We’ve all heard that increasingly common search marketing mantra, “content is king.” It certainly sounds nice, and if it was true, it could really be a wonderful thing, because it suggests that all you need to have a well-performing, highly ranking and highly converting site is to treat people the way they want to be treated and give them an awesome site to experience.
Now, I am not Matt Cutts and I am not omniscient, but I do know this: Content is not king. And here’s how I know this: I can point to innumerable instances of poorly written, ugly, blatantly spammy pages ranking well for keyphrases that they did not “deserve” to rank for. And these nasty, thin, worthless pages far outnumber (even post-Panda) instances where beautiful, engaging pages that just focus on “building a community” and “great content,” but do not have strong ranking signals, rank well. (more…)
Web analytics involves researching the habits of your website’s visitors through a number of metrics ranging from total visits, page views, and many other statistics. The most popular tool these days is Google Analytics; a powerful free tool available to any webmaster. A quick install of the Google Analytics code is all that is needed to drive more data to your eyeballs than your bucket can hold. In this sea of information lies some real big fish. To get to those fish you must be able to take a step back and look at trending from the macro level and answer a few basic questions.
#1 “How many people are visiting my site?”
Old School Hit Counter
This is represented as visits, which is then separated into total and absolute visits. Absolute visits are determined by the total number of unique ids issued by the tracking software, and total visits is a summed total of all visits to the site. In the old days of the internet this was referred to as “hits,” and we certainly all remember those cool hit counters everyone had on their pages. The simple takeaway here is: are more or less people visiting my site?
#2 “How are they finding my website?”
Within Google Analytics you can separate this into four topical level metrics: non-paid (organic) search results, paid search (PPC), referring sites, and direct visits. Referring site visits are generated when someone clicks on a link to your page from another website, and a direct visit is when someone types in the URL directly. From this view point you can see what is working for you and where you need to invest in the future. Does your SEO campaign show results? Is your PPC campaign matching your spend? Are people typing in the URL you put on your last event flyer? Does Facebook work for you? Lastly, where do I go from here with my online marketing efforts?
#3 “What are your goals for the website, and how do you achieve those in a visit?”
Is your goal simply eyeballs for an onboard advertising package, or are you trying to gain leads for your start-up? The website is no longer a “If you build it, they will come” turnkey package. It takes a lot of strategic planning to build a conversion funnel to fulfill your needs. Identify your core bottom line goal, i.e. profitability, and reverse engineer what it takes to push that goal. Does your website reflect that? Is your sales funnel streamlined enough to avoid confused or frustrated customers? Follow the entire pathway of the ideal visitor, or simply a pathway you presume a normal visitor would take.
#4 “What are your visitors actually doing?”
Are your visitors finding the right thing?
Take a glance at the top landing pages and bounce rate. Are your visitors “sticking” to your site, or are they hitting the eject button and bailing on you? Do the top landing pages fall somewhere in your above goal path, or are they finding your favorite stuffed chinchilla fan page? Look at the top viewed pages to get an idea where people are either landing or migrating to on your site. You want to see from a top level what your visitors are engaged with when they enter and navigate through your site.
The answers to these questions will arm you with the information you need to redefine your online marketing strategy and get the most out of your time and spend. These are questions that we ask ourselves when we engage a client’s website from a macro level. They are simple questions with often simple answers, but don’t let that distract you from the fact that they are highly effective. When used properly they are game changing.
SXSW 2011 – Metrics Driven Design Session with Joshua Porter
March 12, 2011
Joshua starts by telling this huge crowd that his twitter account just got hacked, so ignore any strange tweets coming from him. That sux! With that angst out of the way, he starts.
Doug Bowman, an ex-Google guy wrote a post that he was leaving Google, the reason? He was fed-up with micro design changes caused by what he considered an overly engineered process of metrics testing to make design decisions. For those of you who may remember it, it was that 41 shades of blue blog. Google was testing to see which one converted best. Google did a 41 bucket split test, a number of users received each of the colors. Marissa Mayer showed a slide of Green & Blue impact on conversion. Green content worked less well than blue content.
Joshua noted that testing was critical, as with the massive numbers of interactions Google receives even a “small” change like this results in very large dollar improvements to the bottom line.
Joshua noted that Paul Ray, UX Manager, Bing, said their Blue color was worth at least $80 million. So at this scale these discussions matter.
The bad effect, according to Doug, was that it makes creativity become paralyzed because of the need for data-driven testing.
The spectrum of design goes from intuition design to data-driven.
On the Intuition side of the spectrum, it’s about:
- Make best guesses
- Rely on previous experience
- Study what others are doing
- Use best practices
- Aesthetics are integral
- Rely on designers
- Every design choice is tested
- Takes other experience with a grain of salt
- Design is a logic problem
- Rely on data
He says utilizing data-driven design will be a bigger issue going forward because of the plethora of design tools that are now available, including Google Analytics and many others.
