Tuesday, September 17, 2013

Google Algorithm Update Status | Syed Alaudeen | Digital Marketing Consultant

In 2005 Google published its “Web Authoring Statistics” report, which provided a unique insight into how a large search engine views the Web at the very basic HTML level.

In August 2009 Matt Cutts invited Webmasters to help test a new indexing technology that Google had dubbed Caffeine. The SEO community immediately fell to rampant speculation about how Caffeine would affect rankings (in fact, the only effect was unintentional).

By February 2010 even I had fallen prey to Caffeine Speculationitis. On February 25, 2010 Matt McGee confirmed that Google had not yet implemented the Caffeine technology on more than 1 data center (at this time, in April 2013, there are only 13 Google Data Centers around the world).

On June 8, 2010 Google announced the completion of rolling out its Caffeine indexing technology. Caffeine gave Google the ability to index more of the Web at a faster rate than ever before. This larger, faster indexing technology invariably changed search results because all the newly discovered content was changing the search engine’s frame of reference for millions of queries.

On November 11, 2010 Matt Cutts said that Google might use as many as 50 variations for some of its 200+ ranking signals, a point that Danny Sullivan used to extrapolate a potential 10,000 “signals” Google might use in its algorithm.

On February 24, 2011 Google announced the release of its first Panda algorithm iteration into the index.

On March 2, 2011 Google asked Webmasters to share URLs of sites they believed should not have been downgraded by Panda. The discussion went on for many months and the thread is more than 1000 posts long. Google engineers occasionally confirmed throughout 2011 that they were still watching the discussion and collecting more information.The next day Wired published an interview with Amit Singhal and Matt Cutts (see below).

On May 6, 2011 Amit Singhal published 23 questions that drew much criticism from frustrated Web marketers. The angry mobs did not understand the context in which the questions should be used.

On June 21, 2011 Danny Sullivan suggested that Panda may be a ranking factor more than just a filter (a view that I and others had also come to hold by that time, but Danny was the first to suggest this publicly).

In mid-March 2013 Google announced that the Panda algorithm had been “incorporated into our indexing process”, meaning it was now essentially running on autopilot. Between February 24, 2011 and March 15, 2013 there were more than 20 confirmed and suspected “iterations” of the Panda algorithm that changed the search results for millions of queries.

Friday, August 10, 2012

Google Analytics Segmentations Using User Defined Tracking | Custom variable user defined variable tutorials | Web analytics Consultant Chennai


The Google Analytics’ user defined report allows analyst to compare visitors from segments that you have defined. I will go through several types of segmentations that you could possibly set. You can define these segments by calling a line of code/function in your web page. So every time a page with the code is requested, a custom value is captured and stored in the user defined variable.
The main approach to execute this is to simply apply a code like this:

pageTracker._setVar(“test_value”);
As an example, one of the sites I work on has English and Japanese sections. Each section will have a value to identify its section, defined as “/viewed/english” or “/viewed/japanese”.
Once you have these segmentations in place, you’ll be able to see how users are behaving differently in each section.

Visitor Type Segmentation
This is a powerful segmentation to get an actionable insight to who is behaving differently. Let’s say you have a form entry that leads to a confirmation page. Assuming the form has a field with values “Engineer”, “Project Manager” and “Director”, and when the form is completed, one these values will be stored into user defined variable.

At certain point, you may learn that Directors are likely to complete the form and convert. This will tell you something about your visitor that you didn’t know.

Landing Page Segmentation

You may have custom landing pages to serve different campaigns or promotions. Identifying the landing pages through user defined report would be a powerful method to analyze effective landing pages or even its campaign effectiveness.
(Example 1) Say you have two different direct mails with different slogan or description. You can have two different friendly URLs set in each of the direct mail. Direct mail A would have a landing page A, and landing page B for direct mail B. When traffic and performance for landing page B performed better than landing page A, that could mean that the direct mail B’s strategy was more effective than version A.
(Example 2) You can have one campaign with a landing page, but a page can receive traffic from other various sources. Setting two different landing pages and segmenting it through user defined variables can show you which landing page is more effective while testing various traffic sources.

A/B Test
Above examples can speak as an example for A/B test. Another example to perform A/B test is testing different call to actions (CTA). Say you have two links; one with an image link and other as a simple text link.
You may distinguish these different CTAs (or possibly link type, position, etc.), and store it into user defined report to assess which criteria performed better.

Referrer Segmentation
Google analytics has a sophisticated campaign tracking method. However, you can choose to use user defined report to parse certain attribute within URLs and apply it to the report. One possible example of such application would be an existing links with identifier in the URL (not compliant to Google’s campaign tracking), where the links are located in two different sites.
Site abc.com with a link “yoursite.com?source=123″
Site xyz.com with a link “yoursite.com?source=456″
Your landing page could parse these source codes and allocate proper value to a segment. Therefore you should be able to assess the referrers’ performance and its effectiveness.

