Google Analytics Project

Google Analysis Project

597 - Introduction to Analytics

There is 4 topic in my web log about Google Analytic, here is the links:

1)  Google Analytics - Platform Principles

http://usa-da.blogspot.com/2014/07/google-analytics-platform-principles.html

2) Google Analytics - Data collection

http://usa-da.blogspot.com/2014/07/data-collection.html

3) Google Analytics - Processing & configuration

http://usa-da.blogspot.com/2014/07/google-analytics-processing.html

4) Google Analytics - Reporting

 http://usa-da.blogspot.com/2014/07/google-analytics-reporting.html

As you know you must add some tracking codes ( provided by Google) to your web site which is responsible for gathering and sending information to you Google Analytic account. here is simple tracking codes:

<script type="text/javascript">

  var _gaq = _gaq || [];
  _gaq.push(['_setAccount', 'UA-XXXXX-X']);
  _gaq.push(['_trackPageview']);

  (function() {
    var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
    ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
    var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
  })();

</script>

You can find more information here:

https://developers.google.com/analytics/devguides/collection/gajs/

There is another option to import your previous gathered analytic data in Google Analytic. Here is the steps which I provides some cost Data for my Google Analytic Property:

        

           1) Creating New Property as CALMAT-Data-Analytic in Google Analytic Admin Page

       2) Create a data set

      3) Prepare the cost data CSV file for upload

There is a great site for generating customized CSV files:

http://www.mockaroo.com/

I created this data, based on Data Schema I got from step 2:

Sample Data:

ga:date,ga:medium,ga:source,ga:adClicks,ga:adCost,ga:impressions,ga:adGroup,ga:keyword,ga:campaign

20110715,cpc,ad network,664,34,55814,Ad group #1,Pictures,Campaign #4

20130616,cpc,ad network,565,146,05781,Ad group #4,Phone,Campaign #4

20111116,cpc,ad network,969,1943,52306,Ad group #4,Pictures,Campaign #1

20110506,cpc,ad network,17,3676,03879,Ad group #1,Phone,Campaign #1

20130305,cpc,ad network,78,61,84502,Ad group #3,Phone,Campaign #4

20121006,cpc,ad network,9,70,48713,Ad group #4,Pictures,Campaign #2

20101229,cpc,ad network,19,66,14264,Ad group #3,Pictures,Campaign #2

20121005,cpc,ad network,28,52,43913,Ad group #3,Phone,Campaign #1

20111221,cpc,ad network,716,60,74781,Ad group #2,Phone,Campaign #4

20101221,cpc,ad network,4,45,34767,Ad group #4,Phone,Campaign #2

20110118,cpc,ad network,427,5731,95009,Ad group #3,Phone,Campaign #1

20110902,cpc,ad network,0,0092,30372,Ad group #1,Phone,Campaign #4

20110508,cpc,ad network,16,9267,86882,Ad group #3,Pictures,Campaign #4

4) Upload cost data using the Management API

There is a link in Data-Import Data-Set for importing Data:

Notice: It takes about 12 hours the Data to be processes by Google then you can see the related reports in Report section of Google Analytic

5) Create and View the Report

In Reporting Section there is Acquisition and Cost Analysis which you can view and customize the report, Here is sample of report for 100,000 records randomly created by step 3:

Realistic Test Data Generetor

Great site for generating CSV file with random and customized data
http://www.mockaroo.com/

Harvard Business Review, Article #3, Why IT Fumbles Analytics

HBR  
Why IT Fumbles Analytics
by Donald A. Marchand and Joe Peppard Jan 2013
http://hbr.org/2013/01/why-it-fumbles-analytics/ar/1

Preface

• Massive amounts of data now available from internal and external sources
• Companies are spending heavily on IT tools and hiring data scientists
• They treat their big data and analytics projects the same way they treat all IT projects 
• Not realizing that the two are completely different animals

Conventional approach to an IT project

• Focuses on building and deploying the technology :
• On time 
• To plan
• Within budget
• The information requirements and technology specifications are established up front, at the design stage
• This approach works fine if the goal is to improve business processes
• Such projects improve efficiency, lower costs, and increase productivity,executives are still dissatisfied. 
• The reason: Once the system goes live, no one pays any attention to figuring out how to use the information it generates to make better decisions or gain deeper

Where is the problem?

