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Tag: online advertising

Quality in programmatic advertising

Introduction

This is a very short post explaining, from a media house’s perspective, how to manage the two-sided online ad market.

Why does quality matter, more than you think?

The success or failure of online advertising takes place through QUALITY. My argument rests on the notion of two-sided markets, along with their distinctive element of network effects. In more detail, bad ads impose a negative indirect network effect geared towards the end users of media. As a result, ad block usage is increasing rapidly in the world. See the graph from the fresh Kleiner Perkins report on Internet trends.

The answer to this, and many other problems of online display advertising, is not more ads (in contrast, it needs to be less, to avoid clutter) or better targeting, but rather the focus on quality.

Why do I say better targeting is not the answer?

Well, many media houses seem to have this wishful thinking that technology provides the answer to what essentially is a human problem. People don’t want to see crappy, intrusive advertising. “Crappy” here means uninteresting, poorly conceived creative implementations – something that is not hard to see if one browses any given media site. Intrusive means the ads jump to your face. While the latter ensures “guaranteed delivery” for one market side – namely the advertiser, it destroys the satisfaction of the other. And a two-sided market cannot function without both parties on board. New media formats, another convenient solution sought by media houses, are also not the answer. While they may fix the intrusiveness, they cannot amend for low-quality ads that are a much bigger problem.

How to solve the quality problem, then?

First of all, ad platforms and media companies need to be stricter with their clients – not every advertiser should in fact have the right to show online ads. It makes more sense in the long term for publishing houses to refuse bad advertisers (and possibly educate them) than to take the easy money in the short term. But in the current climate, where Facebook and Google are eating their lunch, media companies are tempted to clinch to every dollar and sell to everyone who wants to buy. In general, it’s not a wise business decision to cater all customer types, and in this particular case where ad quality is not uniformly distributed, it’s a decision of shooting yourself in the leg.

In practice, publishing houses need to create strict rule-based systems to control advertising impressions, instead of guaranteeing delivery, which is currently the case for many of them. Although advertisers may want guaranteed delivery, this is not the best choice for the overall (two-sided) market because of the aforementioned quality problem. If guaranteed delivery is to be given at any circumstances, there needs to be a credible commitment from the advertiser’s part to deliver high-quality ads. And how can this be confirmed? By running limited pilot campaigns and verifying the end user response is satisfactory. By no means the verification is a question of a marketing executive saying “Oh, these banners look nice, so this must be high-quality advertising.” That approach is old-fashioned and detrimental to both the industry and the individual company.

Moreover, media companies also need to practice vertical integration by offering creative services and data on best practices with online advertising. They need to show commitment for improving ad quality, both to end users and to advertisers. In strategic terms, they need to become “channel captains” that drive the positive change. Eventually, this will lead to a triple-win scenario where the end users are shown high-quality advertising, advertisers get satisfactory results and media houses in consequence receive a higher share of their clients’ media spend. In the current situation, none of these outcomes are realized.

Conclusion

Targeting and new media formats can be a part of the solution, but they will never be the core solution to the problem which is essentially a human problem. Only humans, not technology, can fix that. Thus, better creative implementations are needed — and the industry needs to collectively move from quantity to quality, or else the triumph of ad blocking persists. Media companies need to take charge and accept their responsibility of the future of online advertising – like Google and Facebook, they need to start accounting and demanding for quality from their clients. The old “anything goes” mentality needs to change, and it needs to change fast.

The author wrote his Master’s thesis on online advertising exchange (available here) and Doctoral dissertation on two-sided markets (available here). He is currently working as a Post-Doc Research at the Turku School of Economics.

Here’s Facebook cheating you (and how to avoid it)

Here’s how Facebook is cheating advertisers with reporting of video views:

How is that cheating? Well, the advertiser implicitly assumes that ‘video views’ means people who have actually watched the video which is not the case here. Say, you have a 10-second video; this metric does not show people who have watched that video till the end, but only those who have watched the first three seconds — possibly just scrolling their newsfeeds and letting the video autoplay accidentally while quickly browsing forward. Essentially, that kind of exposure is worth closer to zero than the 1 cent Facebook usually reports.

