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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.

Using Napoleon’s 19th Century Principles for Email Writing

“In this age, in past ages, in any age… Napoleon.”
(The Duke of Wellington)

This is a short post reflecting upon Napoleon’s writing on war and efficient management. I think many of his principles are universal and apply to communication — my special consideration here is writing of emails, which is a vital skill because 1) you want your message to be read and replied! and 2) to get to that end, you need to learn how to write in a concise way.

Napoleon will help you to get there…

Quote 1:

“Reconnaissance memoranda should always be written in the simplest style and be purely descriptive. They should never stray from their objective by introducing extraneous ideas.”

First of all, write simple text. Avoid using complicated words and ambiguity (– expressions that can be interpreted in many ways). Oftentimes I see sentences that have ambiguity (or, in fact I myself writing them — when that happens, I instantly make it more clear so that there is absolutely no room for misinterpretation).

Quote 2:

“The art of war does not require complicated maneuvers; the simplest are the best, and common sense is fundamental. From which one might wonder how it is generals make blunders; it is because they try to be clever.”

The goal should never be to appear smart of whatever type; only to communicate your message efficiently. As I’ve said in other contexts, clear writing reflects clear thinking — and especially when it comes to writing emails, this is the only image you want to convey of yourself.

Quote 3:

“Think over carefully the great enterprise you are about to carry out; and let me know, before I sign your final orders, your own views as to the best way of carrying it out.”

In other words, make it easy for people to reply by asking for their opinion (when it’s such a matter their opinion would be useful). Write so that it’s easy to reply — e.g., don’t give too many choices or add any unnecessary layers of complexity.

Oftentimes I see messages which require considerable thinking to reply, and then it of course gets delayed or canceled altogether. Writing an email is like servicing a client; everything from the recipient’s part needs to be made as easy as possible.

Quote 4:

“This letter is the principle instruction of your plan of campaign, and if unforeseen events should occur, you will be guided in your conduct by the spirit of this instruction.”

This is actually the only quote where I disagree with Napoleon. Let me explain why. His rationale was based on the information asymmetry between him and his local officers. The officers have more immediate information; first of all, because of this it’s impossible to write a detailed instruction which would optimally consider the local circumstances, especially since they might change in the course of delivering the message (remember, in Napoleon’s day communication had a delay of even up to many days depending on the troops’ location).

Second, if the local officers were to verify each action, the delay in communication would result in losing crucial opportunities. In a word, decentralization of decision-making was essential for Napoleon. Napoleon himself explains it like this:

“The Emperor cannot give you positive orders, but only general instructions (objectives) because the distance is already considerable and will become greater still.”

However, in email communications the situation is different. First of all, there’s no communication lag, at least in the practical sense. Second of all, leaving things “open” for the recipient requires more cognitive effort from them, which in my experience leads to lower response rates and delays.

So, I’d say: Tell exactly what you want the other party to do. Don’t hint or imply – if you expect something to happen, make it clear. Oftentimes I see messages that are thought half-way through: the sender clearly implies that the recipient should finish his or her thinking. Not a good idea. Think the course of events through beforehand so that the recipient doesn’t have to.

More about Napoleon can be read from his memoirs, available at http://www.gutenberg.org/ebooks/3567

The author teaches and studies digital marketing at the Turku School of Economics.

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.

Problem/Solution Space: A Startup Perspective

I was inspired to write this post by the following pictures that I’d included in my lecture material a few years. Writing it in a bit of a hurry since the class starts soon! (but it’ll good enough to make the point)

(You can find the original source for the pictures by googling.)

Okay, a couple of things.

First, it’s highly important for a startup to define both the problem space and the solution space relating to their product. This includes the particular pain points that the customer whose problems we’re solving is experiencing – at minimum, solving one pain point, if substantial enough, suffices to make a successful business. The solution space includes the competition — here, it is super important to consider not only the direct competition (a common mistake) but also the indirect competition.

