IP address tracking

Queries, Keywords, And Search Terms

Queries, Keywords, and Search Terms

In this article we discuss the loss of search terms, explain the current relevance of queries within Google, and outline our current strategy for building content based on focus keywords. November, 2017.

The goal is to understand how your choice of words influences your Marketing and Sales efforts.

Executive Summary – Bullet Points

  • ‘keywords’ and ‘search terms’ are now called Queries, by Google
  • you can no longer see which visitors used which queries
  • you can still see which Queries bring you traffic, but only via Adwords – for bidding (and rank) purposes
  • the queries or keywords you use when building webpages are still central to your success
  • take-home message: the point is to use words that will lead visitors to your site (via search engines)
  • Access this data via the ‘Search Analytics Report’ within the Search Console in Adwords
  • the solution? smaller Pages and highly relevant content

Search Terms No Longer Available

About 10 years ago, in the heyday of SEO, search terms, and keyword ranking, the Marketing Dept. had it better than they knew. It was possible to see, for every visitor coming into a site, which words they had typed into a search engine. This information, coupled with their clickstream gave direct insight into buyer needs, wants, thinking, and personas.
Search term encryption began slowly, and today, search term and keyword data are only available for paid search (non-organic) traffic within Adwords.

Google (via Chrome) was not the only player involved – Mozilla (Firefox) and Apple (Safari) also encrypted by default.
Google obviously needed a way of keeping search terms visible for their advertising clients, so you can still see and buy Queries via Adwords, but they are anonymized, and used for ranking and conversion, as opposed to visitor tracking. It is important to keep in mind, however, that the overall statistics for any query are front and center. In other words, queries and click-through rates are the key determinants in deciding how relevant content is, and how much traffic costs.

The point of this article is to summarize what we have learned from encryption and the disappearance of search terms and keywords, 5-10 years on.

Definition: (focus) keyword. The focus keyword is the keyword, or query, you want to lead visitors to your page from Google. In other words, the search queries, terms, and phrases you want your page to rank (display) for.

Google’s Search Analytics Report

Google Webmaster Tools has been replaced by the Search Console, within which you will find the Search Analytics Report, and this description:

…for example, choose “Queries” to group data by search query terms…
The Search Analytics Report shows how often your site appears in Google search results. Filter and group data by categories such as search query…to improve your site’s search performance, for example:
1. See how your search traffic changes over time, where it’s coming from, and what search queries are most likely to show your site.
2. Learn which queries are made on smartphones, and use this to improve your mobile targeting.

Take-Home Message
[bottom line] See which pages have the highest (and lowest) click-through rate from Google search results.

In English, please? Smaller Pages and highly relevant content

When the loss of search terms first hit home, SEO experts were at a loss to see the bright side – less just means less in this case. The upside is that your most embarrassing searches – “cashew stuck in child’s nose” or “how to get my iphone to do that thing where the screen tilts again” (portrait orientation) -remain private.

So there is a ‘void’ where search term information is concerned.
How have we responded to the change? By reverse-engineering content to drive traffic. In other words we target the language (products and services in our case) and write content which speaks to that topic. The required/ recommended keyword density (number of times query or phrase appears, is 1-3%), meaning that the surrounding language also plays a role.

In the past – you could check how people coming in to a site behaved, based on search terms. Now, you can see how your Google advertisements behave, based on queries. Obviously there are still (conversion) metrics involved, but there has been a definite shift.

The solution

Specifically, what we do is write small concise descriptions of what we do (sell), and publish these on separate pages. In this way – we can see which pages bring in traffic – and that way, we know what our audience is really interested in. That’s the bottom line.

IP address tracking

Queries, Keywords, and Search Terms

Queries, Keywords, and Search Terms

In this article we discuss the loss of search terms, explain the current relevance of queries within Google, and outline our current strategy for building content based on focus keywords. November 2017.

The goal is to understand how your choice of words influences your Marketing and Sales efforts.

