Online Privacy Issues

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Online Privacy Issues

We receive many questions asking us about what tracking services can and can’t do, questions about ‘online profiling’, ‘digital blueprints’ and leaving a ‘data trail’. We are also posting numerous articles on the site explaining what tracking services can do.

Summarised overview of online privacy issues

In this article you will find definitions of:

  • Anonymity
  • Merging clickstream data & personal information
  • Personal contact information
  • Personally identifiable information
  • ‘Computer information’
  • Internet protocol (ip) addresses

In this article you will find discussion of:

  • Why did we write this article on online privacy issues
  • Collecting clickstream data
  • What are we  with this data
  • Capturing email addresses
  • Tracking of individuals
  • The trade-off in privacy

Online Privacy issues

We receive many questions asking us about what tracking services can and can’t do, questions about ‘online profiling’, ‘digital blueprints’ and leaving a ‘data trail’. We are also posting numerous articles on the site explaining what tracking services are doing. In this article, we explain what tracking services, and Opentracker in particular, cannot do.

Online Privacy issue is an important topic on the internet. Much of the discussion is characterised by hype, and preys on fear. This is apparent from looking at the wide range of ‘spyware protection’ products available on the internet, and the language used to promote these products. Without knowing the realities of how their surfing patterns are tracked, and what do they do with that information. It is a concerning topic for many internet users.

The essential point on repeat throughout this article is that by far the vast majority of information collected is in no way connected to personal contact information.

The primary reason for this is that email addresses are not transmitted by surfing.

What is the purpose of this article?

We have written this article, in the hopes of providing information to increase public awareness of what they are doing with your tracking information.
The specific issues we address are anonymityemail addresses, and personal contact information.

Privacy is a topic of great concern on the internet. This is especially the case as many privacy and surfing issues are non-regulated.

At the moment technology is changing very quickly, so that it is difficult for rules and procedures to be established and enforced, as change is the only constant. Perhaps the greatest cause for concern is the unknown. Surfers do not know when and if they are being tracked, who collects that information, how it is done, and for what purposes.

We hope that by explaining what tracking services in general, and Opentracker in particular, can and cannot do, that we can help to dispel some myths. We feel that fear, while a good way to sell protection products, is not a rational basis for developing privacy guidelines or stimulating discussion. Technically speaking, the ‘anonymous surfing’ that many protection products guarantee is already the status quo.

Of course there are many legitimate security concerns, particularly in terms of viruses, but in terms of privacy the dangers are often over-hyped. The primary concerns, as we see them, are information security, in terms of safe data transferal, back-up, and storage of data, and the encryption and safety of information such as credit card info, passwords, etc.

The main information that tracking services collect: clickstream data

In terms of individual information relating to surfing habits and patterns: clickstreams, or click-paths, comprise the essential data that we collect.

The clickstreams that we record on behalf of our clients are not attached to physical or electronic contact information of the people who are visiting the websites. In other words, there is no information that connects people to the statistics we are recording. We do not collect email addresses of surfers. This means that there remains an essential element of anonymity.

The possible exception to this is the IP (internet protocol) address. IP addresses, however, are owned by companies and the ISPs who provide them to their customers. This means that in the great majority of cases this information cannot be used to locate a specific user, unless the ISP itself, or company, make that information public.

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Opentracker couples the visitor’s profile with the clickstream. Each profile contains technical stats of visitors, also known as ‘computer information’. Computer information is different from ‘individual profiling’ and ‘online contact information’. Computer information tells us the technical specifications of a user’s browser: their screen resolution, operating system, router, ISP, etc. This information is not linked to personal contact information.

On our site, we provide a link to ARIN a public IP lookup database and the contact information provided by ARIN can put you in touch with the owner of the IP address of your visitor. Most often, this is the ISP corporation that owns the IP number. The exception is larger companies that do not outsource their internet infrastructure.

Full screen Facebook user-data in Opentracker clickstream

We are providing an example of a clickstream and personal profile to the right, which you can enlarge by clicking. If you would like to interact with a clickstream, please login to our demo and take a look. For starters, you will able to see your own clickstream across our site.

