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Far from average ecommerce conversion rate analysis - part two

Alex Brown — Tue, 02 Apr 2013 01:38:29 +0000

Creating A Measurable Advantage

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Creating a measurable competitive advantage using data in Ecommerce is nothing new. Companies like Amazon and eBay have built their entire business operations model around leveraging data to best affect and at the heart of optimisation strategy is precision.

So what techniques can you start using to improve site conversion rate today?

Analytics Diplopia (Double Vision)

Visitors are counted more than once when web analytics tools are poorly implemented.

The good news is that this means conversion rate performance is most probably being under-reported by your business. Unique visitor (and visit) metrics used by many companies as the denominator for calculating conversion rate are most probably, being (artificially) inflated. The standard ‘out-of-the-box’ configuration that comes with leading web analytics tools will double-count unique visitors who navigate between domains during their user journey. Why you ask? Well imagine that a copy of the original visitor with slightly different DNA has to be created to ensure tracking continuity (using cookies).

For example, if a visitor comes via Google organic search keyword “shoes” to an online store (http://www.myshop.com) and adds a product to their basket. The visitor is transferred to a different domain for secure payment (https://www.mysecurepayments.com) for check out. The shopper will be counted as two separate “unique” visitors because different tracking cookies for each site (domain) were used. Imagine what the actual conversion rate could be after these people are measured correctly!

Solution: Readers using Google Analytics should learn about how to improve visitor tracking and because every web analytics tool has a slightly a different method, it is best to ask your vendor or alternatively contact us for expert, impartial advice.

Lore and Orders

Traditional web analytics tools have been known to report purchases more than once.

The bad news is orders measurement is most probably being over-reported too. Web tracking tools use a special piece of JavaScript code or “tag” that triggers when a successful purchase has been made and the “thank-you” confirmation page is loaded. This method of data collection is prone to error because of the way customers behave onsite. Order duplication arises when an online customer chooses to save, bookmark or refresh the confirmation page in their browser causing the page to reload and the web analytics tags to count another “successful” order. Thankfully, some technologies automatically prevent the order confirmation page reload from being counted. However, a number of platforms do not and if you are concerned, build a quick report and accurately determine the answer.

Use the orders metric and group by the dimension that contains a (unique) purchase transaction id. As a rule of thumb, where there is more than one order recorded per transaction id there is a problem. Calculate the impact historically by pulling data for previous periods and remove these orders from future conversion rate reports. Reader using Google Analytics can use this pre-built duplicate orders report.

Solution: The best long-term plan will require a web developer to adjust the confirmation page HTML code or template so that the ecommerce analytics tag only loads once per purchase so as not inflate the orders metric.

Top Tip! While the developer is changing the ecommerce tag, ask them to rename reloaded confirmation pages (e.g. /purchase_confirmation/reload/). This will help to build more accurate purchase conversion funnel reports by using the authentic success page and bring purchase confirmation Pageview numbers into better alignment with the orders metric total.

Preaching To The Converted

Fix cross-domain tracking in order to evaluate marketing activities properly.

Now having read this far, you could be forgiven for thinking that if both components of the conversation rate formula are inflated, why not just leave them and the equation remains balanced? The answer is not quite since there is probably more duplicate visitors than there are duplicate transactions. However, the real reason to improve tracking is because of customer communication and the accurate evaluation of marketing performance.

Broken traffic source data originating from cross-domain tracking means that marketing campaigns don't get the recognition they deserve especially campaigns targeting existing customers. Channel attribution modelling and ROI analysis are problematic to attempt also with the inflated revenue from duplicate order transactions.

Timely Promotion

Get customers to more buy than once the easy way.

Part of Amazon’s success has been attributed to their recommendations engine that serves tailored site content to would-be customers based on site navigation behaviour and product combination that previous customers have made. Whilst this technology infrastructure is not within reach for many sites, there is another option available to increase conversion and it is often underutilised by many online stores (including Amazon)…

Recommend complimentary products to customers immediately after a successful purchase has been made. Why does the immediacy of this promotional technique work you ask?

Clickstream analysis of ecommerce sites regularly shows a small, but consistent percentage of visitors who immediately go back and purchase again after successfully completing their first order. The Pareto principle at work perhaps?

Most likely people are just anxious about making a purchase online.

A. “Will my payment get accepted?”
B. “How big are the actual shipping charges?”
C. "Is the site really secure?”

These are all familiar questions and psychological barriers that potentially deter visitor from becoming customers. On the other hand existing customers have laid to rest any doubts and are far more likely to buy again as a result. How can the recommendation technique be implemented?

Solution: Dedicate a highly visible area within the order confirmation page for ‘Recommendations’. Insert products that are regularly purchased with the same item(s) that have just been ordered. Extend the recommendations approach and tailor order confirmation email content. You’ll be surprised at the impact of benefiting from favourable open rates.

Top Tip! Motivate and incentivise repeat purchase behaviour by adding time-based offers.

Test It: Which of the order confirmation pages below would perform best?

Send your answer and reasons via email: talktoalexbrown@gmail.com

Analysing Basket Cases

Discover cross-sell and up-sell opportunities through data mining and increase conversion

From a Merchandiser’s perspective, the hardest part about cross-sell is deciding what products go well together and displaying them on site. The starting point for anyone not wishing to rely on gut feel alone is usually the analysis of existing customer order records as the process can yield surprising results. And in case anyone reading this article has heard of a famous supermarket data mining example, here are the real beer and diaper facts via Daniel Power. After completing the next challenge you’ll be left with an actionable report for cross-sell analysis to customise.

Download the Product Affinity Excel Template

How to get started with product affinity (or market basket) analysis.

Use a customer database to obtain order level data for transactions with more than one product purchased (Item Qty>1).

Use a manageable but representative time period for analysis and include the following fields: Transaction Id, Product Name and Quantity [optional]

This task can be done using web analytics tools but most reporting interfaces come with data query and export limitations that are time consuming to work around.

