The premier machine learning conference
Predictive Analytics World
November 18-19, 2019 - Estrel Hotel Berlin


Guiding Theme 2019

From Data Lab to Data Ops

The time for experimentation is over. Companies expect their data labs to deliver what the AI hype has promised them: more customers, higher revenues, more efficient processes and much more. But many projects are stuck in the PoC trap: they work as prototypes – but not in real operation. Data science must become a data industry: we ourselves must learn to become more efficient and effective – by identifying the really critical challenges in the company, developing the appropriate solution ideas, translating the ideas quickly into functioning models, developing scalable solutions from the models and finally ensuring that these solutions are used profitably by the specialist departments. This requires a new self-image: we are not the experimental laboratory of the companies – but their machine room: Data Ops instead of Data Labs.

Focal points & industries are:

  • Data Engineering & Model Management
    • Data Lakes & Pipelines
    • Data & Software Architecture
    • Model Automation & Evaluation
    • Feature Engineering & Management
    • Microservices & Data/Model-as-a-Service
  • Data Management & Strategy
    • Data & Design Thinking
    • Customer Data Platforms & Data Management Platforms
    • Data Labs vs. Data Ops
    • Data Culture & Literacy
    • Meta Data & Data Quality Management
    • Data Sourcing & Governance
  • Machine & Deep Learning
    • Data, Text, Stream, Process & Network Mining
    • Times Series Models
    • Bayesian Learning
    • Ensemble Learning
    • Transfer Learning
    • Reinforcement Learning
    • RNN, CNN & GAN
    • Markov-Ketten & Monte-Carlo-Simulationen
  • Marketing & Sales
    • Marketing Mix Modelling
    • Predictive Lead Scoring
    • Customer Lifetime Value
    • Affinity Scoring
    • Churn Prevention
    • Chat Bots
  • Advanced Marketing & Sales Analytics
    • Dynamic & Multitouch Attribution
    • Marketing Mix Modelling
    • Churn Prediction & Prevention
    • Customer Lifetime Value
    • Lead & Affinity Scoring
    • Customer Segmentation vs. Persona
    • Demand & Revenue Forecast
    • Response & Uplift Modelling
    • Recommender Systems
  • Marketing & Sales
    • Marketing Mix Modelling
    • Predictive Lead Scoring
    • Customer Lifetime Value
    • Affinity Scoring
    • Churn Prevention
    • Chat Bots
  • E-Commerce & Online-Marketing
    • Dynamic Attribution
    • Dynamic Pricing
    • Dynamic Couponing
    • Bid Optimization
    • Website Personalization
  • Supply Chain & Process Optimization
    • Demand Forecast
    • Inventory Optimization
    • Route Optimization
    • Process Mining
  • Finance & Insurance
    • Risk Scoring
    • Fraud Detection
    • Anomaly Detection
    • Visual Inspection
    • Robo Advisory
  • HR & E-Learning
    • Churn Prediction
    • Applicant Scoring
    • Intelligent Assistants

Predictive Analytics World in numbers






Total Events








What do our attendees say?

Ben Rollins - Global Advanced Analytics Specialist
Bain & Company
“The PAW business conference is a great way to meet peers within the industry and to see what everybody else is doing to make sure you don’t fall behind.”
Emre Yayıcı - Managing Partner
Analytics Center
„The agenda/ content and operations were both handled very professionally. As a person attending to many conferences (and also as an organizer), I can say that this is rare. Thanks for all!“
Rainish Lalai - Senior Analytics Specialist
Etihad Airways
“An excellent conference with high-quality speakers, the right companies partnered, and overall a very knowledgable experience.”
Silvan Rath - Founder
„PAW was a phenomenal event. Packed with actual practitioners. And none of the typical „hurray“ presentations from vendors trying to sell you up. Really enjoyed the vibe.“


Intensive Learning. For more machine learning knowledge.

Dean Abbott
November 20, 2019
Predictive Analytics for Practitioners mit Dean Abbott
Dean Abbott
November 20, 2019
9:00 am - 5:00 pm
895 €
Predictive Analytics for Practitioners mit Dean Abbott

Der Workshop mit Dean Abbott wird auf ENGLISCH gehalten

Die Workshopplätze sind limitiert – sichern Sie sich ihren Platz rechtzeitig!

Intended Audience:

  • Practitioners: Analysts who would like a tangible introduction to predictive analytics or who would like to experience analytics using a state-of-the-art data mining software tool.
  • Technical Managers: Project leaders, and managers who are responsible for developing predictive analytics solutions, who want to understand the process.

Knowledge Level: Familiar with the basics of predictive modeling.

