What comes to your mind when you hear the word Analytics? What exactly does it mean? How it is that the Web Analytics is done & why use it? What for & to what Capacity is it used?

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Web Analytics - Get to know of complete
Methodology
What comes to your mind when you hear the word Analytics?
What exactly does it mean?
How it is that the Web Analytics is done & why use it?
What for & to what Capacity is it used?
If you found yourself asking any of these questions at any point in the Past or if these questions get you curious
or even remotely interested in, this piece or Article is definitely meant for you.
Let us start from the basics, the first thing we do to understand any new Concept is we get familiarized with the
term. So that's what we are going to do just about now.
Web Analytics is the Measurement, Collection, Analysis and Reporting of web data for purposes of
understanding and optimizing web usage.
We all know or have heard at a point that the Majority usage & Potential of the Data Analytics is growing at an
unbelievably fast pace in E-Commerce. So to give you the idea, it is safe to say that-In a commercial context
Web Analytics refers to the use of data collected from a web site to determine which aspects of the website
achieve the business objectives.
To simplify it further I give you My Personal Favorite i.e. - "Study of the impact of a website on its users &
their behavior." The Reason for this definition being my favorite is its simplicity & the connection it establishes
with the End User.
Web analytics is not just a process for measuring web traffic; it's more than mere the number of visitors to a
website and the number of page views. But it is the process of identifying key business scenarios & the trends,
then to use that information to meet the end Goals i.e. often related to the end User's behavior (Browsing
habits).
In this Post I will be connecting the dots, so by the end of the Article you have a clear & concise picture of what
is the true meaning & Capacity in which Web Analytics is Implemented Today & About its scope in near future.
To start with we need to know the Key Web Analytics keywords & various related terms- Data Analytics, Data
Mining, OLAP, Data Warehouses, Data marts, Business Analytics, Business Intelligence, KPI's, DSS. I'm not
trying to overwhelm you by throwing all these fancy terms at you, but it is required to know these in & out to
Paint the Complete picture of how they all are inter-connected & how they relate to Analytics.
Let's start by looking at the Visual of the complete procedure to understand where Web Analytics fall in the
spectrum- i.e. at the very end & at the very start, The Data Analytics uses a Source of relevant data- & the
major chunk of data often comes from Users & their browsing patterns. Also the end game of the whole
procedure is to let Users feel the close relationship with the decision making (Changes derived) that further
affects the end users & derives their behavior in return.

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It all starts with CRM (customer relationship management), which usually analyzes data about an
enterprise's customers and presents it so that better and quicker business decisions can be made. CRM
Data, flat files or Data from other sources (Including the Online Data gathering) are then subjected to ETL
(Extraction, Transformation & Loading) processes to gather the Data into Data Warehouses.
Data analytics is basically the science of examining raw data with the purpose of drawing conclusions about
that information. Data analytics uses OLAP (online analytical processing), OLAP data is stored in a
multidimensional database called as Data Warehouse or Data Marts. A multidimensional database considers
each data attribute (such as product, geographic sales region, and time period) as a separate "dimension".
So using these Dimensions data can be manipulated as per the exact need of the hour, & if the need changes
the Dimension changes & same data & Processing engines are used to turn the data into meaningful content.
These Steps are usually completed by a DBA altogether.
Next in the process comes the Data Analysts who uses Technologies like Big Data/Hadoop (We all heard
names of) to get the relevant information discovered through the process of Data Mining. They identify
undiscovered patterns and establish hidden relationships.
After that the process turns towards Business Analysts, who utilizes the Information discovered in the mining
process & presents it in a relevant format for all the Stakeholders to Consume in their preferred format.

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The Presented data from the Business Analysts is then taken into consideration by key Shareholders & Major
Decisions are made to manipulate the product/processes in an attempt to meet the Users requirement better
(or to provide what is actually required).
Data analytics focuses on the process of deriving a conclusion based solely on what is already known by the
researcher. Which is then reflected to the end user & their behavior/moto manipulates due to the Data Analysis
part.
To further simplify this, let's have a look at the Self-explanatory Image.
So Right Data/Web Analytics is a two Way Street, What goes around comes back (eventually).
And Yes contrary to our general believes the Processing of data into information (making sense of the data) is
just a Step/ part of the whole methodology, & Analytics in itself is worthless.
KPI's (Key Performance Indicators) is a quantifiable measure used to evaluate the success of an
organization, employee, etc. in meeting objectives for performance & is the Crux & reason of getting into
Analytics. A KPI is a metric that helps you understand how you are doing against your objectives.

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DSS - Decision Support System is a conceptual framework for a process of supporting managerial decision-
making, usually by modeling problems and employing quantitative models for solution analysis
BI - Business Intelligence is a subset of DSS, It's an umbrella term that combines architectures, tools,
databases, applications, and methodologies
BA - Business Analytics is a subset of BI, The key feature of Analytics is to apply the decision models
directly to business data while assisting in making strategic decisions & not the other way around.
WA - Web Analytics is actually a subset of BA The application of business analytics activities to Web-based
processes, including content & e-commerce
before we take it further, Feel free to re-read it, what? You think you don't need to!
Ahh...Either you are just being lazy or I'm not doing a good job I thought I were. Seriously though Scroll above
& give it all a quick read, It will help you get a better stringent & accurate picture which will come in handy in
what is about to come.
Now as you see the complete Image & are able to Digest the complete procedure & already know the reason
why Web Analytics is actually done let's move to the last but most Important Question of all-
In what Capacity can it be used? To get to know the full capacity & future capabilities we need to give a
deep look at our current need, i.e. majorly Customer Centric.
The Key question Answered here is -"How is the website doing in terms of delivering for the customer?"
The Industry is currently applying Analytics to tackle the following roadblocks-

