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How to Audit International Facebook Advertising? A 37-Item Checklist

This is a joint article written with Mr. Tommi Salenius who works as a digital marketing specialist at Parcero Marketing Partners.

Introduction

Facebook advertising is a powerful form of online marketing for many purposes ranging from direct response campaigns to brand visibility and awareness. However, the competition in the ad platform is increasing every year, as companies are increasing their investments due to the fact that Facebook advertising, relatively speaking, works very well.

Figure 1 shows how Facebook’s revenue, comprising almost exclusively from advertising, has grown during the last nine years. Last year, almost $40,000,000,000 (that’s forty billion dollars) were spent on Facebook ads.

Figure 1. Facebook worldwide ad revenue statistics from Statista.com.

Increasing budgets imply increasing competition which means that in order to maintain the same visibility, advertisers need to increase their bids. For this purpose, in order to make profit in Facebook, advertisers need to continuously optimize their accounts.

To illustrate the power of Facebook advertising for online sales, Figure 2 shows an example from profitable Facebook account targeting direct online sales.

Figure 2. Example from Facebook account targeting direct online sales.

In this example, every euro invested in Facebook ads has generated direct online sales worth of €10. This means that with budget of €100,000 you can make sales worth of €1,000,000 if your target group is large enough and there is demand for your product (assuming that the sale grow linearly, of course).

The case of international Facebook advertising

Facebook is also one of the best choices to advertise globally, given its user base of more than two billion monthly active users (source: Statista.com).

Using the Locations feature in Facebook Ads targeting, several geographic targeting criteria can be chosen:

  • worldwide (type “Worldwide”)
  • country group or geographic region (e.g., type “in Europe”)
  • free trade area (e.g., type in “GCC, the Gulf Cooperation Council”)
  • sub-regions within a country (e.g., type in “Washington”)
  • other features (e.g., type in “Emerging markets”).

Figure 3 illustrates the Facebook targeting interface.

Figure 3. Targeting interface in Facebook Ads.

At the time of writing (October, 2018), the global targeting options in Facebook include the following:

Country groups

  • Africa
  • Asia
  • Caribbean
  • Central America
  • Europe
  • North America
  • Oceania
  • South America

Free Trade Areas

  • AFTA (ASEAN Free Trade Area)
  • APEC (Asia-Pacific Economic Cooperation)
  • CISFTA (Commonwealth of Independent States Free Trade Area)
  • EEA (European Economic Area)
  • GCC (Gulf Cooperation Council)
  • MERCOSUR
  • NAFTA (North American Free Trade Agreement)

Other Areas

  • Android app countries (paid)
  • Android app countries (all)
  • Emerging markets
  • Euro area
  • iTunes app store countries

Despite the tremendous potential of global advertising in Facebook Ads, companies often do not exploit this potential to the fullest. Moreover, we have observed that large international accounts tend to be messy and not well optimized. Therefore, in the following, we provide a checklist that can be used to audit such international Facebook Ads accounts.

Checklist for auditing international Facebook advertising

Here is a checklist for auditing Facebook paid advertising for international companies. This checklist is a concrete tool that can be used to evaluate your Facebook ad account’s current performance and identifying development areas that can get you toward desired results. There will be four sections: A) Account setup, B) Ad campaigns, C) Organic content, and D) International aspect.

Section A: Account setup

1. Is Facebook Business Manager activated? Benefit: Gain more control over user rights and possibility to operate with partners.

2. Is Facebook pixel is installed and configured? Benefit: Makes it possible to track business-related goals, for example, sales, visitors, blog reading times etc.

3. Is additional software being used besides Facebook Ad Platform? Benefit: Specific tools (e.g. Smartly, AdEspresso, Qwaya) can enhance Facebook performance by providing special features. If they are not used, at least they should be explored.

4. Is international Facebook page feature acclaimed? Benefit: This feature enables unified follower count for country pages but separated content on the country basis.

5. Is ‘business locations’ option used? Benefit: This feature enables to input specific geographic business locations.

Section B: Ad campaigns

6. Are Facebook campaign goals aligned with business goals? Benefit: The campaign goals (e.g. reach, engagement, traffic, sales, leads) should be traced back to overall marketing strategy to ensure they match what is wanted.

