SEM

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EVE Online (or Spreadsheets in Space as it is also known) is a MMORPG with strong PVP gameplay. There are a large number of other ways to play EVE Online, from the market through to PVE, but it is PVP that stands out in the game. It has been called a ganking game, which is a fair comment, as there is a real risk of loss of gear and skills (comparable to levels in other games). Loss of gear and skills creates behaviours aimed at minimising this risk while maximising rewards. In other MMORPGs with little or no chance of loss, PVP activity tends to be restricted to the market.

Winning at PVP in EVE Online

Winning at PVP in EVE Online

Wining at Spreadsheets in Space

PVP in EVE Online is not fair. In fact the challenge in PVP in EVE Online is in setting up these unfair encounters. In most MMORPGs, the actual act of combat consists of a few mouse clicks and some waiting. EVE Online is no different. It is the risk of losing stuff that makes players focus on everything before the actual combat a lot more. It is taking the right mix of ships, avoiding being out-numbered and cornered by a superior foe and acting before the opponent even knows they are in a fight where player skill starts to make a real difference.

Why SEM is like EVE Online PVP

Search Engine Marketing (SEM) in very similar to PVP. It is a zero-sum environment where operators compete for a resource through actions governed by a set of rules and environmental factors generated through user behaviour. There are a few principles that carry over from EVE Online PVP to SEM.

  • Situational awareness is king
    • Know how the advertising network works
    • Understand competitive activity
    • Understand how the market behaves
  • Observe, act and assess
    • Analysis without an accompanying action is useless
    • Assess the effectiveness of activity & reassess decision making model
    • And repeat…
  • Know where you can compete and where you can’t
    • Don’t waste time & resources competing directly with advertisers intent on outspending you
    • Find alternative ways of reaching potential customers.

Information is the key. Understanding how the query space works, having good situational awareness, and knowing where in the sales funnel certain terms are is valuable. It won’t save you from the SEM equivalent of a gate camp (high margin and ‘branding’ campaigns with large budgets), but it is essential for remaining competitive without burning through your budget.

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In my last post, “Too soon for decisions”, I discussed applying a consistent set of rules to campaigns to assess the performance of new ads and targeting. However, in practice, assessment and tracking an AdWords or Facebook campaign can be an interesting exercise.

The data generated by a campaign is not a true representation of the population. The data is a snapshot limited by the markets targeted and the visibility available for the budget spent. Any single campaign can be exposed to direct competition over the whole market or specific subgroups. For example, just because “Campaign A” does not get traffic from Victoria does not mean that no-one in that state is searching for “Keyword B”.

A competitor could simply be focused on that market and value the traffic more. Other factors to consider are the effectiveness of the competition’s creatives and offers, the appeal of their product, efficiency of their site in turning clicks into sales and how much they return per conversion. All of these factors will influence their budget, and how much they are willing to spend per click or impression. Tools provided by the advertising networks that increase the efficiency of campaigns like Remarketing are also worth considering.

According to Wikipedia, a confidence interval is defined as:

…a particular kind of interval estimate of a population parameter. Instead of estimating the parameter by a single value, an interval likely to include the parameter is given. Thus, confidence intervals are used to indicate the reliability of an estimate. How likely the interval is to contain the parameter is determined by the confidence level or confidence coefficient. Increasing the desired confidence level will widen the confidence interval.

In use here, it is assumed that between similar competitors, the average Cost Per Acquisition (CPA) within the group is likely to be within a 95% confidence interval of the known CPA.

Confidence interval and estimated CPA

Confidence interval and estimated CPA

Confidence interval can give you an estimate of what other bidders may be paying for a conversion, assuming they are operating as efficiently as you are. In the graph included above, confidence interval of the CPA is used to estimate the most likely highest possible CPA a campaign can still compete on. In conjunction with Cost Per Click data, it is fair to assume that the competitors in the query space are willing to spend over the highest likely observed CPA. Reasons for their bidding strategy can vary from shutting out competitors by absorbing a short term loss, to a higher sustainable CPA. In a query space where a number of different verticals are competing for the same traffic, this metric is considerably less useful and your mileage may vary. For comparing CPA campaigns, creating a model for understanding the market, or simply to assess which ads are potentially performing a lot better or worse than your target in the face of direct competition, it is a useful tool.