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• Why customers do not stay on a website for more than a minute?
• What content on the website is directly tied to driving Macro and Micro Conversions?
• What sections of the website might be most valuable to the visitors?
• What content areas seem very expensive to create (hence more important to measure if
they are adding any value!)?
• What cross-sells and up-sells do the business pumping across the site?
• What does the top navigation and left/right navigation groupings tell us about priorities?
• Why sales are very low? What could be the possible reasons?
• What is the top five problems users experience on a website?
• What is the most influential content on the website?
• What is the impact of the website on user services?
• What are the most productive inbound traffic streams? Which sources are missing?
The Reason to Implement Analytics to deliver the solution to all of the above problems is already a part
of the Analytics industry which was made possible in the first place because of the following salient
features that is embedded in the Procedure-
• Realization that continuous tracking of the visitor's behaviors is very important for the
improvements in the customer acquisitions process
• Focus on some valuable/measurable actionable data- this gives enterprise necessary
foresight and opportunity for conversion
• Always trying to achieve defined Website & Business Goals/objectives
• Suitable & Easiness to Implement for both B2B and B2C
• Realize Return on Investment
Let me shed some light on the Majority Keywords that are ruling the Analytics Industry to fill all its current
needs as of now.
Most popluar events that we track through the Analytics are- Page Tracking, Link Tracking, Page Views, Page
Visits, Session, Event Tracking (Articles , Videos, downloads etc..), Social Media Tracking(Twitter, G+, FB,
Share etc.), Ad tracking, Campaigns tracking, Goal/Conversion, Funnels, Bounce Rate, Exit Rate: Exits/Visits.
WEB Analytics "keywords"- The Keywords are usually identified by IDs and tracking codes on your pages
Event- Events are user interactions with content that can be tracked independently from a web page or a
screen load.
Hits-A request for a file from the web server. Available only in log analysis
Page Views- A request for a file whose type is defined as a page
Visits/Sessions-A series of requests from the same uniquely identified client with a set timeout, often 30
minutes. A visit contains one or more page views
Click Paths- The sequence of hyperlinks one or more website visitors follows on a given site
Segmentation-New v/s Returning Visitors, Visit Duration vs. Content Type, Geographic Location vs. Content
Type
Session: A session is defined as a series of page requests from the same uniquely identified client with a time
of no more than 30 minutes and no requests for pages from other domains intervening between page requests.

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In other words, a session ends when someone goes to another site, or 30 minutes elapse between page views,
or whichever of the two come's first
Goal/Conversion: The successful completion of any specified event, as determined by the end user.
Funnel: The series of steps that move a visitor towards a specific conversion event, such as an order or a
registration signup.
Bounce Rate Vs Exit Rate: A "bounce" is recorded when a person visits and leaves within a second or two,
usually before the page is even done loading. Top exit pages show you which pages people visit immediately
before they leave. E.g. If the page contains a "thank you" message after a customer places an order, a high
exit or bounce rate would be expected. However, if your product pages are some of your top exit pages, it may
be because your descriptions are unclear, or maybe your prices are too high.
Identify core events and use them as key metrics
Tracking Methods- Page tagging using JavaScript, Event tagging, Custom hooks, Custom logs for Offsite
Analysis
Potential Pitfalls- One page view (Bounced Visitors), Exclude these from your reports, Time on last page,
inaccurate calculation, Averages hide behavior, Segment your visits
1. Typical phases followed for Analytics are -TrackingàData MiningàAnalysisàOptimizationà
Data Mining & it's journey involves but not limited to the following phases Identify key KPIs, Common
Data source for each KPI, Define dashboard template, Data extraction and reporting, Performance
monitoring, Automation feasibility for reporting, could be an offshore offering à(Will have cost benefits)
Analysis phase covers the following- Define business and customer objective, Application analysis
to drive actionable outputs, Conversion % trends analysis, Current customer base, Identify customer
segmentations, Analyze traffic trends, Analyze success of marketing campaigns, Measure success of
any updates to application
Optimization Phase covers the following- Improve UE, How to generate more traffic, Improve conversion %,
Impact on marketing decisions based on data, Impact product launch based on analytics data, Competitive
analysis

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1. Testing strategy : Include process driven conversions, social media tracking, payment methods,
extreme conditions etc.
2. Reports/Dashboards: Help visualize data as per need and helps track goals & Funnels
3. Testing Tools: Should support validation of all tags & query parameters while being user friendly and
browser independent.
Web Analytics Testing should validate & cover the following key areas-
• The defined tags exist on the page/website
• Gets fired on a particular event as defined
• The values against those tags are correctly captured
• Timing of the firing of the tags is appropriate
• All the data captured against tags reflects correctly on the reports
Key Documents & Artifacts used to implement an Analytics solution includes the following-
Tagging Matrix- Tagging matrix in an excel sheet that contains page names and its details, Attributes of
Tagging Target, Value (in case of static tags) /Implementation (in case of dynamic tags).
SDR (Solution Design Reference)- This document is a bible for tagging guidelines. It defines about all the
variables
Test Scripts- Test Scripts are excel document that contains attributes like Page Name, Test Phase, Priority,
Designer, Creation Date, Category, Steps to Reproduce, Expected result, Actual result

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Other reference documents- like Wireframes/Use Cases/presentations, these are the word documents or
presentations that client provides for reference to create tagging matrix /test cases.
The aim of Testing should be to identify most important variables to improvise user experience resulting in
improved business results & to identify concrete areas to achieve better results by analyzing reports. It should
also capture the viability of implementing the latest market trends and how do they perform with respect to the
channels. Forecasting variable performances and future trends is also an Important aspect & should not be
ignored.
-Abhimanyu Sood