Executive Summary - Bullet Points

  • ‘keywords’ and ‘search terms’ are now called Queries, by Google
  • you can no longer see which visitors used which queries
  • you can still see which Queries bring you traffic, but only via Adwords - for bidding (and rank) purposes
  • the queries or keywords you use when building webpages are still central to your success
  • take-home message: the point is to use words that will lead visitors to your site (via search engines)
  • Access this data via the ‘Search Analytics Report’ within the Search Console in Adwords
  • the solution? smaller Pages and highly relevant content

Search Terms No Longer Available

About 10 years ago, in the heyday of SEO, search terms, and keyword ranking, the Marketing Dept. had it better than they knew. It was possible to see, for every visitor coming into a site, which words they had typed into a search engine. This information, coupled with their clickstream gave direct insight into buyer needs, wants, thinking, and personas.
Search term encryption began slowly, and today, search term and keyword data are only available for paid search (non-organic) traffic within Adwords.

Google (via Chrome) was not the only player involved - Mozilla (Firefox) and Apple (Safari) also encrypted by default.
Google obviously needed a way of keeping search terms visible for their advertising clients, so you can still see and buy Queries via Adwords, but they are anonymized, and used for ranking and conversion, as opposed to visitor tracking. It is important to keep in mind, however, that the overall statistics for any query are front and center. In other words, queries and click-through rates are the key determinants in deciding how relevant content is, and how much traffic costs.

The point of this article is to summarize what we have learned from encryption and the disappearance of search terms and keywords, 5-10 years on.

Definition: (focus) keyword. The focus keyword is the keyword, or query, you want to lead visitors to your page from Google. In other words, the search queries, terms, and phrases you want your page to rank (display) for.

Google's Search Analytics Report

Google Webmaster Tools has been replaced by the Search Console, within which you will find the Search Analytics Report, and this description:

…for example, choose "Queries" to group data by search query terms…
The Search Analytics Report shows how often your site appears in Google search results. Filter and group data by categories such as search query…to improve your site’s search performance, for example:
1. See how your search traffic changes over time, where it’s coming from, and what search queries are most likely to show your site.
2. Learn which queries are made on smartphones, and use this to improve your mobile targeting.

Take-Home Message
[bottom line] See which pages have the highest (and lowest) click-through rate from Google search results.

In English, please? Smaller Pages and highly relevant content

When the loss of search terms first hit home, SEO experts were at a loss to see the bright side - less just means less in this case. The upside is that your most embarrassing searches - “cashew stuck in child’s nose” or “how to get my iphone to do that thing where the screen tilts again” (portrait orientation) -remain private.

So there is a 'void' where search term information is concerned.
How have we responded to the change? By reverse-engineering content to drive traffic. In other words we target the language (products and services in our case) and write content which speaks to that topic. The required/ recommended keyword density (number of times query or phrase appears, is 1-3%), meaning that the surrounding language also plays a role.

In the past - you could check how people coming in to a site behaved, based on search terms. Now, you can see how your Google advertisements behave, based on queries. Obviously there are still (conversion) metrics involved, but there has been a definite shift.

The solution

Specifically, what we do is write small concise descriptions of what we do (sell), and publish these on separate pages. In this way - we can see which pages bring in traffic - and that way, we know what our audience is really interested in. That’s the bottom line.

1-click User-Tagging

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1-click User-Tagging

Tag and add notes to Visitor Profiles

We are very pleased to finally release User-Tagging, which is now available.

User tagging allows you to actually edit the Visitor Profiles for each and every visitor with a simple mouse click.

User-tagging demo video from Opentracker on Vimeo.

This new feature turns Opentracker into a very powerful CRM system - you can combine contact information with actual website browsing history.
Example: send out an email and combine response to your newsletter with insight into what each recipient (email address) actually looked at on your website.

user tagging username in Opentracker clickstream

 

Here are the fields that you can add/ edit:

  • Email address
  • Title
  • First name
  • Last name
  • Description
  • Gender
  • User name
  • Phone number
  • Mobile nr.
  • Fax nr.
  • Source
  • Currency
  • Website
  • Conversion
  • Rating
  • Email opt-out
  • Age
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Definition & Differences Between Hit, Page, and Web Counters

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Definition & Differences Between Hit, Page, and Web Counters

Executive Summary and Article Navigation

In this article, you will find discussion and technical definitions of:

You will also find information about:

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Hit, Page, and Web Counters –– the difference

All three are counters; devices which display the number of visits which have been made to a website or page within a website. Some important differences are:

  • how many pages are measured (single or multiple)
  • the cost of the tool (free vs. paid)
  • how you access your website or page data
  • how far back this data is available
  • security feature which prevents recording of repeat clicks

The main reason for having a counter is to let you and your visitors know how many people have visited your page or site.

