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Tag: platforms

Platform metrics: Some ideas

Published by Joni on August 11, 2018

I was chatting with Lauri [1] about platform research. I claimed that the research has not that many implications for real-world companies apart from the basic constructs of network effects, two-sidedness, tipping, marquee users, strategies such as envelopment, and of course many challenges, including chicken-and-egg problems, monetization dilemma, and remora’s curse (see my dissertation on startup dilemmas for more insights on those…).

But then I got to think that metrics are kind of overlooked. Most of the platform literature comes from economics and is very theoretical and math-oriented. Yet, it’s somehow apart from practical Web analytics and digital metrics. Those kind of metrics, however, are very important for platform entrepreneurs and startup founders.

On the face of it, it seems that the only difference that platforms have compared to “normal” businesses is their two-sidedness. If we have supply side (Side A) and demand side (Side B), then the metrics could be just identical for each and the main thing is to keep track of the metrics for both sides.

However, there are some dynamics at play. The company has one goal, one budget, one strategy, at least typically. That means those metrics, even though can be computed separately, are interconnected.

Here are some examples of platform metrics:

  • Number of Users/Customers (Side A, Side B)
  • Revenue (Side A, Side B)
  • Growth of Revenue (Side A, Side B)
  • Cross-correlation of Number of Users and Revenue (e.g., Side A users => Side B revenue)
  • Cost per User/Customer Acquisition (Side A, Side B)
  • Support cost (Side A, Side B)
  • Average User/Customer Lifetime (Side A, Side B)
  • Average Transaction Value (Side A, Side B)
  • Engagement Volume (Side A, Side B)
  • Profitability distribution (Side A, Side B)

Note the cross-correlation example. Basically, all the metrics can be cross-correlated to analyze how different metrics of each side affect each other. Moreover, this can be done in different time periods to increase robustness of the findings. Such correlations can reveal important information about the dynamics of network effects and tell, for example, whether to focus on adding Side A or Side B at a given point in time. A typical example is solving the cold start problem by hitting critical mass, i.e., the minimum number of users required for network effects to take place (essentially, the number of users needed for the platform to be useful). Before this point is reached, all other metrics can look grim; however, after reaching that point, the line charts of other metrics should turn from flat line to linear or exponential growth, and the platform should ideally become self-sustainable.

Basic metrics can also be used to calculate profitability, e.g.

Average Transaction Value x Average Customer Lifetime > Cost per Customer Acquisition

Business models of many startup platforms are geared towards “nickel economics,” meaning that the average transaction values are very low. In such situations, the customer acquisition cost has to be low as well, or the frequency of transactions extremely high. When these rules are violated, the whole business model does not make sense. This is partly because of competitive nature of the market, requiring sizable budgets for actual user/customer acquisition. For platforms, the situation is even more serious than for other businesses because network effects require the existence of critical mass that costs money to achieve.

In real world, customer acquisition cost (CPA) cannot usually be ignored, apart from few outliers. The CPA structure might also differ between the platform sides, and it is not self-evident what type of customer acquisition strategies yield the lowest CPAs. In fact, it is an empirical question. A highly skilled sales force can bring in new suppliers at a lower CPA than a digital marketer that lacks the skills or organic demand. Then again, under lucrative conditions, the CPA of digital advertising can be minuscule compared to sales people due to its property of scaling.

However, as apparent from the previous list, relationship durations also matter. For example, many consumers can be fickle but supplier relationships can last for years. This means that suppliers can generate revenue over a longer period of time than consumers, possibly turning a higher acquisition cost into more a more profitable investment. Therefore, churn of each side should be considered. Moreover, there are costs associated with providing customer support for each side. As Lauri noted based on his first-hand experience working for a platform company, the frequency and cost per customer encounter differ vastly by side, and require different kind of expertise from the company.

In cases where the platform has an indirect business model, also called subvention because one side is subventing the cost of the other, the set of metrics should be different. For example, if only Side B (paid users) is paying the platform but is doing so because there is Side A (free users), Side B could be monitored with financial metrics and Side A with engagement metrics.

Finally, profitability distribution refers to uneven distribution of profitable players in each market side. This structure is important to be aware of. For example, in e-commerce it is typical that there are a few “killer products” that account for a relatively large share of sales value, but the majority of the total sales value is generated by hundreds or thousands of products with small individual sales. Understanding this dynamics of adding both killer products and average products (or complements, in using platform terms) is crucial for managing the platform growth.

Footnotes:

[1] Lauri Pitkänen, my best friend.

Managing business development of an ad platform

Published by Joni on July 20, 2017

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.

Conclusion

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.

Thoughts on Remora’s Curse

Published by Joni on May 21, 2017

Remora’s curse takes place when startup attaches itself to a large platform in the attempt to solve the chicken-and-egg problem of getting users. The large platform then exercises its greater power to void the investments made by the startup into the platform, essentially causing more or less deadly delays and needs for re-design. The idea originates from Don Dodge who wrote about the Remora Business Model.