Friday, July 27, 2012

Google Webmaster Tools Index Status | Google Webmaster Tools Tutorial | Syed Alaudeen | Digital Marketing Consultant Chennai

Google web master tool will now let you know how many pages of your website are currently included in Google’s index. The new feature can be found by visiting the Health menu. Once there, you’ll find a graph with a year’s worth of data
.
google-wmt-index-status
The Basic tab show the cumulative total of all URLs on your site crawled by Google, though not all crawled URLs get indexed.
If you need to go deeper to identify potential crawling or indexing issues, you can check out the Advanced tab to see how many pages Google:
  • Has crawled.
  • Knows about but can't crawl because it is blocked by your robots.txt file.
  • Has not included in its search results, either because they are "substantially similar" to other pages or they have been redirected to another URL.
google-wmt-index-status-advanced-tab
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Tuesday, July 24, 2012

Google analytics Filters | Google analyticsFilter Type | Syed alaudeen | Online marketing consultant chennai

Attention all Google Analytics users around the world: you don’t have to be an expert in regular expressions to use filters. Why? Because this post will help you, that’s why!

No long and drawn-out lead-in to the story this time – here are 5 filters that you can create for your Google Analytics profile(s) that will tidy up your data and make you a happier analyst.

1. Excluding your own traffic from reports

Why: Chances are that your own visits to your own web site aren’t racking up that many visits and page views. Nonetheless, you can still permanently remove your own traffic statistics from appearing in your Google Analytics profile(s).

How: First, grab your IP address from whatismyip.com (or, ask an IT person). If you have administrative access to your account, click on your account’s name, then click on your web property’s name. Next, click on the filters sub-tab (within the profiles tab), click on “Add Filter“, and do the following:

Method: Create New Filter
Filter Name: Exclude my IP Address
Filter Type: Custom Filter >> Exclude
Filter Field: Visitor IP Address
Filter Pattern: ^192\.168\.25\.25$
Case Sensitive: No

Replace the IP address in the example above with your own IP address, but leave the ^, the $, and the three \ symbols (just replace the numbers). Click Save, and you’re done!

2. Lowercasing your hostnames
Why: A hostname is a domain that has sent you visitor data. In other words, a hostname is a URL where your Google Analytics tracking code is present and has at least sent you 1 visit during the selected date-range that you’re looking at. If you ever toggle your report dimension by hostname, or switch the viewing table to show hostnames, you could see mixed cases (upper and lower), which leads to many different variations of your same domain name appearing. That also means you need to work on your SEO re-directs – but that’s something for another time.
How: Go through the same steps as you did in the last filter to get to the filter creation screen. Once there, do this:

Method: Create New Filter
Filter Name: Lowercase Hostnames
Filter Type: Custom Filter >> Lowercase
Filter Field: Hostname

Click Save, and you’re done! You can also create additional lowercase filters to do the same thing to other pieces of data that may look unsightly (one of them might be the Request URI filter field, which represents everything after the .com part of your URL).

3. Search for long, bulky page name; Replace with short, clean page name.
Why: Page names can get long and bulky. There’s probably an important page in your top ten that’s just an eye-sore. How about we shorten it and clean it up some?
How: Follow these filter creation steps – but remember to change the page names to your own, as the following is just an example:

Method: Create New Filter
Filter Name: Search & Replace: Long page with “/john.php”
Filter Type: Custom Filter >> Search and Replace
Filter Field: Request URI
Search String: /your-very-long-and-bulky-page.php?id=1234567
Replace String: /john.php
Case Sensitive: No

4. Add the visitor’s browser to the visitor’s operating system
Why: Why not? Google Analytics lets you create some powerful, advanced filters that let you do something cool (and efficient) like adding the visitor’s browser to the operating system that they’re using. This way, you can see a visitor’s browser along side a visitor’s operating system, without having to apply a secondary dimension (saving your secondary dimension option for something else).
How: Here’s how you do it:

Method: Create New Filter
Filter Name: Operating System + Browser Platform
Filter Type: Custom Filter >> Advanced
Field A -> Extract A: Visitor Operating System Platform -> (.*)
Field B -> Extract B: Visitor Browser Program -> (.*)
Output To -> Constructor: Visitor Operating System Platform -> $A1 – $B1
Field A Required: Yes
Field B Required: No
Override Output Field: Yes
Case Sensitive: No

For Field A and Field B, choose the filter field as described, and then in the blank form field, type in (.*) as shown.

