered:
what does “neutral” mean though?
After which I decided to write a post about it, since the idea is challenging to explain in 140 characters.
So, what is a neutral algorithm? I would define it like this:
“A neutral algorithm is a decision-making program whose operating principles are minimally inflenced by values or opinions of its creators.” 
An example of a neutral algorithm is a standard ad optimization algorithm: it gets to decide whether to show Ad1, Ad2, or Ad3. As opposed to asking from designers or corporate management which ad to display, it makes the decision based on objective measures, such as click-through rate (CTR).
A treatment that all ads (read: content, users) get is fair – they are diffused based on their merits (measured objectively by an unambiguous metric), not based on favoritism of any sort.
The roots of algorithm neutrality stem from freedom of speech and net neutrality . No outsiders can impose their values and opinions (e.g., censoring politically sensitive content) and interfere with the operating principles of the algorithm. Instead of being influenced by external manipulation, the decision making of the algorithm is as value-free (neutral) as possible. For example, in the case of social media, it chooses to display information which accurately reflects the sentiment and opinions of the people at a particular point in time.
Now, I grant there are issues with “freedom”, some of which are considerable. For example, 1) for media, CTR-incentives lead to clickbaiting (alternative goal metrics should be considered), 2) for politicians and electorate, facts can be overshadowed by misinformation and short videos taken out of context to give false impression of individuals; and 3) for regular users, harmful misinformation can spread as a consequnce of neutrality (e.g., anti vaccination propaganda).
Another limitation is legislation – illegal content should be kept out by the algorithm. In this sense, the neutral algorithm needs to adhere to a larger institutional and regulatory context, but given that the laws themselves are “fair” this should impose no fundamental threat to the objective of neutral algorithms: free decision-making and, consequently, freedom of speech.
I wrote more about these issues here .
Inspite of the aforementioned issues, with a neutral algorithm each media/candidate/user has a level playing field. In time, they must learn to use it to argue in a way that merits the diffusion of their message.
The rest is up to humans – educated people respond to smart content, whereas ignorant people respond to and spread non-sense. A neutral algorithm cannot influence this; it can only honestly display what the state of ignorance/sophistication is in a society. A good example is Microsoft’s infamous bot Tay , a machine learning experiment turned bad. The alarming thing about the bot is not that “machines are evil”, but that *humans are evil*; the machine merely reflects that. Hence my original point of curbing human evilness by keeping algorithms free of human values as much as possible.
Perhaps in the future an algorithm could figuratively spoken save us from ourselves, but at the moment that act requires conscious effort from us humans. We need to make critical decisions based on our own judgment, instead of outsourcing ethically difficult choices to algorithms. Just as there is separation of church and state, there should be separation of humans and algorithms to the greatest possible extent.
 Initially, I thought about definition that would say “not influenced”, but it is not safe to assume that the subjectivity of its creators
would not in some way be reflected to the algorithm. But “minimal” leads into normative argument that that subjectivity should be mitigated.
 Wikipedia (2016): “Net neutrality (…) is the principle that Internet service providers and governments should treat all data on the Internet the same, not discriminating or charging differentially by user, content, site, platform, application, type of attached equipment, or mode of communication.”
 Algorithm Neutrality and Bias: How Much Control? <https://www.linkedin.com/pulse/algorithm-neutrality-bias-how-much-control-joni-salminen>
 A part of the story is that Tay was trolled heavily and therefore assumed a derogatory way of speech.
March 30, 2017
PPC bidding requires strategic thinking.
Introduction. Wow. I was doing some SEM optimization in Google AdWords while a thought struck me. It is this: Advertisers actively following “Opportunities” in AdWords risk bid wars. Why is that? I’ll explain.
Opportunities or not? The “Opportunities” feature proposes bid increases for given keywords, e.g. Week 1: Advertiser A has current bid b_a and is proposed a marginal cost m_a, so the new bid e_a = b_a+m_a. During the same Week 1: Advertiser B, in response to Advertiser A’s acceptance of bid increase, is recommended to maintain his current impression share by increasing his bid b_b to e_b = b_b+m_b. To maintain the impression share balance, Advertiser A is again in the following optimization period (say the optimization cycle is a week, so next week) proposed yet another marginal increase, et cetera.