Use an Excel based reporting plugin or API where available as an alternative. Readers using Google Analytics can try Excellent Analytics for Excel or the Core Reporting API. Product Category analysis is also possible using the same technique.
Export the transactions data from the platform and save the file somewhere safe on your local machine (e.g. desktop) in .CSV format.
Open the file and prepare the dataset by standardising any product names or transaction id anomalies. For example, altered text strings (“&” instead of “&”) and encoded characters that might cause a problem when matching records.
Download a software package called, “R” from www.r-project.org. The site cosmetics are less important than the powerful and free software it holds but be sure to find the download files that are appropriate to your operating system from the mirror country locations.
After the download has completed, run the .EXE file install ‘R’ using the menu wizard provided and open the software package. R console version 2.15.3 (below).
Using the ‘R’ console command prompt > copy & paste the following string then press [enter]:

install.packages("arules");
Select a mirror location from which to install the ‘arules’ package and click [ok]
After successfully installing the “arules” package at the ‘R’ command prompt > copy & paste the following string then press [enter]:

library("arules");
Find the .CSV file prepared earlier and note the location:

C:\Users\[Insert Name]\Desktop\Transactions.csv
Change the original folder location path back-slashes “\”to double-back-slash (or alternatively, use a single forward-slash “/” as preferred):

C:\\Users\\[Insert Name]\\Desktop\\Transactions.csv
At the ‘R’ command prompt > copy & paste the following string then press [enter]:

txn = read.transactions(file=" C:\\Users\\[Insert Name]\\Desktop\\Transactions.csv", rm.duplicates= FALSE, format="single",sep=",",cols =c(1,2));
At the next ‘R’ command prompt > copy & paste the following string and hit [enter]:

basket_rules <- apriori(txn,parameter = list(sup = 0.002, conf =0.5,target="rules"), appearance = list(default = "both"));inspect(basket_rules);

Adjust the sup and conf values to find the best rule criteria for your dataset
Copy results to the Product Affinity Excel Template and customise.

Support is the proportion of transactions which contain these items (frequency)
Confidence is the estimated probability of the event occurrence (likelihood)
Lift is predicted performance when applying rules to the total population (potential)

After the data has been properly mined and analysed, then comes the interesting bit of finding out whether or not the cross-sell works in real life.

Happy Optimising!

What other techniques are you using to improve site conversion?

We would love to hear your opinion in the comments section.

About the Author

Alex Brown is a Digital Analytics and Site Optimisation expert who works as an independent freelance consultant. The opinions shared in this blog are based on personal experiences gathered over a decade of data crunching and technology evaluation. The author makes no attempt to be grammatically, politically (or otherwise) correct. Spelling was never a strong point and for practical reasons, not all vendors in the market are referenced in the article - no hard feelings.

Related Products & Services

With in-depth knowledge of both the market place and major technology vendors, allow us to help select, negotiate, support and manage web analytics implementation for your business. Contact us for a confidential and informal discussion to see how our solutions can improve your business today.

Resources	(In Alphabetical Order)
Amazon	Retail Site
Daniel Power	Beer and Diapers
Ebay	Auction Site
Google Analytics (1)	Cross Domain Tracking
Google Analytics (2)	Duplicate Orders Report
Google Analytics for Excel	Excellent Analytics
Google Analytics Query Builder	Reporting API
Pareto Principle	80/20 Rule
Statistical Software Package	R-Project
Recommendation Technology	Engine
Wikipedia (1)	Cross Sell
Wikipedia (2)	Market Basket Analysis

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Credit card image courtesy of Naypong / Freedigitalphotos.net
Graph image courtesy of Winnond / Freedigitalphotos.net
Maze image courtesy of Pakorn / Freedigitalphotos.net

Far from average ecommerce conversion rate analysis - part one

Alex Brown — Tue, 19 Feb 2013 00:00:00 +0000

Re-evaluate eCommerce Success

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Evidence suggests that increasing numbers of people are venturing online to purchase goods particularly within the higher income and “silver surfer” households. So why do so many e-Retailer continue to witness low single digit conversion rates and high cart abandon rates?

In the first of a three-part series, we explore how to re-evaluate eRetail success and identify common points of failure.

Your biggest competitor is you

Segment or repent - Conversion calculation methods used by companies are never identical so, don’t benchmark performance against your neighbour. The best indicator for comparison is historic data from within the business. Instead, start by asking how can we double or, triple our existing conversion rate?

Analysis: It might be fine to use an average site-wide conversion rate for board meetings but, that figure is useless when it comes to finding real insight. As not all visitors are equal, segment the different types to find the dynamite that will lead to an explosion in conversion performance. Start by using the following conversion rate reporting filters:

Marketing Channels: {Direct, Email, Paid Search, etc}
Internal Promotions: {Sale offers, Shipping discounts, etc}
Product: {Business specific}
Seasonality: {Industry specific}
Technology: {Device & Browser}

Of course that doesn’t mean to say, we take our eye off the ball either. Keep tabs on how the competition are doing by reviewing industry trends using free and paid solutions. A comprehensive list of sources is summarised at Smart Insights and a few more include FireClick Index, Alexa, Think and Compete.

Watch out for ROPO cop

Add offline data - If call-center and in-store sales data isn’t integrated with web eCommerce data, any conversion rate number reported to the business is going to under value web influence. Market trends indicate that a significant number of people (+40%) research online and purchase offline (ROPO). Find out more useful stats over at consumer barometer.com

Measuring Call Centre conversion - Visitor transfer from online shop to call-center can be easily monitored using call tracking technologies. Be certain to select a provider that integrates seamlessly with your existing web analytics solution. Here are a few well known providers to consider, Response Tap, Mongoose Metrics, Marchex, IfByPhone and PowerMyAnalytics.

Manually capture offline orders - Use market research and/or in-store surveys to provide a proxy for the ROPO % in your business. This method requires relatively little investment and will allow for aggregated offline sales (ePOS) data to be reports outside of the web analytics tool. Apply a manual calculation using the proxy to acknowledge web influence and visualise for business users by combining the data in Microsoft Excel.

Including offline orders automatically - Successful web analytics solutions in the e-Retail sector like IBM Coremetrics have long since provided a means by which offline data integration processing (DIP) can be achieved. However, many platforms now also offer the ability to collect data in an automated way via an Application Programming Interface (API). Leveraging this data transfer technique allows businesses to measure web influence more accurately and reliably. The growth API technology adoption amongst online eCommerce companies has been well documented. However, the benefits of a merged online and offline data ecosystem come with a challenge - additional transaction volumes and the visualisation of new data types.

Enhanced visualisation - Traditional web analytics tools offer standardised web reports that are pre-canned for speed optimisation but, hold limited value when it comes to customised conversion analysis between online and offline data sets. To handle the vast volume and iterative data interrogation an alternative approach is recommended (that is unless you’re prepared to re-mortgage the house for Adobe (Omniture) Insight).

Desktop installed software like Omniscope, Tableau and QlikView are great options for visualisation that integrate with many analytics platforms. There are hosted solutions too, like SumAll, Sweet Spot or iJento. And finally, for e-Retailer who want more control in a blizzard of data there’s Snowplow.