Capturing email addresses

The question we receive most is about the possibility of capturing the email addresses of people who surf on a website. As far as we know: it is not possible to automatically collect the email address of a person who surfs to a website. That does not mean that this technology does not exist, or that somebody is not developing it, but that we have not heard about it.

The technical reason that we are not able to capture a visitor’s email address is that this piece of information is not listed in a user’s browser. The information that tracking services record comes from the user’s browser.

What can and does happen is that a person voluntarily enters their email address for one reason or another. The obvious examples are logging in, entering contact info for an online purchase, signing up for newsletters, and “unsubscribing” to spam. Again, to our knowledge, this is the only way that email addresses are captured.

It is possible to purchase email address lists that have been compiled by companies who sell this information.

As a precaution, if you are concerned with your privacy, setup an email account that you always use to fill in a required email field, if you are not sure where the information is going. Do not connect your physical contact information to this email address.

It is important to keep in mind the possibility that once a person has entered their email at any point into a site, their email address can be stored with their clickstream in a process called tagging. This means that a connection can be made between, for example, login info, and clickstreams. This possibility will lead to a direct connection between surfing habits and personal contact information. That means that Amazon.com, for example, have the potential to keep a record of every page a visitor has looking into their site, and combine this information with purchase history, and billing details.

An important aspect of this potentiality to remember is that each site can only see what visitors are doing on their site, not across the entire internet. That means that the internet is still highly compartmentalised, in terms of tracking surfers.

What happens in the scenarios presented by privacy advocates is that ‘personally identifiable information’ is collected so that ‘online contact information’ (email address) may or may not be merged with ‘physical contact information’ (billing address). This is called ‘merging clickstream data with personally identifiable information’. This is an understandably worrying scenario presented by privacy advocates, in which a person might receive a catalogue in the mail advertising similar products to those viewed online. In this sense, it seems to be sexual products and information related to adult-content websites that calls for safeguards to individual privacy. 

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So what is the information that we collect designed to do?

The scenario that we are presenting above is a worst-case scenario. In the case of Opentracker, there is no personal contact information linking a particular person or email address to a clickstream. We do not collect email addresses. The only personal piece of information captured is the IP number. IP addresses are owned by the companies (i.e. aol, sprint, earthlink) that provide them to their customers. Additionally, some companies and corporations are introducing round-robin IP numbers, whereby IP addresses are re-assigning on a regular basis.

This means that in the case of tracking services similar to Opentracker, the user’s anonymity is preserved. Anonymity is defined as a condition in which ‘your true identity is not known’.

The information that we are collecting on behalf of our clients is designed to be aggregated and used to identify traffic patterns. This activity is referred to by one privacy group as ‘affirmative customisation’. We do not engage in ‘individual profiling’, nor do we provide ‘online contact information’.

The information that we collect and present is passively generated by users browsing through the site’s of our clients.

The information that we collect is designed for various purposes. Essentially, it tells webmasters what is happening on their sites. The information is designed for purposes of marketing, advertising, updates, ad campaigns; essentially content management. By studying clickstreams, webmasters learn which pages are important and which pages need help. They learn about their traffic, i.e. what countries it comes from. We aggregate the data to give a lot of averages: average number of pages viewed, time spent, etc.

Additionally, we do not sell, lease, trade, etc, the information that we collect to anybody. It ‘belongs’ to the webmasters of the sites that we measure.

 

Tracking of individuals

Specific to individuals, we track visitors over the long term. That means that for each visitor to a site, we maintain a record of every click they have made on a website. We can only do this for the pages on which our code is installed. It is possible for webmasters to inspect these clickstreams, and see what an individual did over many months. The only ‘name’, or ‘tag’ that these visitors have is the time of the last click that they made.

Therefore, technically, visitors remain anonymous, as there is no contact information linking a person to their clickstream. Visitors remain statistics collected together into aggregated site stats. These site stats reveal, for example, that the average visitor comes to a site’s homepage 2 times a week, and stays there for X amount of time.

The trade-off in privacy

This is a quote from a privacy advocate group:

“However great the potential benefits of online tracking, they remain incomparable to the grave implications of Internet users’ loss of privacy.”

