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How to Find Great Web Analysts [Video + Summary]

You know you need a good web analyst who can do more than just “puke out data” - but where do you find these talented people?

At Search Engine Strategies San Jose, 2008, I caught up with Avinash Kaushik, author of Web Analytics An Hour A Day, Google Analytics evangelist and Elastic Path webinar guest, and asked this very question.

RSS/Email Subscribers: Can’t see video? Click here.

Here’s the Coles Notes version of the interview:

One of the biggest mistakes when crafting job requirements is putting too much emphasis on knowledge of the tools, whether Omniture, Google Analytics or Coremetrics or whatever. If your candidate already has knowledge of the tool, fantastic, but it shouldn’t be a requirement.

In a few weeks you can teach the tool, but you can’t teach the mindset and techniques that make a great analyst. Without curiosity and out-of-the-box thinking, your analyst will simply “puke out data.”

Avinash shares that in his experience, women and younger people make great analysts. You want to find someone who has a range of life experiences (even jail time is useful!) and “gets the power of the web” - whether it’s Flickr or blogging or other social media or other technologies - because today’s web is stale tomorrow. You want someone who thinks like your customers.

Often you will find star analysts emerging from finance or advertising careers - they can apply this real-world experience to make good conclusions from numbers and data.

The Forgotten Metric: Direct Traffic Signals Brand Preference

We all want to know which sites, search engines and keywords are sending us traffic. But what about direct type in traffic? When people access your site URL by typing it in from memory, it can be a great indicator of your brand preference, success of your offline and online marketing efforts and customer satisfaction.

If you’re smart and lucky, you named your site your main-keyword-dot-com and you get search traffic from visitors who use their address bars as search engines. For example, a search on “reusable bags” sends you automatically to “reusablebags.com” which sells…you got it, reusable bags.

Other types of type-in visitors could be:

  • Loyal, repeat customers

  • Late stage buyers who’ve already visited your site through search, PPC, email or affiliate link but needed time to make the purchase decision or comparison shop
  • First time shoppers pre-sold from a newspaper article, blog post, social media reviews or word-of-mouth

Type in traffic may even be your highest converting traffic. This post will cover how to create a direct traffic report in Google Analytics, and how to compare direct traffic conversion against your site average. Plus, you’ll learn how to exclude IP addresses for non-customer visits.

How to Access Direct Traffic Reports in Google Analytics

We’re interested in looking at trends in direct traffic to see if the strength and awareness of your brand is increasing over time. Here’s how you get the trend data:

1. Log into your Google Analytics account and click on “Traffic Sources” and “Direct Traffic” in the left hand menu.

You’ll want to make sure you change the default date range to your last quarter, or the past year:

After you change the first date box, don’t forget to click in the second date box once to make the “Apply” button live, then click “Apply.”

2. Changing your graph to “Month” will show you an average figure per month, rather than each daily or weekly record. Believe me, this is much easier to work with. Just remember the figures are monthly averages:

Rolling over any point will show you the month average. The above image is Photoshopped to show you the year over year growth in type-ins for Get Elastic. Comparing this July’s traffic over 2007, I see the blog’s direct visits grew 330%, from 2152 per month to 7142.

3. Compare your direct visit conversion rates to your site average. If you click the little arrow beside “Visits,” you can make some pretty useful graphs.

In this case, we’re going to compare the type in segment to overall Goal 1 conversions for the past year. (Stay in “Month” mode)

The graph will even show you the spread between site and segment (not just for type in, you can use this for other segments of traffic like search engine and keyword):

Don’t Forget IP Filters

But there’s likely another type of visitor you’re tracking - yourself! One thing that can really mess up this metric is tracking your own IP address and the IPs of others who frequent your site but are not customers. These visits could represent SEO, PPC, web design and IT consultants, employees’ home computers, the office IP block, SEOs and other web consultants, ecommerce bloggers (wink) and even competitors.

Direct traffic is not the only stat that suffers when you neglect to filter IP addresses. As mentioned in 8 Stupid Things Webmasters Do To Mess Up Their Analytics, it also:

  • Understates your conversion rates. Your direct type-ins could be your highest converting traffic source, but tracking visits from employees, stakeholders and consultants dilutes your real conversion rate for this segment.