Before starting the next section, let's agree that web analytics tools should not be used in place of financial reporting tools. E commerce order processing platforms will always be more accurate and reported numbers between the different counting technologies will never match. However, the gap can be mitigated…

Accounting for change (revenue)

Currency exchange rate conversion - Accounting principles dictate that real-world events like currency fluctuation be incorporated when reporting financial performance so, why not reflect that in the web analytics data too. e-Retailers operating across multiple regions already perform some form of currency conversion. Let’s elaborate on the choices:

Use an exchange rate feed provided by a third party that belongs to or, integrates with web analytics solution - daily refresh (Google, ECB, OER)

+ Low maintenance, closely tracks Global market fluctuations, more “accurate”
- Not aligned to businesses internal rates, requires FX neutral reporting effort
Use an internal FX rate manually refreshed - daily/monthly (Web Dev Team)

+ Aligned with Finance reporting
- Human error factor more likely
Use a fixed rate of exchange that is hard-coded (Clifton)

+ Easy to maintain, consistent across
- Data mismatch with financial reporting

Clearly with so many options available, eRetailers must decide carefully on the most appropriate method.

Reconciling changes (orders)

Fulfilling orders - When items purchased online are cancelled or, modified life becomes more problematic for eCommerce site owners that are purely reliant on site metrics for conversion analysis. Order management systems don’t match web analytics tools leading to further suspicion about data accuracy of the latter.

To align web analytics tools more closely, consider two common methods:

Sending negative order transactions via API
Uploading order modification files via FTP Server

Ask your existing vendor about what available choices as more options may be available.

Conversion metric selection

[ f = Orders / Visitors ] vs [ f = Orders / Visits ]

Re-open denominator debate - The ‘unique visitors’ metric is far from what many preconceive to be a unique person or, individual and yet it is championed as the denominator metric in every eCommerce conversion beauty pageant. Web analytics solutions were designed to use a combination of visitor device (cookies) and browser (sessions) to determine uniqueness which is fundamentally inaccurate. Fast-forward to today’s online shopping behaviour and that becomes even more obsolete with individuals’ using multiple devices (desktops, laptops, mobiles and tablet) and browsers (Chrome, Firefox, Internet Explorer) to browse and purchase. Even when fingerprint authenticated internet browsing is enforced, tracking individuals’ using web technology will not be accurate and if it’s “all about the trends” as our industry rhetoric would suggest, why not just use ‘visits’ as the denominator for e-commerce conversion measurement?

This idea is certainly not an original one (Belkin) and there is prominent opposition to it too (Kaushik) but, let’s agree to disagree and consider how a business might adapt to using visits. You’ve got a hard sell within the business to change from ‘unique visitors’ as people are familiar with the web terminology, comfortably ignorant about what it actually means and unhappy to see conversion rates plummet from 3% to 0.03%. However, think about how much easier eCommerce conversion investigation would become. Analysis by marketing channel or, visit and device type would reflect tracking technology counting methodology and comparing different tools or time periods would be less problematic (Longden). Change is never easy but, if you built it, they will come.

A measure of honesty - A few years ago, whilst working at Skype, the metric used to report the health of the business to investors was the number of ‘registered’ users. A hollow and vain metric on account of the fact that there were registered users that never bothered to actually log-in and use the product and also some people tended created multiple accounts. Internally however, the use of ‘active users’ was championed to measure conversion - The number of people who logged in during a given period after all was a better reflection of the VOIP providers’ peer-to-peer network strength and community. These days the Skype CEO talks openly about ‘connected’ (rebranded active) users externally and sure this metric isn’t bullet-proof either but, is at least an honest account of performance. At the last count, Skype has +600 million registered or, +250 connected users and continues to grow at pace, albeit with a different baseline for conversion. And finally...

Not all new visitors are welcome

Exclude the unwanted - Not everyone that visits your online store wants to buy something. Visitors may have arrived at your site by mistake or, might simply be killing time browsing the internet aimlessly. Don’t beat yourself up too much and focus on the visitors with the most potential to convert.

How about excluding unwanted visitors from the conversion rate calculation entirely to report a more realistic number back to the business?

Tracking single page visits or, time-on-page metrics can be misleading due to the underlying way that analytics tools track. create a new metric to segment with…

Create a ‘timing event’ using javascript that is triggered after a visitor has spent a specified amount on time on site. Each web analytics tool captures information differently and as an example, here’s how Google Analytics does it:

setTimeout("_gaq.push(['_trackEvent', 'read', 'ten'])",10000);

After the site code is added the “adjusted” conversion rate analysis can begin by excluding sessions by New Visitors that don’t include the timing event.

*Small print - Adjusted tracking involves collecting additional requests or ‘hits’ so it is wise to calculate any increase before implementing. If you’re an Adobe (Omniture) SiteCatalyst user, check the secondary server call quota and for Google Analytics Free users, be sure to come in under 10million/mth.

That completes the first instalment on eCommerce site conversion success. Pop back again soon to read about tracking enhancements and lost opportunities in part two.

Like this article? a) tell us b) share it c) download it and d) keep updated

How are you measuring eCommerce site success?

We would love to hear your opinion in the comments section below so don't be shy.

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About the Author

Related Products & Services

Resources	(In Alphabetical Order)
Adobe Omniture	Insights
Alexa	Competitor Site Information
Avinash Kaushik	Unique Visitor Conversion Rates
Brian Clifton	Converting Foreign Currency
Compete	Competitor Information
Coremetrics	IBM Analytics
Engaget	API Growth Infographic
European Central Bank	Daily FX feed
Fireclick Index	Digital River Conversion Rates Information
Google Analytics	Adjusting for Site Bounce
Google FX	Ecommerce Currency Conversion
Google Research	Consumer Barometer Information
Google Think	Other Consumer Insights
IfbyPhone	Phone Call Tracking
iJento	Data Integration and Reporting Services
Jonny Longden	Unique Visitor Conversion Issues
Marchex	Call Tracking Solution
Matt Belkin	Use Visits for Conversion not Unique Visitors
Monetate	API Growth Infographic
Mongoose Metrics	Call Phone Measurement
Open Exchange Rate	FX Currency Conversion Solution
Power (formerly Pimp) My Analytics	Visit to Call Monitoring Solution
Qlikview	Data Management and Visualisation Software
Response Tap	Website to Phone Measurement
Smart Insights	Useful Sources of Market Statistics
Snowplow	Versatile Digital Analytics Solution
SumAll	Data Integration and Visualisation Service
Sweet Spot	Information Integration SaaS Solution
Tableau	Data Visualisation Software
Timing Events	HTML School
Visokio Omnisciope	Powerful Data ETL and Visualisation

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Ecommerce image courtesy of Digitalart / Freedigitalphotos.net
Reject stamp courtesty of Stuart Miles / Freedigitalphotos.net
Spirit level courtesy of Arvind Balaraman / Freedigitalphotos.net
Alien image courtesy of Simon Howden / Freedigitalphotos.net

Web Analytics Book Prize Draw

Alex Brown — Sun, 09 Dec 2012 00:00:00 +0000

Free Entry Prize Draw [closed]

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Now that the Festive season is in full swing and the spirit of giving is officially here, we've decided to join the party fashionably late and start a Free Web Analytics book give away.