Indeed, other view-based metrics such as CPV should be calculated based on somebody watching the video till the end, but in FB it’s 3 SECONDS after which they calculate it as a view. In effect, this will multiply the real CPV by order of several magnitudes, in some cases I’ve seen it’s 10x more than the figure reported by Facebook.

But aren’t they telling this honestly? Sure, they show the correct definition, but a large part of advertisers do not bother looking at it, or are unsuspecting misguiding definitions. After all, you should be able to trust that a big and reputable player like Facebook would not screw over advertisers. However, those of us who have played the game for many years know it’s not the first time (remember their definition of “click” a couple of years ago?).

What’s more, there’s no metric for the real CPV in the reports, so advertisers need to calculate it manually (at which point, based on my experience, it’s revealed that Facebook video views are are typically 5x more expensive than on Youtube).

How to avoid this shenanigans? Simply look at the metric ‘video views to 100%’. This is the real video views metric you should use – calculate your spend with that number, and you will get your true CPV. In other words:

ad cost / views to 100%

Keep your eyes open, my fellow advertisers!

UPDATE: Another good tactic, pointed out by my colleague Tommi Salenius, is to bid for 10-second views in your video campaigns. This is a relatively novel feature in Facebook, and although it doesn’t fix the problem, it’s a decent workaround. He also recommended to optimize “average % viewed” metric – you can do that e.g. by comparing different demographic segments. Finally, Facebook video ads can be seen to have a “social advantage” which refers to people’s ability to comment and like videos – sometimes this does take place 🙂 The advertiser can also include more text than in Youtube video ads which has a positive effect on ad prominence. It is then up to the advertiser to consider whether these advantages are worth the cost premium Facebook tends to have in comparison to Youtube.

Facebook Ads: too high performance might turn on you (theoretically)

Introduction

Now, earlier I wrote a post arguing that Facebook has an incentive to lower the CPC of well-targeting advertisers because better targeting improves user experience (in two-sided market terms, relevance through more precise targeting reduces the negative indirect network effects perceived by ad targets). You can read that post here.

However, consider the point from another perspective: the well-targeting advertiser is making rents (excessive profits) from their advertising which Facebook wants and as the platform owner is able to capture.

In this scenario, Facebook has an incentive to actually increase the CPC of a well-targeting advertiser until the advertiser’s marginal profit is aligned with marginal cost. In such a case, it would still make sense for the advertiser to continue investing (so the user experience remains satisfactory), but Facebook’s profit would be increased by the magnitude of the advertiser’s rent.

Problem of private information

This would require that Facebook be aware of the profit function of its advertisers which as for now might be private information to the advertisers. But had Facebook this information, it could consider it in the click-price calculation. Now, obviously that would violate the “objective” nature of Facebook’s VCG ad auction — it’s currently set to consider maximum CPC and ad performance (negative feedback, CTR, but not profit as far as I know). However, advertisers would not be able to monitor the use of their profit function because the precise ad auctions are carried out in a black box (i.e., asymmetric information). Thus, the scenario represents a type of moral hazard for Facebook – a potential risk the advertisers may not be aware of.

Origin of the idea

This idea I actually got from one of my students who said that “oh, I don’t think micro-targeting is useful“. Then I asked why and he said “because Facebook is probably charging too much from it”. I said to him that’s not the case, but also that it could be and the idea is interesting. Here I just elaborated it a bit further.

Also read this article about micro-targeting.

Micro-targeting is super interesting for B2B and personal branding (e.g., job seeking).

Another related point, that might interest you Jim (in case you’re reading this :), is the action of distributing profitable keywords by the platform owner between advertisers in search advertising. For example, Google could control impression share so that each advertiser would receive a satisfactory (given their profit function) portion of traffic WHILE optimizing its own return.