5. Include your domain (and, ONLY your domain!)
Why: Unfortunately, server caching and having your tracking code outright stolen and placed on someone else’s web site is something that we sometimes have to deal with. So, from time to time, you must write a filter that will prohibit the collection of data from every domain except for your own web site.
How: Create your include filter like this:

Method: Create New Filter
Filter Name: Include my domain
Filter Type: Custom Filter >> Include
Filter Field: Hostname
Filter Pattern: mywebsite\.com$
Case Sensitive: No

Click Save to stop the nefarious ones from sending you irrelevant data!

We could write about filters until the next Presidential election, because there is just so much on the topic, and, so many different things that you can do with filters. Even though you can copy the steps outlined in the above 5 filters directly, I still urge you to use caution. Filters are sensitive, temperamental, and must be precise, to say the very least. A poorly-created filter can cause permanent damage, so tread lightly.

What about you? What filters do you like to use? What problems are you experiencing? We’d love to hear your thoughts below!

Thursday, April 26, 2012

A Social Media Dashboard for Google Analytics | Syed Alaudeen | Online marketing consultant Chennai


Social Dimensions & Metrics: Foundation for the Dashboard

To build a social dashboard you use the social dimensions and metrics. These are the same dimensions and metrics that generate the Google Analytics social reports. Here’s a quick overview:

Data Hub Activities: The social data hub is an open data collection platform. Any social network can send their social activity to Google Analytics. This metric is the total data hub activities for a given site.

Social Network: This dimension is a list of all the social networks that drive traffic to a site. These networks are automatically identified by Google Analytics.

Social Source Referral: This is a simple flag that indicates if the traffic source is from a social traffic source. This dimension is very useful if you want to create a widget that just contains data for social media.

Social Source & Action: This dimension is the name of a social network and an action that is specific to that social network. This track social sharing ON a site. GA will automatically track social interaction with Google + tools but needs to be configured to track other social sharing tools.

Social Entity: This is a URL that shared via social media. It’s any URL from your site.

Social Type: This is a simple boolean that indicates if a visitor is socially engaged, meaning they used a social sharing tool on your site. GA will automatically track social interaction with Google + tools but needs to be configured to track other social sharing tools.

The Social Media Dashboard

I’ve divided the dashboard into three sections: Off-site activity, On-site activity and Conversions/Outcomes. This makes it easy to evaluate user activity throughout the conversion process.
Feel free to download the Social Media Dashboard for Google Analytics and customize it.

Google Analytics Social Media Dashboard
It's easy to create a social media dashboard in Google Analytics.

Offsite Activities

This section is about what happens off of the site and some of the attributes of traffic that comes from social.
First is some basic context: total visits to the site. This puts all of the social data into context. You can quickly gauge when looking at a widget if social is a large or small percentage.
Next I wanted to get an idea of new traffic from social. So I included the % New Visits metric segmented for traffic from Social. When looking at this metric it’s a good idea to remember your social strategy. Are you trying to attract a new audience from social or trying to bring people back to the site? Your strategy will drive the context for this metric.

%New Traffic from Social Media

%New Traffic from Social Media

Now a widget to trend traffic and bounce rate from social. Here I can see how traffic from social changes over time. And we can use the total number of visits to the site to put this data into context. I also have bounce rate in this widget to gauge the quality of the social traffic. Do these people stick around or take off quickly?
Traffic and bounce rate from social sources.


A trend of traffic and bounce rate from social sources. How much traffic do you get from social and does it engage with your site?

The next widget is a plot of Social Data Hub Activities and Site Visits. I like this plot of offsite activities and site traffic. It’s a quick way to identify if any offsite actions resulted in traffic to the site. Many times with social media the activity happens somewhere else and there is no impact on the site.

Remember, this is activity from the social data hub partners, not the entire world of social media.

Offsite social activities vs. site traffic.
A plot of Google Analytics Data Hub Activities vs. Site Traffic. Is there any correlation?

Now let’s get a bit more specific about which social sources are driving traffic to the site. The final widget in this section is the top social sources based on their traffic. This is a classic segmentation of source. And I’m using bounce rate as a gauge to determine if people stick around or leave immediately.

Traffic from various social networks.
This widget lists traffic from the most popular social networks. It also uses bounce rate an a gauge of quality from each network.

On-site Social Activities

Moving on to on-site activities we can include a number of things. Onsite activity is about what content people are interacting looking at and content that people might share using some type of social button (Google +1, Tweet, Like, etc.) This is a good way to understand which social networks people like to share content on.
Onsite social actions.
A widget that measures on-site social actions. This shows how people are sharing content on your site.

