Archive for the Google Adwords tag

Joni

Google and the Prospect of Programmatic

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Introduction

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

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

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

Digital convergence – you what?

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

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

But now, to Google…

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

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

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

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

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

Contact email: [email protected]

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

Online ad platforms’ leeching logic

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I and Mr. Pitkänen had a discussion about unfair advantage in business – e.g., a gift card company’s business model relying on people not redeeming gift cards, investment banker’s relying on monopoly to take 7% of each new IPO, doctor’s controlling how many new doctor’s are educated, taxi driver’s keeping the supply low through licenses, governments inventing new taxes…

It seems, everywhere you look you’ll find examples of someone messing with the so-called “free market”.

So, what’s the unfair advantage of online ad platforms? It’s something I call ‘leeching logic’. It’s about miscrediting conversions – channel x receives credit for a conversion while channel y has been the primary driver to it.

Let me give you two examples.

EXAMPLE 1:

You advertise in the radio for brand X. A person likes the ad and searches your brand in google. He clicks your search ad and buys.

Who gets credited for the sale?

radio ad – 0 conversions
google – 1 conversion

The conclusion: Google is leeching. In this way, all offline branding essentially creates a lift for search-engine advertising which is located at a later stage of the purchase funnel, often closing the conversion.

EXAMPLE 2:

You search for product Y in Google. You see a cool search ad by company A and click it. You also like the product. However, you need time to think and don’t buy it yet. Like half the planet, you go to Facebook later during that day. There, you’re shown a remarketing ad from company A but don’t really notice it, let alone click it. After thinking about the product for a week, you return to company A‘s website and make the purchase.

Who gets credited for the sale?

Google – 1 conversion (30-day click tracking)
Facebook – 1 conversion (28-days view tracking)

In reality, Facebook just rides on the fact someone visited a website and in between making the purchase also visited Facebook, while they learned about the product somewhere else. They didn’t click the retargeting ad or necessarily even cognitively processed it, yet the platform reports a conversion because of that ad.

For a long time, Facebook had trouble in finding its leeching logic, but now it finally has discovered it. And now, like for other businesses that have a leeching logic, the future looks bright. (Good time to invest, if the stock’s P/E wasn’t somewhere at 95.)

So, how should marketers deal with the leeches to get a more truthful picture of our actions? Here are a few ideas:

  •  exclude brand terms in search when evaluating overall channel performance
  • narrow down lookback window for views in Facebook — can’t remove it, though (because of leeching logic)
  • use attribution modeling (not possible for online-offline but works for digital cross-channel comparisons)
  • dedupe conversions between channels (essentially, the only way to do this is by attribution modeling in 3rd party analytics software, such as GA — platforms’ own reporting doesn’t address this issue)

 

Joni

A Few Interesting Digital Analytics Problems… (And Their Solutions)

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Introduction

Here’s a list of analytics problems I’ve devised for a class I was teaching a digital analytics course (Web & Mobile Analytics, Information Technology Program) at Aalto University in Helsinki. Some solutions to them are also considered.

The problems

  • Last click fallacy = taking only the last interaction into account when analayzing channel or campaign performance (a common problem for standard Google Analytics reports)
  • Analysis paralysis = the inability to know which data to analyze or where to start the analysis process from (a common problem when first facing a new analytics tool 🙂 )
  • Vanity metrics = reporting ”show off” metrics as oppose to ones that are relevant and important for business objectives (a related phenomenon is what I call “metrics fallback” in which marketers use less relevant metrics basically because they look better than the primary metrics)
  • Aggregation problem = seeing the general trend, but not understanding why it took place (this is a problem of “averages”)
  • Multichannel problem = losing track of users when they move between online and offline (in cross-channel environment, i.e. between digital channels one can track users more easily, but the multichannel problem is a major hurdle for companies interested in knowing the total impact of their campaigns in a given channel)
  • Churn problem = a special case of the aggregation problem; the aggregate numbers show growth whereas in reality we are losing customers
  • Data discrepancy problem = getting different numbers from different platforms (e.g., standard Facebook conversion configuration shows almost always different numbers than GA conversion tracking)
  • Optimization goal dilemma = optimizing for platform-specific metrics leads to suboptimal business results, and vice versa. It’s because platform metrics, such as Quality Score, are meant to optimize competitiveness within the platform, not outside it.