The decision you must make when deciding what product to use is the cost you are willing to pay and the type of information that you are looking for.

As with many forms of technology, you generally get what you pay for. Counting and tracking options which you pay for give you more detailed information of a higher quality. Additionally there may be some work involved, in terms of generating and interpreting your stats, depending on which option you choose.

Why are many counters free? Many counters are free because in return for installing a counter on your page, you give the company who’s product it is a back-link. Back-links are used to obtain a high listing in search results.

Additionally, most services offer the bare minimum, a counter, for free. If you wish to learn about your traffic in any detail, an upgrade and payment is necessary.

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Hit Counter

A hit counter measures and displays the number of times visitors have viewed a single page on a website. Hit counters are typically displayed on homepages. Hit counters can be public or non-public. If they are non-public, or ‘invisible’, only the webmaster can see how many times the page has been viewed. Technically, hit counters measure requests sent by a visitor’s browser to a server. Each time a visitor’s browser requests to see a page on your site, this request is relayed through your server, and is called a hit.

There is a high potential for confusion here, because log analysis also involves the interpretation of ‘hits’. The hits recorded by log files are much more numerous, and do not individually represent individual human ‘hits’ or ‘clicks’. The hits recorded by log files each represent a single pieces of information (for example a graphic such as a gif) which, when taken together, make up page views. In log file analysis a single page view can generate dozens of hits, depending on how much information is being called up, in terms of graphics, etc. Therefore it is important to always find out the definition of a hit in the system that you are using.

The advantage of hit counters is that many are free, easy to install, and can be graphically altered to fit in with the feel of your site. Additionally, there are also numerous scripts available for free download that can be used to make your own hit or page counter, if you have the time and know-how. To have a look, type “hit counter script download” into your favorite search engine.

The drawback of hit counters is that they will not tell you how many unique visitors you have had. Nor do they always tell you the time period which has been measured. Often the data stretches back to the installation of the counter, which can be interesting, but will not help to analyze trends.

Page Counter

Essentially, a page counter is the same thing as a hit counter: a line of code and a graphic device used to display the number of visitors who have viewed a page on your site. A page counter only measures and presents statistics for the page it is installed on. Technically, a hit counter is a page counter, as it measures and presents the same information, the only difference being in name. Page counters often provide a service that measures page views on multiple pages of a website, as opposed to hit counters, which are typically used to count hits on a single page.

Web Counter

Much like a hit counter or a page counter, a web counter is a combination of code and graphic device that allows you to measure and display the number of visitors a web site has received. Web counters are typically used to measure multiple pages. A web counter is a step closer towards visitor tracking, as some web counters offer additional statistics, such as both the number of visitors, and the number of pages viewed, so that, for example, 200 visitors will have looked at 345 pages over a given period. Additionally, web counters offer analysis, for example, by providing a comparative overview to show which pages receive the most visitors.

Tracking systems

The next step is a tracking system. Tracking systems offer the additional feature of charting the progress that visitors make from page to page, by recording clickstreams. A tracking system can also tell you which search terms were used to find your site. Opentracker provides both of these services. Additionally, tracking services provide complete aggregate reports available for any given period during which data was being recorded. Tracking systems are designed to answer specific questions. For example, by matching click-streams and visitor profiles, to identify specific markets. If you have a product or service you are offering, you can determine where interested customers come from: a particular referrer, or ISP, and expand your efforts to reach that audience. You can go a step further and measure the effect of any changes you make, from the colors on your homepage to a new marketing strategy.

Using a tracking system, it is not necessary to guess what visitors are doing. If you know, to the click, what your visitors do, decisions about content management are much easier to make. Opentracker was designed primarily with this goal in mind: the ability to effectively analyze and respond to web traffic.

Opentracker is relatively unique in being a step beyond log files and log file analysis. We record highly detailed visitor activity and visitor profile data. We track unique visitors over the long-term. We host this data and make it easily available. There is no software to install, nothing to download, and no reports to generate. Your password protected statistics can be accessed remotely, only connection to internet is necessary.

To summarize:

Some important questions to ask when considering counters:

  • What do you want to measure, exactly?
  • Do you want to count a single page, or multiple pages?
  • Do you want to count unique visitors?
  • Do you want to make comparisons between pages?
  • Will you have easy access to your stats?
  • How far back will data be available?
  • Do you want that information to be public?
  • How is a ‘hit’ defined in the system you are using?