Confidence interval can be a guide to how much your competitors expect to spend per conversion, assuming a lot of similarity in product and business practices. Arbitrage and industries with heavily commodified products are prime candidates for this, as well as campaigns with a very aggressive high cost bidding strategy, such as those competing directly with another member of your industry.

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When can you start to assess and optimise online advertising campaigns in a meaningful way? Adwords, Yahoo! Search Marketing, LinkedIn and Facebook ads allow for an amazing level of market segmentation. Small, highly specific populations can be targeted by a large number of different variables. By their very nature these highly specific campaigns sometimes only generate low levels of traffic and sales, and consequently have a high level of apparent volatility over short time periods.

This volatility is an interesting challenge for account management, and can create a risk in responding to rapid changes. Low traffic and conversion numbers make it difficult to collect meaningful data over short time periods, making it hard to tell the difference between an emerging negative trend and an outlier. This creates problems in both managing low activity campaigns, campaigns in highly competitive and volatile markets, and new campaign testing.

Averages, Standard deviations and Confidence intervals

Averages, Standard deviations and Confidence intervals

Averages, standard deviation and confidence intervals are a few statistical tools available for analysing the data. The actual figures used to determine response will vary from campaign to campaign even for the same product, due to other factors such as the size of the audience and the means used to reach them. The tools used to explore the information and create heuristics for guiding analysis often will not change.

The sample data tracks a gradual upwards trend in the average cost per conversion in a focused Adwords campaign. There is an outlier at double the mean that skews the mean and standard deviation in the third reporting period. Normally this would be discarded, as its apparent effect on both the reporting period and ongoing mean and standard deviation is significant.

It is only on the fourth reporting period that the data starts to become consistent. While there is still some volatility in each reporting period, the reporting period mean remains within one standard deviation of the ongoing mean. The hypothesis that the ongoing campaign mean at four reporting periods can be used as a guide for this campaign is supported by the confidence interval of the whole campaign data set. In the example campaign, it can be assumed that after four reporting periods, there will be enough information to make decisions regarding optimising this campaign.

The figures based on the whole campaign can be used as a guide for assessing the effectiveness of specific ads, placements and keywords while minimising the risk of removing a creative that can still be productive. These metrics provide a guide for deciding when a campaign, keyword or creative needs direct intervention, or may just be having a bad week.

The model that you create using data from previous and current campaigns is ultimately only a guide. It can be used as a framework for assessing creatives and traffic, but these heuristics will only ever be as good as the data they are derived from. There is need to review of any model used to guide the decision making process periodically. The market is constantly changing, be it SEM, social or display advertising. Factors such as seasonality of the product, external environmental factors and competitor activity can have a significant impact.

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You have just built a great Google Adwords, Yahoo! Search marketing or Microsoft adCenter campaign. Viewed from the web or local interface, the data is skewed towards analysis by keywords entered into the campaign. Assuming they are not set to exact match, this will not show the whole picture. It is important to dig a little bit deeper, and examine the phrases that trigger your ads.

When you look at the phrases that actually trigger your ads, such as in an Adwords Search Query Performance report, you will start to see patterns emerge, the most interesting being terms that are common across phrases that triggered conversions. Taking a closer look at Clusters of Queries like these can reveal a lot. Most SEM campaigns will have a subset of keywords that might not even be set up in the account itself that correlate strongly with sales. There will also be phrases or words that obviously do not fit or inflate the campaign’s CPC by crossing over into another industry with an aggressive SEM space.

Keyword performance will always be influenced by a number of factors. The effect will vary depending on other descriptive and qualifying terms. Some queries will naturally return more on investment, yield more traffic or be genuinely cheaper. Not all of this will be the product of consumer behaviour. Competition within the query space is incredibly important, especially once you start to examine the performance of specific clusters of search terms.

For example, suppose a pet shop called ‘I Like Turtles’ has set up a campaign to sell turtles. The landing page, with a strong call to action and a robust cart has been built, and a campaign built around phrase match on species terms like ‘Box Turtles’ has been launched. The traffic is not performing as well as expected. The CPC is unsustainable and the traffic is not engaging on the landing page at all. A quick look at the searches triggering the ads suggest a long list of additional negatives and reveals that some descriptive terms are performing better than others, while some are not even appearing at all. The same is true for locations. Reported first page bid levels can provide a guide to what is happening in the market, but it won’t reveal the reason.

