To define success, you must be able to measure it. To improve upon your success, you must be able to measure it.
Successful search campaigns are won by those who know where they are weak and where they are strong. To be a winner in search, you need to have a solid metrics foundation. There isn’t any magic solution or formula; it comes down to measuring, testing, analyzing, and interpreting data. If you want to improve your search campaigns, you will need to exhaust all the data you have, beginning with the data closest to the user or customer and then expanding out.
Fix and improve what you can control first (your website and the customer experience), then try to fix the larger issues (competing and moving up the rankings by building more links for competitive words). A good approach to working with data is to look at data about the user (words and searches bringing them to your site), data throughout your site (what they are doing on your site), and off-site data (influence over offline actions such as buying a product in a store), and then to tie all of this back to data from the search engine (determining whether the engines are interpreting your site’s content as you feel it should be interpreted). The success of your search campaigns depends on recognizing that you have access to user behavior as well as user intent. The engines try to understand user intent in order to provide the best experience possible and pass this along every time a user comes to your website through the referring URL. Knowing the users’ intent should help us shape our entry points, as well as the overall experience that people have when engaging with our sites.
Search—be it SEO, SEM, or site search—is a very simple concept. The search engine’s goal is to deliver the right content to the right person in the right place and at the right time. The engines are always looking to perfect this. The question is, how do you know if your business is a winner?
Tools you will need in this chapter:
Search analytics introduces some qualitative data from the search term coupled with many quantitative data points in the form of click-through rates, traffic volume, conversion rates, and more.
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Qualitative data measures behavior and the reasons driving that behavior. For example, surveys and questionnaires can provide qualitative data; in our case, this may also come from search parameter patterns over repeat sessions.
Quantitative data is numerical data. Examples of this are the number of visits to a website, or the number of people who purchase a product.
Clickstream data measures the actions users take on your website by tracking what and where they click.
You can get some insight into users’ intent and decision-making processes by looking at groups of search terms as qualitative data points. For example, if a user comes to your site from three different search terms, looking at those terms may show some of the decision making that has occurred. For instance, if the search pattern looks like “ACME widgets”→“ACME widget reviews”→“ACME widget sale”, we can see that the user started out looking for a product, was then influenced by the reviews, and then looked for a discounted price. You can further amplify this qualitative data with site surveys. By bringing together qualitative data and quantitative data, we can start to get a better idea of the intent of our users, and optimize their experience.
You can get better ideas of “intent patterns” through search when you merge your external search data with your on-site search data. Perhaps many users come to your site through branded terms, meaning words that are specific to only your brand. An example of this would be a search for “iPhone”: that is specific to Apple, and there is a brand association there. When users show up from a branded term, what secondary searches do they perform on your site?
Have you enabled your clickstream analytics to capture the referring search terms and the associated site search terms? Are you recognizing that many users who come to your site on branded terms are looking for a specific product? Or perhaps there are a great number of support searches. If you have paid for the click-through SEM, maximize the value by learning what users are looking for and develop landing pages to bring this needed information closer to the user. In the case of SEO, you cannot always dictate what page will rank, but you can improve your site’s general navigation to include links to these deeper pages. Recognizing your customers’ needs and providing content that helps them will in the long run also help improve your business.
Search also gives you insight into the language of your customers. The goal of keyword research is to understand the language landscape of the search engines. What are the search volumes like, and how competitive are other sites on these terms? This is data that can be fed to other marketing channels. Why not apply this keyword research to your email deployments or your in-store flyers, bringing online and offline insights together to create subject lines and in-store banners with the language customers use?
When we talk about branded versus nonbranded words, I see all too often a confusion with internal versus external language. Internally you may want to call your product “the super best product ever!” while users may simply call it “widget.” Unless you have the dollars and branding resources to get people to change their language, you may need to recognize that it is easier to get people to think about your product by creating at least a small word association to the word “widget.” Ignoring the elephant in the room and calling your product anything but may result in people not having that “ah ha!” moment and realizing that your widget is really also “the super best product ever.” Search is as much about measuring word use and linguistic needs as it is about measuring clicks, inbound links, and other data points.
Beyond these factors, SEO has unique challenges in that all the major search engines operate as black boxes. The search engines do not let anyone know the recipe for their secret sauce, or in this case the algorithm that makes them run. To better understand these algorithms, SEO specialists have had to try to reverse engineer them. It’s also important to understand that each engine runs different algorithms—for example, Bing runs a different algorithm than Google. Each engine’s algorithm is proprietary to that engine; in some cases, other engines may lease these algorithms (as Yahoo! now leases Bing’s algorithm), but each major algorithm will have its own quirks and issues to test against.
The best way to reverse engineer something such as a search algorithm is to look at data points, examining them to try to determine which return positive feedback and which return negative feedback. Analytics help take a lot of the guesswork out of the SEO’s job.
Search analytics is not just about measuring traffic delivered, but also about landing page optimization (LPO) and conversion rate optimization (CRO). LPO is focused on retaining and moving people through your site; it acknowledges that not everyone comes to your site through the home page. You typically start by optimizing your high-volume entry pages and work down from there. CRO is focused on moving people through a funnel to a goal. This conversion may occur over several visits as part of the overall life cycle and decision-making process of purchasing a product. CRO takes into account the stages of this process and the needs of a user to help that user make a decision as quickly as possible.