(http://www.cdt.org/privacy/guide/start/track.html)

While we acknowledge the potential for concern. We feel that by using the aggregated statistics that we provide, our clients can make their websites responsive to the surfing and clicks made by their visitors. The point here is that the internet can become increasingly interactive, when traffic statistics and analysis are applied. Also, if webmasters do not know what is happening on their sites, there is simply too much guesswork involved.

Obviously there is a very real concern for a lot of people that their privacy is somehow being abused. We would like to respond to these concerns, primarily through education, but also by opening up a dialogue on any related questions or ideas. Please feel free to write to us, or post any feedback on our forum.

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The Surprising Way Data Science Helps in Cancer Research

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The Surprising Way Data Science Helps in Cancer Research

Summary

In this article, you will learn about data science and its effects on cancer research. In particular, you will learn about:

  1. How data science helps in early cancer diagnosis
  2.  How data science can help find the cure for cancer

Introduction

data science cancer research

In light of the World Cancer Day which took place on February 4, we thought that it might be a nice way to show you how data science is actually helping in the research for the cure and treatment of cancer. Millions of people lose their lives or loved ones to cancer each year, and while scientists are doing their best to cure it, there has been only limited progress in finding the cure. A lot of reasons why cancer is not detected in its early stages- where most types of cancers are curable- is because of the lack of technology and information available (until now) to help doctors diagnose them at the right time.

The problem with cancer treatment is that while a lot of valuable data is available to doctors everywhere around the world (the internet has a lot to do with this), only a few have experience with clinical trials. This is because of the general attitudes of patients once they hear that they have cancer. It’s almost as though just hearing that one has cancer is enough to make a lot of people lose their will to fight the disease at all. Another problem with cancer is that not all symptoms are the same for everyone. Same goes with the medicines. A medicine that works for one patient might not work for another. There are thousands of pharmaceutical companies that are pushing new medicines out every day and, in spite of the massive data available, every doctor may not have heard about every new release.

This is where Big Data comes in. The American Society of Clinical Oncology (ASCO) has started an initiative called CancerLinQ which collects data about these new medicines, their usage and results in real time which doctors can use to look at the various symptoms and the required medicine to increase the chances of treatment.

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Data Science in Early Cancer Treatment

Data Science can be integral to the early diagnosis and subsequent treatment of cancer in many patients. This is because knowing a patient’s symptoms and prognosis, and then comparing it to a database of people with the same symptoms can help medical teams decide how they want to treat cancer and begin the treatment process. Big Data helps analyse this massive amount of information available and also helps to categorize data according to age, race and gender so that more detailed information is available, forming patterns which doctors can use to treat cancer. Big Data is also able to predict long term solutions based on the availability of previous cases, both successful or not, and thus help doctors determine the best course for actions.

data science cancer research

Researchers also use Big Data to help understand the genetic changes that lead to the formation of these cancers, studying from a large pool of case files of cancer patients from diverse backgrounds. This, combined with the ability to sequence the DNA of different kinds of tumours, combined provides a very strong framework for researchers to build their research on. This helps to develop medication which can be used to target the various different kinds of tumours and also understand how to fix the genetic change which leads to them in the first place. By doing so, not only can cancer be treated from the very beginning, but the continuous addition in the database will help future scientists with their research as well.

 

Curing Cancer

Big Data cannot cure cancer on its own. However, scientists can make use of it, along with other intelligent machines, to study the complex ways in which cancer cells multiply and form tumours. Before Big Data, it took scientists and researchers decades to realize the link between lung cancer and cigarettes. Now, with the help of modern technology, research facilities only need a hospital’s approval to check its records with histories of cancer cases. Instead of putting a lot of manpower and work into looking for patterns, these scientists can easily rely on Big Data and AI to help analyse and understand patterns.

Many government organizations dedicated to finding the cure for cancer have realized that it is going to be near possible to find a cure for cancer without using AI and Big Data. Because of this, there is a lot of development happening and organizations such as Million Veteran Program in the United States and the Cancer Genome Atlas in the UK are working towards using Big Data to help create human genomes, open to researchers for analysis via the cloud. The point is to study as many cases as possible in order to get newer insights as quickly as possible. The sooner we understand how all types of cancers are formed, the sooner we will get to actually curing them all, once and for all.