  • Overstates average time on page. You think your visitors are reading every jot and tittle of your copy, when it’s really your marketing team.
  • Messes up your Content stats. Your “Top Content,” “Landing Pages” and “Exit Pages” will may be skewed by tracking the wrong visitors.

How to Filter IPs in Google Analytics

Once you’ve gathered all the IP addresses you need to exclude, go into your Analytics Settings, and find the Filter Manager in the bottom right:

Set up IP filter

You can also filter a range of IPs or use an advanced, cookies-based filter in your office which will compensate for dynamic IPs.

Then add each filter, naming each one intuitively so it’s easy to make edits in the future.

If you add a filter after reading this post, keep that in mind when you create reports in the future. Your direct traffic could drop significantly after creating the filter, and you can’t apply the filter today and change yesterday’s data.

Your trends may not show an upward trend, either. They may spike seasonally or after certain promotions you ran, help wanted ads or other “buzz” about your company. It really depends on you and your business.

Stop Google Analytics From Stealing Your Valuable AdWords Keyword Data

Are you a Google AdWords advertiser using Google Analytics? STOP! You MUST read this post because you are losing money daily and we are going to help you stop the bleeding.

There is a problem with the default functionality of Google Analytics when used in conjunction with AdWords. Google Analytics (GA) doesn’t report the actual phrase a shopper entered into the search bar, only the keyword phrase you are bidding on.

Let me explain:
- You bid on the keyword ’shoes’ using ‘broad match’
- A shopper searches for ‘blue suede shoes’
- The Traffic Sources > Keyword report in GA shows the search as just ’shoes’

Even worse, Google likes to use synonyms when your terms are under the broad match type (called automatic matching or extended broad match).

- You are bidding on the keyword ‘running shoes’ using ‘broad match’
- A shopper searches for ‘Adidas Gazelle’
- Google shows your ad, but wait, you don’t carry Adidas shoes

Why would Google do that?

The shopper searched on blue suede shoes, not shoes! You don’t sell blue suede shoes. You have been making decisions based on inaccurate data.

Follow these simple steps to start seeing the EXACT phrases people are using when they click your AdWords ads.

It will help you find terms to add to your negative keyword list. You can also start honing your ad and landing page copy to better reflect how shoppers search.

The Google Analytics Exact Query Solution…

This solution comes from our friends at VKI Studios, a Google Analytics Authorized Consultant and overall great bunch of people (see their analytics blog for some great tools and tips). Specifically, Brian Katz. They have evaluated various means of cracking this nut, and we have their final solution. Credit and comparison of other methods are at the bottom of this tutorial.

1. Create a new Google Analytics Profile

We do NOT want to overwrite any core data, so a new profile keeps everything intact. Even Google says it is a good idea.

Google Analytics - Create New Profile

Select Add a Profile for an existing domain, select which domain, and enter any name for the profile you choose - the more descriptive the better. You will not have to add any tracking code or tag anything, so no need to get the ponytail guys involved.

2. Create the first filter

Locate your newly created profile and click Edit under the Settings column. Then click Add Filter.

Filter 1 for exposing AdWords keyword data

Field A -> Extract A: Referral: (\?|&)(q|p|query)=([^&]*)
Field B -> Extract B: Campaign Medium: (cpc|ppc)
Output To -> Constructor: Custom Field 1: $A3

3. Create the second filter

Locate your new profile again and click Edit under the Settings column. Then click Add Filter.

Filter 2 for exposing AdWords keyword data

Field A -> Extract A: Custom Field 1: (.*)
Field B -> Extract B: Campaign Term: (.*)
Output To -> Constructor: Campaign Term: $B1 ($A1)

As with almost all multi-part filters, sequence is critical and must be ordered accordingly using the “Assign Filter Order” page for the profile.

That’s It!