What ads are triggered

What ads are triggered for a long search phrase?

There are a number of different ways one campaign can compete with others in the same query space by either bidding directly on the same keywords or bidding on other keywords that appear in ad triggering search phrases. There are a few that can create a skewed impression of the query space for any single account. Campaigns targeting specific areas within a larger query space for another coupled with specific geographical regions, devices and time periods can aggressively reduce the larger campaign’s visibility in otherwise profitable spaces.

Either through less wasted coverage leading to a better return or a more aggressive bidding strategy one can lock a more general campaign out of lucrative query groups. These competitors do not even have to be selling the same product to the same people; there can be a lot of overlap in terms used for the SEM campaigns of different businesses.

For ‘I Like Turtles’, their targeted query space may also have a toy store using “Buy Turtles”, travel and accomodation companies targeting the name of their city, a conservation organisation raising awareness on river conservation and a DVD store selling Ninja Turtles movies. These companies do not need to be selling the same product to have an impact on the ‘I Like Turtles’ SEM campaign. Travel companies may bid higher than the pet shop and be less precise with their negatives, and increase the CPC. The DVD store might time their ads to run for the weekend, and periodically bury the pet shop’s ads, and the conservation company and toy store may increase competition on terms that often form a part of a search that would trigger an ad for ‘I Like Turtles’. Each of these actions can increase the effective cost of bidding for the campaign and make it harder to appear for one of the higher converting search strings.

Increasing bids around peak buying times can place ads in front of people more likely to buy for certain products. Any increase in return on traffic can increase the amount spent per click. The same applies for more efficient landing pages and sites, and effective targeting geographically and by device. The more likely a sale, the more that can be spent per click.

This activity within a more general campaign can have an effect on how effective certain query clusters may seem. It can also reduce a campaign’s visibility for certain terms. A Search Query Performance report does not tell the full story of what is happening with your campaign and the targeted terms, but it can hint at competitive activity.

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Almost no-one accesses the Internet. What most users access is a selection information determined to varying degrees by their own behaviour, and the nature of the gatekeepers, such as ISP, browsers, applications, DNS, platforms, language, social networks and online nodes such as search engines and portal sites. The Internet has always been a media especially prone to creating silos of information and homogeneous communities, however increasingly behavioural and real world factors are having a greater impact than before.

Organic factors such as user interest and active social behaviour has always influenced what a person might see and experience online. Someone with no interest or no friends who are interested in esoteric information like Babylon 5 are not likely to hear much about it if they do not actively seek it. There is less chance of being exposed to information they have no interest in on the Internet than in most mass media. Of course the larger the social network of the individual and the greater their direct involvement, the more information outside of their immediate sphere of interest they will be exposed to.

None of these factors are unique to the Internet. The tools available online make it possible to interact with more people in some way then was even possible before. The speed and diversity of content that can be shared online has no parallel in history, but ultimately, it’s just people being people online. What has become increasingly important is the influence of location, device, software and sites or platforms that actively use user generated data to shape your online experience.

The technology to change what is shown by IP, cookies, logged in accounts, OS and browsers are not a new innovation. Their implementation online is becoming more apparent with more obvious use and a proliferation of Internet capable devices in the population. This trend covers commercial sites, social media, news and search engines. It affects content from advertising, articles through to search listings.

Personalised Search

Currently, one of the most interesting things about Personalised Search is the averages users complete ignorance of it. Personalisation of content thought to be consistent for all who access it will have interesting social ramifications. Most users are not actually aware that their own behaviour, and at some point the behaviour of people they are connected to through their Google products, will have an effect on what appears where in their search results.

Google has for a lot of people become a portal, with users retrieving information through the search box with keywords they have learnt, or told to use. This change in user experience of information retrieval for sites other than brand and generic terms may discourage users from being so totally dependant on Google Search acting as a replacement for sharing and directing accessing URLs.