It's a prize draw and you've got to be in it, to win it so follow the instructions below and best of luck.

Prize Draw

There are a couple of books on offer that have been well used and remain in good condition. Talk about 'must have' stocking fillers for any discerning Digital Analyst.

Web Analytics - An Hour A Day First Edition by Avinash Kaushik	Advanced Web Metrics with Google Analytics Second Edition by Brian Clifton

Free copy	Free copy

Rules

Simply enter your email address and select one of the book titles. Entries must be placed before Wednesday 19th December, 2012 when the draw will take place. The lucky winners will be chosen at random and contacted via email the same day to supply shipping address information. Only one entry per email address is permitted. There are no geographical restriction imposed on the prize draw so anyone can enter with; a valid email and postal address.

Shipping

The books will be delivered using standard class postage. Given the increased volume of mail during this time of year, no guarantees will be made on behalf of the postal service regarding the time taken to receive the items. However, every effort will be made to ensure they are dispatched as soon as possible.

Questions

Please submit any questions about the prize draw in the comments sections below.

Author Rankings

The bar graph below shows the number of requests submitted for each author title. Data is updated daily.

Image courtesy of Posterize / Freedigitalphotos.net

3 Essential Google Analytics Improvements For SEO Website Analysis

Alex Brown — Mon, 29 Oct 2012 00:00:00 +0000

An SEO Booster Kit For Google Analytics

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Website traffic analysis using Google Analytics contains so many powerful features but, one area where it potentially punches below its weight is organic search. Surprisingly, the "out-of-the-box" configuration contains sub-optimal information about the most important aspects of search engine optimisation.

This post will share a few tips to get valuable SEO information into the existing reports and improve SEO analysis opportunities.

Why do standard Google Analytics SEO reports suck?

1. Recognised Search Engines

Within Google Analytics, the Organic Search report section displays traffic from a small number of default search engines. Data from ‘unrecognised’ search engines finds its way into the ‘Referral’ traffic report, making it difficult to see the complete SEO picture. To compound matters, the recognised search engines are consolidated under engine name (e.g. google) instead of the regional domain variations (e.g. google.co.uk .fr .de etc) or, search domain type (e.g. images.google.co.uk). For a business operating across different countries this is a major drawback to understanding SEO by market.

Standard reports provide the ability to see what default search engine domain drove traffic (e.g. google) however, it is not possible to isolate the specific domain:

a) com

b) images

c) video

d) translate

Solution: Tell Google Analytics to add a new search to the recognised list when tracking the visitor referral.

First of all, locate existing traffic from unrecognised search engines by looking in the ‘Referral’ traffic source report.

Second, visit the search engine site and identify the query string used. For example, unlisted domain BT.com is powered by Yahoo search engine and when looking for the phrase, “what is seo” the search results page URL displays the query keyword identification used as “p”

http://search.bt.com/result?p=what+is+seo

Next, add a line to the Google Analytics Tracker Code (GATC) for each new search engine domain:

_gaq.push(['_addOrganic', 'search.bt.com', 'p', true]);

Place _addOrganic() before the function _trackPageview() and keep the extra optional parameter set to “true” to list new search engines first.

If more than one account is being used for reporting purposes (e.g. regional and global accounts) be sure to insert two calls at the appropriate code locations:

Regional Account: _gaq.push(['_addOrganic', 'search.bt.com', 'p', true]);
Global Account: _gaq.push(['t2._addOrganic', 'search.bt.com', 'p', true]);

In some cases the custom search engine domain list can become quite long and therefore, it might be worth placing the code within a JavaScript file instead. This keeps the code looking tidy and makes it easier to maintain.

Download the list used for this site: ga-custom-se-domains-v2-1.js

Add a line of code to the standard Google Analytics Tracking Code and be sure to replace the example file location below (mysite.com) with the actual location hosted on your site:

<script src="http://www.mysite.com/ga-custom-se-domains-v2-1.js" type="text/javascript"></script>

Note: Soon functionality like adding custom search engines will be moving into the administrative console as part of the "Universal Analytics" changes. Read more details over at Justin Cutroni blog post.

2. The Big Fat Keyword, "Not Provided

Since Oct 2011, visitors finding a site using Google Search while logged in to their Google Account had their referring organic search term replaced with “(Not Provided)”. This is great at telling us how many people use their Google account whilst searching but, for most site owners this sucks as between 5-30% of traffic can fall into this ‘unknown’ bucket (depending on what audience the site caters for).

The loss of organic search keyword data is here to stay and the volume is set to increase. In March 2012, Mozilla Firefox set the built-in Google Search Bar to secure by default thus encrypting all results. Similarly, the latest release of Apple operating software version (iOS6) on mobile means organic visits from on Safari’s built-in Search Bar will appear as ‘Direct’ instead impacting visit data from October 2012.

Solution (Partial): Link accounts between Google Analytics (GA) and Google Webmaster Tools (GWT) and compare GA ‘Keyword’ data at page level with GWT ‘Search Queries’. Bear in mind that GWT data is known to be a bit ‘flaky’ so observe trends instead of absolute numbers. To learn more about this approach visit Cardinal Path for a detailed explanation.

3. Keyword Rank

Analysing keyword rank over time can help a business understand if their investment in SEO is paying off. Strangely, a metric like ‘keyword position’ is not available by default in Google Analytics. By linking the profile to a Webmaster Tools account it is possible to see ‘Average Position’ but this only has limited value. Like all averages this metric can hide a multitude of sins.

Solution: Collect keyword position from Google search engine.

First of all, create a custom filter within the Admin profile area. Select dimension ‘Referral’ for Field A and input: (\?|&)(cd)=([^&]*) Leave Field B empty and select dimension ‘User-Defined’ for the field called, ‘Output To’ and add the following free text: (Rank: $A3). Field B and Case-sensitive options are not required as show below:

Secondly, allow sufficient time to pass and collect new data. The changes will not be applied to historical data so, be patient.