Here are what the results should look like when you run the Traffic Sources > Keywords > Paid report in Google Analytics:

The following set of results were obtained using an in-line filter to show bid-terms that would be different from the search terms

Exact Keywords from AdWords using a Google Analytics filter

An unfiltered result would look as follows:

Unfiltered results of a AdWords Keyword report in Google Analytics

The above technique provides useful data as is but it does have some shortcomings in that it does not associate the newly overwritten Campaign Term field with Transactions, as is shown in the following screen shot:

Filter can omit transaction data - a fix is in the works

It is probably the result of using session-based values (e.g.: all the Campaign fields) and pageView-based values (e.g.: Referral). Caught in the middle are the event-based eCommerce transactions.

In his book “Advanced Web Metrics with GA” (Page 199) Brian Clifton documents a method attributed to Shawn Purtell of ROI Revolution that uses 3 filters to show each Transaction with its bid and search terms appended.

We are experimenting with a combination of those filters and the ones described above to extend the solution to include eCommerce and will post the solution when we have it. So make sure you subscribe to the RSS Feed or by Email to be notified when it is available.

Hat Tips to Others Tackling this Problem

The original solution for this came from Brian Clifton, formerly of Google.

The solutions (Using Filters):
- How to Get Detailed PPC Keyword Data from Google Analytics
- NUDE: AdWords Keyword Data Exposed With Google Analytics!

An updated solution from ROI Revolution (Using JavaScript):
This solution uses the User Defined variable so it won’t be appropriate if you’re using the User Defined variable (created with _setVar()) already
- Exact Keyword Tracking with Google Analytics, Revisited
- Exact Keyword Tracking with ga.js

Comparison of the two methods

I checked out the two methods (Filters vs. JavaScript) . Since readers commented saying the filters did not work or “no longer worked”, I took a closer look. The devil is in the detail. Errors in their implementations may have been the cause of the malfunctions.

JavaScript vs. Filters

JavaScript
The two methods both extract data from the and Referrer and Campaign Medium checking the latter for “ppc” of “cpc” using regular expressions. They both concatenate the bid and search terms. The JavaScript method goes 1 step further by looking for the gclid value unique to Google AdWords. That may also be done in the filters but I don’t believe it would enhance the filter solution.

The JavaScript performs its magic at run time. It uses the “troublesome” _setVar() cookie to store the bid and search terms in the User Defined field. It does so using a generally accepted “kludge” to work around _setVar()’s issues (a topic all of its own).

The greatest disadvantage to this method is that it monopolizes the User Defined Value. With all its troubles, it is an invaluable resource that most will (should ?) be using to segment visitors. Since it is stored in a domain specific cookie it cannot store profile-specific data to different profiles (well, it can be pushed to greater limits but that is a blog post all of its own).

It should be possible to rewrite the URL of the landing page before ga.js writes the Campaign cookie (again a topic of its own)

Filters
The filters run at data processing time and so, I expect those may prove marginally more reliable than JavaScript and cookies (although all subsequent visits from the AdWords campaign will rely on the keyword and other campaign data being extracted from cookies by ga.js or urchin.js) so that is no reason to choose one above the other.

By default, however, I am biased in favor of filter-based solutions because they are independent of the implementation and so don’t require updates to a site’s GA coding. Implementation is quicker and easier, as is propagation of the solution across profiles and GA accounts. In fact, in the time it takes to update the code on some sites (those that are not tagged as efficiently as they might have been) or in the time to get a site’s 3rd party developers to make the changes, a GA consultant could implement the solution for a number of accounts, regardless of the level of access the consultant has to the coding.

Note: Analysis and much of the technical write-up done by VKI Studios, Brian Katz

Book Review - Web Analytics: An Hour a Day by Avinash Kaushik

Web Analytics: An Hour A Day CoverLast Christmas, I picked myself up a copy of Avinash Kaushik’s Web Analytics: An Hour a Day. (I read about 50 marketing and ecommerce blog feeds each week, so it’s a real treat to read top-notch marketing material offline from time to time). And now with the warm and sunny weather, I’m finding myself sneaking outside for around an hour a day to give it a second run through.

Since we’re having Avinash as our webinar guest this month, I figured now’s a good time to share my review of the book with Get Elastic readers.