A search strategy is not concerned simply with delivering traffic to a site; it must take into consideration the handoff of that traffic, as well as the pathing of that traffic to each goal and objective. Site search also helps facilitate the measurement of on-site navigation issues and needs. Think about moving beyond measuring traffic volume, and measuring business objectives and goals. Think about measuring to improve those objectives and goals.
First, let me come right out and say it: why do SEO and SEM people always have to prove which is more important? Why can’t we just all be search people? A well-defined search program should utilize both SEO and SEM tactics to provide maximum coverage and exposure to the right person at the right time, to maximize your revenue. I do not believe that SEO and SEM should be optimized separately from each other; in fact, there should be open sharing and examination of your overall search strategy. With that said, each practice has its own needs and methods that may be unique to it. Still, remember that the goal is to maximize your revenue by investing smartly. This means that you should invest in traffic that will convert at the maximum value for you.
Search optimization—particularly SEO—is traditionally thought of as improving the rankings of pages. Therefore, pages are optimized for search engines. My personal opinion on this is that if this is your only objective, you are doomed to fail. The simple fact is that the engines are changing every day. There are no “rules,” just best practices that have been adopted because they show positive results in rankings and can have positive results for the end user. To me, it’s not how much traffic you get that counts, but what you do with the traffic.
Your goals in improving search results should include positive impacts to your customers, and ultimately, positive impacts to your revenue streams. Optimizing entry pages for SEO is about improving the flow of traffic into your site to achieve a positive outcome for both your customers and your business. Users arriving from search should have an even better customer experience than those who enter through your home page. Search analytics are just as much about what happens on your site as what drives people to your site.
On top of all this, the online world has brought us an overwhelming multitude of information points. SEO in particular moves at such a rapid pace of change that keeping up with changes in the algorithms is practically impossible. Eric Schmidt, former CEO of Google, claims Google uses over 200 ranking factors to establish what shows up on every organic search result (http://searchengineland.com/schmidt-listing-googles-200-ranking-factors-would-reveal-business-secrets-51065). On top of this, there are over 500 tweaks made to the algorithm every year—more than one change per day. Optimizing to the engines is not a game you can win, but optimizing to people and their behaviors is.
Relevancy can also have an impact on paid search. Google AdWords (the largest of the paid search options) measures relevancy through Quality Score, a metric that takes into account the click-through rate (CTR) of the keyword, the historical CTR of all ads and keywords in your account, the CTR of the display URL in the ad group, the quality of your landing page, the relevance of the keyword both in the ad group and to the search query, and other factors. The better your Quality Score is during each search, the less you will pay per click. It should also be noted that the Quality Score used to determine the cost per click is generated for every search and is not a direct reflection of the Quality Score you see in AdWords (http://adwords.google.com/support/aw/bin/answer.py?hl=en&answer=21388).
Beyond this, the real power of paid search is that you have full control over the user experience: everything from what copy and text the users will see to what pages they will be directed to. You can control the time of day results will be displayed, and you can even target specific device types (mobile or desktop) or geographies (particular cities or countries). The amount of direct and immediate control you have over your paid search campaigns means a greater opportunity to optimize and improve results.
Site search can vary from site to site. How effective is your site search? How frequently is your site search used? There are a great deal of data points specific to search; the challenge is figuring out what points need to be used to answer specific sets of questions. Data only has value if it enables someone to do something.
For those of you used to practicing website measurement through Google Analytics or other clickstream tools, there will be some familiarity, though search analytics also use off-site factors, as well as user experience (UX) and information architecture (IA) factors. This book will introduce several programs that will enable you to capture data and explain how to use some of these programs. Some are paid and some are free, but the most important things to consider will be which tools enable you to make insights that help you meet your business needs, and which ones you feel most comfortable using. Sometimes the free options can be just as good as an enterprise-level paid option, and whenever possible, this book will use the lowest-cost option in each example. An extensive list of tools is provided in the Appendix A.
Search analytics requires a bit of psychology; because we are dealing with words and people, we are given partial insights into our users’ thoughts. Think of a search box as a word association test. People provide a word or a group of words describing or identifying what they are looking for. The engine’s job is to interpret the user’s intent and match that word or words to the page or pages it thinks will best serve the user.
Further, a great deal of data aggregation is carried out to identify patterns that groups of searchers follow. At times you may need to make some assumptions. When you find this to be the case, I strongly urge you to use surveys to help eliminate this guesswork. Qualitative data can go a long way. For example, you can ask people on the page with the highest abandonment rate, “What are you looking for?”
When you need to make an educated guess, it’s important to remember it is just a guess. It can act as a starting place, but it’s only ever a hypothesis. Be prepared to follow a different avenue if it turns out that you are wrong. Like a good detective, you should be able to use your analytics to eventually answer questions or support theories you may have, but until you have supporting data, your hypothesis will only ever be an unproven guess. Also, because people change, you will never be able to stop measuring your site if you plan on improving sales and the user experience.
A recent SEMPO and Econsultancy report (http://econsultancy.com/us/reports/sempo-state-of-search) revealed that both SEO and SEM are in conflict with what the engines tell us to do—namely, “provide good content.” Instead, what most are trying to do is “drive traffic,” without regard for the quality of that traffic. The report shows that over 40% of companies cite driving traffic as the main objective for their SEO programs. This a pretty vague goal, and we can assume websites are already getting traffic from search engines, even if they are not optimized. It’s not only volume that counts, though: it’s important that conversion rates remain the same and that the additional traffic is as engaged as the current traffic. Why not set some deeper action than simply driving traffic as your goal for SEO? That is, assuming your site does not generate revenue only through display ads—and if it does, why not set your goal to be driving more repeat traffic? Keep them coming back for more!
