web traffic and seo tools: 9/23 SEOmoz Daily SEO Blog

SEOmoz Daily SEO Blog

So You Call Yourself an Analyst? Part 1: Asking the Right Questions

September 22, 2010 at 10:13 PM

Posted by JoannaLord

Today I am going to talk about something that plagues companies and consultants everywhere--half baked analysis. It's something we've all done at some point, and something a lot of us still do on a regular basis. It's unfortunate because as online marketers we all understand the power of good data mining, but time and time again we revert to generic inquiry, at best, and default report templates.

Disclaimer: Origionally I attempted to write about the five steps I follow for solid data analysis in one post, but as I approached my 6th page of content, I realized it may be best to break up into a series.

Alas, this will be the first of three posts, tackling a five-step process toward good data analysis. The three topics are:

Asking the Right Questions
Identifying What is Going Wrong
Turning Data Into Action

Yup that's right...cancel that afternoon meeting because you my friend are giong to be stoked about data analysis in 3...2..1...

Rethinking the Questions

A few weeks ago at our SEOmoz PRO Seminar I spoke on "Analyzing What Matters & Ignoring the Rest" and I challenged the attendees to rethink the questions that guide their data research. Too often we get caught up in asking questions that simply put-- don't really matter. Let me explain. It will always be important to know things like "How much has traffic increased" and "What referrers are performing better this month," but this sort of inquiry does not qualify as marketing analysis.

Sure it's valuable to report that to your clients or boss, but as an analyst you are tasked with much more. You are tasked with finding things others can't. You are expected to dive into the data head first and find issues before they become huge problems. You are also responsible for finding opportunities a.k.a. the "game changer" for your company...that is your job. If you don't like the way that sounds, please stop calling yourself an analyst. You are stressing me out.

So what questions should you be asking? Bigger ones to start.

I know they sound uber-top level, but don't roll your eyes just yet. I challenge each of you to write these out and really think about the answers. I think you'll be surprised with what you come (or can't come) up with. I'm going to apply this to SEOmoz as an example.

An outsider would look at our site and say we are -

Trying to sell PRO memberships
An increase or decrease in completed goals would show us if we are being successful
Losing traffic to our sign-up page, and a lower traffic count would be detrimental to our success

Well that is great, but honestly SEOmoz can't succeed solely on increasing PRO memberships. The truth is, there is a lot more to it than that. We have a recognized brand with expectations on it, and a community of over 200,000 people that come to us for the latest SEO information on the web. We can't afford to lose ground on either of those two. These are defining qualities of SEOmoz, and strong advantages over our competitors. So my three questions would leave me more complex answers, something like this:

Increase organic traffic on "Learn SEO" type queries, increase branded term searches, increase YOUmoz member engagement, and increase signups
More referrals from links to our resources, more traffic from people researching SEO, more YOUmoz submissions, more comments, improved engagement metrics on site, higher sign up attempts, higher signup completions, etc.
Decline in branded term searches, decline in organic traffic to resource pages, decline in time on site for YOUmoz members, etc.

So now what? You are left with a handful of metrics to investigate. Those metrics should be the base of your analysis efforts. I urge all of you to revisit the reasons why you analyze what you analyze, you'll be surprised to learn that you don't really have a good reason most of the time. After you have your new questions nailed down and you know what metrics you want to analyze, it's time to jump in the data.

Start Macro and Go Micro

This is when I highly suggest you fill your coffee cup, or grab another Red Bull. I also support locking your office door, or putting up a "Do Not Disturb, I am Data Mining You Silly Non-Analyst" sign up on your cubicle. Okay anyway...so the main roadmap to solid analysis includes five steps and they are:

*Please note that Analyze, Value, and Action will be covered in upcoming posts in this series.

What Do We Mean by Macro Analysis?

