Archive for the search advertising tag

Joni

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

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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/

Joni

Keyword optimization routine for search-engine advertising (AdWords)

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In this post, I’m sharing a simple optimization process for search-engine advertising. I’ll also try to explain its rationale, i.e. explanation of why it should work. The process is particularly applicable to Google AdWords due to availability of metrics, but for the most parts it applies to Bing Ads as well.

First, take a list of your keywords along with the metrics defined in the following.

Then, sort by cost (high to low). Why? Because you may have thousands of keywords, out of which a handful matter for generating results — the Pareto principle is strong in search advertising. It makes sense to focus your time and effort on optimizing the keywords that make up most of your spend.

In metrics, look at

  • relevance (subjective evaluation)
  • match type –> if broad, switch to exact
  • impression share –> if low (below 70%), increase bid (all else equal)
  • cost per converted click –> if high (above CPA target), reduce bid
  • avg. position –> if low (below 3), increase bid (all else equal)
  • Quality Score –> if low (below 6), improve ad group structure, ad copy and/or landing pages

Relevance is the first and foremost. Ask yourself: is this a keyword people who are interested in my offering would use? Sometimes you may include terms you’re not unsure of, or because you want to achieve a certain volume of clicks. If you are able to achieve that volume with relative ease, you don’t need expansion but reduction of keywords. Reduction is started from the keywords with the lowest relevance – interpreted firstly by the results of a keyword (data trumps opinions) and secondarily by qualitative evaluation of the keywords according to the aforementioned rationale.

A common strategy is to start with broad match, and gradually move towards exact match. Take a look at the search terms report: are you getting a lot of irrelevant searches? If so, it definitely makes sense not only to include negative keywords but also to change the match type. Generally speaking, as the number of optimization cycles increases the number of broad match keywords decreases. In the end, you only have exact terms. However, this assumes you’re able to achieve click volume goals.

Are you getting enough impressions? Impression share indicates your keywords’ competitiveness in ad auctions. If relevance is high and impression share low, you especially want to take action in improving your competitiveness. The simplest step is to increase keyword bid. Depending on the baseline, performance, and SEA strategy, you may want to increase it by 30% or even 100% to get a real impact.

Regarding the goals, you should know your CPA target. A very basic way to calculate is by multiplying average order value with average profit per order, i.e. calculate your margin. The amount equivalent to margin is the maximum you can spend to remain profitable or at break-even. (Of course, the real pros consider customer lifetime value at this point, but for simplicity I’m leaving it out here.)

Average position matters because an ad with a high rank gains a natural lift. That is, you can run the same ad in position 3 and position 1 and get better results in position 1 just because it is position (not because the ad is better). This in turn influences your click-through rate and indirectly boosts your Quality Score which, in turn, reduces your CPC, all else being equal. Other ways to improve QS are to re-structure ad groups, usually by reducing the number of keywords and focusing on semantic similarity between the terms, writing better ad copy that encourages people to click (remember, no ad is perfect!), and improving landing page experience if that is identified as a weak component in your Quality Score evaluation.

This is what I pay attention to when optimizing keywords in search advertising. Feel free to share your comments!

Joni

Controlling ad quality in programmatic buying

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Highway to ad quality.

Ad quality is an issue in programmatic buying where ad exchange takes place via computer systems. In traditional ad exchange, there’s a human supervising the quality of advertising, but in a programmatic system it’s possible to receive spammy, illegal, or otherwise undesirable advertising without publishers (ad sellers) being aware of it. Likewise, the quality of performance such as clicks, likes or even impressions might be compromised by fraudulent bot behavior.

In the lack of humans, how to control for quality? Well, some ways include:

  • bot detection — this is what Google uses to filter invalid clicks likely caused by bots. It includes i.a. detecting anomalies in click behavior. Facebook, too, has mechanisms for detecting bots. How well these systems function should be from time to time audited by neutral 3rd parties due to the inherent problem of moral hazard by ad platforms.
  • performance-adjusted pricing and visibility — again, used by Google and Facebook in Quality Score and Relevance Score, respectively. What works cannot be wrong, essentially. The ads with the best response get the most views for the less money. However, this does not directly solve the problem of removing undesirable ads from the system.
  • reporting — again, both Facebook and Google enable reporting of ads by end users. This shows to advertisers as negative feedback – once negative feedback reaches a certain threshold, the ad stops showing. It is in a way crowdsourcing the quality control to the end users.
  • algorithmic analysis of ad content — for example, Facebook is able to detect nudity in the pictures and consequently disqualify them. This is among the best methods, albeit technically demanding, because machine can treat many millions of ad content units in batches. With constantly developing machine learning solutions the accuracy of automatic detection of undesirable content approaches human classifiers.
  • finally, we can have human fail-safe as a “plan B”. Again, both Facebook and Google use manual detection of click-fraud but also in treatment of advertisers’ complaints over refused ads. However, the solution is expensive and does not scale over millions of ad units, so it can be seen as a backup at best.