The solutions

  • Last click fallacy → attribution modeling, i.e. accounting for all or select interactions and dividing conversion value between them
  • Analysis paralysis → choosing actionable metrics, grounded in business goals and objectives; this makes it easier to focus instead of just looking at all of the overwhelming data
  • Vanity metrics → choosing the right KPIs (see previous) and sticking to them
  • Aggregation problem → segmenting data (e.g. channel, campaign, geography, time)
  • Multichannel problem → universal analytics (and the associated use of either client ID or customer ID, i.e. a universal connector)
  • Churn problem → cohort analysis (i.e. segment users based on the timepoint of their enrollment)
  • Data discrepancy problem → understanding definitions & limitations of measurement in different ad platforms (e.g., difference between lookback windows in FB and Google), using UTM parameters to track individual campaigns
  • Optimization goal dilemma → making a judgment call, right? Sometimes you need to compromise; not all goals can be reached simultaneously. Ultimately you want business results, but as far as platform-specific optimization helps you getting to them, there’s no problem.

Want to add something to this list? Please write in the comments!

[edit: I’m compiling a larger list of analytics problems. Will update this post once it’s ready.]

Learn more

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

Joni

Using the VRIN model to evaluate web platforms

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Introduction

In this article, I discuss how the classic VRIN model can be used to evaluate modern web platforms.

What is the VRIN model?

It’s one of the most cited models of the resource-based view of the firm. Essentially, it describes how a firm can achieve sustainable competitive advantage through resources that fulfill certain criteria.

These criteria for resources that provide a sustainable competitive advantage are:

  • valuable
  • rare
  • imperfectly imitable
  • non-substitutable

By gaining access to this type of resources, a firm can create a lasting competitive advantage. Note that this framework takes one perspective to strategy, i.e. the resource-based view. Alternative ones are e.g. Porter’s five forces and power-based frameworks, among many others.

The “resource” in resource-based view can be defined as some form of input which can be transformed into tangible or intangible output that provides utility or value in the market. In a competitive setting, a firm competes with its resources against other players; what resources it has and how it uses them are key variables in determining the competitive outcome, i.e. success or failure in the market.

How it applies to web platforms?

In each business environment, there are certain resources that are particularly important. An orange juice factory, for example, requires different resources to be successful than a consulting business (the former needs a good supply of oranges, and the latter bright consultants; both rely on good customer relationships, though).

So, what kind of resources are relevant for online platforms?

I first give a general overview of the VRIN dimensions in online context. This is done by comparing online environment with offline environment.

Value:

The term ‘value’ is tricky because of its definition: if we define it as something useful, we easily end up in a tautology (circular argument): a resource is valuable because it is useful for some party.

  • critical for offline: yes (but which resources?)
  • critical for online: yes (but which resources?)

The specific resources for online platforms are discussed later on.

Rarity:

One of the key preoccupations in economic theory is scarcity: raw materials are scarce and firms need to compete over their exploitation.

  • critical for offline: yes
  • critical for online: no

Offline industries are characterized by rivalry – once oil is consumed, it cannot be reused. Knowledge products on the web, on the other hand, are described as non-rivalry products: if one consumer downloads an MP3 song, that does not remove the ability for another consumer to download as well (but if a consumer buys a snickers bar, there is one less for others to buy). Scarcity is usually associated to startups so that they are forced to innovate due to liability of smallness.

Imitability:

This deals with how well the business idea can be copied.

  • critical for offline: yes
  • critical for online: no

in “traditional” industries, such as manufacturing, patents and copyrights (IPR) are important. They protect firms against infringement and plagiarism. without them, every innovation could be easily copied which would quickly erode any competitive advantage. Intellectual property rights therefore enable the protection of “innovations” against imitation.