Who It’s For

Web Analytics: An Hour a Day by Avinash Kaushik is a great primer on web analytics for any webmaster, business owner, programmer or marketer. You don’t have to be a techie to “get it.” In fact, if you’re not a techie, you should read it simply to understand the basics of how data is collected on the web.

Because web analytics is as much art as science (perhaps more so), even seasoned web analysts can glean from Avinash’s strategies, tips and tactics. Plus, page 85 to 92 is all about what makes a great web analyst (aren’t you curious?), and there is an advanced analytics chapter that is sure to challenge your thinking.

Although Avinash is Google Analytics’ ambassador, it’s not a Google Analytics guide, nor is it biased to any particular analytics tool. The principles can be applied to free, mid-sized or enterprise tools.

Format

Web Analytics: An Hour a Day starts off with a few chapters to bring you up to speed on what analytics is, the different tools available and how they work, types of data and common challenges of web analytics. You’ll learn the “what,” the “why you should care” and the “what you should care about” of each item.

This is followed up with invaluable advice on what to look for in a Web analyst, a detailed guide to choosing the best analytics solution for your business (without finding out after spending a ton of money that it was the wrong tool) and how to ensure your tracking is set up properly.

Once you’re settled with your tool, the book continues with an 8 month plan for understanding the major capabilities of web analytics in — you got it — an hour a day. Each day you’ll do a little bit of reading, and the rest of the hour you’re hands on with your data. Kaushik covers all the bases - SEO, search engine marketing (PPC), internal site search, email marketing, multi-channel marketing, blogging and RSS tracking.

The content in Web Analytics: An Hour a Day is valuable for anyone who is involved in ecommerce - even if your title is not “Web Analyst.” Just like you must understand financial statements and balance sheets, even if you have an accountant, Web Analytics: An Hour A Day will help you understand your analytics data and reports.

Win a Copy of Web Analytics: An Hour a Day

If you haven’t read Web Analytics: An Hour a Day (or even if you have), please sign up for our upcoming Webinar, Thursday, July 17. Avinash will be presenting 3 Things to Die For: Web Analytics Unleashed, and every participant is eligible to win one of 6 signed copies of Web Analytics: An Hour a Day. And if you have a burning question for the analytics master, this is your chance as there is time for questions at the end. For details, and to sign up click here.

You’ll also do yourself a favor by checking out Avinash’s blog Occam’s Razor. It’s top notch content, and he responds thoughtfully to every comment and email. There’s an archive of podcasts and other media coverage on his blog, which will tide you over until next Thursday.

Google Analytics Posting Delay: Ecommerce Data May Be Lost

Just a heads up for Google Analytics users, your data may not be accurate for April 30 - May 5, 2008:

Google Analytics Posting Delay

“System Message: Analytics Processing Delay from April 30th to May 5th

Google Analytics experienced a data processing error from April 30th to May 5th. Almost all of the data has been recovered and is currently being reprocessed. The recovered data will be reflected in your reports within a few days. Please note that a small percentage of data, particularly in the area of e-commerce reporting, was not recoverable from those dates.

We sincerely apologize for this processing issue and are taking every precaution to prevent such disruptions from occurring again in the future. For more information, please read through our common questions.

The Google Analytics Team”

Webinar Recap: Web Analytics for Online Retailers

Web Analytics for Online RetailersIt was a privilege to have esteemed author, researcher, consultant and speaker Eric T. Peterson join us for this months webinar: Web Analytics for Online Retailers - Technology & Satisfaction 2008

Eric is the founder and CEO of Web Analytics Demystified, where you can find more helpful information and research on the strategic use of web analytics, staffing issues, business process and measurement.

As always, if you missed the live call we have a replay for Web Analytics for Online Retailers in our webinar archive at ElasticPath.com.

Eric presented the findings of a study of web analytics practitioners and I highly recommend you watch the replay to see the full charts because there is a lot of data you may find interesting that is not covered in the highlights below.

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Webinar Recap: Web Analytics for Online Retailers »

Web Analytics for Online Retailers: Must-See Webinar

Analytics Webinar HeroIntimidated by charts and stats? Suffering from “analysis paralysis?”