• It’s crucial to understand how people create and use information
• Project teams need members well versed in the cognitive and behavioral sciences, not just in engineering, computer science, and math
• Deploying analytical IT tools is relatively easy. Understanding how they might be used is much less clear
• No one knows the decisions the tools will be asked to support 
• No one knows the questions they will be expected to help answer
• Therefore, a big data or analytics project can’t be treated like a 
• conventional
• large IT project 
• with its defined outcomes 
• required tasks
• detailed plans    

Five guidelines for taking this voyage of discovery

• Place People at the Heart of the Initiative

• WRONG : The logic behind many investments in IT tools and big data initiatives is that giving managers more high-quality information :
• More rapidly will improve their decisions 
• Help them solve problems 
• Gain valuable insights
• Because: It ignores the fact that
• Managers might discard information no matter how good it is
• They have various biases
• They might not have the cognitive ability to use information effectively
• The reality is that many people, including managers, are uncomfortable working with data
• It must place users, the people who will create meaning from the information, at its heart
• How they do or do not use data in reaching conclusions and making decisions

• Emphasize Information Use as the Way to Unlock Value from IT

• People don’t think in a vacuum; they make sense of situations on the basis of their own knowledge,mental models, and experiences
• They also use information in different ways, depending on the context
• An organization’s culture, can frame how people make decisions, collaborate, and share knowledge
• People use information dynamically and iteratively
• The design of most IT systems takes into account the data that have been identified as important and controllable
• That approach is fine for activities that are highly structured and whose tasks can be precisely described
• It is ideal for moving information from the human domain into the technology domain
• The problem is that many organizations mistakenly apply same design philosophy to the task of getting data out of the technology domain and into the human domain 
• Analytics projects succeed by challenging and improving the way: 
• Information is used
• Questions are answered
• Decisions are made

• Here are some ways to do this:
• Ask second-order questions, that is, questions about questions
• Discover what data you do and do not have
• Give IT project teams the freedom to reframe business problems

• Equip IT Project Teams With Cognitive and Behavioral Scientists
• Most IT professionals have engineering, computer science, and math backgrounds
• They are generally very logical and are strong process thinkers
• Big Data needs people who understand how people:
• Perceive problems 
• Use information 
• Analyze data in developing solutions ideas and knowledge
• This shift  economics to behavioral economics
• Organizations that want employees to be more data oriented in their thinking and decision making must train them to know 
• When to draw on data 
• How to frame questions
• How Build hypotheses 
• How conduct experiments
• How interpret results

• Focus on Learning
• Big data and other analytics projects are more akin to scientific research and clinical trials than to IT initiatives
• In short, they are opportunities for discovery
• How make learning a central focus of big data and analytics projects:
• Promote and facilitate a culture of information sharing
• Expose your assumptions, biases, and blind spots
• Strive to demonstrate cause and effect
• Identify the appropriate techniques and tools

• Worry More About Solving Business Problems than About Deploying Technology
• Conventional IT project management is risk-averse
• Big data should focus less on managing the risks of deploying technology and more on solving business problems
• The paradox is that the technologies that were supposed to help manage data are now causing a massive deluge

Harvard Business Review, Article #2, The New Patterns of Innovation

The New Patterns of Innovation
How to use data to drive growth 
by Rashik Parmar, Ian Mackenzie, David Cohn, and David Gann Jan 2014


http://hbr.org/2014/01/the-new-patterns-of-innovation/ar/1

Idea in Brief

• THE CHALLENGE
• Established companies are notoriously bad at finding new ways to make money, despite the pressure on them to grow.
• THE ANALYSIS
• Most companies own or have access to information that could be used to expand old businesses or build new ones. These opportunities exist because of the explosion in digital data, analytic tools, and cloud computing.
• THE SOLUTION
• Answering a series of questions—from “What data can we access that we’re not capturing now?” to “Can we deliver one of our capabilities as a digital service?”—will help companies find ways to unlock new business value.

Traditional, tested ways of framing the search for ideas

• Competency based: 
• It asks, How can we build on the capabilities and assets that already make us distinctive to enter new businesses and markets?
• Customer focused: 
• What does a close study of customers’ behavior tell us about their tacit, unmet needs?
• Changes in the business environment: 
• If we follow “megatrends” or other shifts to their logical conclusion, what future business opportunities will become clear?
• Fourth approach: 
• It complements the existing frameworks but focuses on opportunities generated by the explosion in digital information and tools
• How can we create value for customers using data and analytic tools we own or could have access to?

