From Customer Insights to Customer Action: How to Capture Intent Through AI, ML & NLP
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Explore ways to proactively detect customer intent through AI, ML and NLP, and convert that intent into insights for optimal activation.
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From Customer Insights to Customer Action: How to Capture Intent Through AI, ML & NLP
- 1. 2019 Zeta Global – Proprietary & Confidential www.zetaglobal.com | 2019 Zeta Global – Proprietary & Confidential www.zetaglobal.com From Customer Insights to Customer Action How to Capture IntentThrough AI, ML & NLP
- 2. 2019 Zeta Global – Proprietary & Confidential www.zetaglobal.com | • Meet Ron Sadi • Case Study Overview • Identity, Signals & Connectivity • What the Data Tells Us • Q & A Agenda
- 3. 2019 Zeta Global · Proprietary & Confidential Meet Zeta Global 3 + Vice President, Data Cloud + 10-year veteran of digital marketing and data strategy + Leads business enablement and data strategy, working with brands and retailers to maximize the value of their 1st-3rd party data + Prior to that, he led Data Partnerships at Disqus before the company was acquired by Zeta in 2017 + Bay Area native Ron Sadi
- 4. What Does Data + AI Strive to Do? Better Audiences Better Inputs Better Insights = Better Outcomes
- 5. Data Cloud
- 6. 2019 Zeta Global · Proprietary & Confidential Identity + Signals Unlock Outcomes
- 7. 2019 Zeta Global · Proprietary & Confidential Fundamentally DifferentApproachTo People-Based Targeting & Effectiveness Identity: “DataAt Rest” 200M & 360M Permissioned Individuals & Emails (US) 4B Digital Profiles (US) 2,500 Data Elements per Individual 1T Content Consumption Signals / Month 10B Transactions /Annually 250B LocationVisits / Month Data Sources: + Proprietary Social Commenting Platform + RTB Bid Request Data + Strategic Partnership with PlaceIQ + 500+ real-time intender segments generated by ML & NLP Data Sources: + Proprietary O&O Digital Properties and Sources + Partnerships with leading: Premium Digital Publishers Offline Data Compilers Credit Bureaus Financial Institutions Intent: “Data In Motion” Zeta Data CloudIdentity Intent Data Sources Zeta’s people-based data enables scalable, integrated omni-channel activation and deterministic measurement
- 8. 2019 Zeta Global · Proprietary & Confidential Make the ubiquitous mileage rewards personal by applying Zeta’s proprietary signals surrounding competitive browse, airline transactions, dominate card in wallet, and interests to stimulate brand usage amongst existing members and frequent travelers SOLUTION CHALLENGE METHOD Loyalty programs no longer drive choice of an airline and program participants have low mileage accumulation to realize the program value 8 8 OWNED EMAIL INGEST DATA 1 CUSTOMER SIGNALS SYNTHESIZE DATA Apply Weighted Algorithm For Individual Scoring ACTIVATE DATA In Close Loop Deterministic Approach 2 3 OWNED SITE RESEARCH OTA BROWSE MILES EARN/BURN FLIGHTS TRAVELED AFFINITY LIFE STAGE CREDIT SCOREPARTNER SPEND SITE OPTIMIZATION PROGRAMMATIC TRIGGERED EMAIL PARTNER RECO EMAILS = ZETA DIFFERENTIATOR KPI Incremental mileage purchases pre-holiday and member net promotion Leading U.S. Airline – Client Introduction
- 9. 2019 Zeta Global · Proprietary & Confidential Leading U.S. Airline – Client Use Cases Conquest of other airline frequent travelers Disrupt OTA channel purchase during preflight research Reactivate lapsed customers through personalized experiences aligned to interest Innovate beyond managing mileage accruals to retain loyalists with new real time partner redemption options
