2. Digital Intelligence
Traditional web analytics techniques were not designed for the breadth of
channels, devices, and speed that fuels today's digital interactions. It is
fundamentally inadequate to accommodate emerging channels, sophisticated
consumers, technical challenges, and the democratization of analytics within
data-driven enterprises. Because subpar analytics puts customer relationships at
risk, Forrester has redefined the modern practice of web analytics as "digital
intelligence."
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3. Some of popular tools
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Adobe Site
Catalyst
Google
Analytics
IBM Digital
Analytics
WebTrends
Splunk> Piwik
«GA has more presence than all
other vendors combined»
4. Old paradigm of Web Analytics
Clickstream
Insight
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Clickstream data is great at the what, but
not why
• What pages did people view on our
website?
• What product did people purchase?
• What was the average time spent?
• What source did they come from?
• What keywords or campaigns
produced clicks?
• What this, and what that, and what
not?
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Valuable Tools for Tracking
• Visits
• Top site content
• Web browser statistics
• Customer activity
• Referring links
• Search keywords
• Path navigation
Traditional Solutions for Digital Analytics
6. But the Business Requires Deeper Intelligence
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What other parts
of the organization
interacted with this
customer?
We’re only looking
at website traffic.
I need to see data
in real-time.
Sorry, our system is
batch.
We need to create
detailed customer
profiles.
We only track
aggregate statistics.
7. Web Intelligence: Challenges & Trends
Existing Solutions - Challenges Emerging Trends
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• Batch processing for clickstream data
• Incremental cost to get raw data
• Point solutions that focus on solving
only clickstream analysis
• Difficult to configure and maintain
analytics tagging for websites
• Inability to conduct advanced
analytics in web analytics solutions
• Increasing need for real-time access to
data
• Correlating clickstream data with
other data sources
• High value data exported to Big Data
solutions (Hadoop, Cassandra etc.) for
advanced analytics
• Need for access to raw data for
customer analytics
8. What?
How
much?
Why?
What
else?
Gold!
The updated paradigm of Digital Intelligence
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Clickstream
Multiple Outcome
Analyses
Experimentation
And Testing
Voice of
Customer
Competitive
Intelligence
Insight
How reliable: IT Operations
• Deployment, operations, maintenance, tuning
and repair web-based applications
What: Clickstream
• Foundational data
• Analyze Visits, Visitors, Time on Site, Page
Views, Bounce Rate, Sources and more
How much: Multiple Outcome Analyses
• Increase Revenue
• Reduce Cost
• Improve customer satisfaction/loyalty
The Why: Experimentation and Testing
• “Experiment or die”
The Why: Voice of Customer
• Surveys, usability testing
What Else: Competitive Intelligence
• Compare your performance vs. others
IT Operations
How
Reliable?
9. Data Collection Mechanism
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Web logs (web server
logs web entities)
Web Beacons (A 1x1 pixel
image, sends page view
data to the third party
server, which in turn
sends image with code
back to the browser to
read cookies and capture
more data)
Javascript tags (Javascript
code executes as the
requested page loads,
sends data to web server)
Packet sniffing
(Software/Hardware
before Web Server that
captures web data and
sends to the server)
10. Log file analysis vs. Page-tagging analysis
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Pros Cons
Log file
analysis
• Access to server-side information (404 pages, 500
errors, time taken, etc.)
• Every resource is counted (images, RSS feeds, etc.)
• Bandwidth information is available
• Provides the most accurate view of what is actually
happening on the Web server.
• Visits by automated bots are tracked, which can
reveal security problems or hack attempts, as well
as search engine spider activity.
• Not as good at counting “live” users that may visit the
website via proxies
• No intrinsic ability to report on browser-side data (resolution,
number of colors, etc.)