Web analytics is essential for measuring your ecommerce performance but can be quite complicated and challenging. Even if you have a dedicated internal team or outside consultants, you need to have an understanding of key concepts and metrics to make effective business decisions from your data.

We’re pleased to have Eric T. Peterson joining us on Thursday, April 17 for Web Analytics for Online Retailers: Technology & Satisfaction 2008. Eric is the author of Web Analytics Demystified, Web Site Measurement Hacks and The Big Book of Key Performance Indicators and the CEO of Web Analytics Demystified Consulting.

Eric will present an overview of how online retailers are doing web analytics today and how satisfied they are with their situation.

In this webinar you will learn:

  • How web analytics has impacted spend on search marketing

  • How satisfied online retailers are with their web analytics vendors
  • What the measurement landscape looks like for online retailers

Live attendees will have an opportunity to fire questions during the webinar. So sign up today.

Thursday, April 17th 2008
9 am Pacific / 12pm Eastern

As always if you miss the event, we will have the replay available in our webinar archive and a companion blog post with highlights.

Checkout Process Split-Testing Tip from Bryan Eisenberg

Path TestingHow should you approach split-testing your checkout process?

This question was asked of Bryan Eisenberg in yesterday’s Google Website Optimizer webinar. Bryan recommended split-path testing, reducing the number of steps in your process and using your analytics data to determine what part of your checkout path needs attention.

What is Split-Path Testing?

The definition of a split-path test, according to GrokDotCom:

Split-Path Test — This test will split your traffic among different linear paths containing multiple pages for each path. This is different in that you’re testing the performance of grouped pages against other grouped pages. For example, you could test a checkout process by splitting it into two variations; one with four steps (or pages), and another with only three steps. Each variation of grouped pages will have the same Goal Page (e.g., order confirmation page). Once the data is collected, the winning checkout process will be the one that converted a higher percentage of visitors.

Reducing Checkout Steps

Different ecommerce stores have different checkout paths, ranging from one-page AJAX checkouts to 6 steps or more. Bryan believes less is more - in fact, he recommends going under 4 steps. But you can find out for yourself if this is so for your website by doing your own testing.

I’ve gathered some examples of checkout steps (many are generally the same aside from labeling) that can give you some ideas for how to simplify your process. For example, you may want to test a new path with a combined billing and shipping page vs. your existing separate steps. Or you may want to ditch a step that may be clogging your funnel, such as “Rewards Program.”

Checkout 9

Checkout 11

Checkout 3

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Checkout Process Split-Testing Tip from Bryan Eisenberg »

Conversion Rates Misunderstood

Conversion RateThis is a guest post courtesy of Invesp Consulting. Ayat Shukairy is a managing partner at Invesp Consulting, an online marketing and conversion optimization company. To read more of her posts, visit www.invesp.com/blog.

Everybody these days is talking about conversion and its significance. But how much of this conversion talk is half-truths, and how much of it is relevant and useful information? The fact of the matter is looking at your conversion rate without deciphering the information is not very meaningful. Conversion must be segmented and assessed from each segment’s perspective rather than lumped into one big number.

Marketers usually talk about conversion as a whole; they don’t talk about these segments because that’s not “elegant” marketing talk. But analyzing your segmented conversion data can make a big difference.

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Conversion Rates Misunderstood »

Why You Should Turn On Google Analytics Site Search Today

Google Analytics ThumbnailGoogle Analytics recently introduced an internal site search feature to its already kick-ass free stats program — aptly named “Site Search.”

This tool works with your existing site search and is invaluable to ecommerce marketers as it gives you so much insight into customer intent and your website’s success at delivering results. For example, you can use search log data to discover:

  • What keywords people search for - what’s hot and what do they want that you don’t carry
  • What search refinements are made, indicating possible “Results Not Found” messages or unsatisfactory results
  • What pages the searches were made from, and where users clicked to

The next 30 days is when this information will be crucial. Customers can’t buy what they can’t find. Maybe you only use the term “notebook computer case” and your customers search for “laptop bags.” You can tweak your product pages and search engine for the various ways customers describe your product until the right pages show up when you test your site.

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Why You Should Turn On Google Analytics Site Search Today »

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