Conclusion

data science cancer research

As you can see, data science is actually helping scientists all around the world look for the cure and treatment of cancer worldwide, as we speak. Future projections show that the investment in Big Data and related technologies by companies and governments all around the world will help further develop this technology. A couple of decades ago, sequencing human genomes would cost around $10 million but now it can be down for less $1000. Research and development in this field are being accelerated as pressure is rising on pharmaceutical companies and researchers to find the cure. Whether this motivation to help save the lives of millions of people is out of good intentions or the greed to control a future pharmaceutical monopoly is unknown, but a growing number of clinics and companies have started to use Big Data to analyse and then determine the symptoms, causes and medication required to treat cancer.

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Why Your Search Terms on Google Don’t Show Up, and What You Can Do About It

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Why Your Search Terms on Google Don’t Show Up, and What You Can Do About It

There used to be a time when it was possible to log into analytics tools such as Opentracker to see a wealth of data about keywords; specifically, the keywords people used to find your website. With this information you could then track conversions, and make improvements in areas that would be the most profitable. Why, then, do you see so few search terms in your Opentracker dashboard?

The first thing to say is that this is not an Opentracker issue. If you use any other analytics software, you will encounter the same thing – hardly any keyword data. The reason is that Google doesn’t divulge the information anymore. It used to, but that all changed when it started encrypting searches.

Basically, this means a huge number of searches done on Google use secure search. When the search is over a secure connection, Google doesn’t make data on the keywords used available to the website. Your analytics provider can only process data that your website has, and if Google isn’t giving it to you, it can’t be shown or used in analytics platforms such as Opentracker.

The keywords affected by this are often referred to as “not provided” keywords as this is the only information that Google gives you, i.e. it is not provided.

This change in Google’s policy began in 2011 when it encrypted searches for users who were signed into their Google account as well as some searches conducted on the Chrome web browser. In 2013 it expanded the policy to cover even more searches, including searches done in the Firefox web browser.

In statements Google has cited privacy as its reason for the policy – protecting the privacy of people conducting searches. It also predicted that the impact would be minimal to the point that it would affect only a small minority of searches. For most websites this prediction has proved inaccurate as the majority of keyword information is held back by Google. In other words, “not provided” keywords often account for more than 50 percent of the keywords you find in your analytics tool.

Getting Around Google’s “Not Provided” Keyword Problem

Whatever analytics tool you use, there is no easy method of getting around Google’s restrictions to get information on the keywords people use to find your website. Most people involved in digital marketing have given up on that strategy and instead tackle the problem from a completely different angle.

The core of the different approach starts with the objective. The objective of using keywords was to improve your website in order to attract more visitors and increase conversions. Keyword data was handy because with that information you knew where to focus your efforts.

Your new strategy should seek to achieve the same objective – improving your website to attract more visitors and increase conversions – by using a different tool, i.e. not keywords.

One way is to display your products or services in smaller groups using multiple pages. You can then monitor which of these pages (or product groupings) gets the most traffic. That is the one that is probably doing best in search.

You can also use social logins to your website. When a person logs into your website using their Facebook or Twitter account, you will get access to data that you can use to tailor your message and improve your website. To make this strategy work you will usually have to offer an incentive for the visitor not only to sign up, but also to give you access to their social media account. Giving away free content – such as an eBook or white paper – is a method that many businesses have successfully deployed.

The bottom line is that Google’s restriction on keyword data is here to stay, whatever its motivation for the encryption policy. To get the most out of your website you should largely forget about keywords and instead use different data points and measurements to learn about how people engage with your website. This will allow you to make more informed decisions on how to improve it.

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“Google search” through all your traffic data

“Google search” through all your traffic data

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We have released an incredibly powerful feature only available in Opentracker:
easily search through all your visitor traffic data in realtime.

Q: What does that actually mean?
A: that means you can enter any search term that interests you and get results based on all your site content and your complete visitor history within seconds.