Imitation is less of a concern online. In most cases, the web technologies are public knowledge (e.g., open source). Even large players contribute to public domain. Therefore, rather than being something that competitors could not imitate, the emphasis on competition between web platforms tends to be on acquiring users rather than patents. (There are also other sources of resource advantage we’ll discuss later on.)

Substitutability:

The difference between imitation and substitution is that in the former you are being copied whereas in the latter your product is being replaced by another solution. For example, Evernote can be replaced by paper and pen.

  • critical for offline: yes (depends on the case though)
  • not so critical for offline: yes (see the example of Evernote)

However, I would argue the source of resource advantage comes from something else than immunity of subsitution: after all, there are tens of search-engines and hundreds of social networks but still the giants overcome them.

‘Why’ is the question we’re going to examine next.

Important resources for online platforms

Here’s what I think is important:

  1. knowledge
  2. storage/server capacity
  3. users
  4. content
  5. complementors
  6. algorithms
  7. company culture
  8. financing
  9. HQ location

Knowledge means holding the “smartest workers” – this is obviously a highly important resource. As Steve Jobs said, they’re not hiring smart people to tell them what to do, but so that the smart workers could tell Apple what to do.

  • valuable: yes
  • rare: no (comes in abundance)
  • imperfectly imitable: no
  • non-substitutable: yes

Storage/server capacity is crucial for web firms. The more users they have, the more important this resource is in order to provide a reliable user experience.

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: yes

Users are crucial given that the platform condition of critical mass is achieved. Critical mass is closely associated with network effects, meaning that the more there are users, the more valuable the platform is.

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: yes

Content is important as well — content is a complement to content platforms, whereas users are complements of social platforms (for more on this typology, see my dissertation).

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: yes

Complementors are antecedents to getting users or content – they are third parties that provide extensions to the core platform, and therefore add its usefulness to the users.

  • valuable: yes
  • rare: no (depends)
  • imperfectly imitable: yes
  • non-substitutable: no (can be replaced by in-house activities)

Algorithms are proprietary solutions platforms use to solve matching problems.

  • valuable: yes
  • rare: no (depends)
  • imperfectly imitable: no
  • non-substitutable: yes

Company culture is a resource which can be turned into an efficient deployment machine.

  • valuable: yes
  • rare: yes
  • imperfectly imitable: yes
  • non-substitutable: yes

A great company culture may be hard to imitate because its creation requires tacit knowledge.

Financing is an antecedent to acquiring other resources, such as the best team and storage capacity (although it’s not self-evident that money leads to functional a team, as examples in the web industry demonstrate).

  • valuable: yes
  • rare: no (for good businesses)
  • imperfectly imitable: no
  • non-substitutable: no (bootstrapping)

Finally, location is important because can provide an access to a network of partner companies, high-quality employees and investors (think Silicon valley) that, again, are linked to the successful use of other resources.

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: no

A location is not a rare asset because it’s always possible to find an office space in a given city; similarly, you can follow where your competitors go.

Conclusions

What can be learned from this analysis?

First, the “value” in the VRIN framework is self-evident and not very useful in finding out differences between resources, UNLESS the list of resources is really wide and not industry-specific. That would be case when exploring the ; here, the list creation was

My list highlights intangible resources as a source of competitive advantage for web platforms. Based on this analysis, company culture is a resource the most compatible with the VRIN criteria.

Although it was argued that substitutability is less of a concern in online than offline, the risk of disruption touches equally well the dominant web platforms. Their large user base protects them against incremental innovations, but not against disruptive innovations. However, just as the concept of “value” has tautological nature, disruption is the same – disruptive innovation is disruptive because it has disrupted an industry – and this can only be stated in hindsight.

Of course, the best executives in the world have seen disruption beforehand, e.g. Schibstedt and digital transformation of publishing, but most companies, even big ones like Nokia have failed to do so.

How to go deeper

Let’s take a look at the three big: Google, Facebook and eBay. Each one is a platform: Google combines searchers with websites (or, alternatively, advertisers with publisher websites (AdSense); or even more alternatively, advertisers with searchers (AdWords)), Facebook matches users to one another (one-sided platform) and advertisers with users (two-sided platform). eBay as an exchange platform matches buyers and sellers.