The rest of the goals for SEO traffic in the report read as follows: generating leads, selling products, increasing brand awareness, and, lastly, improving customer satisfaction and customer service. Only 2% of companies cited improved customer satisfaction as their main goal. At least they are defining more actionable goals, but what about the lifetime value of the customer? Where is the foresight for long-term value?
Even as a secondary objective, improved customer service still ranks as the lowest goal for SEO, cited by only 5% of companies. Agencies also fell into a similar pattern, although their primary objective was to generate leads, followed by driving traffic.
If the search engines tell us that we must create great content and provide good customer experiences to rank well organically, but our primary goals are instead driving traffic or creating leads, how do we bridge this gap? Is an improved customer experience mutually exclusive of driving traffic or generating leads? I would suggest not, but where should the priorities be placed?
Speaking from my own experience, metrics that bring together both voice-of-the-customer data (for example, where customers are given a questionnaire and provide written feedback) and clickstream data that tracks conversion and site usage show that improved customer satisfaction measured through the customer surveys has typically led to improved site usage, improved conversions, and, more importantly, longer repeat customer relations. Avinash Kaushik echoed this point in a post on his web analytics blog (see the entry http://www.kaushik.net/avinash/2007/04/the-three-greatest-survey-questions-ever.html) where he cited the advice he had given to a Fortune 100 company looking to improve its website and increase sales.
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One of the first things I ever discovered that was cool about voice-of-the-customer data was how it made segmenting and thinking about site data so much cleaner. I used to look at the data as a whole, thinking “My god, we only have a conversion rate of 3%” or whatever the conversion rate was.
Then one day when I was looking at a voice-of-the-customer survey, I realized that not everyone is coming to buy products. Some are coming for support, some are coming to download software, some are coming to purchase, and others are simply coming to learn about a product.
This got me to thinking that if I know that only 35% of the site visitors are potential paying customers, our 3% conversion rate is most likely misrepresentative. If I were to filter out the 65% of site visitors who have come with little or no intention of making a purchase, our conversion rate would suddenly jump up to 8.5%.
The voice-of-the-customer survey also showed me that to maintain long-term relationships with existing customers, I must make sure that the website fulfills the needs of the other 65% of the site visitors.
Voice-of-the-customer surveys allowed me to better illustrate what our conversion rate was with users who intended to purchase (8.5%) and, with refinements, how many intended to purchase online versus offline. Filtering out the customers who intended to purchase offline made that 8.5% increase significantly. This finding prompted me to ask the rest of the team, “How can we get people who intend to purchase offline to commit to an online sale?” We decided to focus on the users closest to making the jump to a purchase first.
The point of this is not to show you how to boost your numbers, but how to make sure you are spending your time smartly. In our case, once I realized that not all of our visitors were coming to the site looking to make a purchase, I saw that we no longer had to worry about getting all the traffic to convert; instead, we could focus on making sure that the traffic that intended to purchase was converting. More specifically, when I saw that we were doing fairly well with users who intended to purchase online, I realized we could boost our conversions by focusing on the users who were looking to purchase offline.
In contrast to the results for SEO and SEM, the SEMPO and Econsultancy report showed that paid search’s primary goals are generating leads and selling products, with 75% of companies citing these as their main goals. Driving traffic was the third highest ranking goal, with 19% of companies striving for this. Improved customer service was the objective of only 2% of these campaigns.
It is interesting to see that when dealing with paid search, the goal is tied to something more tangible than simply driving traffic. Perhaps because you have to pay for these ads, companies feel they must show a more palpable result. Again I have to ask, why not also look at creating a great customer experience and working at making that dollar last longer by trying to increase repeat business and focusing on retaining your customers?
The SEMPO and Econsultancy study also asked the companies and agencies surveyed what they considered to be the three most important metrics for search and SEO. Table 1-1 shows how they broke down and identifies the chapters in this book where you can find information on the metrics identified.
Table 1-1. Most important SEO and SEM metrics (data from 2011 SEMPO and Econsultancy survey)
| Metric | Percentage of responses for SEO by companies | Percentage of responses for SEO by agencies | Percentage of responses for SEM by companies | Percentage of responses for SEM by agencies | Chapter covered in |
|---|---|---|---|---|---|
| Site traffic | 57% | 43% | 14% | 11% | Chapter 3 |
| Conversion rate | 33% | 40% | 59% | 50% | Chapter 3 |
| Click-through rate | 28% | 20% | 37% | 34% | Chapter 2 |
| Page rank | 28% | 18% | N/A | N/A | Chapter 9 |
| Position | 27% | 37% | N/A | N/A | Chapters 2 and 4 |
| Number of sales/leads | 25% | 34% | 40% | 33% | Chapters 2 and 3 |
| Brand awareness | 22% | 14% | 12% | 7% | Chapter 8 |
| Return on investment | 15% | 28% | 31% | 33% | Chapter 2 |
| Customer engagement | 13% | 9% | 5% | 5% | Chapter 3 |
| Number of links | 10% | 9% | N/A | N/A | Chapter 9 |
| Cost per sale/Cost per acquisition | 8% | 11% | 23% | 38% | Chapter 2 |
| Value of sales/leads | 8% | 10% | 12% | 10% | Chapters 2 and 3 |
| Customer satisfaction/advocacy | 6% | 1% | 3% | 1% | Chapter 1 |
| Cost per click | 5% | 5% | 26% | 29% | Chapter 4 |
| Cost of generating sale offline | 2% | 3% | 4% | 3% | Chapter 3 |
| Profitability of sales | 2% | 7% | 9% | 11% | Chapters 2 and 3 |
| Return on ad spend | 2% | 6% | 17% | 27% | Chapter 2 |
| Other | 1% | 1% | 1% | 1% | — |
Interestingly, across the board, conversion rate ranks fairly high in terms of what is being tracked, yet customer satisfaction ranks low. Also, the importance of ROI increases as spend awareness increases. ROI is given more weight when agencies are involved, and with paid as opposed to organic search. Site traffic remains the top metric measured for organic search, which indicates to me that organic campaigns have not reached the same level of metrics engagement as paid search campaigns.