Examples:

  • Facebook stopping “friends of friends” access
  • Twitter killing ecosystem players (cf. Meerkat)
  • LinkedIn killing Developer program
  • Google’s Panda update dropping sites

The popular platform is not your friend. If their interests collide with yours, they will walk over you. Period.

Solutions:

  • diversify – don’t be dependent on only one platform
  • limit the overall dependence on platforms; i.e. do not make integration your secret sauce (aka “never build your house on rented land”)
  • capture the users (envelopment): when you get them to visit for the first time, make them yours; e.g. email subscription, registration

Purposefully limit the role of platform to user acquisition as opposed to being core value prop. Platforms, seen this way, are just like other marketing channels – if they work, scale. if not, kill. The benefit of platform integration is that it may partially solve the cold-start problem: get faster traction and accelerate user growth.

Read more about Remora’s curse in my dissertation: Startup dilemmas – Strategic problems of early-stage platforms on the internet

Affinity analysis in political social media marketing – the missing link

Published by Joni on March 30, 2017

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.

Footnotes

[1] Posts in Finnish:

https://www.facebook.com/joni.salminen.33/posts/10212240031455606

https://www.facebook.com/joni.salminen.33/posts/10212237230465583

Total remarketing – the concept

Published by Joni on March 30, 2017

Here’s a definition:

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

Channels:

  • 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.

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

Published by Joni on March 30, 2017

Introduction

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.

Conclusion

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.

References

[1] http://www.internetlivestats.com/google-search-statistics/

[2] http://venturebeat.com/2016/09/27/4-graphs-show-the-state-of-facebook-and-googles-revenue-dominance/

Facebook Ads: remember data breakdowns

Published by Joni on March 30, 2017

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”.)

Digital marketing in China: search-engine marketing (SEM) on Baidu

Digital marketing in China: search-engine marketing (SEM) on Baidu

Published by Joni on March 30, 2017

Introduction

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.

About Baidu

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.

Facts about Baidu

I have collected here some interesting facts about Baidu:

  1. Baidu is the fourth most visited website in the world (Global Rank: 4), and number one in China [1]
  2. Over 6 billion daily searches [2]
  3. 657 million monthly mobile users (December 2015) [3]
  4. 95.9% of the Baidu visits were from mainland China. [4]
  5. Baidu’s share of the global search-engine market is 7.52% [5]
  6. Baidu offers over 100 services, including discussion forums, wiki (Baidu Baike), map service and social network [6]
  7. Most searched themes are film & TV, commodity supply & demand, education, game and travel [7]

The proliferation of Internet users has tremendously influenced Baidu’s usage, as can be seen from the statistics.

How to do digital marketing in Baidu?

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 [8].

How does Baidu advertising work?

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.

How to access Baidu advertising?

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.

Alternatives for Baidu

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 [9]. 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.

Sources

[1] Alexa Siteinfo: Baidu <http://www.alexa.com/siteinfo/baidu.com>
[2] Nine reasons to use Baidu <http://richwaytech.ca/9-reasons-use-baidu-for-sem-china/>
[3] Baidu Fiscal Year 2015 <http://www.prnewswire.com/news-releases/baidu-announces-fourth-quarter-and-fiscal-year-2015-results-300226534.html>
[4] 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>
[5] 50+ Amazing Baidu statistics and facts <http://expandedramblings.com/index.php/baidu-stats/>
[6] 10 facts to understand Baidu <http://seoagencychina.com/10-facts-to-understand-the-top-search-engine-baidu/>
[7] What content did Chinese search most in 2013 <https://www.chinainternetwatch.com/6802/what-content-did-chinese-search-most-2013/#ixzz4G59YyMRG>
[8] 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>
[9] Baidu Paid Search <http://is.baidu.com/paidsearch.html>

Platform strategy: How can media companies co-align their operations with incentives of social platforms

Published by Joni on March 30, 2017

Introduction

Platform integration is a major issue for publishers. The question, as interpreted by some of them, takes the form: friend or foe? Although it would be naïve to answer “friend”, platforms such as Facebook and Twitter are not foes either. At minimum, they are necessary evil to cope with, at maximum they are strategic leverage. But somewhere along this axis the strategic response of media companies has to be, as readers and content consumers are spending the most of their online time in social platforms. Hence the need for a platform strategy – an issue this post touches upon.

“Remora’s curse” in action

Some time ago, Upworthy and a few other “new media companies” that base their business logic on identifying viral hits and recycling (or “curating”) content of not their own doing, experienced a noticeable decrease of traffic from the social media giant Facebook. In my dissertation, I’ve labelled this as Remora’s curse, a condition whereby a startup builds its house on “rented land”, essentially becoming dependent on the host platform in the attempt to solve the chicken-and-egg problem associated with user acquisition.

Countering Remora’s curse

However, Buzzfeed, although at surface a similar business than the other new media companies, was left intact in terms of traffic and visibility in Facebook. How come?