It would be useful to assess how well each of them score in the above resources and how the resources are understood in these companies.

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

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.

Joni

A simple formula for assessing the feasibility of AdWords cases

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Update [24th March, 2017]: In addition to the formula explained in the post, I would add the following general criteria for a good AdWords case: 1) Low-Medium competition (high CPCs force to look for alternative channels), 2) Good website/landing pages (i.e., load fast, easy to navigate, have text information relevant to the keywords.

Introduction

Google AdWords is a form of on-demand marketing which matches demand (keywords) with supply (ads). Because it provides good relevance between demand and supply, it efficiently fulfills the core purpose of marketing which is, again, to match supply and demand. However, while this property of AdWords makes it generally much more effective than other forms of online marketing, it also leads to a major limitation: the campaigns cannot scale beyond natural search volumes.

I often tell this to my students participating in the Google Online Marketing Challenge (GOMC), but a few of them always fall into the “trap of low search volume”. I will explain this in the following.

Selection criteria

First, the relevant dimensions for assessing the potential in AdWords are:

  • geographic range: the based on the company’s offerings
  • product range

These can vary from low to high so that

Low geographic range x Low product range = Trap of low search volume

Low geographic range x High product range = Potential risk of low search volume

High geographic range x Low product range = Potential risk of low search volume

High geographic range x High product range = High search volume (Best case for AdWords)

In other words, this formula favors companies with nationwide distribution and large product range. These campaigns tend to scale the best and offer the best ratio between cost and value of optimization. In contrast, local business with one or two products or services are the least feasible candidates.

What does the trap of limited search volume mean?

Well, first of all it means the spend will be low. In GOMC, this means some teams struggle to spend the required $250 during the three-week campaign window.

Second, and more importantly, it means these cases are less interesting for marketers. They offer little room for optimization (because spend is low and there is very little data to work with).

Also for this reason the management cost of running these campaigns (=the amount a marketer can charge for his/her services) can become unbalanced: for example, if the yearly spend of a low-volume campaign is, say $400 and the marketers charges $100 per hour for his/her work, there is no point for client to pay for many working hours, as their cost quickly exceeds that of the media budget.

Conclusion

As a marketer, you always want to select the best case to amplify with your skills. You can think of it through two dimensions:

  • marketing
  • product

By multiplying them, we get the following.

Bad marketing x Bad product = Bad results

Bad marketing x Good product = Okay results

Good marketing x Bad product = Bad results

Good marketing x Good product = Good results

The same in numbers:

0 x 0 = 0

0 x 1 = 0

1 x 0 = 0

1 x 1 = 1

In other words, it makes sense to choose a case which is good for you as a marketer. A good case will work decently with bad marketing, but not vice versa. And only coupled with good marketing will the maximum potential of a good product be achieved.

Author:

Joni Salminen
Ph.D., marketing

Joni

How to calculate metrics for an AdWords campaign plan

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I teach this very simple formula to my students when they are required to write a pre-campaign report for the Google Online Marketing Challenge (GOMC).

You want to report metrics in a table like this:

budget    ctr      cpc    clicks    impressions
250         0,05   0,2     1250    25000

(The numbers are examples.)

To calculate estimates for a campaign plan, you only need to know three figures:

  • budget
  • goal CTR
  • goal CPC

In the case of GOMC, the budget is set to $250. In other marketing cases, it is based on your marketing plan.

Goal CTR is what you want to accomplish with your ads. I usually say a CTR of 5% is a good target. Based on bidding strategy and competition, however, it can range between 3 and 10%. Less than 3% is not desirable, as it indicates poor relevance between keywords and ads.

Goal CPC is what you want to pay for clicks. Ideally, you want the CTR to be as high as possible and CPC as low as possible to maximize traffic (website visitors). The actual figure will be based on competition as well as your quality score (to which CTR contributes, among other factors of relevance).
Quality score can be enabled by customizing columns in keyword view; the bid estimates for your keywords can be retrieved via Keyword planner, as well as by looking at bid estimates (first-page and top-of-page) in the keyword view. In Finland, I usually say €0.2 is a good target for average CPC. In other markets, the CPC tends to be higher.