7. What is Facebook strategy of the current campaigns? Benefit: In auditing, it is useful to mentally classify the types of campaigns used in the ad account. These can include:

  • technology oriented — e.g., using dynamic ads for advanced targeting
  • content oriented — e.g., using creative concepts to get noticed
  • systematic advertising — i.e., customers need to be reminded regularly
  • ad hoc campaigns — i.e. running ads sporadically without clear purpose

8. Is there something that works already? Benefit: Verifying what already works enables to focus efforts on proven areas (e.g., some campaigns generate sales with low cost, data shows that specific creatives are working, different demographics are responding to ads).

9. Are there budget delivery problems? Benefit: Deliver issues are a common concern in Facebook Ads. Potential reasons: low ad relevance scores, low budget or bids, or not enough conversions (minimum 100 per month), wrong optimization goal. Solutions: change your optimization goal, e.g. from purchases to link clicks, test new target groups and ads, increase budget and bids.

10. Does campaign structure follow best practices? Benefit: Clear division of campaigns provides better tracktability and optimization. There should be different campaigns for all goals: prospecting and retargeting, upselling and cross-selling, reach and sales etc.

11. What auction type is used? Benefit: Auction vs. fixed price: with auction you get better results if you beat competition.

12. What placements are used? Benefit: Performance varies across placements, therefore, they should be tested. Facebook ad platform offers these placements: Facebook, Instagram, Audience Network, and Facebook Messenger. Based on our experiments, Audience Network usually performs poorly, and Instagram is more expensive than Facebook. Moreover, Messenger ads might be thought of more annoying than other placements because they are invading the user’s private space (the inbox).

13. What ad content types have been tested? Benefit: A good account has tested various different ad types (incl. carousel, link ad, instagram story, video, image, canvas).

14. What retargeting types have been used? Benefit: A good account has applied multiple retargeting types (incl. website retargeting, email retargeting, content retargeting).

15. What levels of retargeting are utilized? Benefit: A good account is “deep retargeting”, meaning that retargeting is specified to particular section of the website (e.g., main page, category pages, products pages, blog articles, cart, upselling, cross-selling).

16. What lookalike audience types are used? Benefit: Lookalike audiences can work because they retrieve similar users by “cross-polinating” the targeted subset of users with Facebook’s known information about other users. These options should have been tested (website, email, page likes, purchased lookalikes).

17. Is A/B testing performed systematically? Benefit: A/B test are a sign of active campaign management (both ad set and ad level). Facebook Ads provides a native option for A/B testing as a special campaign type (this campaign type can be used e.g. for testing different creatives, target groups or technical settings).

18. How well are the assets structured? Benefit: Clear naming principles make it easier to analyze and optimize (e.g., are campaigns, ad sets, and ads named systematically).

19. Is UTM tagging used? Benefit: UTM parameters enable tracking visitor performance in other analytics software, such as Google Analytics. The tagging can be done manually or automatically; the main point is that it should be done.

20. What attribution model is used? Benefit: Choosing a different attribution model can drastically change the interpretation of account performance. There are two types of conversions in Facebook: view conversions and click conversions. To get a more conversative view, include only the click conversions with a short attribution window (e.g., 1 day). To get a more rosy picture, include view conversions with a long attribution window (e.g., 28 days). There is no absolutely right or wrong attribution model.

21. Is dynamic advertising used? Benefits:

  • dynamic advertising can be used both in retargeting and in new customer acquisition
  • it offers wide range of options, if technical setup is made correctly, e.g., automated price promotions

22. Is advanced configuration of dynamic advertising used? Benefit: This is underused, yet highly potential feature of Facebook Ads — it enables to customize automatic advertising (e.g., prefer products with high gross margin, geographically show right products for right areas).

23. Are rules used for optimization? Benefit: Rules enable the monitoring and automatic response to business critical conditions (e.g., notification from data anomalies, adjusting budget based on results etc.).

24. Is the budget spent effectively? Benefit: Facebook Ads can waste budget, but there can also be much potential for upscaling the spend — based on performance metrics, one should analyze if the budget should be decrease/increased, what is the potential reach of target groups, how well are those target groups reached, and with what impression frequency.

25. What bid strategy is used? Benefit: A good account has tested several options, including: Lowest cost (standard), lowest cost with bid cap (risk of delivery issues), or Target cost (can be used for scaling up the budget).

Section C: Organic content

26. Is there enough quality content to be believable on the eyes of customers if they visit the Facebook page? Benefit: Visitors may want to check the quality of the page. Having little or no organic content creates mistrust.

27. How active are the Facebook followers of the page? Benefit: There can be a possibility to get insights from followers or turn their enthusiasm into more business. Engagement rate is a good metric, i.e. divide post responses by post impressions.