Cross Platform Content Consumption

Not all content works on all platforms. Mobile browsers are far more sophisticated than they were when WAP was the standard, and most web content is now easily accessible on mobile devices, with a few notable exceptions such as flash. Due to differences in screen size and interface some sites will serve a different site to different devices.

Location Aware

With IP addresses, the ability to serve different content to users from different locations has been available for ages. No where is this more apparent then in search. What is new is how location aware applications are now using device APIs to access information from the GPS chip. This location data makes it possible to serve information based on a far more granular level than is possible through IP addresses.

User Experience and Advertising

Delivering the right message to the right person in the right place at the right time is as important for advertising as it can be for sharing information. Delivering relevant information from trusted sources in the right place and time to a user who has demonstrated an interest does go a long way towards managing the huge volume of information available. There is a cost associated with this, including privacy and an increasingly myopic view on the Internet, especially with content that is currently assumed by the average user to be consistent for all.

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Adwords has just added a new tool for brand advertisers. Advertising inventory on the Content Network is now divided into above the fold and the whole site. Advertisers can now exclude below the fold inventory on sites in the Content Network in Adwords. Google has turned their Content Network into two different products, with different levels of value.

By default, Content Network bids will be on all advertising space on the site, both above and below the fold. To display above the fold, below the fold placements need to be excluded and bids made for placement on the whole site need to be beaten.

On the Adwords blog post explaining this change it was stated that:

Our goal with this release is to give brand advertisers greater control over where their ads appear, and make the Google Content Network an even more powerful, controlled environment for running high performing brand campaigns.

In practice this will increase the perceived value of one form of placement over the other. A direct result of this will be a concentration of market participants, and allocated budget competing for one of the two kinds of placements. The above the fold placements will be seen as the more valuable of the two, and as a result, the average cost per click will rise. Many advertisers will diversify their campaigns and bid at different levels on both above the fold only placements and whole site placements for as long as they see value in doing so.

Content Network Above and Below the Fold

Content Network Above and Below the Fold

There is also a shift towards using online advertising in branding campaigns. With a greater perceived value in search and display advertising for promoting brand building content, the value of certain traffic sources has been inflated. Google Adwords has talked about branding and search marketing a few times already. By selling advertising on branding value and separating the value of an ad from an incremental per sale return increases the amount of money that most organisations can justify internally on paying more for impressions and clicks.

By leveraging different perceptions of value created by these changes to the Content Network, Google Adwords is increasing competition and consequently their margin per click. Separating cost per click from the profit margin on conversion for some markets in the minds of advertisers will also raise the perceived value of impressions and clicks on both search and websites.This trend will increase the actual value of traffic in a market where there is very little competition among suppliers.

Ironically it was Google with their entry into the market that created that first shift towards linking cost of traffic to profit from sale. The introduction of Analytics and Adwords along with using Adsense to grow their inventory were the main drivers in this shift for most marketers new to advertising online.

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Search is an interesting creature.  As well as a way to generate traffic, it is an interesting study of language and intention. Ignoring for a moment how search engines also function as a Skinner box with the effect this will have in consumer behaviour, what someone types into a search engine is an indicator of where they are in the sales funnel and what their intention is.

With long tail search queries it is hard to clearly see what is working and what is not, unless you group traffic around commonalities. With search traffic, the most relevant is the actual phrase, as this reflects user behaviour and can provide a guide for future SEO activity. Time of day, search engine used and the user’s browsing history are also useful.

Multivariant statistics are good for this, especially Cluster Analysis. I pulled a quick sample of some search query data via Google Webmaster tools for a demonstration. I am aware that there is more than one search engine, and I know that data on terms a site appears on is meaningless without information on clicks or search volume per query. This is what you might call a convenience sample.

As I do not have SAS Enterprise Miner on this machine, this analysis will be simple. Each cluster will be split on a commonality that is greater than 20%. If there is no such commonality, then it is exhausted.

Cluster Analysis

Cluster Analysis and Search Queries

As is demonstrated within the sample, there is still a significant dissimilar longtail. A few very niche groups identified were also identified in the sample. Ultimately, this data is not a true representation of user behaviour. Just because a number of different individuals found your site using the same small cluster does not automatically mean that they are after the same thing. More information is required to make those conclusions. This is just a model. It can help guide your decisions, and it can indicate points of interest worth investigating. What it is not, is gospel.

