The document discusses customer experience management (CX) as the product of interactions between organizations and customers, emphasizing the importance of understanding customer journeys, behaviors, and preferences. It outlines key questions for analyzing customer interactions and suggests various use cases for improving customer retention and satisfaction through data analytics and real-time decision-making. The content promotes TigerGraph's capabilities in managing customer relationship data and enhancing marketing analytics.

1. Customer Experience
Management
1
Steven Fuller - Sr Solutions Engineer

2. © 2020. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | CONFIDENTIAL INFORMATION |
2
“customer experience (CX) is the product of an
interaction between an organization and a customer
over the duration of their relationship. This interaction
is made up of three parts; the customer journey, the
brand interactions, and the channels the customer uses
to discover an organization's products and services,
and purchase behaviors.”
Digital transformation begins and ends with the customer

3. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Customer Experience Management
3

4. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
DX: Customer Experience Trends
4

5. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
It's all about the Journey
5
● How do we identify the shopping stage for a
person and/or groups
● What are the interaction patterns for
shopperings during the “Discovery” phase?
● What is the Time-to-Purchase from first visit
to purchase?
● What behaviors/patterns cause shoppers to
abandon the journey?
● What Marketing activities influence moving
from “Discover” to “Choose”?
● What Channels drive the most purchases?
● What are the customer Outbound
preferences?

6. © 2020. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | CONFIDENTIAL INFORMATION |
Use Cases
● Customer360
● Marketing Analytics
● Find Best Customers
● Segmentation / Similar Customers
● HTAP Platform
● Customer Journey
Customer Experience Demonstration

7. © 2020 TigerGraph. All Rights Reserved
Customer Journey

8. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Upsert Data in Real-time
ACID Compliant
Post Processing
Immediately Available
HTAP Platform
8

9. © 2020 TigerGraph. All Rights Reserved
Relationship Data
Management
Understand customer segments and
groups to help improve customer
retention and loyalty management
3
Customer Insights
Analytics to understand customer behaviors,
patterns, and preferences
2
Real-Time Decisioning
Instance decisions for product
recommendations, next best action, and
follow-up procedures
1
Why TigerGraph For Customer Experience
9

10. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Business Impact
10
● Improve Cross-Sell and Up-Sell
○ Recommendations
○ Next Best Action
● Improve Customer Satisfaction
○ Personalization
○ Channel Preferences
● Customer Retention
○ Purchase Patterns
○ Improved Offers
○ LifeTime Value

11. © 2020 TigerGraph. All Rights
Reserved
Deep Link Analytics : A Graph Capability
• An expandable and Inter-linked system of diverse data sets for the Marketing Analytics that
can scale to Terabytes, while performing the ad-hoc, deep-link analytics under few seconds.
11
Data Quality
Data Connectivity
(1) Diverse Data Sources
(2) Not Interlinked
(3) Privacy & Security on
Connectivity
(4) Hence,No Ad-Hoc
Querying
(5) Poor Performance on
Speed And Scale
(1) Diverse Data Formats
(2) Millions of Entities
(3) Thousands of
Attributes
(4) Data Consistency
Connected Data
Perform at Scale
Perform at Speed
Cover Future Needs
Choosing Graph

12. © 2020. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | CONFIDENTIAL INFORMATION |
TigerGraph Toolkit Customer Experience?
TigerGraph Accelerators for Customer Analytics and Experience, Recommendation
Use Cases, and Graph Database (sample Use Cases):
● Customer360
● Marketing Analytics
● Segmentation and Targeting
● Product Recommendations
● Customer Services Preferences
● Community Detection
Includes widgets / visualization / apps for:
○ Customer Journey Visualization
○ OmniChannel Analytics
○ LifeTime Value
Contains Foundational components for:
○ Native Graph Database and Schemas
○ Data Ingestion accelerators for Spark and Kafka
○ Graph Algorithms for Machine Learning
12

13. © 2020. ALL RIGHTS RESERVED. | TIGERGRAPH.COM | CONFIDENTIAL INFORMATION |
steven.fuller@tigergraph.com
13
Steven Fuller