Your customers’ inboxes are overflowing with unread email. Even social media and text messages are ignored. There is simply too much digital content and too little time to consume it. So how do you stand out and get your customers’ attention? You need to go beyond traditional Customer 360 efforts. To get your customers’ attention, you need to maximize personalization based on context.

1. Accelerating
Personalization to Cut
Through Digital Noise
Aaron Wallace – Product Manager
Precisely Spectrum Context

2. • The Challenge of Digital Noise
• Digital Personalization to Get a Clear Signal
• Modern Data Management Supports Personalization
• Graph Databases Provide Foundations
• The Role of the Data Fabric
Agenda
2

3. 33
E-Commerce Optimization
(Shipping/E-Com)
Customer 360/MDM
(Omnichannel, Personalization, CRM)
CRM Optimization
(Telco, Retail Site, Bank Branch)
Data Governance
(GDPR, CCPA, Self-Service, BI & Analytics)
Financial Crimes
& Compliance
Insurance
Underwriting Risk
Governance, Risk &
Compliance
Digital Transformation
& Customer Experience
Solving for the needs of B2C organizations

4. Digital Noise – Getting a Signal
4

5. Digital Noise in the Age of Modern Data Architectures
5
• Digital transformation and innovations within analytics and data
management provide a foundation for personalization
• 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)
• 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
• 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 effective personalization
strategies
5

6. Trends in Personalization
6
• Key to growth/improvement on strategic metrics –
Marketing & Sales efficiency – amongst others
• Individually focused messaging provides foundation for
customer relationships
• Solid, comprehensive quality data is a prerequisite
• Drive relevant recommendations
• Privacy and security concerns
• Location context increasingly important
• How much is too much?
6

7. Personalization = Expectation
7
• Smart businesses are prioritizing personalization
• 70% of customers feel frustrated when their customer experience is not personalized
• 98% of marketers say personalization enhances customer relationships
• Consumers expect personalization and are generally willing to share data to get it
• 91% are more willing to shop with brands that deliver a personalized experience
• 72% say they only engage with personalized messaging
• 56% of online shoppers are more likely to return to a website that offers personalized recommendations
• 42% of consumers are annoyed with content isn’t personalized
7

8. Modern Data Management – A
Strategic Foundation
8

9. Managing Information for the Enterprise
Model ProfileDiscover Cleanse Master Operationalize
Define Key Entities
Define Key
Relationships
Whiteboard Style
Model
Business + IT
conversations
Find Relevant
Datasets
Semantic
Discovery
Determine State
of Key Data
Identify
Necessary
Cleansing Rules
Enrichment
Deduplicate
Standardize
Validate
Enrich
Link
Consolidate
Persist
Federate
Real time Services
Visual Queries
Data Visualization
Search
Edit/Maintain
Govern
Precisely Confidential and Proprietary - do not copy or distribute

10. Data Management and Personalization
10
Observations Multi-Dimensional
Views
Clickstream
Segmentation
2 3
Trade Area Analysis
4 5
Recommendation
Engine
6
1
Integrate Disparate Data Sources to Consolidated Single View of Customer
Delivering Insights and Business Value

11. Context – Weaving the Data Fabric
11

12. Digital Personalization and the Data Fabric
Digital Personalization
• Revenue growth is dependent on providing
key information and data
• Interactions must be in real time and enable
seamless transition between all touchpoints
• Intent-based personalization and marketing
drives greater wallet share and increased
brand loyalty
• Physical spaces must be reconsidered in
light of Covid-19
• Data supports personalization across
channels and in the physical space
Data Fabric
• Data Virtualization – Graph databases enable
federated access to business users
• Modern Data Management – connectors across a
varied enterprise data ecosystem
• Data Quality – rules driven approach to
validation, matching, linking
• Semantic layer – supporting self service by key
business side roles
• Graph – property models provide a common
language
• Predictive Analytics - Machine Learning & AI to
drive deep personalization
12

13. Client Challenge: Pockets of knowledge across
systems of records, insights and interactions
Where are you?
What’s happening around
you?
What are you
doing?
Who is with you?
What’s your intent?
What mood are you
in?
Engagement
ContextInsights
Lifetime Value
Retention
Opportunity
Satisfaction
Loyalty
Share of wallet
Profitability
Needs
R-F-M
Attitude
Persuadability
Credit Risk
Activities
Transaction History
Purchases, Payments,
Bills, Invoices,
Statements
Service History
Requests, Tickets,
Complaints
Engagement history
Calls, Emails, SMS, Web
journey, In-store visits
Social Media activity
Posts, Check-ins, Likes, Awards
Preferences
Method, Content, Frequency
Marketing, history
Offers, Responses, Coupons
Relationships
Household
Organizational
Memberships
Social
Places
Individual
Core profile
Identity
Name
Age
Gender
Employment
Portfolio
…

14. Key Technologies Enable Personalization
14
Data Fabric
Powered by Graph
Persona Driven UX
Metadata
Cloud
Applied Machine Learning
This is where information
intersects to provide context

15. Evolution of the Data Fabric
15
• Traditional approaches to data integration and MDM can no
longer accommodate modern variety and volume
• Typical organizations access customer data from 100s of
different databases and file systems
• Effective delivery on a personalization strategy requires a deep
and broad understanding of the data fabric
• Non-technical personas require a semantic understanding of
data models representing key business processes
• Information and access should be democratized
• Security and role based access must be maintained
• AI and ML drive deep learning to enhance model analytics
and outcomes
• Location information for additional fabric layers
• Graph databases focus on relationships to support dynamic
and varied integration and delivery requirements

16. Location – Spatial Context Adds Dimensionality
16
• 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

17. Graph Databases Form the Core of the Data Fabric
17
• Graph technologies enable innovation in several areas, including data and
metadata management
• Graphs will accelerate preparation and adaptability of data for increasing
analytics demands and enable more complex solutions
• Graph databases allow a better understanding of customer behavior by
providing a more accurate representation of customer data and all the
associated connections – key to a strategy focused on deep personalization
• 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 – used to identify social media influencers

18. Artificial Intelligence and Machine Learning
18
• AI/ML technologies increasingly used to drive deeper personalization
• Provide assistance in data consolidation and verification
• Significant gains and efficiencies around fine-tuning complex matching and
consolidation rules across disparate data sources
• Automate self-service mappings for semantic models across enterprise data
landscape
• Integrate customer digital behavior into predictive algorithms
• Fine-tune offers around recommendations leveraging non-obvious
relationships
• Automate social media marketing through targeted measures based on
centrality algorithms

19. Precisely Context Graph - business & technologists
19
For the business
Deep understanding of relationships in data
through knowledge graph enabled modeling
Delivering highly accurate and “clean” data to
advise strategic business decisions
Tapping location intelligence to deliver
contextually relevant views of customers
For the technologist
Agile information management approach
supports rapid prototyping to help technologists
deliver fast, efficient results
Single Platform, built around graph database,
enables technologists to better understand
relationships between data
Self-service and SaaS capabilities provide
ultimate flexibility for busy technologists

20. More info available: Context Graph
Product sheet Case Studies Whitepaper

21. Thank You