Imagine a metaphor where design is a couple of mountains, a lower mountain, where you are near the top, and a higher mountain next to your mountain. Intuition is really the potential to make really big changes, like jumping from one mountain to the next. However, data is small tweaks, like taking a few steps higher up your mountain. Engineering using data-driven is only going slightly higher to the top of the design, like taking small steps to go to the top of your smaller mountain, it can’t make the big leaps to transition to a completely different, but higher mountain. That’s the problem with data-driving design. As an example, this happens when A/B testing runs out of things to test, because there’s no more copy or colors of buttons to test that make a big enough difference.
The secret is there’s a way to get to the higher mountain, but it takes changing the design in a more radical way. The goal is to maximize the potential improvement.
The model Joshua recommends is there’s two phases of design, do ideation and make changes, then do optimization where you make smaller, more refined improvements to hit the top of the hill. Once there, then you think out of the box and again make the leap to the other mountain.
Optimization asks: What works best in the current model?
Design innovation asks: What is the best possible model?
What are metrics? Joshua’s definition is metrics are numbers that measure the effectiveness of your business. He says, “I’m getting more jobs of “we need to improve conversion by xx%.” That’s the future, you’ll get more of that. Look for it.”
Metrics are actually a designer’s best friend:
- Metrics reduce arguments based on opinion. You can talk about metrics with the team, that’s valuable.
- Metrics give answer about what really works. If you do testing, and have valid data, it will give answers on what really works.
- Metrics show you where you’re strong as a designer. Also shows where you’re weak. Kevin Hale at Wufu said, based on data-driven design, I’m not as good a designer as I thought I was. This was a really big change for a really good designer, to say that. Designers can see where they are strong. I’m stronger in copywriting and flow, other designers are better with visual hierarchy. I think this is a valuable thing because it goes beyond “does it look good,” which is typically how we’re graded.
- Metrics allow you to test anything you want. It’s a lot easier to go into a discussion when you have data that proves you can test anything you want.
- Clients love metrics. Testing allows you to show the most effective design to go with. But the question is; what metrics do I need? The answer is; they will be as unique as your business.
One distinction, a lot of people are probably using Google Analytics. People look at visits, etc. But what design decisions are made on GA? None. The analytics are really not actionable at that level.
Metrics must be actionable. (He shows a shot of all those hit counters that used to be at the bottom of every web page like 10 or 15 years ago, remember those? Talk about un-actionable data!). That’s an example of metrics that are not actionable.
The Usage Lifecycle – As people go through your website people go through a cycle of use, more or less:
- Beta user
- Passionate customer
Between each stage are hurdles:
There are the hurdles between each lifecycle stage. These are the big hurdles that everyone is dealing with. Here’s how you measure for each:
Acquisition Metrics. Get people aware of what you’re doing, get them to the website. Cost Per Acquisition is a really big metric. CPA. Life Time Value, LTV is very important, especially if CPA is higher than LTV, that means you’re in trouble.
Case study: Dropbox, ran Google adwords campaign. CPA = $233-$388 but LTV was $99. They watched the metrics and quickly realized that was not a good channel for them. So, they created a referral program, dropbox used a 2 sided incentive program, both referrer and referee receive incentives, getting additional storage. This sent 2.8 MM direct referral invites, increased sign-ups by 60% permanently.
Comparative Metrics are huge for acquisition models. This is gold (Google vs. Twitter vs. whatever, PRO TIP: Joshua says email is still the most important channel, the best of all channels). If you are doing anything for acquisition, do emails.
Conversion Funnel Analysis. Look at numbers of people that falloff as they move through a funnel. Case study; a 4 page process lead to only 14% people getting to the last page of the funnel. He designed a new 2 page funnel, and the people getting to the last page went to 86%.
Engagement metrics. Hits, page views, visits, unique visitors, returning visitors, registered users, customers, frequency, time on site, daily active users.
Another type is cohort analysis, break groups up into segments of customers by when they started, and how long they stayed a customer, or did some other important engagement activity.
Prevention Metrics. Another example, prevent the user doing something you don’t want them to do. In this case, design a way to decrease or prevent people deactivating their Facebook accounts. Julie Zhou, of Facebook used a message on the deactivate page: “these people are going to miss you” showing the user’s friends pictures. Joshua says, it stopped me from deactivating my account! Deactivation numbers are an interesting engagement number. For SAS, who is leaving the service is an important metric.
Satisfaction Metrics. Showing Net Promoter Score, how likely is it that you would recommend our company to a friend or colleague? Scale is 0 to 10. Detractors are 0-6, passives are 7-8, only 9 and 10s are promoters. Mint.com uses NPS, they have a great NPS, but not a very high viral coefficient (Jason Putorti, Lead Designer, Mint.com).
Emersion Metrics. These are not obvious metrics at first, but by looking at behavior you realize these are important. As an example FriendFeed, once a friendfeed user found five friends, they became active users. (Bret Taylor, Friendfeed).
At blogger, it was the number of posts, an increase in posts would bring more readership, which would drive more users, which would drive more posts.
