In an omnichannel world, organizations struggle to gain complete, 360-degree views of engagement with current and potential customers. Organizations are digitally transforming business processes and customers’ engagement preferences are changing due to upheavals such as the coronavirus pandemic, so traditional data management cannot deliver a complete view. To get anywhere close, organizations have to spend valuable time and resources knitting together numerous data silos and dealing with complicated replication and redundant data preparation. They must lean on specialists who can code and model routines that should be part of data management. It’s time to tap innovations in data management such as graph databases and geolocation intelligence to gain faster, easier, and more complete views of customer engagement. Organizations need to reduce friction in how they find, connect, and share customer data points, and they need to evaluate how nontraditional data management can help. Join this TDWI Webinar to learn how you can take advantage of innovations to drive smarter personalization, targeted marketing across channels, and more satisfying customer engagement. Topics to be discussed include: - Common pain points organizations are facing in trying to gain 360-degree views of customer engagement and how to overcome them with innovative data management - Graph databases: how they can improve views of data relationships, enhance customer analytics, and take burdens off data scientists, analysts, and users - Important trends in unifying data about customers and their behavior, including graph databases, geolocation intelligence, master data management, and semantic data integration - Governance, security, and customer data privacy: how graph databases and related innovations can help

1. WEBINAR
Capturing Data Relationships to
Develop Meaningful Customer
Engagement

2. Sponsor
2

3. DAVID STODDER
Senior Research Director
Business Intelligence
TDWI
dstodder@tdwi.org
@dbstodder

4. Agenda
• Customer engagement in today’s
multichannel environment
• Data challenges involved in
understanding customer
engagement
• Technology trends and solutions
• Concluding thoughts and poll
question
• Precisely presentation
• Audience Q&A

5. Customer Engagement: Critical, But Changing
• Engagement: Building meaningful
connections between customers and
consumers and your company’s brand,
products, and services
• Foundation of loyalty: long-term
engagements can reduce the cost of
finding new customers
• Change is the constant: Customers’
preferred engagement styles and
channels change (Gen X, Millennials,
Gen Z)

6. Multichannel Engagement Adds Complexity
• Disconnected journeys:
Search to compare prices and
brands; look to see if a local
store has it; consult reviews and
comments, etc.
– Attribution: Who influences their
decisions? What messages?
– Can you measure engagement?
• Digital transformation: More
parts of organization could use
customer insights as channels
shift (e.g., inventory, fulfillment)
Credit: www.taxseductible.wordpress.com

7. Marketing & Customer Engagement
• Customer-centric understanding of what will
create, stimulate, or influence behavior
– Clear, personalized, and empathetic
communication at right point of engagement
• Needing the big picture: Multichannel
blind spots in knowing customer journey
– Looking for engagement throughout, not just
at the beginning or at end with purchase
decision
• Flexibility: Don’t want to be locked into “the
way we’ve always done it” when customer
preferences shift

8. Delivering Value from Data for Engagement
• Personalization: Using data to improve
targeting and optimize marketing
– Driving automated, real-time data-driven
engagement
• Effective engagement relies on gathering and
integrating data from multiple channels
– Monitor, measure, and analyze feedback;
performance metrics
• Growing demand for embedded analytics
– TDWI research: 23% currently embed analytics
in CRM, SFA, or marketing applications; 19% in
externally facing websites and portals
Credit: Getty images

9. Impact of Covid-19 on Data/Analytics Projects
Q. Due to Covid-19, how has the nature of your work changed?
(Please select all that apply.)
• 54%: We are being asked to answer new kinds of questions based on the
economic impact of Covid-19 on the company
• 38%: We are being asked to add new attributes/features to our analyses
• 30%: We need to update our models and other analytics to deal with
changing customer behaviors (e.g., retraining models, recasting customer
segments)
• 30%: We’re running analytics more frequently given the constantly
changing landscape
• 24%: We are incorporating new data sources to our data systems
Source: TDWI research survey of analytics and data professionals, July 2020

10. Data Challenges: Poor Visibility, No 360 View
• Customer data lives in siloed systems
– Multiple online and offline channel data tied to point
applications and processes
– TDWI research: 51% cite “too many data silos to
connect” as one of biggest challenges
• Hard to gain anything close to 360-degree view of
customers that unifies all activity
– Single view of relevant data is vital to personalization
as engagement involves multiple channels
• Poor visibility into the entire customer journey
– Cannot see what is most or least effective in meeting
marketing goals or raising satisfaction
Credit: Namos Solutions

11. Data Challenges in Analyzing Relationships
• Data systems must make it faster and easier to
analyze data relationships across channels
– Customer’s buying journey across multiple online
channels, social media, and in-store experiences
– View data relationships between traditional and
new types of data including sensors and in-store
beacon technology for motion tracking of customer
traffic patterns in retail stores and malls
• View and analyze intersections between
customers and in social & geolocation networks
• Governance benefits: monitoring collection and
analysis of PII
Social network visualization