If we turn m into a multiplier, then the bid will eventually be b_a = (b_a * m_a)^c, where c is the number of optimization cycles. Let’s say AdWords recommends 15% bid increase at each cycle (e.g., 0.20 -> 0.23$ in the 1st cycle); then after five cycles, the keyword bid has doubled compared to the baseline (illustrated in the picture).
Figure 1 Compounding bid increases
Alluring simplicity. Bidding wars were always a possible scenario in PPC advertising – however, the real issues here is simplicity. The improved “Opportunities” feature gives much better recommendations to advertisers than earlier version, which increases its usage and more easily leads into “lightly made” acceptance of bid increases that Google can show to likely maintain a bidder’s current competitive positioning. From auction psychology we know that bidders have a tendency to overbid when put into competitive pressure, and that’s exactly where Google is putting them.
It’s rational, too. I think that more aggressive bidding can easily take place under the increasing usage of “Opportunities”. Basically, the baselines shift at the end of each optimization cycle. The mutual increase of bids (i.e., bid war) is not only a potential outcome of light-headed bidding, but in fact increasing bids is rational as long as keywords still remain profitable. But in either case, economic rents (=excessive profits) will be competed away.
Conclusion. Most likely Google advertising will continue converging into a perfect market, where it is harder and harder for individual advertisers to extract rents, especially in long-term competition. “Opportunities” is one way of making auctions more transparent and encourage more aggressive bidding behavior. It would be interesting to examine if careless bidding is associated with the use of “Opportunities” (i.e., psychological aspect), and also if Google shows more recommendations to increase than decrease bids (i.e., opportunistic recommendations).
March 30, 2017
China is an enormous market, amounting to 1.3 billion people and growing. Out of all the BRIC markets, China is the furthest in the adoption of technology and digital platforms, especially smartphones and applications.
Perhaps the most known example of Chinese digital platforms in the West is Alibaba, the ecommerce giant with market cap of over 200 $bn. Through Ali Express, Western consumers can order Chinese products – but also Western companies can use the marketplace to sell their products to Chinese consumers. However, this blog post is about Baidu, the Chinese equivalent to Google.
Baidu was founded in 2000, almost at the same time as Google (which was
founded in 1998). Google left China in 2010 amidst censorship issues, after which Baidu has solified its position as the most popular search engine in China.
Most likely due to their similar origins, Baidu is much like Google. The user interface and functionalities have borrowed heavily from Google, but Baidu also displays some information differently from Google. An example of Baidu’s search-engine results page (SERP) can be seen below.
Figure 1 Example of Baidu’s SERP
A lot of Chinese use Baidu to search for entertainment instead of information;
Baidu’s search results page support this behavior. In terms of search results, there is active censorship on sensitive topics, but that is not directly influencing most Western companies interested in the Chinese market. Overall, to influence Chinese consumers, it is crucial to have a presence on Baidu — companies not visible on Baidu might not be considered by the Chinese Internet users as esteemed brands at all.
I have collected here some interesting facts about Baidu:
The proliferation of Internet users has tremendously influenced Baidu’s usage, as can be seen from the statistics.
Baidu enables three type of digital marketing: 1) search-engine optimization (SEO), 2) search-engine advertising (PPC), and 3) display advertising. Let’s look at these choices.
First, Baidu has a habit of favoring its numerous own properties (such as Baidu News, Zhidao, etc.) over other organic results. Even up to 80% of the first page results is filled by Baidu’s own domains, so search-engine optimization in Baidu is challenging. Second, Baidu has a similar network to GDN (Google Display Network). It includes some 600k+ websites. As always, display networks need to be filtered for ad fraud by using whitelisting and blacklisting techniques. After doing that, display advertising is recommended as an additional tactic to boost search advertising performance.
Indeed, the best way to reach Baidu users is search advertising. The performance of PPC usually exceeds other forms of digital marketing, because ads are shown to the right people at the right time. Advertising in Baidu is a common practice, and Baidu has more than 600,000 registered advertisers. Currently advertiser are especially focusing on mobile users, where Baidu’s market share is up to 90% and where usage is growing the fastest .