Workshop Description:
Predictive Analytics for Practitioners

Predictive analytics has moved from a niche technology used in a few industries, to one of the most important technologies any data-driven business needs. Because of the demand, there has been rapid growth in university programs in machine learning and data science. These teach the science well, but do not describe the tradeoffs and the “art” of predictive analytics.

This workshop will cover the practical considerations for using predictive analytics in your organization through the six stages in the predictive modeling process:

  1. Business Understanding – how to define problems to solve using predictive analytics
  2. Data Understanding – how to describe the data
  3. Data Preparation – how and why to create derived variables and sample data
  4. Modeling – the most important supervised and unsupervised modeling techniques
  5. Evaluation – how to match modeling accuracy with business objectives to select the best model
  6. Deployment – how to use models in production

Practical tips are given throughout the workshop including:

  • Which transformations of data should be used for which algorithms?
  • Which algorithms match what kinds of problems?
  • How does one measure model accuracy in a way that makes sense for the business?
  • How does one avoid being fooled with predictive models, thinking they are behaving well when in reality they are brittle and doomed to fail?

Case studies that illustrate principles will be used throughout the workshop, drawn from Mr. Abbott’s more than 20 years of consulting experience in data mining and predictive analytics. The techniques are software independent, but Mr. Abbott will illustrate them using several commercial and open source software packages.

Every registered attendee will receive a copy of Mr. Abbott’s book “Applied Predictive Analytics”

This workshop will benefit anyone who has worked with data, whether in spreadsheets, statistics programs, or commercial predictive modeling software, and would like to learn the practical side of predictive analytics.

Attendees receive a course materials book and an official certificate of completion at the conclusion of the workshop.

Workshop Schedule:

  • 09:00
    • Software installation
  • 09:15
    • Workshop program starts
  • 10:30 – 11:00
    • Morning Coffee Break
  • 12:30 – 13:30
    • Lunch
  • 15:00 – 15:30
    • Afternoon Coffee Break
  • 17:00
    • End of the Workshop


Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics, Inc. in San Diego, California. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology.

Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a highly-regarded and popular speaker at Predictive Analytics and Data Mining conferences and meetups, and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate as well as the UCSD Data Mining Certificate programs.

He has a B.S. in Mathematics of Computation from Rensselaer (1985) and a Master of Applied Mathematics from the University of Virginia (1987).

Jim Sterne
November 20, 2019
Introduction to Artificial Intelligence for Marketing mit Jim Sterne
Jim Sterne
November 20, 2019
9:00 am - 5:00 pm
895 €
Introduction to Artificial Intelligence for Marketing mit Jim Sterne

Der Workshop mit Jim Sterne wird auf ENGLISCH gehalten.

Die Workshopplätze sind limitiert – sichern Sie sich ihren Platz rechtzeitig!

From market research to direct mail metrics to web analytics to Big Data, the job of “marketing” has changed dramatically over time. We have arrived at a fundamental shift in marketing that is as impactful as the advent of the Internet: Artificial Intelligence and Machine Learning.

This workshop introduces marketing professionals of all ranks to the theory, the language and the practical application of these disruptive technologies.

This workshop will not teach you how to be a data scientist.

It will teach you enough about the language and implications to speak cogently with your colleagues, and determine where to apply this innovative technology first. You will also get a firm grasp on how these new tools will change your job and what you can do to remain relevant in tomorrow’s marketing department.

Key Takeaways

  • How Machine Learning Works for Marketing
  • What Machine Learning Cannot do for Marketing
  • How to Onboard AI and ML into Your Organization

This workshop is for marketing professionals who are comfortable consuming analytics outputs, but must now face a new, educational learning curve.

This workshop is for data scientists to help them understand the problem the marketing department is trying to solve and the available data sets.

This workshop is for marketing managers who must respond to the C-level insistence that the marketing department “get with the times” (management by in-flight magazine).

This workshop is for those who need to survive in these changing times even though they are not data scientists, algorithm magicians, or predictive analytics statisticians.

Each participant will receive a copy of Jim’s book.


Jim Sterne, Founder, Marketing Analytics Summit

Jim Sterne has been in data processing since 1979, an online marketing consultant since 1993, and an online marketing analytics consultant since 2000. Sterne focuses on proving the value of digital communication as a medium for creating and strengthening customer relationships. He is the founding president of the Digital Analytics Association and producer of the eMetrics Summits. Sterne was named one of the 50 most influential people in digital marketing by the United Kingdom’s premier interactive marketing magazine and one of the top 25 Hot Speakers by the National Speakers Association, to which he credits his degree in Shakespeare. He has consulted to some of the world’s largest companies; lectured at MIT, Stanford, USC, Harvard, and Oxford; and sat on a plane to Las Vegas grading the CRM strategy plans of a Nigerian mobile phone company for a course he taught in Singapore produced by a training company in Shanghai. Sterne is the author of 15 books on online marketing and analytics including his latest, “Artificial Intelligence for Marketing: Practical Applications” (Wiley, August, 2017)

Martin Szugat
November 20, 2019
Data Thinking for Marketing & Sales mit Martin Szugat
Martin Szugat
November 20, 2019
9:00 am - 5:00 pm
895 €
Data Thinking for Marketing & Sales mit Martin Szugat

Die Workshopplätze sind limitiert – sichern Sie sich ihren Platz rechtzeitig!


