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Modellenboek Workshop 9 januari - Menno van der Steen
The document discusses advanced topics in data strategy, AI, and personalized marketing, emphasizing the importance of utilizing data-driven decision-making and predictive models to enhance advertising effectiveness. It highlights the challenges posed by evolving privacy regulations, the need for new decision-making strategies in a saturated media landscape, and the relevance of trait-based personalization for achieving better consumer engagement. Moreover, it underscores the necessity for businesses to develop actionable marketing models and adapt to continuous changes in the digital marketing environment.
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Modellenboek Workshop 9 januari - Menno van der Steen
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- 2. Let’s meet! DATA STRATEGYAI | FOUNDER SILVERLABS PARTNER ONBRDNG ACE INCUBATOR MENTOR RAAD VAN ADVIES & PhD UVA ASCOR DOCENT IRIS ACADEMY EX IPG MEDIABRANDS, GROUPM, RABOBANK & SCHIPHOL Menno van der Steen SilverLabs X
- 3. Companies X Internships Video Production& E-commerce Business Development, Research, Digital Transformation, Marketing Communication, Advertising, Media Data & Tech Data Driven Decision Making | Strategies | Tooling
- 4. Contents STUFF I HAVEBEEN WORKING ON SOME RELEVANT DISRUPTIVE TRENDS CHALLENGES IN ADVERTISING EXPERIENCE WITH THEORETIC MODELS IN DAILY PRACTICE THE EFFECTIVENESS OF TRAITS-BASED PERSONALIZATION QUESTIONS & DISCUSSION X
- 5. Stuff I havebeen working on… MANAGEMENT OF MARKETING SCIENCE TEAMS UNDERSTANDING AND PREDICTING MARKETING ROI AND KEY BUSINESS DRIVERS PHD ABOUT THE IMPACTS OF PERSONALIZATION STRATEGIES PRODUCTIZING DATA PROPOSITIONS APPLICATION OF AI, MACHINE LEARNING, NEW DATA SOURCES IN MARKETING & RESEARCH CONSULTANCY IN DATA STRATEGY, PRODUCT DEVELOPMENT, INSIGHTS, ROI & MEASUREMENT X
- 6. Value in the4th (AI) revolution A WORLD OF ABUNDANCE AND CONTENT OVERLOAD: DEVALUATION AND CHAOS OWNERSHIP ACCESS X
- 7. Decision-making strategies ina world of abundance GREAT IF YOU ARE AN EXPERT NO ISSUE IF YOU DON’T REALLY CARE NEED OF FILTERS, RECOMMENDATIONS, CURATION, PLAYLISTS NOT 1 BEST CHOICE, BUT A LOT OF GREAT CHOICES WE NEED NEW DECISION-MAKING STRATEGIES AND TOOLS X
- 8. Personalization and curation DEEPNEWS:AI POWERED NEWS FEED TO FIGHT FILTER BUBBLES ENABLING FILTERING AND CURATION ON THEMES, PEOPLE, PLACES, COMPANIES ETC. Screenshots of MVP 2021 X
- 9. In search ofbusiness models AI POWERED PERSONALISATION LIKE ARTIFACT FAILED AI POWERED PERSONALIZATION Artifact’s mission was focused on delivering the most relevant stories to users through AI, utilizing proprietary technology to provide curated news and content experiences. As a trusted guide to the internet, Yahoo helps people achieve their goals online, including connecting with high quality content they care about. This investment advances Yahoo’s commitment to bringing trusted news and information to hundreds of millions of users globally, and accelerates our vision to offer a more personalized experience for discovering news and information across platforms. X