Out of the previous figures, you can calculate other metrics:

  • clicks = budget / cpc
  • impressions = clicks / ctr

The calculation assumes full usage of budget, which is not always possible when organic search volumes limit the growth (this is just a general limitation of search advertising).

Joni

Bugs and problems in Facebook Ads [UPDATED 10/08/2016]

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Introduction

I’ve been doing a lot of Facebook advertising. Compared to Google AdWords, Facebook Ads is missing a lot of features, and has annoying bugs. I’m listing these problems here, in case anyone working at Facebook would like to have an advertiser’s opinion, and that people working with programmatic ad platforms see how difficult it is to create — if not perfect, then at least a satisfactory system.

A caveat: although I’m updating the list from time to time, it might be some bugs are already corrected and the missing features added. The ones fixed have been pointed out by strike-through.

Acknowledgments: A big thanks goes to Mr. Tommi Salenius, who is my right hand in digital marketing.

[UPDATED 10/08/2016]

  • add ‘like disavow tool’ (cf. Google’s link disavow)
  • ‘Facebook marketing partner’ –> expanding to smaller agencies (cf. Google Partners)
  • save target groups when making targeting in ad creation tool
  • add possibility to exclude saved audiences
  • ads receive an unequal number of impressions; if many ads in one ad set, most of them receive zero impressions
  • de-duping target group frequency across campaigns (overlapping audiences: avoid inflation of total frequency by de-duping)
  • distribute budget automatically between campaigns and ad sets
  • Split option in Power Editor does not split an existing audiences, but actually creates a new (complementing) one
  • add possibility to exclude age groups (could be done with exclusion of saved audiences)
  • sorting columns does not work in Power Editor reports section
  • sorting based on conversions does not work properly in Ads Manager columns (it calculates some sort of average)
  • re-position image in Power Editor –> not possible to see preview
  • in web interface impossible to make advance connection with parameter OR – now it uses AND – for example, fans of my page AND friends of fans makes target group impossibly small
  • does not show total budget (or any totals) in campaign view (UPDATE: partly fixed for some metrics, but total budget still not visible)
  • impossible to target competitors’ fans (what are the barriers for making this happen?)
  • breakdowns not possible based on e.g. education level (more breakdown possibilities)
  • possibility to set budget at campaign level
  • no possibility to filter campaign (cf. adwords) –> trying to find a campaign quickly is a pain
  • utm tagging missing –> impossible to track from 3rd party analytics
  • shared budget feature is missing –> you should copy this feature from AdWords
  • when copying campaigns, impossible to change goal (really stupid, cannot test performance with different goals)
  • campaign reporting –> no trends, no graphs –> impossible to assess long-term development of campaigns (compared to AdWords)
  • campaign page –> no possibility to change metric for graph (much better in AdWords where two metrics can be freely chosen)
  • no frequency cap (again, possible in AdWords)
  • no ‘compare to previous time period’ option in reports (unlike AdWords)
  • no possibility to delete images in image gallery –> wtf, makes it very difficult to manage
  • too small image size in image gallery –> again, hard to manage images
  • not possible to copy numbers in power editor (!!!) –> sometimes, you’d want to copy numbers between campaigns or into excel
  • power editor loses text field content when changing ad (field)
  • power editor does not enable image variation
  • web version does not show all image variation ads in first pageload
  • unable to copy ad sets in web interface –> impossible to make quick new versions targeting e.g. newsfeed vs. right column
  • doesn’t show pause status in ads while in review
  • power editor does not copy ad statuses while duplicating ad sets
  • rotate evenly option missing –> compare to AdWords
  • cta not possible to be removed in powereditor once put into ads
  • unable to revert to suggested image in web interface after choosing image from gallery
  • facebook ads no sound in video preview
  • missing bid modifiers: e.g. for ad placement, e.g. -50 %, right column

Problems in Page Insights:

  • inability to answer standard questions such as: what are the all-time most liked posts? how many posts did we do last month?

Want to contribute? Send me bugs and/or missing features and I’ll list them here.

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]