For an advertiser, Baidu offers similar functionalities than Google. Search-engine advertising, often called PPC (pay-per-click), is possible in Baidu. In this form of advertising, advertisers bid on keywords that represent users’ search queries. When a user makes a particular serch, they are shown text ads from the companies with winning bids. Companies are charged when their ad is clicked.
The following picture shows how ads are displayed on Baidu’s search results page.
Figure 2 Ads on Baidu
As you can see, ads are shown on top of the search results. Organic search results are placed after ads on the main column. On the right column, there is extra “rich” information, much like on Google. The text ads on Baidu’s SERP look like this:
Figure 3 Text ads on Baidu
The ad headlines can have up to 20 Chinese characters or 40 English characters, and the description text up to 100 Chinese characters or 200 English characters. There is also possibility to use video and images in a prominent way. Below is an example of Mercedez Benz’s presence in Baidu search results.
Figure 4 Example of brands presence on Baidu
It can be easily understood that using such formats is highly recommendable for brand advertisers.
Baidu’s search advertising platform is called Phoenix Nest (百度推广). The tools to access accounts include Web interface and Baidu PPC Editor (百度推广助手).
To start Baidu advertising, you will need to create an account. For that, you need to have a Chinese-language website, as well as send Baidu a digital copy business registration certificate issued in your local country. You also need to make a deposit of 6500 yuans, of which 1500 is held by Baidu as a setup fee and the rest is credited to your advertising account. The opening process for Baidu PPC account may take up to two weeks. Depending on your business, you might also need to apply for Chinese ICP license and host the website in mainland China.
There are other search providers in China, such as 360 Search and Sogou but with its ~60% market share in search and ~50% of overall online advertising revenue in China, Baidu is the leading player. Additionally, Baidu is likely to remain on top in the near future to its considerable investments on machine learning and artificial intelligence in the fields of image and voice recognition. Currently, some 90% of Chinese Internet users are using Baidu . For a marketer interested in doing digital marketing in China, Baidu should definitely be included in the channel mix.
Other prominent digital marketing channels include Weibo, WeChat, Qihoo 360, and Sogou. For selling consumer products, the best platforms are Taobao and Tmall – many Chinese may skip search engines and directly go to these platforms for their shopping needs. As usually, companies are advised to leverage the power of superplatforms in their marketing and business operations.
 Alexa Siteinfo: Baidu <http://www.alexa.com/siteinfo/baidu.com>
 Nine reasons to use Baidu <http://richwaytech.ca/9-reasons-use-baidu-for-sem-china/>
 Baidu Fiscal Year 2015 <http://www.prnewswire.com/news-releases/baidu-announces-fourth-quarter-and-fiscal-year-2015-results-300226534.html>
 Is Baidu Advertising a Good Way to Reach Chinese Speakers Living in Western Countries? <https://www.nanjingmarketinggroup.com/blog/how-much-baidu-traffic-there-outside-china>
 50+ Amazing Baidu statistics and facts <http://expandedramblings.com/index.php/baidu-stats/>
 10 facts to understand Baidu <http://seoagencychina.com/10-facts-to-understand-the-top-search-engine-baidu/>
 What content did Chinese search most in 2013 <https://www.chinainternetwatch.com/6802/what-content-did-chinese-search-most-2013/#ixzz4G59YyMRG>
 Baidu controls 91% mobile search market in China <http://www.scmp.com/tech/apps-gaming/article/1854981/baidu-controls-91pc-mobile-search-market-china-smaller-firms>
 Baidu Paid Search <http://is.baidu.com/paidsearch.html>
March 30, 2017
In space, nobody can hear your advertising.
Earlier today I wrote about convergence of media agencies and creative agencies. But let’s look at it from a different perspective: Which one would survive? If we had to pick.
To answer the question, let us first determine their value-provided, and then see which one is more expendable.