I also fear that in this case, site traffic metrics are simply measuring how much traffic was delivered to a site. This is a largely worthless metric. If your site is monetized through traffic, how is it monetized? Through ad impressions? If so, you should measure number of ad impressions served as opposed to traffic. Site traffic is an archaic metric with little actionable value. The same can be said for click-through rate (CTR). Again, this metric is worthless on its own. What I would rather know is what percentage of the CTR traffic bounced, and whether that is good or bad. This indicates how successful I am at retaining users—that is, whether I am helping them to complete their objectives and meet their goals.
The last piece of the SEMPO and Econsultancy study we will consider is what challenges companies identified in relation to their search programs. The report indicates that 44% of companies have difficulty measuring ROI for SEO and 40% of companies for SEM. Beyond this, obtaining executive buy-in, getting budget allocations, and making the business case for investment account for 53% of the challenges SEO marketers face and 45% of those faced by paid search marketers.
I believe these challenges can be overcome by presenting clear and concise metrics that show ROI, share of voice, and lifetime value. These are all issues that typically are raised either by more senior people or people in other departments who have to decide where is the best area to spend the budget. These other departments are accountable for what they spend, so when investing in search there should be an expectation to have a measurable data point.
Lastly, 40% of companies identified optimizing destination pages as an SEO challenge, and 42% as a paid search challenge. This is a somewhat vague description that may refer to optimizing either to rank well or to push traffic further into the site. Both of these issues are very relevant and real concerns, each of which can be addressed through analytics and landing page optimization. By defining the goal of a page and what action a user may take on that page, and also by understanding the overall business objectives of the company, you can begin to define what you need to track beyond traffic volumes.
Every business has a reason for being. It has a goal. It has a market. Before you even begin to build a metrics program, conduct interviews throughout your business to find out what the goals are. Find out what your customers need. Set up voice-of-the-customer surveys: iPerceptions offers 4Q, which is free and an excellent start if you are working with a limited budget.
Learn what your business is attempting to do both online and offline. While you may not be able to easily measure offline results, you should do your best to translate those into online goals.
To capture offline goals in online instances, you will have to think outside of the box. In some cases, you can capture offline results through coupon codes by using custom URLs that append tagging, such as www.acme.com/promotion redirecting to www.acme.com/directory/some-promtion.html?cid=campaign-variable. Tagging and marketing URLs that redirect to longer URLs can help you track an offline campaign back to an online action. In this example, the variable that comes after “cid=” is used to identify a specific marketing campaign that is tied to a business objective.
By clearly setting out the business objectives, understanding what your customers see as issues today, and knowing your market and who your competitors are, you will ensure that you are better informed as you start to establish an analytics-driven strategy. You should be aiming to develop a plan that will encompass these needs and provide ways to translate online actions into measurable events.
Beyond setting business objectives, you will also have the task of improving your search programs. These programs should be tuned to deliver results that help you meet your business objectives. There are some basic business objectives that will come out early, the first of which will likely be ROI. To impact this, you will have to look at improving what happens on your site. Your job is not to simply deliver traffic to a site, but to ensure that traffic is successful in meeting the goals of your business.
You will use data to answer the question of how successful your search programs are. To optimize your programs, you will need to make data-driven decisions. Part of your responsibility may be keeping the big bosses (HiPPOs) happy, or you may be the big boss yourself. Often, the data they or you need is much different than the data your marketing person needs, and it will be very different than the data the person running your SEM, SEO, or site search program needs.
The software, browser plug-ins, and websites you select should be items you are comfortable with, or are able to utilize efficiently. The important thing to remember is that you do not have to spend a lot of money to get the best auditing tools. As you will quickly discover, there is great variance in cost. The tools you select may depend on the scale of your programs or the size of your company. For example, Kenshoo, Marin Software, Adobe, and ClickEquations all provide very powerful SEM tools to manage and track paid search campaigns with budgets ranging from $25,000 to millions of dollars a month; however, these may not be the best solution for a small business. Do not get caught up in individual software or tools; instead, learn what data points and information will help you further understand any holes or issues in your search strategies and focus on those.
The following sections provide a brief summary of the types of tools you will come across. Specific packages will be introduced in later chapters as examples demand, and a full list is provided in the Appendix A.
Having some way to track traffic through your site will be critical to success. Website analytics tools typically provide clickstream tracking. Data from these tools shows where and how customers move through your site. Today, most are implemented through tagging. The problem with tagging is that it does not always capture what search spiders do on your site. Clickstream tracking tools that rely on tagging usually require a JavaScript tag or a beacon, such as an image, to be placed on every page to capture actions. JavaScript tagging only captures the actions of users that can execute JavaScript. This excludes visits from users on screen readers, users who prefer to disable JavaScript, and most search engines. The use of image tagging allows for some information to be captured should JavaScript tagging fail.
