4 Types of Data in Clickstream Measurement

“A sense is a physiological capacity of organisms that provides data for perception.” – Wikipedia

In the world of digital we often deal with “listening, viewing and touching” (touch to click or swipe). It is becoming critical for businesses to collect the right data from digital channels for accurate perception of our customer’s excitement, enagement, confusion and frustration.

Collecting the right data in the right way would increase the accuracy of understanding our customers better.  Lets look at the kind of data available, how can we collect and why:

System Data, User generated data, application generated data and meta data
System Data, User generated data, application generated data and meta data

1. System Data such as  Visitor’s browser details, OS, user network IP, time of visit and other user-agent data.,  This data needs to be collected from the visitor’s browser.  To collect this data most clickstream anlaytics tools provide basic javascript which would require absolutely no or minimum configuration.  Optionally, this data would also be available in your Server logs. But, using server logs has some drawbacks that we talk in the rest of the article.

How does this data help you?
This data mainly helps to itentify a visitor journey leakage in a segmented cluster.  In additon, visitors network IP allows to reverse lookup for their geo-location details.

Some of the use cases would be “ensuring the compatibility of your digital application(s) accross different technological devices and browsers”, “better target sales offers or customized targeted content to address different visitors from targeted states or locations”.

2. User generated Data – data gets generated everytime the user touches, clicks or swipes on your digital application. That’s critical data that we can’t miss.  Do we collect all of the data? Mostly not all but some as it required element wise scripting/tagging at User Interace(UI) level.

There are technologies coming to the market to collect all of the data thru DOM manipulation. But the data is often used at a single session or one visitor level.

3. Application generated Data – our digital applications also generate data based on user actions.  For example, number of search results, no search results, order completion status etc.,  This data needs to be collected from the visitor’s browser/app.

4. Meta Data – Page or Screen Names, Product SKU’s, Classifications.  This type of data is generally available with us and doesnt require to retag and collect them again from the visitor’s browser.

Meta Data helps to read and perceive the data elements better.

 

(This is a live document and keep updating..)

Big Data & Digital Analytics

Adobe Marketing Cloud, Google Analytics, IBM Core Metrics and Web Trends – all of these tools provide end to end digital data collection, data processing and provide you standard template based reports.  All of these come as one package as ASP based model where you just load the software application in the browser to implement or use.

In late 90’s or early 2000’s- there were no options available for businesses but to rely on this software as storage and computing were quite costly to invest in-house.

Digital Analytics
Digital Analytics

Digital Analytics Tools Landscape – All the above digital analytics tools revolve around the four key steps.

Lacking Flexibility: Proprietary software lacks flexibility to meet unique business needs.

Cost: High License Fee, often over paying for the solutions offered.

Not standalone: Often require an additional technology or resource for meeting the business needs.

Scalability: Scaling is not immediate.

Takes a large amount of time to process the data and this makes system unavailable

With the proprietary measurement frameworks, enterprises have no control in how the data is collected, stored, processed and visualized.  They often paying for multiple tools to meet one business requirement.
The Next Big Opportunity is in your Data and this data is across the globe in different formats.

digital_data_everywhere

digital analytics data in cloud and everywhere

Data is everywhere, it is imperative for businesses to collect this Data from across channels and combine the insights to optimize the multi-channel customer interaction processes and marketing.

But, the challenge is most enterprises have already locked or invested heavily to deploy the existing tools which operate in silo and never speak to each other fully.

However, with the current generation of cloud computing and Hadoop frameworks – it is working out very cheap to store and process the data the way your business needs and no more need to compromise the business with one size fit all software solutions.
Augment the current technology platforms in Digital Analytics in preference to :

Big_Digital_Analytics

Big Data Technology Stack “cloud computing and hadoop frameworks”

Tag Management Essentials

Tag Management is the second generation digital analytics process, this was introduced to universally tag the pages for collecting data for multiple data collection/measurement tools –  One Tag to address all your data collection & re-marketing needs.

Some of the widely used “Tag Management Tools” in the market are – Google Tag Manager, Adobe Dynamic Tag Manager, Ensighten Tag Manager, Tealium.  All tools listed here serves the core purpose of digitally collect and distribute data to multiple systems with minimum tags in the page.

Lets explore if we really need a tag management tool for measuring clickstream behavior of our digital visitors.

If we segregate complete click-stream measurement into three different categories:

1. System Data such as Browser language, version, OS, user network IP and  other user-agent data.,  This data needs to be collected from the visitor’s browser.  To collect this data most clickstream anlaytics tools provide basic javascript which would require absolutely no configuration thru tag management tools as this collects data from all standard browsers in default way.

2. Meta Data is the data about data such as page names, classifications, product skus, targeted language & country and so on.  This data is defined by us and available with us.   We have absolutely no reason to recollect this data using the tag management.  I have always seen 80% of the data collected currently about the data we already have with us. There are different ways to send the meta data back to the tools to avoid page tagging.

3. Dynamic Clickstreamstream Data, this type of data is non-standard for each visitor and each click.  This data is not known to us nor can be collected in a default like Meta Data. This data generally includes number of units, sum of revenue. If you really have a shopping-cart and visitors can add multiple items into the cart or you want to know what keywords are used on your site search. This becomes significant for you.  though the data you collect is less than 1% most times, the value is above 80%.

The conclusion is if you don’t have dynamic data generated by your visitors..you are fine to deploy basic tags on the page thru a common javascript file and get out of tag management from investment perspective.