1. Web Analytics In the Bigger Picture of
Cross-Channel Analytics
Eric Tobias
Director, Analytics Services
Unilytics Corporation
eric.tobias@unilytics.com
(416) 441-9009 x228

2. Topics We’ll Cover
What We’re Seeing
•
What is Cross-Channel Analytics?
•
Common Goals
•
Challenges Encountered
•
Key Terminology
•
Components
•
Best Practices
•

3. What We’re Seeing
500+ engagements in North America across numerous verticals:
• Self-service
• E-commerce
• Government
• Consumer packaged goods (CPG)
• Professional & association organizations
• Service providers
• Consulting
• Intranet
• Legal

4. Web Analytics Adoption Phases
We categorize analytics adoption in five phases:
1. Implementation – Software acquisition and installation
2. Basic Analysis – Reporting and monitoring of page views, visits, visitors, etc.
3. Optimization – Campaigns, visitor segmentation, multivariate testing
4. Automation – Dashboards and alerts
5. Integration – Core business systems, cross-channel analytics
We have observed a marked increase in phase five implementations in
the last year.

5. What is Cross-Channel Analytics?
Cross-channel analytics is the collection, analysis, measurement, and reporting
of customer interaction with a company, product, service, or brand.
It is based on a hierarchy:
Company
↓
Channel
↓
Touchpoint
↓
Customer

6. What Channels?
We generally work with four types of channels. Those channels, along
with touchpoints in each, are:
• Digital – Web, e-mail, chat, online advertising, web 2.0, surveys
• Phone – IVR, phone support, telemarketing
• Print – Forms, publications, mail, coupons
• In-person – Service counter, point of sale

7. Benefits of Cross-Channel
Obtain a consolidated view of customer interactions
•
Optimize customer interaction across channels
•
Achieve more holistic view of customers
•
Correlate data from various channels
•
Extract trend and growth metrics across channels
•
Identify drivers that cause cross-channel “churn”
•

8. Challenges Encountered
There are a few challenges to these projects:
• Political issues when working with managers from each channel
• Frequent lack of common identifiers
(e.g., customers, activities, topics) requires translation infrastructure
• “Time” has mixed meanings between systems
• Integrated data is large, tends to require BI approach
• Huge volume of measures and metrics requires classification and a
degree of automated handling

9. Examples of Cross-Channel Projects
A few examples of our clients currently executing cross-channel projects:
• An e-commerce client is reducing customer service costs by transitioning
customers from phone-based support to web-based self service.
• A major government agency is reducing their annual costs for forms and
publications by providing web-based versions to the public.
• A telecommunications client is ensuring customers receive the same
corporate message and experience in each channel and touchpoint.
• A CPG client is implementing a system for measuring effectiveness of print-
based promotional campaigns to drive traffic to their brand sites.
• An IT consulting company is proving ROI for a self-service web site by
comparing development costs to cost savings in transitioning customers to
web-based self-service.

10. Standard Components
Most cross-channel implementations will use the following:
• KPI Paradigm
• Cross-channel customer segmentation
• Time standardization
• Metric scoring

11. KPI Hierarchy
Goal High level company goal
Items that are vital for a strategy to be
Critical Success Factors successful
KPI Special metrics that tell you how you are doing
Relationship of measures -
Metrics ratios, averages, rates, or percentages
Raw numbers and data
Measures (web analytics, off-line touch-points, customer
databases, email marketing)

12. KPI Paradigm
Goal
KPIs are driven by company goals
KPI BUT…
KPIs are constructed from Measures
Measures

13. KPI Paradigm Example #1

14. KPI Paradigm Example #2

15. Cross-Channel Customer Segmentation
An example from one of our CPG clients:
Customer Coupons Top Recipes Engagement Personal
Segment Redeemed Printed Demographics Demographics
•Young Mother •Brand X •Recipe 254 •Frequent visitor •Female
•Brand Y •Recipe 786 •Visited 8 times •Married
•Recipe 990 •Within last 3 wks •21-25 y/o
•Registered user •2 children
•Male college student •Brand M •Recipe 123 •Infrequent visitor •Male
•Visited 2 times •Single
•Within last 6 months •18-23 y/o
•Guest status •No children
•Female Retiree •Brand G •Recipe 822 •Frequent visitor •Female
•Brand L •Recipe 890 •Visited > 10 times •Over 65 y/o
•Brand X •Recipe 992 •Within last week
•Recipe 1022 •Registered user

16. Time Standardization
It is not generally feasible to store real-time data from cross-channel
systems, therefore it is necessary to roll measures and metrics up to a
predefined level when integrating cross-channel systems.
Time standardization also handles the discrepancies that exist in
different channels for standard “time” definitions.
Standardizing time requires a survey of the various systems being
integrated and assembling a master list of “time”.

17. Metric Scoring
Key Concepts & Terms :
• Goals – Target values the metric should achieve with a timeframe in
which it should be achieved.
• Valuation – An assessment of the value of the metric at any given
time.
• Change classification – A means for classifying the degree of
change in the metric.
• Impacting factors – A historical perspective on changes that have
had an affect on channels and touchpoints.