- 10. 2019 Zeta Global · Proprietary & Confidential Our Approach: We capture a user’s identity, pair it with their behavioral, transactional and location signals and identify their intent. Vanessa’s Identity Graph We know Vanessa is likely recently engaged and can activate on this life event through: VANESSA | Zeta ID: 4188210 Postal Demographic Web Behavior Location Transaction + Members of household increase from 1 to 2 + Marital Status = Single + Occupation = Marketing Technology + Higher spending at Etsy and arts & crafts brands + Viewing articles on wedding planning + Researching honeymoon destinations, resorts and amenities + Visiting country clubs, event halls and wedding venues + Increased visits at high-end department stores Recently Engaged 0.91
- 11. 2019 Zeta Global · Proprietary & Confidential Select Publishers
- 12. 2019 Zeta Global · Proprietary & Confidential AI-derived Intent Drives Audience Creation Methodology for Determining Intent: + Define clusters tied to consumer life-stage (e.g., new parent) and client requirements (e.g., profile targets) + Classify keywords and actions that provide signals at varied confidence levels + Score individuals based on the semantic relationships created by their content consumption Bring to life via high-value audiences and 1:1 real- time messaging deployed through Zeta platform 12
- 13. 2019 Zeta Global · Proprietary & Confidential Data Cloud Understands Page Level Content URL Title Domain Audience Segment Confidence https://thepointsguy.com/reviews/hotel- banke-autograph-collection/ Paris Right Banke: A Review of the Hotel Banke, Autograph Collection ThePointsGuy.com Resorts 0.92 Resorts 0.92
- 14. 2019 Zeta Global · Proprietary & Confidential Domain Domain Domain Domain Zeta Score Zeta Score Zeta Score Zeta Score https://www.msn.com/en- us/travel/adventuretravel/14- dreamy-honeymoon-destinations- that-will-excite-you-more-than- the-wedding/ss-BBPqdwt https://www.oyster.com/articl es/57735-5-luxury-hotels-in- las-vegas-and-their-cheaper- but-similar-alternatives/ https://abcnews.go.com/GMA /Travel/royal-wedding- honeymoon-destinations-fit- royalty/story?id=55207068 Domain Zeta Score https://www.nomadicmatt.com /travel-blogs/best-walking- tours-paris https://thepointsguy.com/revi ews/hotel-banke-autograph- collection/ Visits in Last 30 days Visits in Last 30 days Visits in Last 30 days 3 21 Visits in Last 30 days 5 Visits in Last 30 days 3 Date of Last Visit October 14, 2019 Date of Last Visit September 28, 2019 Date of Last Visit September 2, 2019 Date of Last Visit October 20, 2019 Date of Last Visit October 12, 2019 0.92 0.90 0.88 0.89 0.82 Scoring of Content and Individual Resort 0.90 VANESSA | Zeta ID: 4188210
- 15. 2019 Zeta Global · Proprietary & Confidential Scoring of Content and Individual Membership in Audiences Behaviors: Recency, Frequency Traits Lookalike Audiences Audience Taxonomy On-Site NLP & Scoring Metadata Full Text Machine Learning & Scoring VANESSA | Zeta ID: 4188210
- 16. 2019 Zeta Global · Proprietary & Confidential Scoring of Content and Individual Membership in Audiences Behaviors: Recency, Frequency Traits Lookalike Audiences Audience Taxonomy On-Site NLP & Scoring Metadata Full Text Machine Learning & Scoring VANESSA | Zeta ID: 4188210
- 17. 2019 Zeta Global · Proprietary & Confidential Data Cloud Data Dictionary Club Warehouse Home Furnishings Home Improvement Store Grocery Health Club Event Tickets Harness Transactional Data Across Brands Combine with Location Data to Drive Business Outcomes MOVEMENT DATA MAPPING DATA Understand foot traffic trends by day and market Identify market opportunities and competitive pressures Reach consumers with targeted messaging across devices based on where they go in the real world TRANSACTIONAL DATA LOCATION DATA VANESSA | Zeta ID: 4188210
- 18. 2019 Zeta Global – Proprietary & Confidential www.zetaglobal.com | Demo Credit Profile/ Ownership Spend IRL Visitation InputWeighting Automated ML-Driven Re-Balancing Based on Feedback Loop Data Optimizes Audiences 18 Intender Audiences Are Multi-Dimensional Behavioral