Conclusion

This idea is not well-developed though; it rests on the notion that there is heterogeneity in advertisers’ willingness to pay (arising e.g., from different in margins, average order values, operational efficiency or such) that would benefit the platform owner; I suspect it could be the case that the second-price auction anyway considers this as long as advertisers are bidding truthfully, in which case there’s no need for such “manipulation” by Google as the prices are always set to maximum anyway. So, just a random idea at this point.

Facebook ad testing: is more ads better?

Yellow ad, red ad… Does it matter in the end?

Introduction

I used to think differently about creating ad variations, but having tested both methods I’ve changed my mind. Read the explanation below.

There are two alternative approaches to ad testing:

  1. “Qwaya” method* — you create some base elements (headlines, copy texts, pictures), out of which a tool will create up to hundreds of ad variations
  2. “Careful advertiser” method — you create hand-crafted creatives, maybe three (version A, B, C) which you test against one another.

In both cases, you are able to calculate performance differences between ad versions and choose the winning design. The rationale in the first method is that it “covers more ground”, i.e. comes up with such variations that we wouldn’t have tried otherwise (due to lack of time or other reasons).

Failure of large search space

I used to advocate the first method, but it has three major downsides:

  1. it requires a lot more data to come up with statistical significance
  2. false positives may emerge in the process, and
  3. lack of internal coherence is likely to arise, due to inconsistency among creative elements (e.g., mismatch between copy text and image which may result in awkward messages).

Clearly though, the human must generate enough variation in his ad versions if he seeks a globally optimal solution. This can be done by a) making drastically different (e.g., humor vs. informativeness) as oppose to incrementally different ad versions, and b) covering extremes on different creative dimensions (e.g., humor: subtle/radical  informativeness: all benefits/main benefit).

Conclusion

Overall, this argument is an example of how marketing automation may not always be the best way to go! And as a corollary, the creative work done by humans is hard to replace by machines when seeking optimal creative solutions.

*Named after the Swedish Facebook advertising tool Qwaya which uses this feature as one of their selling points.

Facebook’s Incentive to Reward Precise Targeting

Facebook has an incentive to lower the advertising cost for more precise targeting by advertisers.

What, why?

Because by definition, the more precise targeting is the more relevant it its for end users. Knowing the standard nature of ads (as in: negative indirect network effect vis-à-vis users), the more relevant they are, the less unsatisfied the users. What’s more, their satisfaction is also tied to the performance of the ads (positive indirect network effect: the more satisfied the users, the better the ad performance), which should thus be better with more precise targeting.

Now, the relevance of ads can be improved by automatic means such as epsilon-greedy algorithms, and this is traditionally seen as Facebook’s advantage (right, Kalle?) but the real question is: Is that more efficient than “marketer’s intuition”?

I’d in fact argue that — contrary to my usual approach to marketer’s intuition and its fallibility — it is helpful here, and its use at least enables the narrowing down of optimal audience faster.

…okay, why is that then?

Because it’s always not only about the audience, but about the match between the message and audience — if the message was the same and audience varied, narrowing is still useful because the search space for Facebook’s algorithm is smaller, pre-qualified by humans in a sense.

But there’s an even more important property – by narrowing down the audience, the marketer is able to re-adjust their message to that particular audience, thereby increasing relevance (the “match” between preferences of the audience members and the message shown to them). This is hugely important because of the inherent combinatory nature of advertising — you cannot separate the targeting and message when measuring performance, it’s always performance = targeting * message.

Therefore, Facebook does have an incentive to encourage advertisers for more precise targeting and also reward that by providing a lower CPC. Not sure if they are doing this though, because it requires them to assign a weighted bid for advertisers with a more precise targeting — consider advertiser A who is mass-advertising to everyone in some large set X vs. advertiser B who is competing for a part of the same audience i.e. a sub-set x – they are both in the same auction but the latter should be compensated for his more precise targeting.

Concluding remarks

Perhaps this is factored in through Relevance Score and/or performance adjustment in the actual rank and CPC. That would yield the same outcome, given that the above mentioned dynamics hold, i.e. there’s a correlation between a more precise targeting and ad performance.