12. Weakness of Traditional Data Platforms
• Data warehouse
– Cleansed and transformed data, but slow
process to get there, leading to more data silos
– Only offers selected data; geared to reporting
and less advanced analytics
– Built on relational db structure; not set up for
flexible analysis of data relationships
• Data lake
– Massive centralized data collection, but
depends on custom, often manual work
– NoSQL; good for unstructured data exploration
and ad hoc analytics & AI/ML, but depends on
custom programs to examine data relationships
Credit: Data Flair
Credit: MemSQL
Traditional Data Warehouse Architecture

13. Looking Beyond Traditional Data Systems
• Beyond traditional relational systems
– Trend to reduce join complexity and slowness:
columnar, NoSQL, cloud DW/data lake hybrids
– Location intelligence and geospatial data analysis
• Master data management and semantic
integration
– Centralizing high-quality, consistent reference
data drawn from multiple sources
– Using data catalogs and business glossaries to
find related data sets; building a knowledge base
about object of interest (e.g., a customer)

14. Missing: Explicit Focus on Data Relationships
• Fast, consistent insight into data relationships is
essential to creating meaningful customer
engagement in a multichannel world – yet it’s not
easy with traditional data systems
• Graph database
– Explicit representation and storage of data
relationships – dependencies between nodes of data
(“edges” representing relationships between nodes)
– Designed for fast retrieval of complex hierarchical
structures; similar to network model databases
(1970s, Charles Bachman) but at higher abstraction
– Relationships are as important as the data itself
Charles Bachman

15. What Graph (Semantic) Database Offers
• Can use in-memory computing and computation advances to
support flexible analysis and visualization of persistent data
relationships
– Retrieval typically using specialized language that avoids complex
join operations
• Customer engagement across multiple channels generates
interconnected data that using a graph database can make easier
to retrieve for analysis and visualization
• Graph plus MDM: Faster understanding of related reference data
for customer analysis, fraud detection, and governance

16. To Conclude: Insights Help Engagement
 Meaningful connections should be explored,
analyzed, and visualized in data
 Organizations can improve marketing efficiency
and effectiveness by improving understanding of
customer data relationships across multiple
channels
 Organizations should evaluate technologies that
can improve speed, consistency, and flexibility in
understanding complex data relationships that
exist across channels
 Share data relationship insights with all business
processes that impact customer journey

17. Thank You!
David Stodder
Senior Director of Research for Business Intelligence
TDWI (www.tdwi.org)
dstodder@tdwi.org
@dbstodder

18. AARON WALLACE
@aaronwatx

19. Capturing Data
Relationships to
Develop Meaningful
Customer Engagement
Aaron Wallace – Product Manager
Precisely Spectrum Context

20. Digital Noise in the Age of Modern Data Architectures
20
• Digital transformation and innovations within analytics and data
management provide a foundation for better understanding of
relationships
• There are more avenues than ever for marketing to our
customers in a hyper-personalized manner
• The challenge of COVID-19 has made the world a more digital
place – in-person interactions are the exception for the
foreseeable future
• The concepts of signal and noise are rooted in physics and
statistical analysis – how clearly can we gain insight (signal)
against a background of many irrelevant or confusing inputs
(noise)
• Customers root through noise in marketing communications
• Analysts try to eliminate noise in data gathered through these
efforts
• A modern approach to gathering, managing and integrating
data across the enterprise drives personalization strategies
20

21. There are two key imperatives that drive change -
often perceived as contradictory
#1 Grow #2 Protect
And if being in a hyper regulated industry could lead to
innovation and growth opportunities
CRM Optimization
Digital Transformation Governance
Risk ComplianceChannel Optimization
Experience
Optimization
Financial Crimes
& Compliance
Data Privacy
Regulations
Fraud
21

22. Presentation name22

23. Graph Databases Provide Foundation for Context
23
• Graph technologies enable innovation in several areas, including data and
metadata management
• Graphs promote agility in preparation of data for increasing analytics
demands and enable more complex solutions for changing business
priorities
• Graph databases allow a better understanding of customer behavior by
providing a more accurate representation of customer data and all the
associated relationships
• Graph technologies are extremely versatile and performant especially with
complex queries and enable faster insights
• Graph technologies provide unique algorithms for understanding concepts of
centrality

24. Location – Spatial Context
24
• Location provides key context for the data fabric
• Geolocation and boundaries provide context along
several dimensions
• Not just a point on a map – relationships between
locations, logistical networks and customers
• Personalization strategies benefit from understanding
customer location and movement
• Can be used to drive real-time interactions and offers
when customers opt in
• Adding location information to a single view or data
fabric brings spatial context lacking in more traditional
views of customer data

25. More info available: Customer 360°
Product sheet Case Studies Whitepaper

26. Thank You

27. CONTACT INFORMATION
If you have further questions or comments:
David Stodder, TDWI
dstodder@tdwi.org
Aaron Wallace, Precisely
Twitter: @aaronwatx
tdwi.org