Macro analysis means you have a solid understanding of the different sections of your site, the different user types that navigate it, and the top-level metrics. You should know these like the back of your hand. In addition to knowing these actual numbers you should know their rate of change (how often does that data point change), the depth of change (how extreme are those changes--big jumps? small steps?), and the way they interact (is there a consistent relationship between two metrics--one goes up/down, the other will too). If this sounds like a lot to continuously track, you are right. Good analysis is a lot of work. Thankfully SEOmoz pays me in cupcakes, and Champagne Wednesdays, I highly suggest negotiating for these perks ;)

At SEOmoz we track our top sections by week, so we can easily identify shifts in the data, and it looks something like this:

(A portion of our weekly analysis for full site stats)

You can see we aren't just looking at our homepage, we are looking at our subdomains, our highest trafficked sections. We also are going beyond visitors, we are pulling top-level stats like pages/visit, time on site, bounce rates, etc. This graph goes around to the entire company once a week. This macro level view helps all of us understand the momentum of our site's growth. It helps us easily isolate problem areas so we can address them before they grow into huge "Oh sh*t" moments. Trust me when I say, if you aren't tracking your data at this macro level, you should start today.

What Do We Mean by Micro Analysis?
This part of the puzzle is the one that most people skip over. Micro analysis means you don't just have a sense how your blog's traffic is doing you know how many comments you get on it, how long they spend on it, how deep they go into your site after reading a post, and how many of your blog visitors end up converting for you. In short, micro analysis means you look at all those secondary data points that you can actually manipulate.

While it's great to go into work on a Monday and say I want to increase traffic to my blog by 20%, it is a big feat to accomplish. Not only will it take a lot of time conceptualizing, writing and sharing that content, it will also, most likely, be less lucrative than if you took the existing traffic and increased its conversion rate by 5%. That sort of move is done by honing in on data at a micro analysis level.

Specifically this is where things like event tracking in Google Analytics and deeper dives into your preferred analytics package come in handy. Everyone has their own approach for micro analysis, but I think a good place to start is see where successful events (downloads, subscriptions, sign-ups, conversions, etc.) are taking place and see if you can come up with common demoninators. If you see that successful pages all have one or more thing in common, you can start testing these on other sections to increase conversions across your whole site. Here is an example of what we pull for SEOmoz:

(A portion of our micro tool usage analysis report)

We can see which tools are performing the best, and analyze those pages to see if we can isolate out page tweaks to roll out across all tool pages. It seems simple, but way too often analysts look into analytics to see how they are doing, and fail to put in the time required to uncover what they could be doing for increased success. You should know, for every single section and user type on your site, what makes it "successful." You need to be tracking these "successes" as closely as you would your visitor count.

Well this post got a little long, but I really wanted to give you guys some real examples on how I approach data analysis both at the macro and micro level. Hopefully, you can take some of this and apply it right away. I know we all have our own unique approach to analysis, and I'd love to hear yours in the comments below!

Next post I will be talking about the "analyze" step of a solid analysis strategy. That post will hone in on quick ways to figure out what is going wrong. I will talk about some GA features that you can use to make your analysis more effective and less time consuming. So stay tuned!

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Google Instant: Fewer Changes to SEO than the Average Algo Update

September 21, 2010 at 9:23 PM

Posted by randfish

Ugh... Part of me just wants to link to this old blog post and leave it at that.

But, since there's actually a bit of data to share helping to show that (at least so far) Google Instant changes less than your average algorithmic rankings update, let's share.

880,000 Search Visits Analyzed

Conductor released some nice research from anonymized data of sites on their software platform making a compelling case:

Search Term Keyword Length for Visits Post-Google-Instant
If Conductor keeps putting out this kind of stuff, they'll be a "must-read" in no time

Hmm... Looks pretty darn similar to me. A tiny increase in 4, 5 and 6 word phrases would seem to go against many of the prognostications and fears that this move would decimate the long tail (though, to be fair, plenty of savvier search folks predicted a slight increase as Google's "Suggest" function would be more obvious/visible to searchers and push them to perform more specific queries).

Google Search Traffic for SEOmoz & Open Site Explorer

While I don't have as much data to share as Conductor, I can show you some tidbits from SEOmoz.

Here's SEOmoz.org's traffic from Google in the past week compared to the week prior:

SEOmoz.org's Traffic Pre-and-Post Google Instant

And here's a similar look at OpenSiteExplorer's Google traffic:

OpenSiteExplorer Traffic Pre-and-Post Google Instant

There's a suspiciously small amount of change in the keyword demand, and although these are certainly un-representative of the broader web, we can be relatively confident that lots and lots of folks in our industry, performing queries that might lead them to these two sites, have awareness of and are using Google Instant.