An example of universal search, demonstrating how to search through website visitors.

In the screenshot above, we have used “pay*” to locate recent conversions for the PDFmyURL website-to-PDF service.

In practice, this means that you can enter any search term you can think of and get results.
Examples: any word, page title, url, term, conversion, user, company, ip address, or strategic point of interest.

We’re nuts about data!

You may ask yourself why you have never used or seen this feature supported by any other web analytics companies before? The answer is that it was a very complicated nut to crack.

Opentracker: don’t get left behind.

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User Behavioral Analytics

Goal-oriented User Behavior

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Visitor Goals

Take-home message: visitor website behavior is goal-oriented. People who visit websites are usually trying to accomplish something. Your job if you work on a website is to facilitate this. There are some essential questions to ask;

  1. What are people who visit your website trying to accomplish?
  2. What are you doing to understand them?
  3. What are you doing to help them?

By studying traffic statistics for the past ten years across thousands of sites we have learned that website behavior is increasingly goal-oriented. In the past people seemed to do more surfing for “fun” – for example visiting company websites out of curiosity.
No-Brainer: Use traffic information to improve your website.

Q: What should you do with this insight?
A: Reverse engineer your website from a user-experience point of view. What does this mean in plain language? Figure out what people are trying to do, make sure they can do it quickly and easily. This applies to improvement of an existing site or creation of a new website.
How-to: improve visitor experience and likelihood that visitors acheive their goals

  1. Know what goals your visitors have
  2. Focus what you are offering – provide what people want
  3. Reduce the amount of clicks needed
  4. Make the process easier for them

Surfing behavior and the average amount of time people spend on website pages has changed. The vast majority of internet pageviews last 1 or 2 seconds. Writing on the internet evolves to reflect new reading habits. Many changes have been understood using website statistics. News websites are a good example. Look at the formatting on BBC, CNN or Yahoo. These sites continously re-invent themselves based on how and where people click, and how long they view. These websites base their design on the behavior of millions of daily visitors using traffic statistics.

Pages of text have been replaced with paragraphs. Paragraphs have been replaced with sentences. Pages are being re-designed to meet specific steps visitors make to reach their goals.

Part 1 of this article, above, is concerned with creating a website that helps visitors achieve their goals, next are webmaster goals.

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Webmaster Goals

A Webmaster Goal is a goal that people who build websites want to encourage, for example signing up, making a purchase, or requesting information.

Encouraging your visitors to convert to your goals
Website designers build sites that encourage visitors to take certain actions (goals). Advertising campaigns are measured in success by the number of goal conversions which take place.

Combine goals for greater success
Action point: combine visitor goals and webmaster goals where possible.

  1. You want your visitors to convert to your goals
  2. Your visitors are trying to accomplish their own goals
  3. Find the overlap between these two activities and win-win

Increase the likelihood of goal conversion 
Strategy from the business point of view: every website has goals. Websites can be reverse-engineered in order to make sure that visitors are led to the goals.

How can statistics be used to increase goal conversion?

  1. Stats can be used to examine Goal Conversion scientifically and determine how to increase goal conversion likelihood &
  2. Visitor behavior, specifically in terms of how visitors find a website; the search terms typed and referrers used can be utilized to grow trends. For example, generating content related to most-used words, topics, and terms on the internet.

Conclusion

based on traffic and clickstream data we know that, more often than not, people are trying to accomplish a goal when they visit a website. Visitor statistics play a central role in increasing website performance and value. As a business, the easier you make it for people to accomplish their goals, the more visitors you will have, and the happier these visitors will be. In other words, the greater likelihood that they will return.

Glossary:

Conversion
Goal
Source
ROI

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Understanding Big Data

Understanding Big Data

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Executive summary and article navigation

Part 1: Introduction – Age of Big Data

Picture the Second Coming of the Internet, we are just entering the 2nd inning. The whole point of the discussion, and of Big Data, is how to ask the data questions. The data is only valuable if it is used to make decsions.