A Little Guide to AdWords Optimization

Hello, my young padawan!

This time I will write a fairly concise post about optimizing Google AdWords campaigns.

As usual, my students gave the inspiration to this post. They’re currently participating in Google Online Marketing Challenge, and — from the mouths of children you hear the truth 🙂 — asked a very simple question: “What do we do when the campaigns are running?”

At first, I’m tempted to say that you’ll do optimization in my supervision, e.g. change the ad texts, pause add and change bids of keywords, etc. But then I decide to write them a brief introduction.

So, here it goes:

1. Structure – have the campaigns been named logically? (i.e., to mirror the website and its goals)? Are the ad groups tight enough? (i.e., include only semantically similar terms that can be targeted by writing very specific ads)

2. Settings – all features enabled, only search network, no search partners (– that applies to Google campaigns, in display network you have different rules but never ever mix the two under one campaign), language targeting Finnish English Swedish (languages that Finns use in Google)

3. Modifiers – are you using location or mobile bid modifiers? Should you? (If unsure, find out quick!)

4. Do you have need for display campaigns? If so, use display builder to build nice-looking ads; your targeting options are contextual targeting (keywords), managed placements (use Display Planner to find suitable sites), audience lists (remarketing), and affinity and topic categories (the former targets people with a given interest, the latter websites categorized under a given interest, e.g. traveling) (you can use many of these in one campaign)

5. Do you have enough keywords to reach the target daily spend? (Good to have more than 100, even thousands of keywords in the beginning.)

6. What match types are you using? You can start from broad, but gradually move towards exact match because it gives you the greatest control over which auctions you participate in.

7. What are your options to expand keyword base? Look for opportunities by taking a search term report from all keywords after you’ve run the campaign for week or so; this way you can also identify more negative keywords.

8. What negative keywords are you using? Very important to exclude yourself from auctions which are irrelevant for your business.

9. Pausing keywords — don’t delete anything ever, because then you’ll lose the analytical trace; but frequently stop keywords that are a) the most expensive and/or b) have the lowest CTR/Quality Score

10. Have you set bids at the keyword level? You should – it’s okay to start by setting the bid at ad group level, and then move gradually to keyword level as you begin to accumulate real data from the keyword market.

11. Ad positions – see if you’re competitive by looking at auction insights report; if you have low average positions (below 3), consider either pausing the keyword or increasing your bid (and relevance to ad — very important)

12. Are you running good ads? Remember, it’s all about text. You need to write good copy which is relevant to searchers. No marketing bullshit, please. Consider your copy as an answer to searchers request; it’s a service, not a sales pitch. This topic deserves its own post (and you’ll find them by googling), but as for now, know that the best way (in my opinion) is to have 2 ads per ad group constantly competing against one another. Then pause the losing ad and write a new contender — remember also that an ad can never be perfect: if your CTR is 10%, it’s really good but with a better ad you can have 11%.

13. Landing page relevance – you can see landing page experience by hovering over keywords – if the landing page experience is poor, think if you can instruct your client to make changes, or if you can change the landing page to a better one. The landing page relevance comes from the searcher’s perspective: when writing the search query, he needs to be shown ads that are relevant to that query and then directed to a webpage which is the closest match to that query. Simple in theory, in practice it’s your job to make sure there’s no mismatch here.

14. Quality Score – this is the godlike metric of AdWords. Anything below 4 is bad, so pause it or if it’s relevant for your business, then do your best to improve it. The closer you get to 10, the better (with no data, the default is 6).

15. Ad extensions – every possible ad extension should be in use, because they tend to gather a good CTR and also positively influence your Quality Score. So, this includes sitelinks, call extensions, reviews, etc.

And, finally, important metrics. You should always customize your column views at campaign, ad group and keyword level. The picture below gives an example of what I think are generally useful metrics to show — these may vary somewhat based on your case. (They can be the same for all levels, except keyword level should also include Quality Score.)