One change that did catch my eye (thanks to some Tweets on the topic) is that Google's Suggest itself seems to have changed a bit:

Querying for SEOmoz in Google Instant

Hard to complain about that :-)

Make the Case for CRO using Simple Math

CRO may be popular on online marketing blogs, but I’m always surprised to learn that most organizations aren’t doing it. At the recent SEOmoz PRO Training Seminar in Seattle, conversion rate guru Tim Ash asked the audience how many of their companies were doing CRO. Of the 300 or so in the audience, only a few dozen individuals raised their hands. Of all the things I’ve worked on in online marketing, nothing has delivered a higher ROI than conversion rate optimization. And yet, it remains less popular than it should.

One explanation I’ve heard is that it’s difficult to get started. But with free tools like Google Website Optimizer, and affordable, yet capable services like Unbounce and Visual Website Optimizer, this excuse is quickly losing ground. The best explanation I can venture is that CRO doesn’t happen because it’s difficult to prioritize against the stack of urgent projects that marketing teams tackle each day.

Your first job should be explaining the potential return-on-investment of a CRO project. If your marketing team, boss or client knew the estimated ROI of CRO using metrics from their own business, they’ll be more likely to prioritize it ahead of other projects. So what’s the best way to make the case for CRO?

Use simple math. Take the numbers of conversions/goal completions from key process of your website, and show what would happen if they performed better. Imagine saying this to your boss or client:

The above example was generated using a simple Excel spreadsheet I created. Download the worksheet and just fill in the white cells with blue text (further instructions are later in this blog post). The spreadsheet will calculate a simple ROI and provide an easy, yet surefire argument.

The boxed quote above reflects the outcome of a retail web site example that has 632 sales a month with an average transaction size of $40. See the details in the screenshot of the spreadsheet below:

What to enter into the spreadsheet:

Experience Name
A friendly name for the User Experience you are considering optimizing using Conversion Rate Optimization. For this example we are using the Checkout Page of an typical retail e-commerce website.

Monthly Visits
I’d recommend the number of total Visits (for an average month) to the first page of the user experience you’d like to optimize using CRO. In this example above, this is how many Visits occurred on the checkout page of a given month. I believe Visits are better than Unique Visitors as they take count someone who visits twice during the same day as two distinct visits. I wouldn’t recommend using Page Views in this cell, since page reloads and other behaviors can make this number larger than it should be.

Monthly Conversions
The monthly conversions or successful completions to this user experience. In this example, the number of times a purchase was made from the Checkout page. For simple websites that have a single purchase experience, this is usually an easy number to determine. If not, make a best guess.

Average Cash Per Conversion
This is how much money you make on average for each conversion that is completed. An optional, but desirable field. A monetary estimate makes for a more compelling argument. For the example above, the company makes an average of $40 for each transaction. If you are a subscription business, this is where you would enter your customer lifetime value.

If you don’t have easy access to monetary values like average purchase size or customer lifetime value, just use the raw number of conversions to make your case. Using the data entered above, that would be the following (note that the Excel worksheet provides both):

Conversion Rate Increase
The estimated improvement that might be achieved using Conversion Rate Optimization. What percentage increase should you use? It’s up to you, but I like to estimate 10% improvement, because it’s believable and if your user experiences are not already very well optimized, this percentage is usually easy to achieve. But in my experience, if executed well, your first test will do, much, much better.

Keep it simple.

This is a simple ROI calculation. Some may argue it's too simple, but it makes a compelling argument that's easy to grasp. The key lesson here is while 10% may not seem monumental, when you see the expected ROI, it often is. And for a low effort with a big reward, it’s a slam-dunk. Use simple math to make your case and you'll have a better chance of getting your organization on board with conversion rate optimization.

What's worked for you?

What’s helped you convince your organization or client to start doing conversion rate optimization? Please let me know in the comments!

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Jamie Steven is the VP Marketing at SEOmoz, and a lover of pumpkin-flavored beverages including lattes and beer—both excellent choices for chilly fall weather.

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