Media agencies. First, media agencies’ value-provided derives from their ability to aggregate both market sides: on one hand, they bundle demand side (advertisers) and use this critical mass to negotiate media prices down. On the other hand, they bundle supply side (media outlets) and therefore provide efficiency for advertisers – the advertisers don’t need to search and negotiate with dozens of providers. In other words, media agencies provide the typical intermediary functions which are useful in a fragmented market. Their markup is the arbitrage cost: they buy media at price p_b and sell at p_s, the arbitrage cost being a = p_s – p_b.
Creative agencies. Second, creative agencies value-provided derives from their creative abilities. They know customers and have creative ability to create advertising that appeals to a given target audience. They usually charge an hourly rate, c; if the campaign requires x working hours, the creative cost being e = c*x. And consequently, the total cost for advertiser is T = e+a. We also observe double marginalization, so that e+a > C, where C is the cost that either agency would charge would they handle both creative and media operations.
Transition. Now, let’s consider the current transition which makes this whole question relevant. Namely, the advertising industry is moving into programmatic. Programmatic is a huge threat for intermediation since it aggregates fragmented market players. In practice this means that the advertisers are grouped under demand-side platforms (DSPs ) and the media under supply-side platforms (SSPs). How does this impact the scenario? The transition seemingly has an impact on media agencies, but not on creative agencies — “manual” bundling is no longer needed, but the need for creative work remains.
Conclusion. In conclusion, it seems creative agencies are less replaceable, and therefore have a better position in vertical integration.
Limitations. Now, this assumes that advertisers have direct access to programmatic platforms (so that media agencies can in fact be replaced); currently, this is not the standard case. It also assumes that they have in-house competence in programmatic advertising which also is not the standard case. But in time, both of these conditions are likely to evolve. Either advertisers acquire in-house access and competence, or then outsource the work to creative agencies which, in turn, will develop programmatic capabilities.
Another limitation is that the outcome will depend a lot on the position towards the client base. Whoever is closer to the client, is better equipped to develop the missing capabilities. As commonly acknowledged, customer relationships are the most valuable assets in advertising business, potentially giving an opportunity to build missing capabilities even when other market players would have already acquired them. But based on this “fictional” comparison, we can argue that creative agencies are better off when approaching convergence.
March 30, 2017
Attribution modelling is like digital magic.
Wow, so I’m reading a great piece by Funk and Nabout (2015) . They outline the main problems of attribution modelling. By “standard”, I refer to the commonly used method of attribution modelling, most commonly known from Google Analytics.
Previously, I’ve addressed this issue in my digital marketing class by saying that the choice of an attribution model is arbitrary, i.e. marketers can freely decide whether it’s better to use e.g. last-click model or first-click model. But now I realized this is obviously a wrong approach — given that the impact of each touch-point can be estimated. There is much more depth to attribution modelling than the standard model leads you to believe.
So, here are the five problems by Funk and Nabout (2015).
This is the main problem to me. The impact of touch-points on conversion value needs to be weighed but it is seemingly an arbitrary rather than a statistically valid choice (that is, until we consider advanced methods!). Therefore, there is no objective rank or “betterness” between different attribution models.
The standard attribution model does not consider the time interval between touch-points – it can range anywhere from 30 minutes to 90 days, restricted only by cookie duration. Why does this matter? Because time generally matters in consumer behavior. For example, if there is a long interval between contacts A_t and A_t+1, it may be that the effect of the first contact was not very powerful to incite a return visit. Of course, one could also argue there is a reason not to consider time, because any differences arise due to discrepancy of the natural decision-making process of the consumers which results in unknown intervals. Ignoring time would then standardize the intervals. However, if we assume patterns in consumers’ decision-making process, as it is usually done by stating that “in our product category, the purchase process is short, usually under 30 days”, then addressing time differences could yield a better forecast, say we should expect a second contact to take place at a certain point in time given our model of consumer behavior.
The nature of the touch or interaction should be considered when modeling customer journey. The standard attribution model assigns conversion value for different channels based on clicks, but the type of interaction in channels might be mixed. For example, for one conversion you might get a view in Facebook and click in AdWords whereas another conversion might have the reverse. But are views and clicks equally valuable? Most marketers would not say so. However, they would also assign some credit to views – at least according to classic advertising theory, visibility has an impact on advertising performance. Therefore, the attribution model should also consider several interaction types and the impact each type has on conversion propensity.