- 19. 2019 Zeta Global · Proprietary & Confidential + Demonstrating Data Cloud Value and ROI Upfront + Aligns Key Business Objectives with Audiences + Expands Identity Resolution with Zeta’ Data Cloud + Deeper Data Driven Insights Driving Key Opportunities Storied Outcomes are Illustrated through Zeta’s Insight Reports Data Cloud Insight Report
- 20. 2019 Zeta Global · Proprietary & Confidential Profile and Identity Analysis
- 21. 2019 Zeta Global · Proprietary & Confidential
- 22. 2019 Zeta Global · Proprietary & Confidential
- 23. 2019 Zeta Global · Proprietary & Confidential
- 24. 2019 Zeta Global · Proprietary & Confidential
- 25. 2019 Zeta Global – Proprietary & Confidential www.zetaglobal.com |2019 Zeta Global · Proprietary & Confidential 3 Thoughts We Want You To Take Away from Today1. Better data inputs allow us to capture intent, build audiences and assign membership 2. Glean insights through AI, ML and NLP by overlaying a lens of behavioral, transactional, demographic and location data on customers and prospects to predict behavior 3. Trigger personalized 1:1 messaging to drive better engagement, increase brand affinity and customer lifetime value.
- 26. Insights that Teach Impressions that Learn and Improve
Editor's Notes
- Neej opening Just placeholder slide to document the overall narrative. We lead/ask questions on some of the slides to make it interactive family feud. Winner gets a white claw delivered (and perhaps a non alcoholic alternative. Fortune cookies?) 1. Setup format, get client and use cases from audience. Focus on one where new movers is relevant 2. Discuss Identity, Signals, Connectivity as baseline 3. Talk about Identity and how it relates to our client/usecase. DEMO: identity explorer 4. Talk about signals, the scale, variety and differentiation for our client/usecase. DEMO: ZDI 5. Talk about connectivity/audiences/merged audiences. DEMO: living-demo 6. Assume we get data from client, get into solutioning with IR report. DEMO: IR 7. PDC pitch/applications. DEMO: screenshot 8. outcomes 9. Sales stages and tools and people to utilize at each. DC team. 10. Q&A 11. Appendix for reference/reading
- Ron
- Ron
- Ron
- Ron
- Sample of publishers where customers are consuming content on a day to day basis Large brands Diverse verticals
- SUV Interest Children's Apparel Pre-Mover Define the taxonomy through our linguistic experts and domain knowledge Classify content on the page level Score individuals in and out of those audiences and at different confidence levels based on their behaviors
- Winnie Introduce you to Vanessa Snapshot of Vanessa's online content consumption behavior We look at the type of content Vanessa is consuming – is it high scoring or low scoring How recently is she looking at that content and how frequently Score Vanessa in and out of the Pre Mover audience and with a corresponding confidence score based on her online interest and intent
- We use NLP & Scoring methodology to classify pages into audience taxonomies then look at our user’s behaviors, recency and traits to move them in and out of those audiences
- Winnie Online and offline spending across a wide array of brands Point of interest data – what places are you visiting, when are you visiting them and how frequently
- Winnie Online and offline spending across a wide array of brands Point of interest data – what places are you visiting, when are you visiting them and how frequently
- Winnie
- Sophia
- Do you have a marketing flywheel? By no means have we cracked the code to solving some of the foundational challenges facing marketers today. But we feel that our approach can help contribute to making consumer outreach more valuable for all parties and are committed to investing to help us all get back to growth.