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Definitions of Big Data

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Definitions of Big Data

Q: Can you please provide me with a definition of Big Data?

A: The definition of Big Data is a moving target.

Opentracker big data definition

In order to make it possible to follow the discussion, as it evolves, we see have started a list of definitions, as we read them on the internet.

Author names: Andrew Brust (ZDNet), Bill Franks (FCW article), PCmag encyclopedia,  John Rauser (Networkworld), John Weathington (TechRepublic Blog), Cory Janssen (Techopedia.com), Mike Gualtieri (Forrester),  John Ebbert (Adexchanger), Edd Dumbill  (O'Reilly Strata), Boyd & Crawford (cited by Leslie Johnston on the Library of Congress), Tim Gasper (cited on TechCrunch), Margaret Rouse (TechTarget), Mike Loukides (O'Reilly Radar), Jimmy Guterman (O'Reilly), Wikibon,  Steven Burke (CRN), Urbandictionary .com, Slashdot (SAP survey), George Dyson (personal correspondence Tim O'Reilly), Doug Laney (Stackexchange), Brian Hopkins and Boris Evelson (Forrester), Bob Gourley (Smartdatacollective),  SAS,  Stephane Hamel.

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  1. "Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization." Cited from Wikipedia
  2. "Big data is the term increasingly used to describe the process of applying serious computing power – the latest in machine learning and artificial intelligence – to seriously massive and often highly complex sets of information." Cited from 4/2013 the Microsoft Enterprise Insight Blog
  3. "We can safely say that Big Data is about the technologies and practice of handling data sets so large that conventional database management systems cannot handle them efficiently, and sometimes cannot handle them at all." Cited from 1/2012 ZDNet Blog by Andrew Brust.
  4. "An easily scalable system of unstructured data with accompanying tools that can efficiently pull structured datasets." Cited from a 4/2013 post on the FCW Blog.
  5. "The definition of big data? "Who cares? It's what you're doing with it,"" Cited from 3/2013 FCW article, quoting Bill Franks.
  6. "The definition of big data refers to groups of data that are so large and unwieldy that regular database management tools have difficulty capturing, storing, sharing and managing the information." Cited from yourdictionary.com
  7. "Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. Big Data includes business transactions, e-mail messages, photos, surveillance videos and activity logs (see machine-generated data). Scientific data from sensors can reach mammoth proportions over time, and Big Data also includes unstructured text posted on the Web, such as blogs and social media." Cited from the pcmag encyclopedia
  8. "Any amount of data that's too big to be handled by one computer." John Rauser cited 5/2012 at networkworld.com
  9. "To define big data in competitive terms, you must think about what it takes to compete in the business world. Big data is traditionally characterized as a rushing river: large amounts of data flowing at a rapid pace. To be competitive with customers, big data creates products which are valuable and unique. To be competitive with suppliers, big data is freely available with no obligations or constraints. To be competitive with new entrants, big data is difficult for newcomers to try. To be competitive with substitutes, big data creates products which preclude other products from satisfying the same need."
    Cited from 9/2012 John Weathington on the TechRepublic Blog
  10. "Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware." Cited from a Cory Janssen post on Techopedia.com
  11. "Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data." Cited from IBM.com
  12. "A more pragmatic definition of big data must acknowledge that:
    Exponential data growth makes it continuously difficult to manage — store, process, and access. Data contains nonobvious information that firms can discover to improve business outcomes. Measures of data are relative; one firm’s big data is another firm’s peanut. A pragmatic definition of big data must be actionable for both IT and business professionals.The Definition Of Big Data: Big Data is the frontier of a firm’s ability to store, process, and access (SPA) all the data it needs to operate effectively, make decisions, reduce risks, and serve customers."
    Cited from a 5/2012 Mike Gualtieri Forrester Blog post
  13. "The world has always had 'big' data.  What makes 'big data' the catch phrase of 2012 is not simply about the size of the data.  'Big data' also refers to the size of available data for analysis, as well as the access methods and manipulation technologies to make sense of the data." Cited from 12/2012 Adexchanger.com article by John Ebbert
