The document outlines a webinar on operationalizing customer analytics using Azure and Power BI, emphasizing the importance of data-driven insights for customer loyalty. It discusses challenges organizations face, including data disintegration and manual reporting, and presents nine key analytic use cases to optimize customer acquisition and retention. The presentation also highlights the process of automating analytics to improve business value and operational efficiency.

1. Operationalizing Customer Analytics
October 18, 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. DRIVING CUSTOMER LOYALTY
WITH AZURE MACHINE LEARNING
– WATCH ON DEMAND
Webinar Series
OPERATIONALIZING CUSTOMER
ANALYTICS WITH AZURE + POWER BI
– TODAY, OCT. 18TH 2017
BUILDING A SCALABLE CUSTOMER
ANALYTICS HUB
– WED, NOV 8 | 11AM
go.ccgbi.com/cloudwebinars.html

5. John Bastone
Dir. of Customer Analytics, CCG
Mike Druta
Solutions Engineer, CCG
SPEAKERS

6. Customer Analytics 101
Operationalizing Analytics
Technology Solutions
Demonstration
Q & A

7. Introduction to Customer Analytics

8. 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”

9. 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.

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

11. 9 Key Analytic use cases every organization should be practicing
Optimizing strategies around the
acquisition, growth and retention
of customers.
Acquisition Analytics - Where can I best find
customers that will drive an inordinate
amount of value?
Next Best Offer - What is the best offer I can
personalize for each customer, as the basis of
my next interaction with that customer?
Churn Modeling - Who among my customers
are at greatest risk of leaving me for a
competitor in the next 30 days?
Providing deeper insights on how
customer perceive every interaction
with your business, beyond just
using purchase data.
Cross-channel analytics - What are the
individual channel preferences each customer
has for how they prefer to be engaged?
Text Analytics - What is the customer
sentiment and immerging topics shared across
social, chat and web channels specific to our
company and competitors?
Marketing Optimization - Which aspects of
my digital strategy resonate best with the
customers that matter most?
Developing a more holistic
understanding of how a customer
engages with every touchpoint of
your business over time.
Omnichannel Analytics – Where are you
interacting with this customer? What is the
most valuable channel?
Customer Journey Mapping - What are the
moments of truth in the sales funnel have the
greatest impact on the conversion of that
prospect to a customer?
Customer Lifetime Value - What can I expect
the customer lifetime value and associated
actions to be for differing customer segments?
Customer
Loyalty
Customer
Experience
Customer
Journeys

12. 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…

13. Operationalizing Customer Analytics
A Practical Approach

14. Desktop ML –> Cloud ML
Load Data
Prepare Data
Build & Train Models

15. Process
 Non-sustainable
- Handling multiple
predictions is untenable
 Repetitive
- Manual work
- Prone to user errors
Data
 Siloed Data
- Not ready for consumption
by a larger audience
- Missing attributes
 Quality Issues
- Timeliness
- Accuracy
Current Set of Challenges
People
 Specialized Skills
- Data Scientist
- Integration Specialist
 Resource Allocation
- Competing priorities

16. Governance
 Data Quality
 Data Security
 Single version of the truth
Next Step: Operationalizing Analytics
Automation
 Programmatically invoke predictive models
 Real-Time or Batch
 Include Predictive Analytics into your ETL processes
Dashboards
 Data Exploration
 Data Visualization
 Self-Service Analytics

17. Practical Use Case: Review and Solution Architecture
Telco Dataset
- Customer Demographics (Gender,
Marital Status, etc.)
- Products sold (Phone line, Internet
Service, etc.)
- Transaction Aggregates (Monthly/Total
Charges, etc.)

18. Demo
Part 1 - Demographics and Product Mix - Data Exploration in Power BI
Part 2 - Deploy and Run the Churn Model
Part 3 - Making Predictive Model Insights Actionable using Power BI

19. Key Takeaways
Using Power BI to get a better understanding of data and customer
Azure ML differentiator: enterprise grade deployment and management
Don’t just predict an outcome: take action on it before your business environment changes.
“You never know who's swimming naked until the tide goes out.”
Warren Buffett

20. What’s Next
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

21. Watch the full version of this presentation on
www.ccgbi.com/resources
Watch the Full Webinar

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