Principles of metrics driven design
- Optimize in small steps, innovate with daring leaps
- No design survives contact with the user
- Small improvements, taken together, yield amazing results
- Testing is empowering, revision is cleansing
- Metrics are not creative, human being s are
- If metrics aren’t actionable, they aren’t useful
- Design is never done
Joshua noted that his slides, along with more information about metrics, is available on his website:
By Craig Tomlin
The online marketing paradox refers to the same concept as that old familiar standby; what came first, the chicken or the egg?
In the case of online marketing, this paradox is; when you examine your spend, do you find yourself torn by a decision that is difficult to make? Where do you place your spend? Is it:
- Do I spend my budget on generating traffic to my website or landing pages? If so, which sources and how much for each? Or…
- Do I spend my budget on improving conversion on my website or landing pages? If so, where in the lead flow should I start and how much should I spend? Or…
- Is there some mix of spend that provides the best possible traffic flow AND conversion? And if so, what is that best mix?
In many cases, the online marketing paradox is caused by a root issue; the lack of accurate and actionable data as it relates to achieving marketing goals across the spectrum of the lead-flow process.
As an online marketing authority with many years of experience in assessing website and lead-gen analytics, we have found that the vast majority of our clients have inaccurate or not fully optimized analytics packages. This forces the online marketing paradox of trying to guess what the correct spend should be. And without solid data, you’re left guessing at what should come first.
Typical reasons for the Online Marketing Paradox
Pick any of these that best describes your current analytics situation:
- Google Analytics, Omniture or related analytics systems were set-up by a technical team without the benefit of a complete set of marketing reporting requirements, resulting in sub-optimal online marketing data
- Changes to our website over time are not accurately reflected in our analytics package, resulting in sub-optimal online marketing analysis
- Some traffic sources provide inadequate or no tracking data into our analytics, resulting in missing or incomplete marketing information
- Back-end systems are not directly connected to our analytics system, thus we are forced to estimate sales conversion and related website metrics, resulting in the potential for error
- All of the above, we’re a hopeless online marketing data mess
There are many reasons why your analytics may not be as optimized as it should or could be, but the key impact this has on you is it forces you into the online marketing paradox.
You just don’t have the detailed information you need to make accurate marketing spend decisions. And that’s not a good place to be when you have to meet or beat your marketing goals. So how then do you get out of the online marketing paradox?
How to fix the Online Marketing Paradox
To fix your online marketing paradox, we believe a robust and deep evaluation of your reporting package is required. Our experience over the years has shown that addressing your marketing analytics first and foremost is the best way to have real, actionable data. This accurate data can be the basis of your online marketing spend decisions. This improved data gets you out of the online marketing paradox, and enables you to adequately gauge where and how to deploy your spend.
Our methodology for addressing analytics optimization is simple, but effective:
- Conduct a detailed analytics audit, paying special attention to cross-domains, as well as to the set-up of goals and the sales reporting funnels
- Rank the issues found in the audit by creating a prioritized list of the items found, ranked by severity
- Evaluate potential solutions for each issue found, some may be easy and quick fixes, while others may require more programmatic solutions
- Create a spreadsheet with four columns; a) issue, b) severity, c) potential solution, d) final ranked order of fixes
- Execute the fixes in order
For the spreadsheet, the last column, column D is probably the most important, because some fixes may by necessity have to wait, while others may need to be addressed immediately. Having the order clearly defined makes it easier for everyone to know what’s being fixed and when, enabling a far more efficient optimization process.
The online marketing paradox resolved
You can only resolve the online marketing paradox after you are confident that the data and reporting from your analytics package is accurate. Having better information will help you determine whether to spend your money on traffic, conversion or some mix of the two. By determining where to spend your marketing dollars wiser, you will most likely help lower your overall Cost Per Lead and ultimate Cost Per Sale (or Acquisition). This helps you achieve your marketing goals, enabling you to finally rid yourself of the dreaded online marketing paradox.
This month’s Web Analytics Wednesday (WAW) – Austin will be held at Little Woodrow’s in downtown Austin on Wednesday, October 8th at 5:30 PM. It will be hosted by Ian Strain-Seymour, Director of Product Development at Apogee Search.
This month’s event will use a “birds of a feather” format. Each table will have an assigned topic, allowing people to easily find others that have similar interests. Attendees will be able to easily network and share knowledge by moving from table to table (the only problem may be making sure your drink order finds you).
Table topics will include:
- Measuring visitor engagement
- Online marketing success
- Web analytics for events
- Offline conversion
Want to add a topic? You can do so on Web Analytics Wednesday’s LinkedIn group.
Details on the event can be found on the official WAW website.
Information on the Austin Web Analytics group can be found on LinkedIn or on Facebook.
What is Web Analytics Wednesday?
Web Analytics Wednesday (WAW) is a monthly networking and educational event. It is held on Wednesdays around the world. It is a great chance for web analytics professionals (and amateurs), online marketers, online merchants, designers, developers, students and anyone else interested in web analytics to meet and share knowledge.