Key points:

  • The key is to understand data as it is; unstructured, using a scalable platform for analysis, processing, and action, in order to unlock value
  • Businesses wishing to remain competitive must continuously learn how to use technology to give meaning to the data they collect
  • Online customers generate a lot of data (data trails) which, combined with social media can be used to add value, generating leads and sales
  • Effective decision-making should be based on current real-time data
  • Information loses its value quickly and should be used efficiently
  • Keeping up with demands of changing clients and conditions requires a structural solution (like real-time management and utilization)
  • Scale is rapidly increasing: the current internet environment is millions of users and associated data-points managed by large websites, combined with round-the-clock smartphone activity, and still growing
You can read a summary of these points in our blog post on Unlocking the Value of Data.
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Definition of Big Data

Wiki tells us: “In information technology, 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.”

In other words, more data than ever before is available as more people & things are connected via internet.

What Opentracker does to solve this problem

In a nutshell; we’ve built a distributed database system that will collect and store anything you throw at it. In keeping with our tradition of simplifying things, we’ve got a powerful api you can use to ask the data any questions you like. Click here for the Opentracker api.

An endless stream of data

An example would be a program to manage exercising. Sounds simple enough, but think about all the data: the individual accounts and separate datastreams; every step taken, start times, finish times, distances, average speed, calories burned, sessions, temperatures, weight, BMI calculations, milestones, etc. Now imagine that every piece of data is a single entry/ signal, every footstep shouting “count me” until there is a tremendous amount of information in a very short time. It takes a large effort to collect, store, manage, maintain and keep this data available.

Tsunami of analytics

There is talk of data scientists, map reduce, hadoop, and big data analytics.

With so many people uploading endless streams of fotos, videos, music, content, consumer choices, likestweets, and chatter into the cloud, it is no wonder there seems to be too much information to act on. This is sometimes referred to as a ‘Data Tsunami’ – the fact that a datastream for even a single user via social media such as Facebook, Instagram, and Twitter, networking via LinkedIn, or a consumer site such as Amazon contains innumerable pieces of information to be counted and put to use.

Some examples – Big Data in action

What industries are collecting and using this data? One example is the health care and insurance industry. They collect large amounts of data in order to derive predictive models for people, costs, treatments, and propensity for disease. The airline industry has successfully developed very complex real-time ticketing systems.

The automotive industry; receiving datatstreams from cars, navigation systems, fuel consumption, oil quality.
B2C retailers: consumption patterns, stock, ordering, returns, sales – and how all of this ties in to online advertising campaigns, conversion, and efforts; ad delivery.

Outsourcing data management – IaaS

In the past, companies kept this data themselves, everything needed was to be found in-house and many work stations were not connected to internet. Now its a requirement to have an internet connection as a resource while developing. Despite this, many companies are hesitant to outsource data management. Data management means managing and storing the data, and more importantly – being able to query it. Traditionally, data has been stored in databases and only later, if ever, consulted.

What’s happened recently though, with the onslaught of data, is that a company has to become specialized in data management just to be able to cope. That translates into infrastructure (hence the new IaaS; Infrastructure-as-a-Service). The alternative is for a company to purchase their own engineers and infrasturuture.

next: Part 2 Ownership, Terminology, & What questions to ask

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Web analytics dashboard – customer engagement

Web analytics dashboard – customer engagement

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Opentracker web analytics dashboard navigation : customer engagement

Enterprise Dashboard

We are very pleased to release a Dashboard, currently available for Enterprise clients.
For ten years we have been perfecting the art of displaying a lot of actionable information in a small intuitive space.

With the release of our dashboard, which reports on visitor/ user/ customer/ client engagement and metrics, we are able to show you everything important about your visitor behavior at-a-glance.

The dashboard consists of seven elements, a Trend Summary table and 6 traffic metric tables.

Opentracker web analytics dashboard navigation : customer engagement

Example customer engagement metrics to display:

  • single event sessions
  • page views per session
  • average time on site
  • number of sessions
  • number of page views
  • number of returning users

Segmentation –
The future of custom visitor reporting has arrived.