To capture the search spider, you need to look at options such as log analyzers that parse your website’s log files to pull data from all visitors to your site, including search spiders. They can usually be configured to exclude or include certain user data.
One final note to point out about relying on clickstream data from tagging solutions is that because this technology relies on cookies, the numbers can be inaccurate. For example, a recent ComScore study showed that 33% of Internet users in Latin America delete their first-party cookies. This figure is, however, much higher than that reported by some experts and ComScore does have a bias because of its business model. These results may also be isolated to the Latin America audience.
For more on website analytics, see Avinash Kaushik’s Web Analytics: An Hour a Day (Sybex), and for some history, see Chapter 5 of Alistair Croll and Sean Power’s Complete Web Monitoring (O’Reilly).
Link tracking tools can be used to give you an idea of who is linking to your domain and its individual pages. Their output can vary in detail, trying to list every page that links to some page on your site or simply listing the domains linking to your domain. In some cases, you may only get a total number of links, without knowing what domains or pages are linking to your site. Some tools will also identify internal links, monitoring which pages of a site link to other pages on the same site. This data is useful in analyzing IA and UX as well as SEO.
Page authority is a measurement first made popular with Google’s PageRank, which assigns a level of authority to a page or domain based on a combination of factors, the most predominant of which is number of inbound links. Pages with a higher PageRank are more likely to show at the top of search results, though Google has made some comments indicating that PageRank is not as important a factor now as it once was.
The folks at SEOmoz have also created some of their own tools to learn about the authority of pages and sites: MOZ Rank and MOZ Trust. Authority and trust are used when looking at links from other sites. Basically, links from pages or sites with higher levels of trust and authority are assigned greater value.
Most likely, one of the first questions anyone running a search program will need to answer is “what position do we rank in for some word or term?” This is also known as the search engine result page (SERP) position. Ranking position tools provide a way of capturing either SEO or SEM rankings (or, in some cases, both).
One more thing to keep in mind is that a trend is emerging for the search engines, or at least Google, to pass along the ranking position of the clicked-on link for some searches. If this trend continues, you may be able to get an idea of SERP position based on referred traffic. The upside of this is that you will be able to determine the impact that position has on traffic to your site.
Sometimes, the best battle to fight is the one no one is fighting. Understanding the volumes of searches on particular terms as well as the number of competitors can help you understand if it’s a fight you want to take on. In some cases, it may be better to try going up a slightly less steep mountain. Keyword search volume and competition tools pull their data from exclusive sources; for example, Google uses its own data set, while Trellian uses numerous sources. There may be great variance between tools with regard to the anticipated search volume on any given word, so take the expected volumes with a grain of salt.
Social media activity (such as Facebook likes and tweets that contain links) has been confirmed as a factor in search rankings by both Google and Bing. The suspicion is that right now it has a lower impact than some other indicators, but this may change over time. Social media sites also have a propensity to push links out to third-party sites, such as blogs, if the noise gets loud enough, or if you are fortunate enough to connect with a well-known blogger.
Social media is still a developing space that is changing rapidly. Not only is it important to monitor the social space from a search perspective, it is also important to know what people are saying about you and your products and to be able to engage in these discussions where they take place. The tools for this category, which are listed in the Appendix A under Social Links and Social Noise, offer some monitoring capabilities to help you with this.
Keyword volume is the measurement of words and terms on a page. Most tools look at the terms as a list of single, double, and triple word combinations. Keyword volume does not take into account meaning or any of the semantics associated with the words on the page. It simply gives you an idea of whether you repeat certain words or sets of words over and over in your pages (which may actually be bad in some cases). Some of the tools help you identify a weight based on words and HTML tags. Note that these tools do not tell you if the text is readable by a human or if it makes any sense. The onus for clear content is always on the author of the text.
Recently, one of the larger shifts for Google has been an emphasis on local search results (for example, returning local business results based on the location of the searcher). Smaller companies are able to rank much more highly on local results, whereas larger companies would dominate otherwise. Furthermore, searches are happening on mobile devices that are becoming even more location-aware, down to specific longitude and latitude values. These types of tools will help you learn about opportunities in different geographies, as well as mobile search volumes, which may differ greatly from PC search volumes on some words or terms.
The search landscape is highly competitive—so much so that you may have competitors in the search results you didn’t even know existed. You will not only be competing against companies that may compete in the same markets offline, but also against information sites like Wikipedia or online stores like Amazon. Tools in this category will offer you some insights into who your competitors may be. Sites ranking higher or generating more traffic may be doing something that you are not. You will also be able to see how they trend over time and where you trend against them.
There are several solutions that offer multiple data points. SEOmoz, Web CEO, and SEO Book each provide a selection of excellent SEO-related tools for keyword research, link building, competitor tracking, auditing, and more. There are also several large-scale enterprise options available that enable you to tie together search, other digital marketing, and offline marketing measurement points.
Before we end this section, there is one other piece of software you will need: a spreadsheet program. You will likely spend lots of time pulling data into spreadsheets to help you keep track of this information, and if you are not putting information into spreadsheets yourself, you will find that many of these tools output into spreadsheets. You don’t need to be a spreadsheet wizard, but you should be prepared to be dealing with them in some form or another.