Patterns

1. Using data that physical objects now generate (or could generate) to improve a product or service or create new business value.
2. Digitizing physical assets.
3. Combining data within and across industries.
4. Trading data
5. Codifying a capability

Pattern 1

Augmenting Products to Generate Data

• Because of advances in sensors, wireless communications, and big data, it’s now feasible to gather and crunch enormous amounts of data in a variety of contexts.
• Such capabilities, in turn, can become the basis of new services or new business models.

PATTERN 2

Digitizing Assets

• Over the past two decades, the digitization of music, books, and video has famously upended entertainment industries, spawning new models such as iTunes, streaming video services, e-readers, ...
• Digitization of health records, of course, is expected to revolutionize the health care industry, by making the treatment of patients more efficient and appropriate

PATTERN 3

Combining Data Within and Across Industries

• The science of big data, along with new IT standards that allow enhanced data integration, makes it possible to coordinate information across industries or sectors in new ways.
• The goal is to encourage the private sector to develop new business models, such as shared-delivery services in specific areas

PATTERN 4

Trading Data

• The ability to combine disparate data sets allows companies to develop a variety of new offerings for adjacent businesses.

PATTERN 5

Codifying a Distinctive Service Capability

• Now companies have a practical way to take the processes they’ve perfected, standardize them, and sell them to other parties
• Cloud computing has put such opportunities within even closer reach, because it allows companies to:
• easily distribute software
• simplify version control 
• offer customers “pay as you go” pricing

COMBINING THE PATTERNS

• The five patterns are a helpful way to structure a conversation about new business ideas, but actual initiatives often encompass two or three of the patterns

Key Questions

• questions designed to inventory the raw material out of which new business value can be carved
• What data do we have?
• What data can we access that we are not capturing?
• What data could we create from our products or operations?
• What helpful data could we get from others?
• What data do others have that we could use in a joint initiative?
• Armed with the answers, the team cycles back through each pattern to explore whether it, or perhaps a modification or combination of patterns, could be applicable in the company’s business context

AUGMENTING PRODUCTS

• Which of the data relate to our products and their use?
• Which do we now keep and which could we start keeping?
• What insights could be developed from the data?
• How could those insights provide new value to us, our customers, our suppliers, our competitors, or players in another industry?

DIGITIZING ASSETS

• Which of our assets are either wholly or essentially digital?
• How can we use their digital nature to improve or augment their value?
• Do we have physical assets that could be turned into digital assets?

COMBINING DATA

• How might our data be combined with data held by others to create new value?
• Could we act as the catalyst for value creation by integrating data held by other players?
• Who would benefit from this integration and what business model would make it attractive to us and our collaborators?

TRADING DATA

• How could our data be structured and analyzed to yield higher-value information?
• Is there value in this data to us internally, to our current customers, to potential new customers,or to another industry?

CODIFYING A CAPABILITY

• Do we possess a distinctive capability that others would value?
• Is there a way to standardize this capability so that it could be broadly useful?
• Can we deliver this capability as a digital service?
• Who in our industry or other industries would find this attractive?
• How could the gathering, management, and analysis of our data help us develop a capability that we could codify?

Success Factors

Harvard Business Review, Article #1, Analytics 3.0

HBR  Analytics 3.0
by Thomas H. Davenport Dec 2013
http://hbr.org/2013/12/analytics-30/ar/1

The Evolution of Analytics
Briefly, it is a new resolve to apply:

• Powerful data-gathering 
• Analysis methods 

not just to a company’s operations but also to its offerings to embed data smartness into the products and services customers buy.


History 

• The use of data to make decisions is not a new idea.
• It is as old as decision making itself. 
• But the field of business analytics was born in the mid-1950s

Analytics 1.0 the era of “business intelligence

• For the first time
• data about production processes 
• sales
• customer interactions 
• and more 

         were 

• recorded 
• aggregated
• analyzed
• This was the era of the enterprise data warehouse

• used to capture information 

•  business intelligence software

• used to query and report it

• Readying a data set for inclusion in a warehouse was difficult

• Analysts spent much of their time preparing data for analysis and relatively little time on the analysis itself

Analytics 2.0 the era of big data

• At the mid 2000s, when internet-based and social network firms primarily in Silicon Valley (Google, eBay, ...) began to amass and analyze new kinds of information
• Big data also came to be distinguished from small data because it was not generated purely by a firm’s internal transaction systems
• Big data couldn’t fit or be analyzed fast enough on a single server
•  So it was processed with Hadoop, an open source software framework for fast batch data processing across parallel servers
• To deal with relatively unstructured data companies turned to a new class of databases known as NoSQL
• Machine-learning methods (semi automated model development and testing) were used to rapidly generate models from the fast-moving data

