Accelerating Personalization to Cut Through Digital Noise
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The document discusses the importance of digital personalization in cutting through digital noise, emphasizing the need for effective data management and integration to enhance customer experiences. It highlights trends in personalization, consumer expectations, and the role of graph databases and AI in driving individualized marketing strategies. Additionally, it outlines how modern data architectures and real-time contextual information can improve customer engagement and business outcomes.
Accelerating Personalization to Cut Through Digital Noise
- 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
Editor's Notes
- #7: how do I personalize for individuals based on what I know about where they live/work/shop? https://www.forbes.com/sites/blakemorgan/2020/02/18/50-stats-showing-the-power-of-personalization/#515e1c9d2a94