Page-tagging
analysis
• More accurate “live” visitor count (for webpages
only), if all pages are tagged correctly
• Access to browser-side data
• Anti-spyware software and security software now block
JavaScript callback methods, leading to untracked users
• No server-side information about the website can be
collected
• Requires more maintenance to get the site set up for
analytics
• No method for tracking items like downloads or RSS traffic
• No information is stored on the server
• No way to detect abusers/hack attempts
• No access to historical data. Statistics start on the date the
tracking code was implemented; data prior to that date is
unavailable
11. Product
Analytics
Marketing
Analytics
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Business
Performance
Analytics
Digital Analytics Types
Actionable Insights
Improved User Exp
12. •Multi channel
analysis
• Create detail
customer profiles
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• Easy correlation
with IT data
• Improve site
performance
• Integration of large
disparate data
•No lag in reporting
• Segmentation on fly
• Historical trending
Adhoc
Analysis
3rd Party
data
Integration
360 degree
Customer
View
Operational
Efficiency
Evolving Digital Analytics Landscape
13. Splunk Turns Machine Data into Digital Intelligence
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Customer
Facing Data
Outside the
Datacenter
Applications
Web logs
Log4J, JMS, JMX
.NET events
Code and scripts
Networking
Configurations
syslog
SNMP
netflow
Databases
Configurations
Audit/query
logs
Tables
Schemas
Virtualization
& Cloud
Hypervisor
Guest OS, Apps
Cloud
Linux/Unix
Configuration
s
syslog
File system
ps, iostat, top
Windows
Registry
Event logs
File system
sysinternals
Logfiles Configs Messages Traps
Alerts
Metrics Scripts Changes Tickets
Click-stream data
Shopping cart data
Online transaction data
Manufacturing,
logistics…
CDRs & IPDRs
Power consumption
RFID data
GPS data
14. Splunk Digital Intelligence Components
Mobile Analytics Social Media Analytics
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Customer/Prodyct
(Web) Anlytics
BigData Analytics
15. Alerting and Monitoring
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Alerts are just short messages or notifications that help
individuals keep informed about certain things that have
happened or potentially will happen:
• Significant change in the traffic patterns on your site
• Traffic reaches a specific threshold you specify
• Hardware/Software critical errors
• Product sales or failed logins
16. Before Splunk State
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Customer Challenges
• Difficult to integrate and correlate data
from digital channels (web, mobile,
social)
• Traditional databases unable to
combine digital data with other data sets
(IT, point of sale, telecom)
• No/Limited drill-down capabilities into
original data and segmentation of data
• No ad-hoc capability on clickstream data
Business/IT Consequences
• Traditional Web Analytics tool provide
analytics on either server or client side
data – no single view to correlate data.
• Very difficult to answer the “Why”
behind the variance in trends
• Unable to show real value in helping
make key business decisions
17. After Splunk State
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Future Vision
• Easy correlation and integration of digital
data for complete customer interaction
• Deep insight into customer activities and
patterns
• Unique insights from combination of client
and server side data for websites
• Ad-hoc capability on clickstream data
• All relevant data in its original format
• Available for full drill-down
Business Outcomes
• Comprehensive view of customer
interaction for user experience
optimization for web & mobile
• Higher productivity and fewer escalations
save money
• Better visibility leads to better decisions
18. > Capture data from client, servers and mobile applications
> Real-time insights into customer behavior and product analytics
> Segmentation on the fly
> Optimize website user experience
> Understand what marketing initiatives are effective and what content is
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most compelling for website visitors.
> Combine & Correlate across digital channels and with IT data
> Ability to drilldown to individual visitors/customers
Summary
3 types of analytics that organizations typically spend their analytics efforts on – Business performance analytics, Marketing Analytics and Product Analytics
Most organizations do not spend time on Product Analytics.
Product Analytics needs deep understanding of user interactions to answer the “why” question.
Most Analytics tool focus on aggregated data with limited to no ability to segment data and perform session or pan session analysis.
Product Analytics is needed for sustained conversion and better user experience to improve repeat customer behavior
4 key requirements:
Adhoc analysis – Large data volume and changing business needs have shifted canned reporting to adhoc analysis with the ability to easily explore data through segmentation and historical trending
3rd party integration – Multi channel reporting and insights needed for identifying user behavior insights. Data stitching is time consuming and painful. Batch processing can further delay deriving insights
360 degree view – Detailed customer profiles are pre-requisite for effective marketing campaigns and customer retention
Operational – Changes in search algorithms and increasing expectations from users for speed/user experience is forcing better site uptime, response time. Easily co-relate web + IT data