One of the first things you need to do for any analytics program is define your key performance indicators (KPIs). KPIs should be objectives and results, sometimes referred to as OKRs (http://blog.anthonyrthompson.com/2010/01/objectives-and-key-results/). The simple idea is to define what and where positive results happen on your site. Objectives help keep your eye on the ball and results help tell you if you got a touchdown. Measuring KPIs can typically be classified into four silos: macro, micro, value, and action metrics. These are defined as follows:
- Macro metrics
Macro metrics look at a large subset of information. In the realm of SEO metrics, examples might include the total number of inbound links to a page, average keyword position for a group of keywords, or total number of visitors from search. These analytics are often useful when meeting with upper management, when responding to marketing requests, and for understanding general ROI values.
- Micro metrics
These metrics examine the smaller parts of a macro metric. For example, if you were looking at the total number of links to a page, the micro metric would look at what domains are linking to that page. Often, these analytics are great for understanding where certain SEO or SEM elements need to be tweaked. Macro metrics often drive the micro metrics that are examined.
- Action metrics
Action metrics capture a user’s input or response (for example, clicking deeper into your website or interacting with 3D demos). Testing out different ad copies to see which one results in the most clicks is an example of an action metric, measuring when the user takes action. Action metrics are an excellent way to measure the usability and experience of your site.
- Value metrics
Value metrics are tied directly to revenue or other goals that are considered the core driving forces of your website. These metrics might include clicking on web banners that create revenue for you, purchasing from an ecommerce engine, or subscribing to your newsletter. Value metrics may be a subgroup of action metrics. Value metrics are also the touchdowns. These are your goals and conversions.
Note
Value and action metrics were first introduced to me by one of my senior executives. It didn’t take me long to realize that he was not only interested in conversion rates and the bottom line; he also wanted to measure the use of the site.
Our site did a lot more than just sell products, and he recognized this through key performance indicators that measured the actions users took that might not be directly related to sales or value. He referred to them as “volume metrics,” but I thought “action metrics” sounded more appropriate, and it was easier to explain the difference to stakeholders.
As for micro and macro analytics, when presenting data I always had two approaches: top-down or bottom-up. Which one I used would change from audience to audience, but when I was presented with a hard question, such as “Why is traffic down for such and such a segment?,” my approach was consistently the same: start at the top and work my way down. First I compared the data to see if traffic really was down, and if so, I checked whether I could see a dip from any specific source. Was search traffic down? Was display banner traffic down? Was bookmarked or direct traffic down? Were there any pages whose traffic suddenly dropped from one month to the next, or year to year? The big question triggered all these little questions and made problem solving much easier.
If you take one thing away from this discussion, it should be to think about how you can segment traffic and then segment it again. When answering questions, smaller portions of data are always easier to deal with and dissect than larger chunks of data, which are usually much better for reporting to executives on.
As Avinash would say, all of these metrics are “data puke” if you cannot make them actionable. Say, for example, you had 1,000,000 visitors to your site, 50% of whom added a product to a shopping cart, 25% of whom began to check out, but only 3% of whom actually did check out.
The 1,000,000 is your macro metric. You would then want to segment this group into better-defined categories, such as users looking for support, users with strong buying intent, and users with weak buying intent. Where do you get the data to segment these users? In the world of search, we can look at how many users have come to the site on keywords that indicate a need for support versus keywords that include product terminology and keywords that have purchase phrases in them.
We can eliminate the traffic that came in on support terms as likely purchasers of our products, and conversely we can say that anyone who came in on terms with “buy,” “deal,” or “offer” in them likely had high intent to purchase. Suppose this group represented 10% of our traffic. We now have a micro metric, which is 10% of 1,000,000, or 100,000.
The users that added a product to their cart can be segmented by the action metric “users that added something to a cart.” This group could then further be segmented into “users who started to check out” to provide us with better insight into how much our “high intent” segment overlaps into these two segments. If there is high overlap, we are likely doing a good job of getting interested buyers through our site. However, what happens at the checkout? Why do we have such a drop-off?
We can track the folks that check out as a conversion or value metric, and again we’d want to see the overlap in our micro metric, but also we want to know how to get more of these people through the full checkout process.
At this point, we have done a fair amount of data analysis: we have identified segments of people, we have identified where people are failing to complete an action, and we have identified a potential untapped area for revenue improvement if we can just figure out how to get users who come to our site on high-intent-to-purchase keywords through the checkout. To that end, I would resort to a simple questionnaire presented to users before they leave the site, which should include the question: “What can we do to improve your experience next time?”
As you develop your analysis strategy, it helps to think of what you are measuring and at what level of detail you are measuring. Referring back to these four groups of metrics will also help you think about your audience and whether you can drill deeper into any information available to you. It will also help you select the right tool for the job when you need to pull data from a resource.
Chapter 14 is dedicated to this subject, but before we dive into any analytics, it’s important to think about who will be looking at the data. The most important questions to ask are “Why should they care?” and “Why do I care?” More often than not, people want more data than they ever use.
In the case of presenting the data, sometimes less is more. When there are too many data points, your audience can become overwhelmed or run into what is called “data paralysis,” which is when a decision can’t be made because there is too much data to absorb or comprehend. This is why you will need to think, “Segment! Segment! Segment!” Present data that is actionable, not fascinating.
Before you begin pulling data, make sure you fully understand what question whoever will be taking action on the results wants answered. Work to answer that question as best you can. It doesn’t hurt for you to look at deeper data, and if you see something that indicates deeper analysis is needed, by all means keep digging. Also remember that the data you present will raise questions; if you don’t have the data you need at your fingertips, know where you can find that information to ensure you can get answers quickly to whomever it is that is asking the questions.