And finally, navigate to Traffic Sources > Sources > Search > Organic > Primary Dimension = Keyword within Google Analytics

        1. Select Pivot report
        2. Choose Secondary Dimension ‘User Defined’
        3. View actual keyword position results

Better still, view the cool motionChart or, alternatively export to the data to Excel and manipulate.

Small print - Be sure to test the filter before using with main reporting profiles. This approach is for Google specifically but alternative adaptations can be used for other search engines. Only certain items within results pages contain the keyword position.

Good luck analysing organic search site performance and thanks for reading this article. Now it's your turn to share...

What's the best way to customise Google Analytics for SEO?

We would love to hear your opinion in the comments section below so don't be shy.

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About the Author

Alex Brown is a Digital Analytics and Site Optimisation expert who works as an independent freelance consultant. The opinions shared in this blog are based on personal experiences gathered over a decade of data crunching and technology evaluation. The author makes no attempt to be grammatically, politically (or otherwise) correct. Spelling was never a strong point and for obvious reasons, only Google Analytics was referenced in the article - no hard feelings to any other vendor out there.

Related Products & Services

With in-depth knowledge of both the market place and major technology vendors, allow us to help setup SEO web analytics for your business. Contact Us for a confidential and informal discussion to see how our solutions can improve your business today.

Resources	(In Alphabetical Order)
Cardinal Path	Not Provided Keyword Analysis
Justin Cutroni	Univeral Analytics
Search Engine Land	iOS6 Safari Search Bar
Slight Paranoia	Mozilla Firefox Search Bar
Yoast	Original Keyword Position Filter

Like this article? a) tell us b) share it c) download it and d) keep updated

Image courtesy of Stuart Miles / Freedigitalphotos.net

Don’t Invest In Real Time Web Analytics - Buy A Lava Lamp Instead!

Alex Brown — Fri, 24 Aug 2012 00:00:00 +0000

Real Time Analytics - Love it or Lamp it?

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Its Tuesday morning and the boss has returned from a long weekend. She's tired and slightly hung over but, glued to her laptop screen and wont budge for an hour whilst consuming her guilty pleasure…

The real time web analytics dashboard

The main take-away seems to be a general sense of well-being and the plausible misconception that one is “in touch” with the business given the immediate proximity to their customers online behaviours.

Friendly advice, if this strikes a chord...

Stop using Real Time Web Analytics and buy a Lava Lamp instead!

The similarities are really quite uncanny.

Lava lamps are just as colourful
They update and move in similarly hypnotic fashion
Time passes quickly when watching them
Viewers feel rewarded without actually doing anything productive
Lots of different styles and all at reasonable prices

Go buy an amazing lamp from legendary British manufacturer Mathmos.

But, wait! Surely real time web analytics is good for something?

Thankfully, there are many organisations leveraging the full potential of real time web analytics and here are a few scenarios to demonstrate successful application…

1. Content Prioritisation

Media and Publishing companies use real time web analytics tools to ensure that the content being served is current, topical and appealing. Nowhere is this more prevalent than in news organisations. Take the New York Times or, Forbes for example. Both leverage Chartbeat to optimise and prioritise content to capitalise on existing site behaviour and response feedback to story popularity.

2. Test & Learn

User generated content site Quora leveraged real time Funnel Analysis by MixPanel to optimise their registration flow and get new users to the most appropriate content quicker. Being able to take immediate action and site design modification based on real time data helped engage more visitors and reduced drop-off.

3. Purchase Flow Optimisation

The benefits of some Real Time Web Analytics tools may extend beyond simply collecting data as in the case of Woopra and their Live Chat feature. This allows site owners to engage in conversation directly with the visitor if support is requested by the shopper or problems be observed by the webmaster. Personally I find the latter of these use cases slightly creepy but, who's to say it wont increase conversion too.

4. Technical Troubleshooting

Staying with eCommerce, StylistPick used real time web analytics to find areas of the site that not functioning as designed. Technical hardware allocation and server side issues were identified and investigated using data from GoSquared.

5. Campaign Scheduling

Iconic fashion house Reiss used Google Analytics Real-Time Beta during a hugely successful sale campaign to monitor site performance and visitor levels in order to schedule the timely release of email batches. This ensured the site remained stable and visitors obtained an experience equal to the premium brand image.

6. Landing Page Optimisation

This site uses Clicky to carefully monitor campaign success in real time and apt strap line copy and landing page layout. This is perhaps most critical for paid marketing activities like PPC but, also applies to non-paid promotions such as Social Media.

Can real time web analytics improve your business?

Chances are that if an organisation takes six months and a committee of ten people to make a decision about changing text font size on the home page, its time to go buy the lamp.

Consider answering these questions before starting the real time journey:

Is the organisation striving to become more data driven or is this just casually thrown into boardroom conversation?
Are Marketing teams empowered and enabled to implement campaign change quickly or do they rely on slow moving, third party agencies?
Is Design resource available for creative changes required post-launch?
How closely aligned are Operations department to other parts of the business?
Does existing technology infrastructure allow for changes to be easily and swiftly deployed?

Ultimately, any business attempting to leverage real time web analytics must adapt to visitor behaviour at speed based. And remember, the people and processes dominate success rates over and above measurement tool functionality.

Good luck with the Real Time Web Analytics journey and thanks for reading this article. Now it's your turn to share...

Real Time Web Analytics: Love it or Lamp it?

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About the Author

Related Products & Services

With in-depth knowledge of both the market place and major technology vendors, allow us to help select, negotiate, support and manage real time web analytics implementation for your business. Contact Us for a confidential and informal discussion to see how our solutions can improve your business today.

Special Thanks

I'm a massive fan of Mathmos products and a special thank you is extended to customer service team for providing the article image at lightning speed.

Resources	(In Alphabetical Order)
Chartbeat	chartbeat.com
Clicky	getclicky.com
Forbes	http://www.forbes.com/
Google Analytics	real-time beta
GoSquared	http://www.gosquared.com/
New York Time	http://www.nytimes.com/
Mathmos	http://www.mathmos.com/
Mixpanel	http://www.mixpanel.com/
Quora	http://www.quora.com/
Reiss	http://www.reiss.com/
Stylistpick	http://www.stylistpick.com/
Woopra	http://www.woopra.com/

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Image courtesy of Mathmos

10 Reasons Why Web Analytics Sucks Big Time

Alex Brown — Mon, 13 Aug 2012 10:39:58 +0000

A Guide To Prevent Web Analytics Failure

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Executive sponsors have become increasingly frustrated that web analytics initiatives fail to deliver any real business insight.

If left unchecked, this sentiment can lead to the reduction or, reallocation of budget and marks the end of hope for a resident web analytics professional.

So, how does this situation arise and what can be done to prevent it?

