1. How to Build a Scalable Customer Analytics Hub
November 8, 2017

2. Introductions

3. CONVERGENCE CONSULTING GROUP
We help companies improve business performance
through the use of Data & Analytics
I would not only recommend CCG to any company, but question why you would
engage with anyone but CCG. - Director of Customer Success
DATA ANALYTICS STRATEGY

4. John Bastone
Dir. of Customer Analytics, CCG
Lexy Kassan
Marketing Analytics Consultant, CCG
SPEAKERS

5. Webinar Series Recap
Trajectory of Data
Scaling Analytics
Stair-Step Methodology
Q & A

6. DRIVING CUSTOMER LOYALTY
WITH AZURE MACHINE LEARNING
– WATCH ON DEMAND
Webinar Series
OPERATIONALIZING CUSTOMER
ANALYTICS WITH AZURE + POWER BI
– WATCH ON DEMAND
go.ccgbi.com/cloudwebinars.html
BUILDING A SCALABLE CUSTOMER
ANALYTICS HUB
– WATCH ON DEMAND

7. TODAY’S WEBINAR
Scale for the enterprise
 Increase data volumes
 Lowest grain transactions
 Historical data
 Add more data sources
 Multiple systems (ERP, CRM, POS, etc.)
 Social data
 Web logs
Solve issues related to volume, variety, and velocity
Increase analytical capabilities
BUILDING A SCALABLE CUSTOMER ANALYTICS HUB
WED, NOV 8 | 11AM

8. Scaling Customer Analytics:
A Practical Approach
Lexy Kassan

9. Scale Out
Ensure Veracity of information provided
Bring Value to other areas
The Five V’s of Enterprise Scale Analytics
Scale Up
Larger Volume of existing data
Extended Variety of sources
Higher Velocity of ingestion and use
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10. Streaming Data
Unstructured Data
Semi-Structured
Data
The Varied Customer Data Ecosystem
Structured
Data
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11. Scaling Up Data Variety
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12. Scaling Up Structured Data Volume
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13. Scaling Up the Telco Churn Model
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14. Scaled Up Feature
Full Database (Volume)
Changes to Contract trigger (Velocity)
Identify abnormal charges above contract (Velocity)
Sensor data flags service outages (Volume & Velocity)
Social media mentions and sentiment (Variety)
Scaling Up the Telco Churn Model
Original Scale
Extract
Contract Type
Total Charges
Tech Support
No sentiment data
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15. Scaling Out to Other Business Areas
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Analytics
Marketing
Customer
Service
Field
Service
Finance
R&D
Sourcing
Network
Eng.
Human
Resources

16. Scaling Out with Integrity
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17. Scaling Out the Data Science Team
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18. Scaling Out the Telco Churn Model
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19. Call Center
Batch & Real-Time
Route high churn propensity
calls to a dedicated
retention team
Re-score churn risk based
on call outcomes
Feed updated scores back
to the database for use in
other applications
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20. Route Management
Batch & Real-Time
Daily routes prioritize high-
value, high-risk customers
for service
Service completion triggers
real-time update of time to
resolve and re-scores
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21. Finance
Batch
Daily batch rolls up revenue
likely to churn in the next
30 days
General ledger is
automatically adjusted to
reflect latest estimates
End-of-Month audit checks
movement from prior
month close
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22. Key Takeaways
 Scaling your Customer Analytics Hub means fundamentally changing the definition of scaling to include
not just more data but also varied data at increased speed used in more ways
 Integrated solutions such as Azure provide more flexibility and future-proofing than point solutions
with lower overhead and management
 Technology is not the only area to address in scaling your Customer Analytics Hub – you must also
consider the processes and the people supporting and using that technology
 Governance and trust in the data leads to reusability across business units
 Activities associated with analytics are a starting point for business transformation that spans well
beyond the point of origin

23. Stair-Step Approach to Customer Analytics
John Bastone

24. Challenges We See
People Process Technology Data
Small Staff
Specific to analytics
supporting a large
scale audience
“Finance, Planning and
Allocation, Merchandising,
Wholesale and Marketing
all have analysis needs to
support”
BI Evaluation
Numerous choices of
reporting and analysis
capabilities
“Reporting is clunky,
requiring us to build
queries and views to
make use of it”
Disintegration
Data sources spread
across the
organization
“We have data
spanning, ERP, Point of
Sale, eCommerce and
Digital residing in
different places”
More Freedom
For line of business to
create their own
reports
“We have 4 different
teams running against
the same data set, but
interpreting it in
different ways”

25. Evaluate how different people
will want to consume
information
Anticipate demands by
implementing processes
for triaging requests
& questions
Prioritize use cases, create
reference architecture, and
maximize technology
investments
Prioritized business
value should shape your
data strategy &
architecture
Business Value should
be evident within 3
months, iterative &
high impact.
What Is Needed – Analytics That Drive Business Value
Whether you are looking at technology investments, data management or advanced
analytic solutions, every initiative must drive towards business value.

26. Where Companies Are Betting On A Payoff
41% of organizations increasing investments in customer
analytics this year
Analytics Investment

27. 1. Analyze
2. Automate
3. Scale
4. Start all over. Now that analysts are pulling from an
enriched, robust data set, can produce more valuable
insights to reapply in the database.
3. Providing access to all and adding more data sources requires an
enterprise grade storage engine, of which there are many options.
1. This short-term phase focuses on data exploration. Often the work of
a single analyst or data scientist to find out something about your
customer’s loyalty, journey or experience that you did not know before.
Stair Step Approach to
Customer Analytics
2. Create processes to remove the need for manual work, socialize the
outcomes, and make customer insights actionable. This medium-term
phase focuses on automation, as well as data and predictive modeling.
4. Rinse & Repeat…

28. Series Recap
1. Analyze 2. Automate 3. Scale
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𝒇𝒇(𝒙𝒙)
“Scale Up”
“Scale Out”

29. Have questions?
Submit them to the right of your screen now.
Q & A

30. THANK YOU!
www.ccgbi.com | 813.265.3239 | info@ccgbi.com