There – I believe these are the most common ways to control ad quality in modern programmatic advertising platforms. If you have anything
to add, please share it in the comments!

EDIT: Came across with another quality control mechanism: private exchanges. They effectively limit the number of participating advertisers making it manageable for a small number of humans to verify the ads. The whole point of the problem is that this works for a handful or so ads, but when there are millions of ad units, humans cannot be used as the primary solution.

Joni

Facebook’s Incentive to Reward Precise Targeting

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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.

Joni

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

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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.

Joni

A Quick Note on Bidding Theory of Online Ad Auctions

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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.

Joni

A major change in AdWords – How to react?

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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]

Joni

The correct way to calculate ROI for online marketing

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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]

Joni

The Digital Marketing Brief – four things to ask your client

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Recently I had an email correspondence with one my brightest digital marketing students. He asked for advice on creating an AdWords campaign plan.

I told him the plan should include certain elements, and only them (it’s easy to make a long and useless plan, and difficult to do it short and useful).

Anyway, in the process I also told him how to make sure he gets the necessary information from the client. These four things I’d like to share with everyone looking for a crystal-clear marketing brief.

They are:

1. campaign goal
2. target group
3. budget
4. duration

First, you want to know the client’s goal. In general, it can direct response (sales) or indirect response (awareness). This affects two things:

  • metrics you include as your KPIs — in other words, will you optimize for impressions, clicks, or conversions.
  • channels you include — if the client wants direct response, search-engine advertising is usually more effective than social media (and vice versa).

The channel selection is the first thing to include into your campaign plan.

Second, you want the client’s understanding of the target group. This affects targeting – in search-engine advertising it’s the keywords you choose; in social media advertising it’s the demographic targeting; in display it’s the managed placements.

Based on this information, you want to make a list (of keywords / placements / demographic types). These targeting elements are the second thing to include into your campaign plan.

Third, the budget matters a great deal. It affects two things:

  • how many channels to choose
  • how to set daily budgets

The bigger the budget is, the more channels can be included in the campaign plan. It’s not always linear, however; e.g. when search volumes are high and the goal is direct response, it makes most sense to spend all on search. But generally, it’s possible to target several stages in customers’ purchase funnel (i.e., stages they go through prior to conversion).

Hence, the budget spend is the third thing to include into your campaign plan.

The daily budget you calculate by dividing the total budget with the number of channels and the duration (in days) of the campaign. At this point, you can allocate the budget in different ways, e.g. search = 2xsocial. It’s important to notice that in social and display you can usually spend as much money as you want, because the available ad inventory is in effect unlimited. But in search the spend is curbed by natural search volumes.

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas: http://goo.gl/QRc11f

Joni

Assessing the scalability of AdWords campaigns

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Introduction

Startups, and why not bigger companies, too, often test marketing channels by allocating a small budget to each channel, and then analyzing the results (e.g. CPA, cost per action) per channel.

This is done to determine a) business potential and b) channel potential. The former refers to how lucrative it is to acquire customers given their lifetime value, and the latter to how well each channel performs.

Problem

However, there is one major issue: scaling. It means that when we pour x dollars into the marketing channel in the test phase and get CPA of y dollars, will the CPA remain the same when we increase the budget to x+z dollars (say hundred times more)?

This issue can be tackled by acquiring enough data for statistical significance. This gives us confidence that the results will be similar once the budget is increased.

In AdWords, however, the scaling problem takes another form: the natural limitation of search volumes. By this I mean that at any given time, only a select number of customers are looking for a specific topic. Contrary to Facebook which has de facto an unlimited ad inventory (billions of ad impressions), Google only has a limited (although very large) ad inventory.

Solution

Here’s how to assess the scalability of AdWords campaigns:

1. Go to campaign view
2. Enable column called “Search impression share” (Modify columns –> Competitive metrics)

This will tell you how many searchers saw your ad out of all who could have seen it (this is influenced by your daily budget and bid).

In general, you want impression share to be as high as possible, given that the campaign ROI is positive. So, in general >80% is good, <10% is bad. (The exception is when running a long-tail strategy aiming for low-cost clicks, in which case <10% is okay.)

3. Calculate the scalability as follows:

scalability = clicks / impression share

For example, if you have an impression share of 40 % with which you’ve accumulated 500 clicks, by increasing your budget and bids so that you are able to capture 100% impression share, you will accumulate 1250 clicks (=500/0,40) which is the full potential of this campaign.

Limitations

Note that the formula assumes the CTR remains constant. Additionally, increasing bids may increase your CPA, so improving quality score through better ads and relevance is important to offset this effect.