  • CTR (as high as possible, at least 5%)
  • CPC (as low as possible, in Finland 0.20€ sounds decent in most industries)
  • impression share (as high as possible WHEN business-relevant keywords, in long-tail campaigns it can be low with a good reason of getting cheap traffic; generally speaking, this indicates scaling potential; I’ve written a separate post about this, you can find it by looking at my posts)
  • Quality Score (as high as possible, scale 1-10)
  • Cost (useful to sort by cost to focus on the most expensive keywords and campaigns)
  • Avg. position (TOP3 is a good goal!)
  • Bounce rate (as low as possible, it tends to be around 40% on an average website) (this only shows if GA is connected –> connect if possible)
  • Conversion rate (as high as possible, tends to be 1-2% in ecommerce sites, more when conversion is not purchase)
  • Number of conversions (shows absolute performance difference between campaigns)

That’s it! Hope you enjoyed this post, and please leave comments if you have anything to add.

Example of Google’s Moral Hazard: Pooling in Ad Auctions

Google has an incentive to group advertisers in ad auction even when this conflicts with the goals of an individual advertiser.

For example, you’d like to bid on ‘term x‘ and would not like be included in auctions ‘term x+n‘ due to e.g. lower relevance, your ad might still participate in the auction.

This relates to two features:

  1. use of synonyms — by increasing the use of synonyms, Google is able to pool more advertisers in the same ad auction
  2. broad match — by increasing the use of broad match, Google is able to pool more advertisers in the same ad auction

Simply put, the more bidders competing in the same ad auction, the higher the click price and therefore Google’s profit. It needs to be remarked that pooling not only increases the CPC of existing ad auctions by increasing competition, but it also creates new auctions altogether (because there needs to be a minimum number of bidders for ads to be launched on the SERP).

A practical example of this moral hazard is Google’s removal of ‘do not include synonyms or close variants‘ in the AdWords campaign settings, which took place a couple of years ago.

There are two ways advertisers can counter this effect:

  1. First, by efficient use of negative keywords.
  2. Second, by resorting to multi-word exact matches as much as possible.

In conclusion, I always tell my students that Google is a strategic agent that wants to optimize its own gain — as far as its and advertiser’s goals are aligned, everything is fine, but there are these special cases in which the goals deviate and the advertisers needs to recognize them and take action.

A Quick Note on Bidding Theory of Online Ad Auctions

Introduction

This is a simple post about some commonly known features of online ad auctions.

Generalized second-price auction (GSP) is a mechanism in which the advertiser pays a marginally higher bid than the advertiser losing to him. It encourages the bidder to place a truthful bid, i.e. one where the price level is such that marginal returns equal marginal cost.

Why is this important?

Simply because:

truthful bid = incentive to bid higher

In other words, if you know a bidder behind is bidding say 0,20 € and you’re bidding 0,35 €, under a standard auction you’d be tempted to lower your bid to 0,21 € and still beat the next advertiser.

In any case you wouldn’t directly know this because the bids are sealed; however, advertisers could programmatically try and find out other bids. When you’re using GSP, manually lowering bids to marginally beat your competition is not necessary. It’s therefore a “fair” and automatic system for pricing.

Of course, for the ad platform this system is also lucrative. When advertisers are all placing truthful bids, there is no gaming, i.e. no-one is attempting to extract rents (excessive profits) and the overall price level sets higher than what would take place under gaming (theoretically, you could model this also in a way that the price level is at equal level in both cases, since it’s a “free market” where prices would set to a marginal cost equilibrium either way).


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

A major change in AdWords – How to react?

Introduction

Google has made a major change in AdWords. Ads are now shown only in the main column, no longer in the right column. Previously, there were generally speaking eight ads per SERP. For some queries, Google didn’t show ads at all, and additionally they’ve been constantly testing the limit, e.g. running up to 16 product listing ads per results page.

But what does that mean to an advertiser?