Common Web Analytics Pitfalls

1. Top down, not bottom up

More often than not, web analytics failure begins during the adoption process when the initiative is being driven from the bottom up. Even if deployment is successful, this approach nearly always results in siloed data usage by Marketing, Design or, I.T. departments and culminates with pointless debates during board meetings about conflicting data sources.

Get CEO support for Web Analytics from the start and demonstrate continual "value-add" by linking outputs back to company objectives that have a direct commercial benefit.
Ensure that an inclusive requirements gathering process is undertaken that allows different stakeholders the opportunity to contribute providing advise and education where appropriate.

Steve Jackson's Cult of Analytics is a great place to start learning more about how to build strong foundations.

2. Integration across the nation

Just having the “big cheese” buy-in to the web analytics program is not sufficient for the initiative to deliver value. Instead, the function needs to be integrated across the business to succeed. However, exercise caution not to align any one business unit too closely as the focus of web analytics resource can become marginalised and narrow in scope.

Set up a data governance team to oversee the direction of web analytics product life cycle. Safe guard against one department running the show.
Position web analytics at the heart of the site by making the team responsible for change accountable for results measurement.

More recommendations on web analytics positioning and governance are available from Adobe Omniture's white paper available to download here!

3. Talent, tools and training (in that order)

"We bought a Ferrari but, only use it to go to the end of the driveway and back,” remarked one head of marketing for a global clothing manufacturer recently. After a frustrating nine months in web analytics, the lessons learned were straight forward enough but, ultimately proved quite costly for the business.

Hire talented people to select and use the tools during early adoption and don't be afraid to get outside help provided they accelerate learning and foster long term self-sufficiency.

Prove the business case for web analytics investment using “freemium” tools before splashing out on a “best-in-class” product.

Ensure internal resources keep up with the ever changing digital measurement landscape. Attending conferences and training courses are mandatory for employee development and retention. Set aside budget as part of the early scoping business case.

“The human skills needed to bring true value to an enterprise from web analytics are scarce, but they are more important than the technology involved.” Frank Buytendijk & Astrid Van Dorst, Gartner Group, April 2001

4. Measure everything and you wont find out anything

Ask a business starting out in online measurement what they want to track and it's fairly common to get the response, “Everything!” in reply. Resist the temptation to track everything and instead answer one question to define tracking requirements…

Why does the web site exist?

The overly simplistic question usually sparks internal debate about company objectives and helps to build out a measurement framework that can be used to inform tracking implementation. Create a measurement framework for your business using design principles and kpi definitions from Avinash Kaushik.

5. Manage ecosystem evolution

Introducing web statistics into an established business data ecosystem is problematic. The reports seldom match figures from other business intelligence tools and nuances in data collection methodology can easily undermine efforts to establish credibility amongst internal stakeholders.

To ensure long term web analytics success, follows a few simple steps…

Do not circulate reports too widely after initial deployment and instead focus on reconciliation activity for a small number of key metrics.

Build a data dictionary to describe each key web metric and highlight the difference in data collection method between other tools using similar metric descriptions (e.g. “orders”). Use it to educate the business and start in the board room.

Restrict access to reporting (at least initially). The number and variety of reports in most web analytics tools is enormous. Customise reports available in the platform interface to ensure users focus on the important stuff.

Target and train web analytics “champions” for each business unit. These people will act as knowledge satellites evangelists and hopefully act as a buffer from requests that come in from around the business.

Start any reconciliation exercise with the six sensible steps suggested by June Dershewitz and when constructing a data dictionary for the business have a look online for examples in your industry sector.

6. Web analytics just isn't enough

Don't be surprised if using web analytics alone is not enough to produce the step change in performance expected by the business. Statistics are great at explaining what happened but, crucially not why it happened. For best results, ccompliment web measurement with customer experience and observational analysis.

Seek opinions from a wider audience during initial design phases with focus groups, online user testing services or even eye-tracking (e.g FiveSecondTest, Gazehawk, etc).

Use specialist tools to observe micro conversions, click activity and in-page site interaction (e.g. ClickTale, CrazyEgg, etc).

Experiment by showing different versions of site content to improve conversion with Testing software (e.g. Visual Website Optimiser, Optimizely, etc).

Add a short site survey to allow visitors to give immediate and unbiased feedback (e.g. Qualaroo, Kampyle, etc)

There are countless methods and tools available to obtain a much more rounded view of new site visitors' and existing customers' behaviour than traditional web analytics data provides alone. Ask us for a recommendation .

7. Avoid the data monkey trap

Shocking news! Most web analysts don't actually analyse anything. Instead companies pay these resources to pull reports and troubleshoot technical issues. So perhaps a change of job description is required. To avoid the "data monkey" trap try the following approach:

Allocate a maximum of 20% bandwidth to ad hoc reporting and have this agreed by the boss - And yes, that's just one day a week.
Setup a ticketing system and to prioritise incoming requests, monitor service levels and track how much resource is spent reporting. Jira is a popular approach for many businesses
Identify the root cause behind reporting requests and don't blindly agree to deliver them - Easier said than done.
Automate at least 80% of standard reports using the web analytics email scheduling tools or, by building pre-populated reports using API's. Most vendors have their own solution and their are really good third party ones like Omniscope and Excellent Analytics
Write a business case and secure budget to help support reporting activities using graduates or, outsourcing companies - Don't wait until it get overwhelmed
Train people on how to pull reports themselves - Its all about self-service these days

Interested in learning how other companies use web analytics resources? Visit Econsultancy who in partnership with Lynchpin published some interesting recent survey results.

8. Don't expect return-on-investment without change

The ultimate acid test and competitive advantage for a data driven company is generated by their ability to implement change based on insights generated from web analysis. Therefore, it goes without saying that return-on-investment can never be achieved until the development road map allows for sufficient resource to deploy code changes and implement recommendations in a timely fashion.

Build robust financial business cases to support web analytics code development
Promote findings internal and petition stakeholders to support further development resource to implement recommendations
Consider tag management solutions as a flexible alternative for web analytics code development

9. Crash test, dummies

Never release web analytics code changes onto Live site without thoroughly testing it first on a Test environment. Failure to adhere to basic quality assurance procedures can have devastating short term effects on data quality and generate longer term trust issues with internal stakeholders. Each Site or situation may require a slightly different approach. We perform the majority of single page audits with Charles and undertake large automated Site-wide audits using ObservePoint. For cookie specific audits and competitor technology information we recommend Pikslme.