18. Metric Scoring Example #1
Example metric: Average knowledgebase searches per visit
Current value: 2 knowledgebase searches per visit
Goals: Short-Term = Reduce by 1 within six months
Long-term = Reduce by 2 within one year
Valuation: 0–2 = “Excellent”
2–4 = “Acceptable”
>4 = “Critical”
Change classification : 0 – 100% = Negligible, do nothing
100 – 150% = Minor, notify assigned analyst
150 – 200% = Noteworthy, notify analysis team
> 200% = Excessive, notify analysis team and channel manager
Impacting factors: Change = New FAQ added to search page
Est. Impact = Reduce metric by 0.5, starting four weeks after release
Act. Impact = Metric reduced by 0.25 within four weeks and then stabilized

19. Metric Scoring Example #2
Example metric: Average daily transfers from web to phone for “Change of Address” transaction
Current value: 450 transfers per day
Goals: Short-Term = Reduce by 100 within six months
Long-term = Reduce by 400 within one year
Valuation: 0 – 200 = “Excellent”
201 – 500 = “Acceptable”
501 – 600 = “Warning”
>600 = “Critical”
Change classification : 0 – 5% = Negligible, do nothing
5 – 20% = Minor, notify assigned analyst
20 – 30% = Noteworthy, notify analysis team
> 30% = Excessive, notify analysis team and channel manager
Impacting factors: Change = Fix intermittent bug that interferes with submit action
Est. Impact = Reduce transfers by 50 per day, starting one week after release
Act. Impact = Transfers reduced by 100 within two weeks

20. Metric Scoring Example:
Dashboards
3
Example #1
0–2 = “Excellent”
2–4 = “Acceptable”
>4 = “Critical”
Example #2
6
0
Goals:
Short-Term = Reduce by 100
Long-Term = Reduce by 400 LT Goal ST Goal
Valuation:
0 – 200 = “Excellent”
201 – 500 = “Acceptable” 0 100 200 300 400 500 600 700
501 – 600 = “Warning”
> 600 = “Critical”

21. Advanced Cross-Channel Components
Some cross-channel implementations will use the following:
• Model scoring
• Text mining
• Topic cross-referencing
• Automated metric handling
• Forecasting

22. Model Scoring
A component that compares known characteristics of customers with
predefined archetypes. The purpose is to identify the “type” of the
customer.
Model / Archetype CRM Web Analytics Phone Support Point of Sale
Young Professional Female 75% 100% 80%
Male College Student 10% 28% 9%
Female Retiree 63% 67%
Middle Age Father 21% 33% 42% 44%
In this example, the customer is recorded as belonging to the “Young
Professional Female” archetype.

23. Text Mining
A component designed to extract “meaning” from large amounts of free-
form text found in many Web 2.0 technologies.
It is designed to find the
general “buzz” about a
company.
For example, is this a good endorsement? Is it an isolated opinion, or is
it representative of others’ views?

24. Topic Cross-Referencing
A component that allows for comparison and correlation of customer
motivation (a.k.a., “driver”) across channels.
Topic Channel Attribute Value
Installation Troubleshooting
Phone
IVR Prompt 1–3–2
Call Topic InstIss004
Digital
KB Article CKB223108, CKB233211
Print
ISBN 978-3-16-148410-0

25. Automated Metric Handling
A component for sifting through the hundreds, and thousands, of
possible metrics and focusing analytic teams on the most important
metrics.
Metric Change Classification Action
Ratio of enquiries to claims
Between 0.60 and 0.75 Notable E-mail analyst
Greater than 0.75 Excessive E-mail team
% of first call resolutions
Between 40% and 50% Problematic E-mail Channel
Between 51% and 75% Notable Daily Report
Greater than 75% Verify E-mail analyst

26. Forecasting
A late-stage component that uses a history of experience with metrics to
forecast values at future dates.
“Based on our prior experience with adding FAQs on top phone call
drivers to the web site, how do we expect the web site traffic will be
affected?”
“What is the current trend for our knowledgebase searches per support
visit, and based on that where will our search volume be in six months?”

27. Wrap-Up
• Cross-Channel has been around for years, but was mainly used by large
companies with physical stores and e-commerce. It is being implemented in a
variety of verticals where companies are entering phases four and five of web
analytics adoption.
• Many benefits to be gained: holistic view of the customer, cost optimization by
channel, company-level view of behavior instead of in isolated silos, and trend and
growth data.
• Many of the challenges encountered by early adopters have been identified and
solutions derived.
• We are consistently receiving calls on how to determine KPIs. The KPI Paradigm is
a best practice to determine the critical metrics.
• Standard components consist of KPI Paradigm, cross-channel customer
segmentation, time standardization, and metrics scoring.
• Advanced components consist of model scoring, text mining, topic cross-
referencing, automated metric handling, and forecasting.