As Funk and Nabout (2015) note, “the analysis does not compare successful and unsuccessful customer journeys, [but] only looks at the former.” This is essentially a case of survivor bias – we are unable to compare those touch-points that lead to a conversion to those that did not. By doing so, we could observe that a certain channel has a higher likelihood to be included in a conversion path  than another channel, i.e. its weight should be higher and proportional to its ability to produce lift in the conversion rate. Excluding information on unsuccessful interaction, we risk getting Type I and Type II errors – that is, false negatives and positives.
The standard attribution model does not consider offline interactions. But research shows multi-channel consumer behavior is highly prevalent. The lack of data on these interactions is the major reason behind exclusion, but the the same it restricts the usefulness of attribution modelling to ecommerce context. Most companies, therefore, are not getting accurate information with attribution modelling beyond the online environment. And, as I’ve argued in my class, word-of-mouth is not included in the standard model either, and that is a major issue for accuracy, especially considering social media. Even if we want to measure the performance of advertising channel, social media ads have a distinct social component – they are shared and commented on, which results in additional interactions that should be considered when modeling customer journey.
I’m still finishing reading the original article, but had to write these few lines because the points I encountered were poignant. Next I’m sure they will propose solutions, and I may update this article afterwards. At this point, I can only state two solutions that readily come to mind: 1) the use of conversion rate (CVR) as an attribution parameter — it’s a global metric and thus escapes survivorship bias; and 2) Universal Analytics, i.e. using methods such as Google’s Measurement Protocol to capture offline interactions. As someone smart said, solution to a problem leads to a new problem and that’s the case here as well — there needs to a universal identifier (“User ID” in Google’s terms) to associate online and offline interactions. In practice, this requires registration.
The criticism applies to standard attribution modeling, e.g. to how it is done in Google Analytics. There might be additional issues not included in the paper, such as aggregate data — to perform any type of statistical analysis, click-stream data is a must have. Also, a relevant question is: How do touch-points influence one another? And how to model that influence? Beyond technicalities, it is important for managers to understand the general limitations of current methods of attribution modelling and seek solutions in their own organizations to overcome them.
 Funk, B., & Abou Nabout, N. (2016). Cross-Channel Real-Time Response Analysis. in O. Busch (Hrsg.), Programmatic Advertising: The Successful Transformation to Automated, Data-Driven Marketing in Real-Time. (S. 141-151). Springer-Verlag.
 Conversion path and customer journey are essentially referring to the same thing; perhaps with the distinction that conversion path is typically considered to be digital while customer journey has a multichannel meaning.
March 30, 2017
Answer: It kinda is.
“Premium publishers” and “premium ad space” — these are often heard terms in programmatic advertising. But they are also dangerously fallacious ideas.
I’ll give three reasons why:
First, publishers define what is “premium” a priori (before results) which is not the right sequence to do it (a priori problem). The value of ad space — or the status, premium or not — should be determined a posteriori, or after the fact. Anything will risk biases due to guess-work.
Second, what is “premium” (i.e., works well) for advertiser A might be different for advertiser B, but the same ad space is always “premium” or not (uniformity problem). The value of ad space should be determined based on its value to the advertiser, which is not a uniform distribution.
Third, fixing a higher price for “premium” inventory skews the market – rational advertisers won’t pay irrational premiums and the publisher ends up losing revenue instead of gaining “premium” price (equilibrium problem). This is the exact opposite outcome the publisher hoped for, and arises from imbalance of supply and demand.
I defined premium as ad space that works well in regards to the advertiser’s objectives. Other definitions also exist, e.g. Münstermann and Würtenberg (2015) who argue the distinctive trait between premium and non-premium media is the degree of its editorial professionalism, so that amateur websites would be less valuable. In many cases, this is an incorrect classifier from the advertiser’s perspective — e.g., placing an ad on a blogger’s website (influencer marketing) can fairly easily produce higher rents than placing it alongside “professional” content. The degree of professionalism of the content is not a major cue for the consumers, and therefore one should define “premium” from the advertiser’s point of view — as a placement that works.