10. Responsive isn't just about web design

The internet is constantly changing and measurement techniques used to track it continually evolve at the same lightning pace. Stay sharp and find out how the latest technological development can help improve the existing web analytics program. Put sufficient time and resource into keeping on top of industry developments and in practical terms this means:

Budget for at least two of the main web analytics conferences a year. Semphonic’s Xchange and Adobe-Omniture's Summit come highly recommended.
Reserve thirty minutes each day to read blogs, articles and other technology news. To save time, set up RSS feeds and have content delivered directly and don't forget there are also great pod casts so that you can learn while you listen. A favourite of ours is found at Beyond Web Analytics

So that completes the top ten gotcha's to avoid when managing a web analytics initiative. Thanks for reading this article and now it's your turn to share.

Web Analytics Sucks: Who's to blame?

We would love to hear your opinion in the comments section below so don't be shy.

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About the Author

Related Products & Services

With in-depth knowledge of both the market place and major technology vendors, allow us to help select, negotiate, support and manage web analytics implementation for your business. Contact Us for a confidential and informal discussion to see how our solutions can improve your business today.

Resources	(In Alphabetical Order)
Adobe Summit	Web Analytics Conference
Audit software	ObservePoint
Automated Reporting	Excellent Analytics
Automated Reporting	Omniscope
Cookie Auditing	Piksleme
Click Analytics	CrazyEgg
Cult of Analytics	Steve Jackson
Customer Experience Analytics	ClickTale
Customer Feedback	Kampyle
Customer Feedback	Qualroo
Data Dictionary Example	Office of Disability and Adjudication Review
Data Governance	Omniture White Paper
Defining KPI's	Avinash Kausik
Eye Tracking	Gazehawk
Measurement Model	Avinash Kausik
Online Usability Testing	FiveSecondTest
Preferred debugging tool	Charles
Podcasts	Beyond Web Analytics
Reconciliation how to guide	June Dershewitz
Survey Results	Econsultancy
Testing	Optimizely
Testing	Visual Website Optimiser
Ticketing Systems	Jira
Xchange Analytics Conference	Semphonics

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Image courtesy of M Bartosch / Freedigitalphotos.net

Mobile Analytics - Evolution or Revolution?

Alex Brown — Tue, 19 Jun 2012 09:59:54 +0000

Apt Measurement For Mobile

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Measuring mobile has never been straight forward and at times felt a little painful especially during implementation and occasionally during analysis with a partial dataset compared to it's web based equivalent.

However, the mobile analytics landscape is about to change for good with the launch of a new product by ClickTale who specialist in customer experience analytics.

In the past, leveraging ClickTale’s web based technology offering for different Clients generated some amazing insights into user behaviour and their new mobile proposition promises to follow suit allowing businesses to view their mobile customers’ true-to-life browsing experience at all levels of detail, from aggregated views to playable videos of individual sessions. An invite to the beta can be requested at clicktale.com/mobile.

During the early days of mobile site development, traditional digital analytics technology vendors (Omniture, Coremetrics, Webtrends) raced to shoe-horn their web tracking platforms into the mobile space. Over time mobile specific analytics technology providers surfaced (Flurry, Bango, Kontagent) with a clear focus on mobile site and app measurement. These combined elements provided the digital analyst with good information about site navigation, custom event tracking and the ability to undertake cohort analysis.

However, the behavioural tracking goal posts shifted with the launch of iPhone in mid-2007 and the introduction of touch screen and gesture based page interaction. Five years later and the first widely available measurement solution has appeared to provide an understanding of these complex user behaviours.

With all this talk of “true-to-life” browsing measurement and video replay for mobile it is easy to get carried away and think that old mobile reports can be dispensed with. However, previous experience has shown that such technology is best leveraged when used in parallel with traditional web analytics tracking. The latter provides an understanding of “what” happened whilst, the former helps explain “why” it happened. Both play a crucial part in understanding site behaviour and identifying key areas to improve site conversion.

Interested in learning more?

Watch a short preview youtube video or akternatively, request an beta invite clicktale.com

Thanks for reading this article and now it's your turn to share.

ClickTale Mobile™: Analytics Evolution or Revolution?

We would love to hear your opinion in the feedback section below so don't be shy.

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About the Author

Alex Brown is a Digital Analytics and Site Optimisation expert who works as an independent freelance consultant. The opinions shared in this Blog are based on personal experiences gathered over a decade of data crunching and technology evaluation. The author makes no attempt to be grammatically, politically (or otherwise) correct. Spelling was never a strong point and for practical reasons, not all vendors in the market are referenced in the article - no hard feelings.

Related Products & Services

With extensive in-depth knowledge of both the market place and major technology vendors, allow us to help select, negotiate, support and manage mobile analytics for your business. Contact Us for an informal discussion and see how our solutions can improve your business today.

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Image courtesy of Graur Codrin / Freedigitalphotos.net

13 Golden Rules For Successful Tag Management Solution Adoption

Alex Brown — Mon, 07 May 2012 00:00:00 +0000

A Tag Management Solution Checklist

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Over a number of years we've helped large multi-national eCommerce companies to implement, fine tune and leverage different Tag Management Solutions (TMS).

This article attempts to consolidate key learnings for organisations who are considering adoption and also build upon recently published work by notable commentators like Stéphane Hamel.

The Golden Rules

1. One size does not fit all.

TMS platforms allow for a simplified deployment using one snippet of code but, adding one “tag” across the Site isn't quite how it works. If a Site contains static (http) and secure (https) pages or, includes dynamic content than the chances are that different variations of code will be needed. Allocate resource and set aside time to investigate Site content and perform an inventory of existing tracking technologies. This will speed up deployment and mitigate against the proliferations of tag variants. If the task of auditing the Site seems overwhelming, consider asking an organisation like Pikslme to help. During the implementation consider using an independent monitoring software like ObservePoint to ensure tags work as intended.

2. Change is inevitable - be prepared.

The business release process is guaranteed to change when adopting a TMS and during the design phase think about ownership and governance of data too. Is it Marketing or I.T. that hold the platform keys?

Similarly, consider which part of the organisation will be responsible for development and on-going costs. What about expertise? Should the business develop in-house technical expertise of the TMS platform or be reliant on a knowledgeable third party be it the Vendor or Agency. How much training is provided if an independent route is preferred?

What about external changes? Using a TMS can also impact relationships with existing Partners. Using attribution methods provided by some TMS vendors allows for a revised assessment of channel performance. Sit down with the third parties used for marketing acquisition as early as possible and explain that reward, recognition and commercials may change. Remember to be nice to those poor Affiliates!

3. ROI calculator rules, ok.

Calculate a true ROI and manage expectations within the business according. Ask the TMS vendor for a full declaration of service costs and build the business case for adoption. Start by estimating the financial impact from faster page load times and more efficient marketing budget allocation but, don't forget to establish all the associated TMS usage costs.