  14. "Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it." Cited from 1/2012 post by Edd Dumbill on O'Reilly Strata
  15. "We define Big Data as a cultural, technological, and scholarly phenomenon that rests on the interplay of:
    (1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
    (2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.
    (3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy." From Critical Questions for Big Data Boyd & Crawford (2012) as cited by Leslie Johnston on the Library of Congress website
  16. "The definition of Big Data is very fluid, as it is a moving target — what can be easily manipulated with common tools — and specific to the organization: what can be managed and stewarded by any one institution in its infrastructure.  One researcher or organization’s concept of a large data set is small to another." Cited from 10/2011 Leslie Johnston Library of Congress
  17. "Big Data is presently synonymous with technologies like Hadoop, and the “NoSQL” class of databases including Mongo (document stores) and Cassandra (key-values)." Tim Gasper cited 10/2012 on TechCrunch
  18. "Big data (also spelled Big Data) is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates -- data that would take too much time and cost too much money to load into a relational database for analysis. Although Big data doesn't refer to any specific quantity, the term is often used when speaking about petabytes and exabytes of data." Cited from a 3/2011 post by Margaret Rouse on TechTarget
  19. "But I do like Roger Magoulas’ definition of “big data”: big data is when the size of the data becomes part of the problem." Cited post by Mike Loukides 2/2013 post on O'Reilly Radar
  20. "Big Data: when the size and performance requirements for data management become significant design and decision factors for implementing a data management and analysis system. For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." Cited from 6/2009 Jimmy Guterman O'Reilly
  21. Big data has the following characteristics;
    1. Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible:
    2. Petabytes/exabytes of data
    3. Millions/billions of people
    4. Billions/trillions of records
    5. Loosely-structured and often distributed data
    6. Flat schemas with few complex interrelationships
    7. Often involving time-stamped events
    8. Often made up of incomplete data
    9. Often including connections between data elements that must be probabilistically inferred,
    10. Applications that involved Big-data can be:
    11. Transactional (e.g., Facebook, PhotoBox), or,
    12. Analytic (e.g., ClickFox, Merced Applications). Cited from Wikibon.org
  22. "We think at the end of the day, big data is not just about analytics, it is about data-centric applications. It is about driving some experience to a customer and causing them to do things in realtime." Paul Mauritz quoted in a 4/2013 Steven Burke article on CRN
  23. "Modern day version of Big Brother. Online searches, store purchases, Facebook posts, Tweets or Foursquare check-ins, cell phone usage, etc. is creating a flood of data that, when organized and categorized and analyzed, reveals trends and habits about ourselves and society at large." urbandictionary.com
  24. "A new survey by SAP suggests that nearly 76 percent of executives see “Big Data” as an opportunity. However, respondents’ definition of “Big Data” varied to a considerable degree. Nearly a quarter of the 154 C-suite executives felt that “Big Data” was the technologies designed to handle the massive amounts of data swamping organizations. Another 28 percent defined “Big Data” as that flood of data itself. Still another group (19 percent) equated “Big Data” with storing data for regulatory compliance. Around 18 percent viewed “Big Data” as the increase in data sources, including social networks and mobile devices." slashdot 6/2012 citing an SAP survey
  25. "Big data is what happened when the cost of storing information became less than the cost of making the decision to throw it away." Tim O'Reilly quoting personal correspondence via email from George Dyson 20 March 2013, regarding Dyson's talk at the Long Now Foundation 19 March 2013.
  26. The Big Data Landscape:
    1. Apps
      • Vertical Apps
      • Ad / Media Apps
      • Business Intelligence
      • Analytics and Visualization
      • Operational Intelligence
      • Data as a service
    2. Infrastructure
      • Analytics Infrastructure
      • Operational Infrastructure
      • Infrastructure As A Service
      • Structured Databases
    3. Technologies"