Analysis

The change means the number of ads shown per SERP (search-engine results page) is effectively reduced. Since the number of advertisers is not reduced (unless rotation is applied, see below), the competition intensifies. And since the visibility of search ads is based on cost-per-click auction, ceteris paribus the click prices will go up.

Therefore, logical conclusion is that when ad placements are cut, either CPC increases (due to higher competition) or impression share decreases (due to rotation). In the former, you pay more for the same number of visitors, in the latter you pay the same click price but get less visitors.

Why Google might in fact prefer ad rotation, i.e. curbing down an individual advertiser’s impression share (the number of times your ads is shown out of all possible times it could have been shown) is because that wouldn’t impact their return-on-ad-spend (ROAS) which is a relative metric. However, it would affect the absolute volume of clicks and, consequently, sales.

In some of my campaigns, I’m using a longtail positioning strategy where this will influence, since these campaigns are targeting positions 4+ which, as said, are mostly no longer available. Most likely, the change will completely eradicate the possibility of running those campaigns with my low CPC-goal.

Why did Google do this?

For Google, this is a beneficial and logical change since right column ads are commanding lower CTRs (click-through rates). This has two implications – first, they bring less money for Google since its revenue is directly associated with the number of clicks; second, as commonly known Google is using CTR as a proxy for user experience (for example, it’s a major component in Quality Score calculations which determine the true click price).

Therefore, removing the possibility of poorly performing ads while pushing the advertisers to an increased competition is a beneficial situation for Google. In the wider picture, even with higher click prices, the ROI of Google ads is not easily challenged by any other medium or channel, at least what I can see taking place in the near future.

However, for advertisers it may easily signify higher click prices and therefore decreasing returns of search advertising. This conflict of interest is unfortunate one for advertisers, especially given the skewed distribution of power in their relationship to Google.

(On a side-note, the relationship between advertisers and Google is extremely interesting. I studied that to some extent in my Master’s thesis back in 2009. You can find it here: https://www.dropbox.com/s/syaetj8m1k66oxr/10223.pdf?dl=0)

Conclusion

I recommend you revise the impact of this change on your accounts, either internally or if you’re using an agency, with them.

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]

The correct way to calculate ROI for online marketing

Introduction

This is a short post explaining the correct way to calculate ROI for online marketing. I got the idea earlier today while renewing my Google AdWords certificate and seeing this question in the exam:

Now, here’s the trap – I’m arguing most advertisers would choose the option C, although the correct one is option A. Let me elaborate on this.

The problem?

As everybody knows, ROI is calculated with this formula:

ROI = (returns-cost)/cost*100%

The problem is that the cost side is oftentimes seen too narrowly when reporting the performance of online advertising.

ROI is the ‘return on investment’, but the investment should not only be seen to include advertising cost but the cost of the product as well.

Let me give you an example. Here’s the basic information we have of our campaign performance:

  • cost of campaign A: 100€
  • sales from campaign A: 500€

So, applying the formula the ROI is (500-100)/100*100% = 400%

However, in reality we should consider the margin since that’s highly relevant for the overall profitability of our online marketing. In other words, the cost includes the products sold. Considering that our margin would be 15% in this example, we would get

  • cost of products sold: 500€*(1-0.25) =425€

Reapplying the ROI calculation:

(500-(100+425)) / (100+425) * 100% = -4.7%

So, as we can see, the profitability went from +400% to -4.7%.

The implications

The main implication: always consider the margin in your ROI calculation, otherwise you’re not measuring true profitability.

The more accurate formula, therefore, is:

ROI = (returns-(cost of advertising + cost of products sold)) / (cost of advertising + cost of products sold)

Another implication is that since the ROI depends on margins, products with the same price have different CPA goals. This kind of adjustment is typically ignored in bid-setting, also by more advanced system such as AdWords Conversion Optimizer which assumes a uniform CPA goal.

Limitations

Obviously, while the abuse of the ‘basic ROI’ calculation ignores the product in the cost side, it also ignores customer lifetime value from the return-side of the equation.

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]