Analytics 3.0 the era of data-enriched offerings

• The pioneering big data firms in Silicon Valley began investing in analytics to support customer-facing products, services, and features
• The common thread in these companies management to compete on analytics 
• not only in the traditional sense (by improving internal business decisions) 
• but also by creating more-valuable products and services
• Any company, in any industry, can develop valuable products and services from their aggregated data
• This strategic change in focus means a new role for analytics within organizations

Ten Requirements for Capitalizing on Analytics 3.0

1. Multiple types of data, often combine
2. A new set of data management options
1. In the 1.0 era, firms used data warehouses as the basis for analysis. 
2. In the 2.0 era, they focused on Hadoop clusters and NoSQL databases. 
3. Today the technology answer is “all of the above”
3. Faster technologies and methods of analysis
4. Embedded analytics
1. Consistent with the increased speed of data processing and analysis, models in Analytics 3.0 are often embedded into operational and decision processes
2. Some firms are embedding analytics into fully automated systems through 
1. Scoring algorithms 
2. Analytics-based rules.
5. Data discovery
6. Cross-disciplinary data teams
1. Companies now employ data hackers, who excel at extracting and structuring information, to work with analysts, who excel at modeling it
7. Chief analytics officers
8. Prescriptive analytics
1. There have always been three types of analytics:
1. Descriptive, which reports on the past
2. Predictive, which uses models based on past data to predict the future 
3. Prescriptive, which uses models to specify optimal behaviors and actions
2. Analytics 3.0 includes all three types, 
3. Analytics 3.0  emphasizes the last
9. Analytics on an industrial scale 
1. Analytics 3.0 provides an opportunity to scale those processes to industrial strength
2. Creating many more models through machine learning can let an organization become much more granular and precise in its predictions
10. New ways of deciding and managing 
1. Some of the changes prompted by the widespread availability of big data will not yield much certainty
2. Big data flows continuously consider the analysis of brand sentiment derived from social media sources and so metrics will inevitably rise and fall over time 
3. Additional uncertainty arises from the nature of big data relationships. Unless they are derived from formal testing, the results from big data generally involve correlation, not causation

Creating Value in the Data Economy

• Analytics 3.0 is the direction of change and the new model for competing on analytics.

Thomas H. Davenport is the President’s Distinguished Professor of IT and Management at Babson College, a fellow of the MIT Center for Digital Business, a senior adviser to Deloitte Analytics, and a cofounder of the International Institute for Analytics (for which the ideas in this article were generated). He is a coauthor of Keeping Up with the Quants (Harvard Business Review Press, 2013) and the author of Big Data at Work (forthcoming from Harvard Business Review Press).

Google Analytics - Reporting

Building Reports with Dimensions & Metrics

The building blocks of every report in Google Analytics are :

• Dimensions : A dimension describes characteristics of your data
• Metrics : Metrics are the quantitative measurements of your data

Reporting API

To use the reporting APIs, you have to build your own application. This application needs to be able to write and send a query to the reporting API. The API uses the query to retrieve data from the aggregate tables, and then sends a response back to your application containing the data that was requested.

Each query sent to the API must contain specific information, including the ID of the view that you would like to retrieve data from, the start and end dates for the report, and the dimensions and metrics you want. Within the query you can also specify how to filter, segment and order the data just like you can with tools in the online reporting interface.

Report Sampling

Report sampling is an analytics practice that generates reports based on a small, random subset of your data instead of using all of your  available data. Sampling lets programs, including Google Analytics, calculate the data for your reports faster than if every single piece of data is included during the generation process.

When does sampling happen?

• During processing
• Modifying one of the standard reports in Google Analytics by adding a segment, secondary dimension, or another customization

Adjusting the sample size

The number of sessions used to calculate the report is called the “sample size.” .If you increase the sample size, you’ll include more sessions in your calculation, but it’ll take longer to generate your report. If you decrease the the sample size, you’ll include fewer sessions in your calculation, but your report will be generated faster.

The sampling limit

Google Analytics sets a maximum number of sessions that can be used to calculate your reports. If you go over that limit, your data gets sampled.

One way to stay below the limit is to shorten the date range in your report, which reduces the number of sessions Google Analytics needs to calculate your request