I call it the “pen and paper” test — can the problem you’re solving, most often with a high degree of technological sophistication, solved with a simpler, non-technological way?

And more importantly, how are the customers solving it now? It takes a lot for them to change their habits, much more than what founders typically think. The customer will not download an app to solve the problem — no matter if it’s free or not — unless the app provides a solution several magnitudes better than what he currently has. So, bear this in mind.

Second, once the gravity of the problem we’ve set to solve has been “validated” by more trustworthy means than guessing (such as customer development), the problem dimensions need to be tied formally into the product features the team is building (the second picture depicts this).

This way, we avoid waste in the startup development process (remember, waste is your biggest enemy because you’re always on borrowed time).

Third, after this the usage of these features needs to be backed up real usage data — in other words, the product needs to be exposed to real users whose behavior is analyzed based on engagement metrics (e.g., time they spend with the product, what features they use, how frequently, etc.). For this, there needs to be a good analytical system built into the product. Follow the Facebook guideline here: you don’t know what data you might later need, so store everything. This enables maximum flexibility for subsequent analyses.

And finally, of course when we get feedback on the usage of the product, we tie it back to the problem we’ve set out to solve and conclude whether or not we’re actually solving it. If the data suggest low engagement, we need to start over and make radical changes to the core of the product. If the data gives us a nice depiction, we’ll still continue with further adjustments to improve the user experience (which, of course, is by definition never good enough).

That’s it. Thank you for reading (and I’m off to class!)

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]

Modern Market Research Methods: A Startup Perspective

EDIT: Updated by adding competitive analysis, very important to benchmark competitors.

EDIT2: Updated by adding experimentation (14th April, 2016)

Introduction

Somebody on Quora was asking about ‘tools’ for validating viability and demand for a startup’s products.

I replied it’s not a question of tools, but plain old market research (which seems to be all too often ignored by startup founders).

Modern market research methods

In brief, I’d include the following options to a startup market research plan:

  1. market statistics from various consultancy and research institution reports (macro-level)
  2. general market (country, city) statistics generated just for your case (macro-level Ă  la PESTLE)
  3. competitive analysis, i.e. benchmarking existing solutions — will help you find differentiation points and see if your “unique idea” already exists in the market
  4. (n)etnography, i.e. going in-depth to user communities to understand their motivations (micro-level, can be done offline and online)
  5. surveys, i.e. devising a questionnaire for relevant parties (e.g., customers, suppliers) to understand their motivations (just like the previous, but with larger N, i.e. micro-level study)
  6. customer development, which is most often used in B2B interviews as a presales activity to better understand the clients’ needs. Here’s an introduction to customer development (Slideshare).
  7. crowdfunding, i.e. testing the actual demand for the product by launching it as a concept in a crowdfunding platform – this is often referred to as presales, because you don’t have to have the product created yet.
  8. experimentation, i.e. running different variations against one another and determining their performance difference by statistical testing; the tests can relate to e.g. ad versions (value propositions, messages) or landing pages (product variations, landing page structure and elements). Here’s a tool for calculating statistical significance of ad tests.

So, there. Some of the methods are “old school”, but some — such as crowdfunding are newer ways to collect useful market feedback. Experimentation, although it may appear novel, is actually super old school. For example, one of the great pioneers of advertising, Claude Hopkins, talked about ad testing and conversion optimization already in the 1920. (You can actually download his excellent book, “Scientific advertising“, for free.)

How to combine different methods?

The optimal plan would include both macro- and micro-level studies to get both the “helicopter view” and the micro-level understanding needed for product adoption. Which methods to to include in your market research plan depends on the type of business. For example, crowdfunding can be seen as a market validation method most suitable for B2C companies and customer development for B2B companies.

The punchline

The most important point is that you, as a startup founder, don’t get lured into the ‘tool fallacy’ — there’s no tool to compensate for the lack of genuine customer understanding.

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]

Dynamic Pricing and Incomplete People Information

One of the main problems in analytics is the lack of people information (e.g., demographics, interests). It is controlled by superplatforms like Google and Facebook, but as soon as you have transition from the channel to the website, you lose this information.