All the data on the dashboard can be segmented by browser, country and platform by default and also be downloaded as raw data.
Opentracker dashboard segmentation : customer engagement

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Why we need Big Data in the field of Psychological Research

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Why We Need Psychological Research based on Big Data

Summary

In this article, we will talk about why Psychological Researchers should rely on Big Data more often than traditional Research methods. Additionally, we will also look at:

  1. The benefits of using Big Data for Psychological Research
  2. How helpful is Big Data in terms of Predictive analysis

Introduction

Big Data has proven helpful in psychological analysis, especially in analysing, detecting and comparing behavioural patterns in social media, to achieve goals that include targeted marketing and political campaigns; Although, the use of this data in Psychological Research is not as common; Yes, Big Data is used in Psychological Research, but given the reliability of such data, it should be rather emphasized.

Benefits of Using Big Data

big data applicationsOne of the greatest benefits of using Big Data in Psychological Research is the fact that Big Data is pure “Data”, therefore it lacks the human error that sometimes accompanies the targeted sample’s facts that are mixed with an opinion or inaccurate facts that are mildly influenced by the environment where the research is conducted. For instance, in a scientific paper published on 2015 in the PNAS (Proceedings of the National Academy of Sciences of the United States of America) by Dr Youyou Wu, what an individual of a certain age group likes or posts on Facebook have proven as far more reliable in terms of predicting personality patterns than personal analysis of the individual’s own personality traits.

Examples of How Big Data Helps with Research

big data applications

Case 1:

Another incident was debunking the theory that there is a correlation between how good someone looks and their attitude; i.e., previous surveys which interviewed people around the so-called attractive individuals, the overall alignment of the study suggested that a majority attractive people do possess a certain degree of narcissism, but there is the unreliability arising from certain human emotions (jealousy, envy; to name a few). The study was later repeated and duplicated in a different environment, the supposedly fertile land for narcissistic behaviour: Social Media.

The Study:

This recent study used machine learning algorithms and web-scraping, to compare thousands of Twitter users with identifiable profile pictures of people that match traditional beauty standards (omitting those with celebrity pictures etc.) while simultaneously keeping track of the tweets of those supposedly attractive people. According to the researcher conducting this study, Dr L. Lui; there is no correlation between physical attractiveness and possessing a grandiose personality.

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Case 2:

Another case is the survey that interviewed men from several states in the USA, asking them whether they felt any sort of attraction for other men, and it was clear that there was a drastic difference between regions that were more tolerant towards gay people than regions that were not, I.e. LGBT+ friendly states like California and Rhode Island had a significantly higher population of gay people than relatively less intolerant states like Texas and Mississippi, and once this information was out, the public went haywire, suggesting two theories, its either that gay men tend to move to places of more tolerance, or that people in less tolerant states are less likely to respond truthfully and freely in surveys regarding their sexuality.

The Study:

Due to big data, the two theories could be put to test, Stephens-Davidowitz (Data Analyst at Google) used data from Facebook to investigate were men who self-identified as gay were born and where they moved. There was some tendency towards movement from less tolerant to more tolerant places.

But, that movement alone could not explain the large regional differences seen in surveys; the next step was tracking search engines of pornography sites users, specifically males seeking gay-male porn, which suggested that roughly 5% of pornography searches in America; regardless of the region, were searching for gay-male porn. The conclusion was that 5% of men in America are attracted to other men, regardless of the states they are from.

Big Data, Predictive Analysis and Psychological Behaviour Patternsbig data applications

Big Data can be used in predicting language patterns of a certain age group, which can be useful for several reasons; one of which is that if education targeted to a certain age group would be more appealing if it closely resembles the language that they speak; another reason is to make it manageable for linguists to track the evolution of language and predict decaying languages that should rather be rejuvenated as a cultural treasure.

Big data was also able to help Data Analysts predict recent drone attacks in the UK which, predictions were not taken seriously until the Gatwick Airport Drone incident, this promoted the idea of using Big Data to predict human behaviour leading to vandalism, and possibly terrorism.

Conclusion

Big Data is surely reliable in terms of Psychological Research, specifically more than the traditional flawed methods, and several types of research suggest that Big Data is actually more cost-effective than traditional Research Techniques; better, Big Data is capable of the mass investigation of human behaviour over social media, this can help prevent future tragedies, and aid in the development of a more effective educational system.