The only reason, I suspect, premium inventory is still alive is due to the practice of private deals where advertisers are more interested in volume than performance – these advertisers are more informed by assumptions than data. Most likely as the buyers’ level of sophistication increases, they become more inclined to market-based pricing which has a much closer association with performance than private deals.
March 30, 2017
From its high point, the sheep can see far.
In Finland, and maybe elsewhere in the world as well, media agencies used to reside inside advertising agencies, back in the 1970-80s. Then they were separated from one another in the 1990s, so that advertising agencies do creative planning and media companies buy ad space in the media. Along with this process, heavy international integration took place and currently both the media and advertising agency markets are dominated by a handful of global players, such as Ogilvy, Dentsu, Havas, WPP, etc.
This article discusses that change and argues for re-convergence of media and advertising agencies. I call this the new paradigm (paradigm = a dominant mindset and way of doing things).
The old paradigm
The current advertising paradigm consists of two features:
1) Advertising = creative + media
2) Creative planning –> media buying –> campaigning
In this paradigm, advertising is seen as rigid, inflexible, and one-off game where you create one advertising concept and run it, regardless of customer response. You are making a one sizable bet, and that’s it. To reduce the risk of failure, creative agencies use tons of time to “make sure they get it right”. Sometimes they use advertising pre-testing, but the process is predominantly driven by intuition, or black-box creativity.
Overall, that is an old-fashioned paradigm, for which reason I believe we need a new paradigm.
Towards the new paradigm
The new advertising paradigm looks like this:
1) Advertising = creative + media + optimization
2) Creative planning –> media trials –> creative planning –> …
In that, advertising in seen as fluid, flexible, and consecutive game where you have many trials to succeed. The creative process feeds from consumer response, and in turn media buying is adjusted based on the results of each unique creative concept.
So what is the difference?
In the old paradigm, we would spend three months planning and create one “killer concept” which according to our intuition/experience is what people want to see. In the new paradigm, we spend five minutes to create a dozen concepts and let customers (data) tell us what people want to see. Essentially, we relinquish the idea that it is possible to produce a “perfect ad”, in particular without customer feedback, and instead rely on a method that gets us closer to perfection, albeit never reaching it.
The new paradigm is manifested in a continuous, iterative cycle. Campaigns never end, but are infinite — as we learn more about customers, budget spend may increase in function of time, but essentially optimization is never done. The campaign has no end, unlike in the old paradigm where people would stop marketing a product even if the demand for that product would not disappear.
You might notice that the paradigm may not be compatible of old-fashioned “shark fin” marketing, but views marketing as continuous optimization. In fact, the concept of campaign is replaced by the concept of optimization.
Let me elaborate this thought. Look at the picture (source: Jesper Åström) – it illustrates the problem of campaign-based (shark-fin) marketing. You put in money, but as soon you stop investing, your popularity drops.
Now consider an alternative, where you constantly invest in marketing and not in heavy spikes (campaigns) but gradually by altering your message and targeting (optimization). You get results more like this:
Although seasonality, which is a natural consequence of the business cycle, does not fade away, the baseline results increase in time.
Instead of being fixed, budget allocations live according to the seasonal business cycles — perhaps anticipating the demand fluctuations. The timing should also consider the carryover effect.
I suspect media agencies and advertising will converge once again, or at least the media-buying and creative planning functions will reside in the same organization. This is already the way many young digital marketing agencies are operating since their birth. Designers and optimizers (ad buyers) work side-by-side, the former giving instructions to the latter on what type of design concepts work, not based on intuition as old-paradigm Art Directors (AD) would do, but based on real-time customer response.
Most importantly, tearing down silos will benefit the clients. Doing creative work and optimization in tandem is a natural way of working — the creative concept should no longer be detached from reality, and we should not think of advertising work as a factory line where ads move from one production line to another, but instead as some sort of co-creation through which we are able to mitigate advertising waste and produce better results for advertising clients.