So, I was thinking this in context of dynamic pricing. There’s no problem for determining an average solution, i.e. a price point that sets the price so that conversion is maximized on average. But that’s pretty useless, because as you know averages are bad for optimization – too much waste of efficiency. Consider dynamic pricing: the willingness to pay is what matters for setting the price, but it’s impossible to know the WTP function of individual visitors. That’s why aggregate measures *are* needed, but we can go beyond a general aggregate (average) to segmentation, and then use segment information as a predictor for conversion at different price points (by the way, determining the testing interval for price points is also an interesting issue, i.e. how big or small increments should you do —  but that’s not the topic here).

Going back to the people problem — you could tackle this with URL tagging: 1) include the targeting info into your landing URL, and you’re able to do personalization like dynamic pricing or tailored content by retrieving the targeting information from the URL and rendering the page accordingly. A smart system would not only do this, but 2) record the interactions of different targeting groups (e.g., men & women) and use this information to optimize for a goal (e.g., determining optimal price point per user group).

These are some necessary features for a dynamic pricing system. Of course then there’s the aforementioned interval problem; segmentation means you’re playing with less data per group, so you have less “trials” for effective tests. So, intuitively you can have this rule: the less the website has traffic, the larger the increments (+/-) should be for finding the optimal price point. However, if the increments become too large you’re likely to miss the optimal (it gets lost somewhere in between the intervals). I think here are some eloquent algorithmic solutions to that in the multi-armed bandits.

The Psychological Cost of Answering an Email

You’re not getting as many replies to your messages as you’d like. Why is that?

Well, there may be many reasons, but I’m discussing one of them here. It’s the psychological cost of processing an email and acting upon it. My hypothesis is simple:

The higher the psychological cost of answering an email, the lower the response rate.

This means that don’t make people think (the same principle applies in UX design!).

So, if you propose a meeting time, don’t give many choices — only give one, if that’s not okay let them process it further (by that time the processing has already begun, it’s like a bait).

If you give many choices, the person has to think between them; also, he knows he still has to wait for your reply which is far higher psychological cost than just replying “ok”.

Remember, even if it wouldn’t seem like much, people get so much email that any marginal increase of complexity is likely to sway them for answering immediately and therefore postponing or even ignoring the message.

Any addition of cognitive effort will reduce the reply rates of your emails. As you’ll be sending many of them throughout your career, non-replies and delays add up and hinder your ability to achieve your goals in a timely manner. Therefore, learning how to write great emails is a hugely important skill. And one way to go about it reducing the psychological cost of the recipient.

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

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.

Google and the Prospect of Programmatic

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]

The Vishnu Effect of Startups (creators/destroyers of jobs)

Background

In the Hindi scripture there is a famous passage in which the god Vishnu describes himself as death; to Westerners this is mostly known through Oppenheimer’s citation:

“Now, I am become Death, the destroyer of worlds.”

But, there is another god in Hinduism, Brahma, that is the creator of the universe.

How does this relate to startups?

Just like these two gods, startups are of dualistic nature. In particular, they are both job creators and job destroyers. One one hand they create new jobs and job types. On the other hand, they destroy existing jobs.

So what?

This dualistic nature is often ignored when evaluating the impact of startups on the society, although it’s definitely in the core of the Schumpeterian theory of innovation. What really matters for the society is the balance — how fast are new companies creating jobs vs. how fast they are destroying it.

I haven’t seen a single quantification of this effect, so it would definitely merit research. Theoretically, it can be called something like SIR, or startup impact ratio which would be jobs produced / jobs destroyed.

SIR = jobs produced / jobs destroyed

As long as the ratio is more than 1, the startups’ impact on the job market (and therefore indirectly on the society) is positive. In turn, if it’s below 1, “robots are taking our jobs”. Or, rather, if it’s above one, Brahma is winning while below one means Vishnu is dominating.

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]