28. Is organic content reaching the target group? Benefit: If not, maybe it should be advertised. Many Facebook pages produce fairly good content that reaches nobody organically.

29. Is there point of focusing organic content or paid advertising? Benefit: The strategic roles of organic and paid should be addressed. What is the role of organic content? What is the role of paid advertising? Note: multiple ads can be advertised and A/B tested without publishing these on the news feed.

Section D: International aspect

30. Are the ads translated? When doing advertising to e.g. 10 countries with different languages, the ads should also be communicated in 10 different languages. Note that one country can contain multiple language groups, requiring localization even within a single country.

31. Is campaign structure supporting multiple languages? Each language should have been placed in separate target groups. For example, campaign could be name after the country, and it should contain different ad groups for each languages.

32. Is there enough budget to advertise internationally to all target groups? If you are targeting several countries, cities, and languages, these all need different budgets. In order to make impact, it is not usually wise to divide budget into too small pieces.

33. Is there other localization besides translation? Often, an error is made to assume localization is only about language. However, it is also about culture, customs, and ethnicity. For example, value propositions of communicated benefits may be entirely different when the same product is promoted to culturally different target groups (e.g., collectivity-individuality aspect might differ). Another example is that imagery matters for ethnic match between the target audience and people shown in the ads.

34. Have the country-basis legal restrictions been taken into consideration? E.g. different countries have different restrictions for promoting alcohol products, and European countries have strict orders for handling the data according to GDPR protocol.

35. How do normalized metrics vary by countries? Compare performance by normalized metrics (e.g., ROI), because that adjusts for variation between the markets. For example, Facebook Ads bids can be ten times more expensive in the US than in Vietnam. Similarly, purchase power differs so avg. conversion value can be one tenth in Vietnam, meaning that advertising would be equally profitable. To account for this, use normalized metrics, such as ROI or ROAS.

36. What are the city-level performance differences? Another common mistake is to assume that country is detailed enough segmentation criteria for performance differences. However, performance can vary greatly by city, e.g. in big countries like China or US. Moreover, rural areas can differ compared to city areas because people’s tastes, values, and behavior is different. To accommodate for this, Facebook advertisers should segment by city in addition to country (e.g., compare TOP 5 cities of each country).

37. What are the segment similarities across countries? Each impression has a cost. And each impression also adds information about customer responses. However, in the Facebook Ads account the performance values are siloed across different campaigns and ad sets. Therefore, to optimize such accounts, data needs to be combined. For example, if targeting 12 countries, the performance by demographic groups can be aggregated to give more statistical power (higher reliability for found similarities and differences).

Conclusion

This list of 37 items is a good starting point for analysing any Facebook Ads account running international campaigns. Besides these steps, Facebook account level data can be used for analysis purposes to find patterns in the data. For example, making country level breakdowns is made easy in the user interface of Facebook Ads platform.

About the authors:

Tommi Salenius is a Digital Marketing Manager at Elämyslahjat.fi, a Finnish e-commerce company that sells experience gifts. Tommi also works at Parcero Marketing Partners as a Lead Digital Marketing Strategist. www.tommisalenius.com

Joni Salminen is a Digital Marketing Manager at Elämyslahjat.fi, a Finnish e-commerce company selling experience gifts. Joni is also a board member at Konvertigo Digital Agency that runs digital marketing campaigns to over 100 countries. www.jonisalminen.com

Identifying opportunities that Google and Facebook can’t handle

It’s almost impossible to beat Facebook’s or Google’s algorithms in ad optimization, because they have access to individual-level data whereas the advertiser only gets aggregates, and even their supply is limited. But, there are two opportunities I see which Google and Facebook don’t handle:

1. Use of CRM data

Especially purchase history (=lifetime value), product margins (=profitability), and other customer information that can be used for user modelling or machine learning as features. But, don’t use Google Analytics for linking this data to website analytics — Google Analytics sucks, because Google keeps individual-level information (=click-stream data) for itself and only shares, again, aggregates. Use Piwik instead.

2. Use of cross-platform data

Google doesn’t have access to Facebook’s data or vice versa, but the advertiser has. Thus, you can create more comprehensive optimization models for bidding and budgeting.

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.

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.

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.

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

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.

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

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”.)

Advertisers actively following “Opportunities” in Google AdWords risk bid wars

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).

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

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>

Media agency vs. Creative agency: Which will survive?

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.