Titlebook: Data-Driven Customer Engagement; Mastering MarTech St Ralf Strauss Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusi

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,Data Readiness and Data Strategies … Without Data, You Are Just Another Person with an Opinion,Data architectures … despite the hype on CDPs … are still in their infancy. New skills and competencies on data management need to be established … whilst experts are not available. The demission of third-party cookies in 2025 latest is pushing data clean rooms, server-side tracking, and first-party

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,Measurement and KPI Optimisation … Oldie, But Goldie,y down to execution and tactical levels. While as a “l(fā)ipstick service” marketing planning receives a huge amount of attention, the cruel reality indicates that the quality of planning and data-driven steering is lagging. Backed by huge data science departments, the future will show a higher degree o

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,Pandora’s AI Box: The AI Tsunami Is Rolling In, of 2023. Still, major hurdles come along with the hype on AI: a lack of knowledge, a myriad of tools touch the corporate ground with distributed, dispersed databases. Beyond the hype, a profound conceptional understanding is missing, similar as fundamentals such as data strategy and data consolidat

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CRM Meets Customer Experience: Moments of Truth for the Customer,d by tremendous costs, going hand in hand with low adoption rates. The comparatively new discussion on Customer Experience Management does not gain traction due to the cross-functional character across the functionally structured organisation, processes, and applications. Companies with a lower leve

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On the Way to Composability: The Changing Role of IT,es across processes, data, and applications. The mantra of service-oriented architectures becomes a true “second life” after an initial hype around 2010+. In parallel, companies are not willing to pay for software which will not be used afterwards. Newer software license models move to a usage-based

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Case Studies: Lessons Learned from Other Companies,, as well as the required time for implementation. The examples include (besides others) a highly elaborated MarTech stack at COOP Switzerland, to the data infrastructure at Lufthansa, Marketing Automation at Qiagen, Ad Optimisation at Vodafone, IT applications built from scratch at Flaschenpost, do

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