Pixel tracking, redirect services, 3^rd Party Vendor support and (in some cases) data extraction and storage all have a knock-on impact in terms of cost. A popular TMS vendor called, Ensighten also provide a nice Excel ROI Calculator template to get you started.

4. Avoid the 'emperors new clothes' trap.

Its easy to be wooed by a technology that promises so much - resist that temptation. Don't scrimp on research during the discovery phase. Shortlist a maximum of three TMS vendors that can exceed internal requirements gather from a cross-section of business stakeholders. Decide whether a hosted solution is needed or, not and in those preliminary meetings, dig beneath the buzz words and rhetoric to establish if the vendor technology is platform compatible, robust, secure and scalable. Ask for a Proof of Concept (Poc) and put the vendors to the test. A popular TMS vendor called, BrightTag has a good introductory article on tagging for starters.
Also worth reading is an overview on TMS by Forrester.

5. Trust the sales people…only kidding.

During pitch time, don't be surprised if all the answers from across the table start with the word, “Yes”. Make sure the IT team are in the room to probe on Technical capabilities. This may be new technology to them too but, the underlying principles about how it works should be stress tested. Ask the vendor to set up a conversation with an existing happy customer and be cheeky by requesting to talk to an ex-customer who hasn't renewed.

6. Establish who ultimately owns third party tag testing.

Tag Management Solutions are working with established marketing campaign tracking companies to build relationships and partnerships. However, don't be surprised if some third party providers choose not to support testing activities if their tags are hosted inside a TMS container and not natively on the page. Get the I.T. department to help out or a knowledgeable third party.

7. Understand data accessibility.

The price of data storage has been slowing coming down over the years and some TMS vendors are moving service into the cloud. Besides cost, other benefits include scalability and reliability but, there are some important considerations. Is your business data easy to access, how long is data stored for and what is the cost of exporting it raw (if required). Be sure to ask about privacy and cookie laws too if within the EU. A popular TMS vendor called, TagMan provide a good summary of considerations that have been detailed in this ePrivacy info sheet.

8. Crash test, dummies!

Sadly, on more than one occasion a company has chose to ignore advice and released new code using a TMS vendor without prior testing on a Development environment to the detriment of Site performance and user experience. Using a LIVE environment to test new code is only ever going to end one way - it will hurt sales or, even the company's reputation… eventually. Resist the temptation or business pressure to deploy quickly and never push code out before the weekend, peak period or a national holiday.

9. Be prepared to re-implement site analytics...argh!

Hosting Site Analytics tracking code within a TMS is perhaps one of the greatest uses of this type of technology. It is important to understand if the TMS has in-house technical experts for your particular Site Analytics Tool(s). Often a complete re-implementation is required and support from the Analytics vendor may not be free. Make sure to ask up front about any charges resulting from additional consulting hours. If your company is data driven, highly focused on measurement and has a sophisticated Site Analytics implementation, look for a TMS vendor who has a proven track record in this field. Mistakes will impact data quality and confidence in the numbers otherwise.

10. Mitigate against 'chinese whispers' style deployment.

During implementation, a TMS vendor will request tracking tags that currently exists on Site to be added within their system. Make it a habit of requesting a copy of the exact code once it has been inserted. On a number of occasions, vendor engineers changed or removed key components the tracking code that rendered them useless. Check and check again to avoid disappointment.

11. Think 'russian dolls' when tagging.

The cornerstone to any successful TMS deployment is that the setup mirrors established organisational structures, taxonomies and marketing campaign naming conversions. For a global company, it might be easy to deploy one “tag” Site wide but, there could be limitations in terms of restricting views for the data collected. Be sure to ask how regional, country or, product data siloes can be achieved (if this is important to the business).

Similarly, when marketing campaign tracking is set up within a TMS vendor, ensure that it at least reflects current activity and naming conventions. If a format does not exist already, follow the Russian Doll rule - start big and end small. For example, the top of a tag hierarchy might start with the broadest element like Geographic Region (e.g. EMEA) and end at the lower hierarchy level with the most minuscule like Campaign Creative (e.g. Promo_Banner_160x600). Performing this exercise when setting up campaigns within a TMS will pay dividends later on as the business grows. For more information on campaign naming conventions, read this post over at E-Nor for a most informative and coherent explanation.

12. Marketing retribution

Tag Management Solutions are in a unique position to provide accurate measurement of marketing performance across disparate media to enable channel attribution evaluation. However, there is a bit of work required to get there. The methods used to detect various referral sources include a mix of; i) landing page detection rules, ii) pixel tracking and iii) click re-direct services. Therefore, in some cases it will be essential to re-deploy existing marketing campaigns with revised tracking links. Don't under estimate how resource intensive this process is and work with channel Partners or us to ease the burden.

13. Get in sync

The way that Site pages retrieve objects impacts the speed at which the page loads. A number of TMS now offer various methods to minimise the weight of their tags including a approach called, asynchronous tracking. Be sure to ask the TMS vendor how their tags work and what impact they have on Site performance. A popular TMS vendor called, Tealium who was an early adopter for asynchronous tracking has an easy to understand explanation of how these tags works article.

Thanks for reading this article and now it's your turn to share.

Tag Management Solutions: Panacea or, Placebo?

We would love to hear your opinion in the comments section below so go ahead and don't be shy.

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About the Author

Related Products & Services

With extensive in-depth knowledge of both the market place and major technology vendors, allow us to help select, negotiate, support and manage TMS development for your business. Contact Us for an informal discussion to see how our solutions can improve your business today.

Resources	(In Alphabetical Order)
BrightTag	101 Guide to Tagging
E-Nor	Campaign Naming Conventions
Ensighten	ROI Excel Calculator for TMS
Forrester	Tag Management Overview
ObservePoint	SiteAudit
Pikslme	Site Cookie Auditing
Stéphane Hemal	Selecting a Tag Management Solutions
TagMan	ePrivacy Info sheet
Tealium	Asynchronous tags

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Image courtesy of Digitalart / Freedigitalphotos.net

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An SEO Booster Kit For Google Analytics

Don’t Invest In Real Time Web Analytics - Buy A Lava Lamp Instead!

Real Time Analytics - Love it or Lamp it?

10 Reasons Why Web Analytics Sucks Big Time

A Guide To Prevent Web Analytics Failure

Mobile Analytics - Evolution or Revolution?

Apt Measurement For Mobile

13 Golden Rules For Successful Tag Management Solution Adoption

A Tag Management Solution Checklist