- 10. AI XXXXLlegislation² ADDITIONAL PRIVACY/IP/AI/SCRAPINGLAW SUITS & LEGISLATION JUST STARTING 2017 Facial recognition not allowed to count and predict audiences 2018 GDPR legislation: Europe’s framework for data protection 2024 AI Act in place – the first comprehensive regulation on AI by a major regulator anywhere 2021 Big tech received big fines by the EU between 2018-2023 2024 Lawsuits of NY Times versus OpenAI, artists complaining about IP, scraping policy in the making 2023 NY Times lawsuit regarding misuse copyrights OpenAI X
- 11. AI today: contentdeals DEAL MAKING BETWEEN DATA/CONTENT OWNERS AND AI DEVELOPERS X
- 12. The future ofsales Source - Gong Labs The Revenue Intelligence Platform https://www.linkedin.com/company/gong-io/ SELLERS WHO USE AI TO GUIDE THEIR DEALS INCREASE WIN RATE BY 35% X
- 14. Conversational support ADVICE, FUNNELASSISTENCE BASED ON PROFILE, DATA PATTERNS X
- 15. Joining the PlatformEconomy • PLATFORMS OFFERING A PRODUCT THAT GENERATES DATA ITSELF ARE TODAYS WINNERS • MORE USAGE LEAD TO MORE VALUE FOR USERS: BETTER UX, MORE FUNCTIONALITY, BETTER RECOMMENDATIONS ETC • ALL CONTACT POINTS WITH YOUR BRAND OR PRODUCT COULD BE MEANINGFUL X
- 16. Data Driven BusinessGrowth Business objectives Definition of what the business is trying to achieve • Revenue growth • Cost efficiency • Customer satisfaction • Speed to market Marketing goals Definition of what marketing is trying to achieve to support business objectives • Net new customers • Loyalty • Share of wallet • … Business strategy Collect & Connect MarTech & data acquisition Activities to support relevant data collection & connection for activation and insights • Data acquisition & governance • Marketing automation solutions • Web & app analytics • 1P data • … Activate & Deliver Data-driven activation Activities that drive dynamic or personalized experiences based on these insights • Audience strategy & personalisation • First-party data activation • Dynamic offerings & bidding • Play concepts • … Measure & Optimise Data-driven insights Activities to extract and generate insights to optimise activation • KPI framework • Measurement strategy • Media effectiveness • Experimentation & optimization • Dashboarding • … Data strategy X
- 17. Push versus Pull PULLIS GROWING: CONSUMER IN ACTIVE ROLE AS A USER NEW SEARCH AND RECOMMENDATION PLATFORMS LIKE SEARCHGPT AND AI AGENTS NEW SALES FUNNELS MIGHT ALLOW MORE DATA COLLECTION THINK OF WHAT IS NEEDED TO BE FINDABLE, VALUABLE, RELEVANT AND WHAT MESSAGE TO TELL X
- 18. Cross-Platform Share ofSearch (Oct 2024) X
- 19. Areas of experience& interest (and some expertise) X
- 20. Challenges in AdvertisingDomains CLUTTER AT A NEW HIGH: AI GENERATED CONTENT ADDS TO THIS ATTENTION SPAN IS DROPPING PRIVACY LEGISLATION MAKES TARGETING DIFFICULT RELEVANCY EXTREMELY IMPORTANT BUT DIFFICULT TO REACH: HIGH EXPECTATIONS OF CONSUMERS AD-FREE ENVIRONMENTS GAIN TRACTION OWNED CHANNELS BECOME AN ASSET TO REACH CUSTOMERS AND TO COLLECT DATA MEDIA LANDSCAPE IS FRAGMENTING EVEN MORE MAKING AD OPERATIONS MORE COMPLEX MEASUREMENT & ATTRIBUTION OF EFFECTIVENESS MORE COSTLY (WALLED GARDENS) WITHOUT COOKIES X
- 21. Facing these challenges AUTOMATIONOF ADOPS WORKFLOWS DATA COLLECTION: UNDERSTANDING WHAT DATA IS NEEDED FOR TARGETING, SEGMENTATION & PERSONALIZTION UNDERSTANDING WHAT DATA IS NEEDED FOR MEASUREMENT OF CAMPAIGN EFFECTS AND IMPLEMENT AN EXPERIMENTAL CULTURE TO TEST PERSONALIZATION, DIFFERENT TYPES OF MESSAGING, NEW CHANNELS, UX INCL USER (DATA) FEEDBACK, ETC X
- 22. HOW IS THISIMPACTING THE USE OF MARCOM MODELS? Models of marketing and advertising more relevant than ever?