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Here’s How Companies Use Data Science To Launch Product Campaigns

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Here’s How Companies Use Data Science To Launch Product Campaigns

Summary

In this article, we’re going to look at data science and its applications in the marketing industry- specifically in developing and launching campaigns. Additionally, we will also learn about:

  1. How data is used to select the theme or topic for a campaign, and
  2. How companies use big data to study trends that will appeal to their target audiences.

Introduction

data science applicationsIf you have been wondering about how data science can be used to manipulate audiences into buying merchandise, then you’re not alone. Thousands of people ask this question every day and yet, there’s nothing that we really do about it. In fact, in order to make it big and run a successful business, it almost seems as though luck has a big role in deciding whether you will succeed or not. While, traditionally that might have been the case; now you don’t have to depend on luck to have a successful business- whether it is an e-commerce store, or a small-scale, home business. All you need to do is promote your product in the correct way. That’s how you will attract the attention of everyone around you in your area, town, city, and even country.

How They Do It

Understanding how to promote products correctly is very important if you want people to buy your products. Just look at all the ‘big’ businesses. Companies like Pepsi, McDonald’s and Adidas spend millions, if not billions of dollars each year promoting, advertising and launching massive campaigns. And they catch our attention because that’s what you need to do to get people to buy your products. You need them to remember your product, catch their attention long enough for them to make the purchase.

That’s really all it is.
Making sure that you remember the product long enough to give in to the trend and buy it.
You will see this theme run across every company- regardless of which sector or how big it is. So, naturally, there’s a lot of competition. And how do you stay ahead of the curb?
You use Big Data to analyze, predict and perfect your new campaign idea.

Choosing Campaign Themes

data science applicationsThe marketing industry has actually been using data science longer than a lot of industries. This is probably due to the fact that promoting campaigns does relay a lot on what sort of products customers would like. After all, you need to make sure that the customer chooses your product at the expense of some else’s. That’s just how it works. But once you’ve got your products made, ready for the world to see them, you need to promote them. In a way that catches everyone’s attention.
Large corporations have the advantage of having entire marketing and research departments dedicated to market research. This used to be more intensive back in the day when surveys from customers were used to decide the theme or tone of upcoming campaigns. Now, while a lot of companies still listen to their customers’ feedback, with the help of Big Data, they’re able to study a lot more.

 

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data science applicationsHow Trends Are Studied

Thanks to the rise in internet usage and social media, it’s become fairly easy to see how different groups of people react to trends. This is why you will often find many brands jumping in on popular trends. They know that people will be keeping track of certain trending topics and they plan to reap profits from it as much as possible. Consumer behavior is fairly simple to track down these days thanks to the voices and feedback of audiences heard via social media. The next step for a company launching a new campaign is to look at the available data and analyze it. With the help of data science, it is now possible to not only process massive amounts of data quickly but to also analyze and ‘predict’ what is likely to happen if a certain campaigning approach is taken. Data science helps companies to analyze rising and falling trends, This way they are able to predict things like consumer behavior, sales and thus are able to price their products according to the state of the current market. This way they are able to form their marketing strategies and the tone of their campaign.

data science applicationsCompanies with bigger budgets will also use data science to predict the right time to launch their campaigns. Again, this is done by processing information and predicting the ‘peaking point’ of a trend. This is the best time to launch a campaign- and it’s all done through market analysis. Of course, this depends on the company’s intentions as well: do they plan on targeting their loyal demographics, or do they want to appeal to a larger audience? Big Data provides them with the most economically beneficial options and then, depending on its profit margins, a company will launch its marketing campaign.

Conclusion

Big Data is a key player when analyzing market trends, and a lot of companies understand this. After all, no one wants to make a loss and everyone wants to make a lot of profit. So, it’s really no surprise that companies have turned to data science to help them understand the massive volume of data available to them. Humans are prone to bias and a single mistake in marketing can cause a significant blow to a company’s reputation as well as its profits. By using data science, they have facts and figures to back their marketing strategies and getting you- the consumer- to pay attention and buy their products.

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