    big dataDerived from an informational diagram on The Big Data Landscape

  27.  The recently updated Gartner definition also recognizes the value aspect: "Big Data are information assets with volumes, velocities and/or variety requiring innovative forms of information processing for enhanced insight discovery, decision-making and process automation." - Doug Laney posting at stackexchange  citing his original piece outlining the 3Vs of big data now republished.Here is the original 2001 paper entitled "3-D Data Management: Controlling Data Volume, Velocity and Variety" by Laney.
  28. "Big data: techniques and technologies that make handling data at extreme scale economical." by Brian Hopkins and Boris Evelson at Forrester 8/2011. In diagram form. big data
  29. "To date, our key message has been that it is the enterprise CTO who is responsible for defining how the term should be used."
    Bob Gourley (who originally posted the big data definition on wikipedia) posting on smartdatacollective.com 12/2012.
  30. “A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex, and diverse types of data. Big Data is often defined along three dimensions — volume, velocity, and variety.”  [Big Data requires] “advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.”
    The TechAmerica Foundation’s Federal Big Data Commission Comprehensive Guide to Best Practices for Big Data, cited by Bob Gourley here 10/2012.
  31. "Big data is a popular term used to describe the exponential growth, availability and use of information, both structured and unstructured.
    Ultimately, regardless of the factors involved, we believe that the term big data is relative; it applies (per Gartner’s assessment)  whenever an organization’s ability to handle, store and analyze data exceeds its current capacity."  SAS.
  32. The simplest definition of "Big Data" is "it doesn't fit in Excel"
    from the full quote; "I have joked that the simplest definition of "Big Data" is "it doesn't fit in Excel" - and when you think of it, it's true for most people who wonder how to make the shift from a traditional approach to a Big Data one."
    Stephane Hamel comment 8/2012 Big Data - What It Means For The Digital Analyst.
  33. More to follow...

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Third-Party Cookies vs First-Party Cookies

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Third-Party Cookies vs First-Party Cookies

Executive Summary and Article Navigation

Discussion and definitions of:

Who wants a cookie?

What are cookies? Here are a few over-lapping definitions;
  1. A small data file placed on your computer by a website that you visit.
  2. A piece of code placed in your browser by a website server.
  3. A text file placed on a hard drive to store and transmit information to the server of websites (re)visited from that browser / computer.
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What is a (third-party) cookie?

A cookie is a small bit of text placed on the hard drive of  your computer by the server of a website that you visit. The cookie is placed there for the purpose of recognizing your specific browser or remembering information specific to your browser, were you to return to the same site.

All cookies have an owner which tells you who the cookie belongs to. The owner is the domain specified in the cookie.

In "third-party cookie", the word "party" refers to the domain as specified in the cookie; the website that is placing the cookie. So, for example, if you visit widgets.com and the domain of the cookie placed on your computer is widgets.com, then this is a first-party cookie. If, however, you visit widgets.com and the cookie placed on your computer says stats-for-free.com, then this is a third-party cookie.

Opentracker provides services that allow the companies and websites to track their visitors with first-party cookies.

Growth of third party cookie rejection

Reports and research on the subject of website tracking tell us that the rejection of third-party cookies is growing. Increasing numbers of people are either manually blocking third-party cookies, or deleting them regularly.

That is why Opentracker utilizes 1st party cookie technology.

The cookies being deleted / blocked are third-party party cookies, as opposed to less problematic first-party cookies.

How many people or software tools delete third party cookies? The numbers given can be as high as 40%. If you count that many anti-spyware applications and default privacy settings also block 3rd party cookies, then it is possible that a high percentage of cookies are being blocked.

Blocking and deleting cookies

Why do far fewer people block first-party cookies? It is estimated that a very low percentage of people block first party cookies, less than 5%. The reason for this is primarily that it is very difficult to surf the internet without accepting these cookies. First party cookies are necessary in order for you to be recognised as an individual. Any site that you login to as an individual requires a way of identifying you as "you". Hotmail, Yahoo, Gmail, online banking, ebay, Amazon, etc.

Additionally, anti-spyware software and privacy settings do not target first-party cookies.

visitors onlineWe use cookies to keep track of long-term visitors. These visitors remain anonymous, the point is to be able to see who returns, if and when, for example, for conversion analysis.

We use first party cookies as our first line of analysis, and ip number with user agent as the secondary line. AOL users are identified more specifically because their ip number changes with every click.

What actually happens when cookies are blocked / rejected?

1st party cookies: it is very hard to login anywhere

3rd party cookies: no adverse effects to surfing

Q: How does this affect tracking systems, when people block / delete cookies?

A: All visits will still be recorded, but a person who has deleted the cookies will not be recognised as the same (returning) visitor.

When cookies are in place, and not blocked or deleted, total visitor counts will remain comparatively low. If a person constantly deletes cookies, they will be counted as a new "unique" visitor with every subsequent visit.

Conclusion

In response to these trends, the first step is to find out if the statistics that you collect utilise first-party or third-party cookies. Ask your statistics or tracking company. Asking questions usually leads to more questions, always a good thing when it comes to gathering and analysing data.

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