Here’s a perfect explanation by Jonah Peretti, the founder of Buzzfeed:

“BuzzFeed is very aligned with the interests of all the major social networks: 1) we are in this for the long term, 2) we continually invest in our content to make it better, 3) we do R&D on new formats and areas (lists, quizzes, explainers, mobile, video, breaking news, long form), and 4) we never game platforms with deceptive headlines, we never trick our readers, we put the reader first in all our decisions. The end result is that we are focused on making content that *readers* love and share and traffic growth on social platforms is only a secondary effect.”

Herein lies the answer: Buzzfeed was more compatible with the incentives of Facebook – especially in terms of providing “authentic” content as oppose to recycled “clickbaits”.

How should media companies approach social platforms?

I think Peretti’s answer encapsulates the perfect approach for any publisher devising their platform strategy.

First, you want to invest in the relationship with the platform. You do this by developing capabilities that are “native” in that platform, learning about that platform’s logic and rules as much as you can, and tailoring content (length, type, format) to it.

Second, you of course want to create engaging content because engaging content is what interests the platform as well (due to positive network effects). You don’t want to try and drive people from the platform to your site, but keep them within the platform enjoying your content (which you will monetize in other ways, such as in-stream ads or instant article integration). You learn through platform analytics (e.g., Facebook Insights) what content works and why.

Third, you want to experiment on the new features as soon as they roll out. This goes back to the first point — continuous investment on the platform. Only by so doing can you become a major player in that platform. You need to have journalists who are Facebook specialists at the same time, or at least willing to develop into such. With greater understanding comes the ability to quickly take advantage of new platform opportunities and enjoy the short but strong pioneer advantages associated with early movers.

Fourth, you don’t want to optimize for the platform but ultimately for the people. This means no “clickbaits” or recycling of others’ content. Instead, you want to create genuinely interesting (and useful) pieces of content which are your own original editorial content. Again, this requires investments in competence and capabilities in order for it to work. Your organizational structure and processes need to reflect online content production, so that you are able to create platform-specific content rapidly and run your production activities as a holy tandem of data-driven creativity.

Conclusion

Essentially, Peretti compares Facebook to a broadcaster that is interested in favoring content that keeps the “viewers” engaged. As a publisher, you want the same. The thing is, the platform won’t give you much “airtime” if you want to lure people away. Therefore, you need to share your best bits of content in the platform.

The author currently works as a researcher at the Turku School of Economics. His interests include digital marketing, startups, and platforms.

Google and the Prospect of Programmatic

Published by Joni on March 29, 2017

Introduction

This is a short post taking a stance on programmatic ad platforms. It’s based on one single premise:

Digital convergence will lead into a situation where all ad spend, not only digital, will be managed through self-service, open ad platforms that operate based on auction principles

There are several reasons as to why this is not yet a reality; some of them relate to lack of technological competence by traditional media houses, some to their willingness to “protect” premium pricing (this protection has led to shrinking business and keeps doing so until they open up to the free market pricing), and a host of other factors (I’m actually currently engaged in a research project studying this phenomenon).

Digital convergence – you what?

Anyway, digital convergence means we’ll end up running campaigns through one or possibly a few ad platforms that all operate according to the same basic principles. They will resemble a lot like AdWords, because AdWords has been and still is the best advertising platform ever created. Why self-service is critical is due to the necessity of eliminating transaction costs in the selling process – we don’t in most cases need media sales people to operate these platforms. Because we don’t need them, we won’t need to pay their wages and this efficiency gain can be shifted to the prices.

The platforms will be open, meaning that there are no minimum media buys – just like in Google and Facebook, you can start with 5 $ if you want (try doing that now with your local TV media sales person). Regarding the pricing, it’s determined via ad auction, just like in Google and Facebook nowadays. The price levels will drop, but lowered barrier of access will increase liquidity and therefore fill seats more efficiently than in human-based bargaining. At least initially I expect some flux in these determinants — media houses will want to incorporate minimum pricing, but I predict it will go away in time as they realize the value of free market.

But now, to Google…

If Google was smart, it would develop programmatic ad platform for TV networks, or even integrate that with AdWords. The same applies actually to all media verticals: radio, print… Their potential demise will be this Alphabet business. All new ideas they’ve had have failed commercially, and to focus on producing more failed ideas leads unsurprisingly to more failure. Their luck, or skill however you want to take it, has been in understanding the platform business.

Just like Microsoft, Google must have people who understand about the platform business.

They’ve done a really good job with vertical integration, mainly with Android and Chrome. These support the core business model. Page’s fantasy land ideas really don’t. Well, from this point of view separating the Alphabet from the core actually makes sense, as long as the focus is kept on search and advertising.

So, programmatic ad platforms have the potential to disrupt Google, since search still dwarfs in comparison to TV + other offline media spend. And in the light of Google’s supposed understanding of platform dynamics, it’s surprising they’re not taking a stronger stance in bringing programmatic to the masses – and by masses, I mean offline media where the real money is. Google might be satisficing, and that’s a road to doom.

Dr. Joni Salminen holds a PhD in marketing from the Turku School of Economics. His research interests relate to startups, platforms, and digital marketing.

Contact email: [email protected]