DEM OGRAPHICS DATA REACH collect sources 0 + 1ST PARTY DATA 2ND PARTY DATA 3RD PARTY DATA DATA PRIVACY trust and transparency DATA ONBOARDING high data accuracy 360° CONSUMER VIEW single source of truth ENRICHMENT predictive & generative ORCHESTRATED ACTIVATION micro targeting ENTERPRISE DATA LAYERS ANONYMOUS USERS IDENTIFIED USERS AUDIENCE SEGMENTATION DATA SCIENCE CONTENT PERSONALIZATION DIRECT MARKETING TARGET ADVERTISING content audiences message time location frequency remarketing GDPR DATA DECISIONS ENGAGEMENT Customer technology supplies and needs data…
DEM OGRAPHICS DATA REACH collect sources 0 + 1ST PARTY DATA collected and owned by company 2ND PARTY DATA partner data shared with company 3RD PARTY DATA owned by others DATA GOVERNANCE DATA PRIVACY trust and transparency DATA ONBOARDING high data accuracy 360° CONSUMER VIEW single source of truth ENRICHMENT predictive & generative ORCHESTRATED ACTIVATION micro targeting FULL FUNNEL DATA STREAMING EVENTS & PURCHASES SOCIAL MEDIA APP & WEB EMAIL, PUSH & ECOMMERCE MONITORING DATA AND COST WORKFLOW ORCHESTRATION BUSINESS INTELLIGENCE PERFORMANCE MEASUREMENT CONSENT LEGITIMATE INTEREST privacy management transparency and consent ENTERPRISE DATA LAYER identity resolution tag management content classification metadata ANONYMOUS USERS IDENTIFIED USERS account email address purchase AUDIENCE SEGMENTATION SMART DATA SCIENCE pCLT modelling lookalike modelling behavior analysis propensity modelling conversion modeling engagement modelling churn prediction automated testing CONTENT PERSONALIZATION personalization and recommendations MARKETING AUTOMATION campaigns flows TARGET ADVERTISING programmatic addressable contextual content audiences message time location frequency remarketing GDPR PDCA … and requires a robust ecosystem
packaged CDP • The cloud data warehouse GCP BigQuery acts as the data foundation • Actions and insights are created on all levels, not limited to customer data • Activation of data directly from the cloud data warehouse environment • A combination of cloud-native services and specialized tools and frameworks
establish a data warehouse on BigQuery • Advanced and more complex use cases • Not want to duplicate data and recreate business rules in different environments • Optimally leverage existing investments in a cloud data warehouse • Unlock the potential of non-customer data • Reduce vendor lock-in of systems and applications • If the cost of the packaged CDP grows 10X while your business grows 2X • Dedicated tools to connect data and tools easily, bringing flexibility of choice • Flexibility in on- and offboarding point solutions in the future
Goals ◦ Future proof setup of first-party data collection and profiling ◦ Acquisition and retention tactics based on data-driven targeting ◦ Advertisements based on context, customer interest and 1p data ◦ Article tagging for content optimization ◦ Democratize data for self-service analytics enterprise level ◦ Governed Generative AI platform (WIP) Solutions ◦ Unified modular martech stack on GCP centralizing enterprise data, analytics, activation and cross channel measurement in a CDP. ◦ Data strategy design and implementation to support functional and technical data driven transformation. ◦ Growing data literacy with inhouse training. Result ◦ Mitigation of EUR 2.000k revenue decrease at risk ◦ 200% increase CPM ad sales ◦ 30% efficiency increase marketing and data analytics teams ◦ Comprehensive insights in ROI of campaigns ◦ Insights is use cases for Gen AI Customer cases on Crystalloids and on Google Cloud website
user stories that add the most value Refine Refined top priority user stories and added them the next sprint and backlog Make sense of opinions Prioritize and refine user stories
up team Product Owner, SCRUM Master, Architect Developer, Data Scientist Build Stand ups, develop, demo, retrospective Design, team up, build and show Developing the solution incrementally
Show and tell to stakeholders 10. Built new use cases Path to realizing benefits 4 - 6 Days in total Development sprints Discovery sprint 1. Co creation backlog of user stories 1. Refinement and prioritization of the backlog 1. Select stories to develop with management incl. high level business case 1. Technical discovery + target architecture 1. Detailed estimation of work (incl. estimation workload for client ) + detailed business case < Go-no go decision > 6. Build selected user stories 8. Test, evaluate, improve 9. Show and tell to stakeholders 10. Built new use cases Development Sprints Results after 4 - 6 weeks
not a tool • Balance between Packaged, ISVs and Headless • Don’t boil the ocean; leave the legacy systems as they are • Build as much as you can on the cloud with cloud native solutions • Add the 3rd party components you might miss • Boost ROI on ESP’s, ecommerce systems etc by feeding them with the right data and decisions • Develop tech, skills and use cases incrementally • Grow employee satisfaction this way
mark3t1ng
techseoconnect
marcelosomers
geoffreycrofte
katarinadahlin
lauravandoore
tmiket
sarafernandez
chrislema
csswizardry
bryan
watany
![Jan Hendrik Fleury - j.fl[email protected] A practical approach to use](https://files.speakerdeck.com/presentations/22a20177c2174f21bc07794c90fba659/slide_0.jpg){kind=link}