Finally, you should also keep in mind that being presented with lots of numbers can sometimes become boring! Yes, I know this is a book on analytics and numbers, and I personally love looking at numbers, but not all people do. Chapter 14 gives some examples of how to improve the display of numbers and data.
Before we even look at a single number, I want to set some expectations and prepare you for some speed bumps you may come across. The first is that numbers can be pulled out of thin air—4 out of 5 people know that. In fact, I don’t know if that is true, but I do know that including numbers in a statement can make it seem more credible. When pulling your numbers, don’t make anything up; the integrity of the data and your own personal integrity should be upheld regardless of any pressures to show improvements even if there are not any.
If you aren’t getting a result that is positive, be prepared to report on something negative. From time to time, you may see a decrease in something where an increase was hoped for, and you may be tempted to fudge the numbers to make the results appear better. Don’t do this! At some point, it will catch up to you. It is better to fail quickly and learn from your mistakes than to misrepresent numbers. Integrity is what makes analytics reliable.
This leads to my second point. As the expression popularized by Mark Twain states, “There are three kinds of lies: lies, damned lies, and statistics.” Just because they are numbers does not mean they are facts. Numbers and analytics can be manipulated to appear both positive and negative. We will see examples of this in the next chapter, where the same statistically correct data is presented in different lights.
Some data sources you use to pull your data may contain sampled data. For example, in Google Analytics, the message “This report is generated in fast-access mode” (Figure 1-1) indicates that the data is sampled and not comparing the same data sets anymore. Most data we will work with is intended not to provide exact numbers but to provide us insights, and therefore we will not have to rely completely on exact data. It is, however, to your benefit to inquire if any of the data sources you use are using sampled data. If you are not familiar with averages, medians, and percentiles, I suggest you take a look at Bruce Fey’s Statistics Hacks (O’Reilly) to learn more about data and sampling.
When comparing two related data sets, I suggest that you use the same tool. For example, if you want to compare your site traffic to your competitors’, do not attempt to compare your clickstream data with their ComScore data, as the results will be misleading. While the ComScore data is less accurate, it is at least comparing the same sampling of users, and your goal should be to compare apples to apples as closely as possible. By consistently using the same sources, you will find less fluctuation in numbers. This also means you will get fewer questions about data integrity and you can focus more on acting on the data.
As another example, you would not want to compare web analytics from two different JavaScript-based sources, such as Adobe SiteCatalyst and Google Analytics; it would be an even worse idea to compare a JavaScript-based source to a log-based source. Different analytics tools use different rules internally to measure a variety of data points, and there may also be gaps, such as JavaScript-based tracking not capturing activity from bots. Remember, settle on one source and establish that as your baseline source when comparing analytics across multiple sites, words, or campaigns.
Before you go any further in this book, try to answer a few of these questions:
What website or websites do you want to monitor?
What keywords do you want to track?
What keywords are considered branded terms? That is, what words are specific to your brand alone?
What keywords are considered nonbranded terms?
What is the goal of your business?
What is the goal of your website?
What one thing should a customer be able to do on a specific page?
What pages are considered success pages (meaning if a customer gets to it, he has completed a task that has a positive impact for your business)?
What are the values of these success pages? Is there a monetary value directly tied to them? If not, can you develop some sort of value estimate for them?
We look at a few of these questions in more depth in the following sections.
While you can find all sorts of sites online, these are the five most common types of websites today:
Each site has its own unique needs and challenges. Each site also has a different way of producing revenue. Revenue is typically generated directly online in three ways:
Advertising (the selling of advertising space, as Google does with AdWords)
Product sales
Signing up users to a service
Media sites and collaboration sites typically generate revenue through ad impressions. When advertising is sold by impressions instead of clicks, it is to that company’s benefit to drive as many people to their site as possible. In these cases, you can find the value of a user based on the average number of impressions you can generate. For example, every ad may be worth 5 cents an impression. If you can generate 10 impressions, then you have earned 50 cents. If that is your average value earned, then each customer is worth 50 cents.
Monetary transactions and subscriptions are typically the driving force on transaction sites and SaaS sites. That is, they generate revenue by selling or getting subscribers to opt into a product or service.
When dealing with product purchases, you will have an average order value (AOV). The average value is calculated by adding up the number of orders and the total amount of revenue, then dividing total revenue by the total number of orders.
Note
AOV = Total Revenue / Total Number of Orders
The last way of generating revenue is through affiliates. Technically, Groupon is not a true affiliate, but it does act on behalf of other companies, selling products and receiving a commission for each sale. In a traditional sense, affiliate sites make money by marketing and creating leads or selling products through a storefront for another company. Affiliates, such as product sales sites, can track average lead value. This is typically the average amount of money each lead to the parent site is worth. This value may be based on actual transactions occurring on the parent site, or it may be good enough simply to generate the lead. Amazon offers an affiliate program rewarding leads based on purchases that happen on its site.
To add more complexity to tracking affiliate sites, if you are the parent company, you may want to get reports and data back from the affiliates. You may want to know how many impressions your products get on these sites, or how many people click on a product on an affiliate site but do not come to your site. You also will want to track which affiliate sites refer more traffic, and which refer the best-converting traffic.
You should have an idea of what type of website you are responsible for. You should also consider any competitor websites you would like to monitor. Deciding which websites you want to track may impact what is measurable. An affiliate site should provide much more data than a competitor site, but regardless, know who you want to track, and build those lists up.