- 23. Experiments need guidance PREDICTIVEMODELLING REQUIRES DATA SCIENTISTS AND TOOLS ‘LEARNING BY DOING’ REQUIRES AN EXPERIMENTAL CULTURE AN EXPERIMENTAL CULTURE REQUIRES UNDERSTANDING OF ‘HOW ADVERITSING WORKS’ MODELS ARE DELIVERING ON THIS X
- 24. MODEL USAGE INDAILY PRACTICE Some examples of experience
- 25. KPI Framework: creatingyour own model X
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- 27. KPI Framework: makeit measurable X
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- 30. RFM Model Bron: Vrijnaar Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378-394. X RECENCY FREQUENCY MONETARY VALUE
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- 37. Personalization Strategies • PERSONALIZATIONSTRATEGIES • CUE-BASED PERSONALIZATION: IN CUE-BASED PERSONALIZATION, THE CUSTOMER'S NAME, ADDRESS OR OTHER INDIVIDUAL FACTS ARE INCLUDED IN THE MESSAGE, RESULTING IN A DIFFERENT MESSAGE FOR EACH CUSTOMER (S. WINTER ET AL 2021) • TRAITS-BASED PERSONALIZATION: WITH TRAITS-BASED PERSONALIZATION, SPECIFIC TRAITS THAT ARE KNOWN BY AN ADVERTISER ABOUT THE RECEIVER, FOR INSTANCE THROUGH ANALYSIS OF DIGITAL TRACE DATA USING MACHINE-LEARNING TECHNIQUES TO INFER TRAITS FROM BEHAVIORAL DATA (HINDS & JOINSON, 2019; WANG & KOSINSKI, 2018), ARE USED TO TAILOR THE MESSAGE. • ALSO, SUGGESTED PERSONAL RELEVANCE (‘JUST FOR YOU!’) RAISES EXPECTATIONS, RELEVANCE, ATTENTION • NOT MUCH GUIDANCE WHAT TRAITS WORK BEST AND WHAT EFFECTS CAN BE EXPECTED X
- 38. Segmentation models: values MentalityModel, Motivaction (1997) Waardentheorie, Schwartz (1992) X
- 39. No Segmentation Model:Byron Sharpe X
- 40. Traits-Based Personalization Study MATCHINGPERSONAL TRAITS AND TAILORING THE MESSAGE TO INCREASE ATTENTION, COGNITIVE PROCESSING (SELF- REFERENCING) AND RELEVANCE WHAT TRAITS WORK BEST? • INTERESTS • VALUES • SOCIODEMO X Hey Menno! Let’s play some drums and drink a 0.0 beer!
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- 42. The Briefing andCreative Process… IMAGINE YOURSELF BEING A: • Verdiepingzoeker • Gezelligheidszoeker • Spanningszoeker • Inspiratiezoeker • Sociale klasse A • Man 50-64 • Vrouw 25-45 • Actieve Sporter • Documentaire liefhebbers X
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- 44. X The Briefing andCreative Process… PRAGMATIC AND COST EFFICIENT SETUP VARIABLES: PROPS, CASTING, VOICE, TEXT, DETAILED INFO VS GENERAL INFO, NON-VERBAL (LAUGHING VS SERIOUS)
- 50. Traits-Based Personalization Model H2A.The effects of trait-based personalization on brand recall, brand recognition, and purchase intentions are positively mediated by Perceived Personalization. H1A: Interest-based, values-based, and socio-demographic-based personalized ads will result in more positive brand responses than non-personalized ads. H2B: The effects of trait-based personalization on brand recall, brand recognition, and purchase intentions are positively mediated by PPR. H1B: Interest-based personalized ads will outperform values-based and socio-demographic-based personalized ads in generating positive brand responses. X
- 51. Traits-Based Personalization Results •INTEREST-BASED PERSONALIZED VIDEO ADS INCREASED PPR AND PP • MEANING THAT PEOPLE RECOGNIZED THAT THESE ADS WERE TAILORED TO THEIR INTERESTS • THE OTHER PERSONALIZED VIDEO ADS (ON VALUES AND SOCIO DEMO) WERE NOT RECOGNIZED AS BEING TAILORED AND DELIVERED A NEGATIVE PPR AND PP SCORE • INTEREST-BASED PERSONALIZED VIDEO ADS GENERATED HIGHER PURCHASE INTENTION THAN NON-PERSONALIZED X
- 52. Learnings PERSONALIZED MESSAGES TAILOREDTO SPECIFIC TRAITS HAVE TO BE RECOGNIZED AS BEING PERSONALIZED: INTERESTS ARE MOST EASY TO TRANSLATE INTO CREATIVES DON’T BE TOO SUBTLE USING TRAITS : MAKE PERSONALIZATION RECOGNIZABLE FOR THE RECEIVER VALUES ARE TOO SUBTLE FOR PERSONALIZATION / DIFFERENTIATION OF MESSAGES X
- 53. Wrapping Up • USEMODELS TO CREATE YOUR OWN TRUTH FOR YOUR COMPANY • MAKE THIS MODEL ACTIONABLE: • GENERATE A KPI FRAMEWORK TOGETHER WITH ALL MARKETING TEAMS • SETUP THE RELEVANT MEASUREMENT INSTRUMENTS • IMPLEMENT OPTIMIZATION PROCESSES AND OPTIMIZATION TOOLS • SET HYPOTHESIS TO TEST ON A DAILY/WEEKLY/MONTHLY BASIS: • PLAN AND MEASURE THESE EXPERIMENTS TO LEARN FROM THEM: WHAT WORKS AND WHAT DOESN’T? • MONITOR PROGRESS AND LOG THIS TO CREATE A ‘COMPANY MEMORY’ • BE CRITICAL ON MODELS THAT CANNOT BE MADE ACTIONABLE X
- 54. MARCOM AS A CONTINUOUS BEHAVIORALSTUDY menno@silverlabs.nl
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