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Text ads on a Search Engine Results Page (SERP) are a disruptive form of advertising. The intention is to distract the viewer whilst they are engaged with one mode of product search, to use an alternative means which produces revenue. In relation to search, the relevance of the ad served is calculated using a different method to that of organic search, and is heavily influenced by both click through rates and money bid per click. AdWords advertising is visible next to and on top of the organic results, on Google Maps, within the AdSense network, in the Search Suggestion box, and so on.

With the option of now adding additional links and other content to an AdWords listing, the look of some AdWords ads is closer to that of organic search. If I were to have a tinfoil hat moment, I might go so far as to say this could potentially lead to the effective monetarisation of organic SERPs returned for branded terms.

NIB Search Engine Result Page

NIB Search Engine Result Page

There is one thing that has driven this renaissance of the text ad by Google, and that is the fact that disruptive advertising can work. AdWords, Yahoo Search Marketing and Microsoft’s adCenter have worked because with all the tracking available the advertiser can prove that the money spent has a return, without falling back on nebulous metrics such as branding. With SEM, disruptive advertising does work, provided it is relevant enough.

One product of effective performance measurement is the emphasis on terms that denote an information search close to the point of purchase. The most competitive terms are those that indicate a pre-purchase search. With the exception of a few groups of generic terms, this mindset has left the research and discovery keywords in most markets ignored and possibly undervalued.

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cpcctrpos

The relationship between Cost Per Click, Average Position and Click-Through Rate is very interesting. While the relationship between Average Position and Click-Through can be demonstrated without digging too deeply, looking at these factors with Cost Per Click as well requires more data.

CPC CTR

Position CTR

The data discussed here was taken from a single keyword that maintained a consistent Quality Score. There were changes in Cost Per Click, Average Position and Click-Through. The data was taken from a few months of operation, and from just Google Search. The keyword experiences a high, stable level of activity, and did not experience any spikes of interest from advertising, PR or related news. Any shift in Click-Through, Cost Per Click and Average Position will probably only relate to changes in the other two variables.

CPC CTR

Position CPC

The relationships between Click-Through and Position, Cost Per Click and position, and Click-Through and Cost Per Click exist, but do not appear to be very strong. Which of the three had the strongest relationship to the others was not clear either at this point. A quick look at correlation between all three variables showed the following:

Avg CPC Avg Position Avg CTR
Avg CPC -0.500039708 -0.492482923
Avg Position -0.500039708 -0.143205517
Avg CTR -0.492482923 -0.143205517

Correlation coefficient of CTR, CPC and Average Position

CPC CTR

CPC CTR

For Average Position and Click-Through, Cost Per Click has the strongest relationship, even though it is not very strong. These figures are not conclusive however, but do serve as a guide to the relationship between Cost Per Click, Click-Through and Average Position.

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Google’s Quality Score is like their search algorithm. No-one knows for sure how Google does it, and it is in the search engine’s interest to keep it that way. In Adwords, even the actual effect of Average Position is very opaque. Despite this metric’s unclear nature (See Search Agents, Average Position is a really perverse metric), its relationship to clicks can be demonstrated.

Position to Click Through

Position to Click Through

Quality Score and the bid are two of the main factors that decide what the Average Position for a keyword or placement will be, but where does click-through sit in this? Click-through is a key metric in determining Quality Score, though it is not the only one.  But does click-through have an effect outside of this measure?

Using three months’ worth of campaign information, I decided to have a closer look at these relationships.  Here are my findings.

Between adgroups with a similar average Quality Score, there were differences in Average Position that seemed to be related to their respective click-through rates. The adgroups with a relatively high Quality Score are marked with a colour on both graphs.

Quality score, Click through and Position

Quality score, Click through and Position

The sample used in this example is flawed. By using Adwords data at adgroup level, this graph does not account for any variance between keywords within the groups sampled.

In this small group of keywords though, the trend is continued. When their Quality Score is the same, click-through is the best predictor for relative position.

Keyword Quality Score and Position

Keyword Quality Score and Position

Keyword Quality Score and Click Through

Keyword Quality Score and Click Through

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