If your business is based on referrals from other sites, such as affiliate sites or affiliate marketing, reach out to your affiliates and request weekly, monthly, quarterly, or annual reporting. If you have the option to install your own tracking methods, ask for your affiliates to set those up too if it will help provide a better picture of what is happening. With affiliates, there are also several pitfalls to watch out for. It is usually in their best interest to report higher volumes, as that is usually what determines if they get paid. Establish some key affiliates to work out pilot programs with, and build from there. It is also key to note that tracking beacons may create privacy issues for some websites.
The impact of privacy on metrics is another important issue you need to be aware of. Today there is talk of “do not track” legislation, and of companies implementing do-not-track technology and opt-outs (http://donottrack.us/). Google has made its own announcement on this, which impacts personalized advertising but not all analytics directly as of yet (http://googlepublicpolicy.blogspot.com/2011/01/keep-your-opt-outs.html). Tracking people through your site needs to be done in a way that respects their privacy, providing you with insights through anonymous data, and there are legal implications that are beyond the scope of this book. Most off-the-shelf software does not cross the privacy lines, but with constantly changing legal stances and concerns, it is a hotly discussed topic.
When dealing with tracking competitor sites, be prepared to get sampled data and estimates. Unless you have a very generous competitor, most numbers you will be able to get will be based on sampling and panels. This means that data on your competitors may be more or less accurate, but you will never get exact numbers, and the degree of variance is unknown. ComScore and Nielsen are examples of sources for this type of data.
Begin to think about what keywords you want to track. You should have some idea of what words you feel best represent your website. Start with that list, and add words to or remove words from it as needed. If you are launching a website with no history at all, you will need to consider what words you think people should use to find your site. Later we’ll explore how to validate whether these are good words or bad words, but for now make that list, keep it close at hand, and be prepared to build it up. You may also want to think about negative words, or words you do not want to be associated with. These could be vulgarities, politically incorrect terms, or words that may create confusion about your site. For example, if your company sells red bicycles as opposed to motorbikes, you may want to add words like “motorbike” and “motor” to your negative list (Table 1-2).
Table 1-2. Keyword list and negative keyword list
| Keywords | Negative keywords |
|---|---|
| Bike | Motorcycle |
| Red bike | Motorbike |
| Bike shop | Motor |
| Austin bike shop | Harley |
| Tom’s Bike Shop | Chopper |
It’s usually best to keep each of these sets of words in a spreadsheet; it will make manipulating and tracking them easier later on. Also, some PPC campaigns allow for bulk uploads of words in spreadsheet format.
Now that you have your word list, go back through it and pick out the terms that are specific to your company, website, or brand. These can also be word pairings, like “Nike shoes.” Separate these terms from the other terms (Table 1-3). If you don’t have any branded terms in your list, sit down and think about what words you might like to trademark. This will be the start of your branded list.
Table 1-3. Keyword list with branded terms added
| Keywords | Branded terms | Negative keywords |
|---|---|---|
| Bike | Tom’s bike shop | Motorcycle |
| Red Bike | Tom’s bike | Motorbike |
| Bike Shop | Tom’s bike repairs | Motor |
| Austin Bike Shop | Harley | |
| Sport Bike Shop | Chopper |
If you have other people to poll, go out and ask them for feedback as well. You can interview people in your company, customers, or partners to find out what terms they link the most to your company and brand.
Take a look at the words still remaining in your list; these are your nonbranded terms (Table 1-4). These should be terms that your competitors would compete on. Later on we’ll try to figure out how competitive the market for those terms is, but for now you should have several ways to segment your keywords besides branded and nonbranded (for example, by product line, or geography, or eventually by the revenue they drive).
Table 1-4. Keyword list changing remaining keywords to nonbranded terms
| Nonbranded terms | Branded terms | Negative keywords |
|---|---|---|
| Bike | Tom’s bike shop | Motorcycle |
| Red Bike | Tom’s bike | Motorbike |
| Bike Shop | Tom’s bike repairs | Motor |
| Austin Bike Shop | Harley | |
| Sport Bike Shop | Chopper |
You may also want to build similar word lists for your competitors, partners, and affiliates. As you develop your word lists, over time you will notice that some words constantly bubble to the top; you may also see some words overlap across partners, affiliates, and competitors. They may be words that are highly competitive, or words that convert extremely well. Make mental notes of these words, as we will look to develop strategies around these terms in later chapters.
A good analyst is always looking for patterns and anomalies. An anomaly may even be the fact that nothing has changed, or that a pattern is too consistent. As someone reading the data, you have to be willing to ask questions and also to recognize when something doesn’t seem right. It’s better to look into a question than to be silent and realize later something has gone very wrong. The best analyst is the one who asks a lot of questions. The following chapters should give you a start in determining what these questions are, and how to best answer them. To become a true master, you will need to learn how to ask the kinds of questions that will help you segment your data and also how to find the answers to those questions.
While we are looking at these problems, I also encourage you to ask yourself questions about other anomalies you might see. Try to figure out how you might solve the problems in the examples with other tool sets. Do not simply limit yourself to the tools reviewed in this book; be willing to try new tools. The search field is only just beginning to develop some sophisticated search analytics tools. If you see something new, take the time to compare it to what you are familiar with. Look at the data; look at how easy it is to use or not use. The tools will enable you to manipulate data, but it will be your brain and inquisitive mind that will provide the insights. Avinash’s rule of investing 10% in your tools and 90% in your people is a good one to live by. Insights are driven by analysts, not tools, regardless of how automated they claim to be.
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