I also like the value of visitors that are socially engaged. This segment of traffic is those that perform some type of social activity, like share using a tweet but or +1 button. I think a lot of people are trying to increase the sharability of their content. It leads to more traffic and, hopefully, more conversions. I would look for this metric to increase over time, depending on the tools that you give your visitors to share content.

Value of socially engaged traffic.
Are those that engage socially on a site worth more? If they are, can you somehow increase social activity?

Another way to look at social sharing is to focus on which content people share. And we can do that using a widget with the Social Entity dimension. I find that it’s important to consider how you are promoting content when looking at this widget. It may be that you are constantly promoting certain content.

Most socially shared content.
Which content gets shared on social media? This Google Analytics widget is a list of pages that get's shared on social media.

Another widget is the social traffic segmented by mobile device. Social and mobile are intimately connected. So much social content is consumed on various mobile devices. The goal of this widget is to get an understanding of which devices are popular with social users.

Social traffic from mobile devices.
I like to view social traffic based on mobile devices. Is one device more popular than another? Are certain social actions popular on certain devices?

Outcomes & Conversions from Social Media

The last group of metrics focus on the outcomes from social. It focuses on goals and ecommerce (if you’re an ecommerce site). This is where you’ll probably need to adjust some of the widgets based on your goal configuration.

It starts with the value of traffic from social. I like the Per Visit Value metric. It’s a good way to compare the economic impact of different sources of traffic. It’s a single number that puts a value on traffic from different places. Some good context for this metric is the amount of effort (i.e. time and money) you spend to generate traffic from social. Do you employ a “social media guru?” If so, how much do you pay them, and how does this translate into revenue?

Per-visit-value of traffic from social media.
Measuring the per-visit-value provides an easy way to compare the value of different traffic source. How does social media compare to other sources for your site?

Now revenue! Here’s a simple widget with the revenue from various social sources and the per visit value for each source. Keep in mind which social networks you are focused on and the effort you put into each.

Revenue from Social Sources
Tracking revenue from your social sources is critical. Are you getting a return on your investment?

NOTE: The one thing that I wish I could add to the dashboard is the Assisted Conversions metric for social. So often social media influences conversions higher up in the funnel. Unfortunately you can’t add the Assisted Conversions metric to the dashboard.


Now for more outcomes: conversion rate for various social sources of traffic. Remember, you’ll need to configure this widget to reflect your specific goal configuration. And you can certainly add more widgets for your various conversion activities. I’m just measuring the conversion rate for people reading an article.

Conversion rate for various social sources.
Here's the conversion rate for various social sources. You can change this widget based on your goal configuration.
What would you put on a Google Analytics social media dashboard?

Remember, this is a shared dashboard, so you can add it to your Google Analytics account. You can keep it as-is or modify it to meet your needs.

3 Analytics Tips for Agencies | Syed Alaudeen | SEO Consultant Chennai


3 Analytics Tips for Agencies

Top 3 Mobile tips

With the explosion in mobile, there was no doubt that it needed to be one of the main topics of the conversation. Eric Feinberg, from Foresee, stated the obvious mobile tip, “Get in or Die!” and delivered these great, forward thinking tips on mobile.

1) Be visual and branded! – Be visual with mobile user experience and maintain your branding. Don’t bore and don’t be afraid to challenge convention. Then again, don’t forget to ensure you at least provide a basic mobile optimized experience. Don’t blow it like Tylenol and Trident Gum.

Mobile Don’t! Tylenol’s Mobile Experience Disaster. It requires Flash.
Tylenol Mobile Experience Disaster
Mobile Don’t! Trident Gum’s Mobile Experience Fails Big Time. It requires Flash.
Trident Gum Mobile Experience Fails Big Time
Mobile Do! Expedia delivers Great Mobile Experience. Simple and great iconography.
Expedia Great Mobile Experience
Mobile Do! Clearly, Kayak put a lot of thought into delivering an optimum mobile experience.
Kayak Great Mobile Experience
2) Align with and understand mobile intent – Believe it or not, even the most trivial products are searched online and users want information relevant to their mobile intent.

Consider the scenario where a tired mother is at the store shelf around 11pm and needs simple to access information in order to select the proper medicine for her for sick, crying child. When you understand that this mobile intent exists, you can create a more useful experience for users searching on the go at the store shelf.

If you deliver, you will make a positive impression on your audience that they won’t soon forget!


3) Provide a custom experience for each device – Great advice, but I disagree that one should create physically separate sites on different domains. This is resource intensive, cost intensive and dilutes SEO authority.
This can be done better with Responsive Web Design which drastically reduces costs, simplifies maintenance and improves SEO authority by consolidating all authority on one domain.