  • Data Scientists und Datenanalysten, welche die Data Science erfolgreich in ihrem Unternehmen etablieren und vorantreiben möchten
  • Fach- und Führungskräfte, welche ihr Unternehmen zu einem Data-Driven Business entwickeln möchten.
  • Projekt- & Produktverantwortliche, welche bereits datengetriebene Lösungen entwickeln und die Entwicklung beschleunigen und fokussieren möchten.


  1. Erfahren Sie, was die kritischen Faktoren für eine erfolgreiche Datenstrategie sowie ein datengetriebenes Unternehmen sind.
  2. Lernen Sie die Methode des Datenstrategie-Designs kennen, um eigenständig eine individuelle Datenstrategie für Ihr Unternehmen zu entwickeln.
  3. Entdecken Sie die vielfältigen Möglichkeiten von analytischen Lösungen und finden Sie heraus, wie Sie Analytics-Projekte effizient und effektiv konzipieren, evaluieren und priorisieren.


Datengetriebene Unternehmen punkten gegenüber ihren Wettbewerbern mit einer durchschnittlichen 6% gesteigerten Produktivität und Effizienz (laut einer Studie des renommierten Massachusetts Instituts of Technology). Um diesen Wettbewerbsvorteil zu erlangen, bedarf es allerdings einer durchdachten und individuellen Datenstrategie. Weder reicht es aus, Data Scientists und Data Engineers einzustellen, um ein eigenes Data Lab aufzubauen, noch genügt es, teure Technologien für Machine Learning, Deep Learning und Artificial Intelligence einzukaufen oder im Sinne von Big Data riesige Data Lakes anzuhäufen. Ohne eine klare Zielsetzung und einen Fahrplan zur Erreichung der Ziele wird das Data Science-Team ziellos agieren, die Technologie stillstehen und die Daten ungenutzt bleiben. In der Summe verlieren Unternehmen dadurch doppelt Geld: sie geben unnötig Geld für Ressourcen aus, die sie nicht benötigen, und die positiven Effekte von potentiellen Analytics-Lösungen wie Umsatzsteigerung oder Kostenreduktion bleiben aus bzw. treten erst mit erheblicher Verzögerung ein.

Mit Design Thinking existiert ein etablierter Ansatz, um aus Anwender- bzw. Kundenperspektive die relevanten Herausforderungen zu identifizieren und innovative Lösungen zu konzipieren. Das Data Thinking erweitert diesen Ansatz um Aspekte der Data Science, um datengetriebene Lösungen zu entwerfen. Mit der Methode des Datenstrategie-Designs stellt die Strategieberatung Datentreiber kostenlose Visualisierungswerkzeuge („Canvas“) und einen erprobten Prozess zur Verfügung, um in interdisziplinären Teams (bestehend aus Data Science, IT und Fachabteilung) schnell die für ein Unternehmen kritischen Anwendungsfälle zu erkennen und machbare Lösungen zu entwickeln. Das Ergebnis des Data Thinkings ist ein Fahrplan zur Steigerung des analytischen Reifegrads des eigenen Unternehmens sowie konkrete Konzepte für die Realisierung erster Lösungen. Auf Basis dieser Datenstrategie können Sie direkt ableiten, welche Ressourcen – Mitarbeiter, Technologien und Datenquellen – sie tatsächlich benötigen.

Der eintägige Workshop „Data Thinking“ bietet Ihnen anhand von zahlreichen realen Beispielen und praktischen Übungen einen schnellen Einstieg in die Themen Design Thinking und Datenstrategie. Am Ende des Tages sind Sie in der Lage, selbstständig Datenstrategien zu entwickeln und zu bewerten und die Canvas-Werkzeuge Datenstrategie, Datenlandschaft und Analytik-Reifegrad mit Ihrem Team anzuwenden. Die Methode des Datenstrategie-Designs wird Ihnen helfen, Ihr Unternehmen schneller datengetrieben voranzubringen und Risiken sowie Unsicherheiten möglichst früh zu minimieren.


09:00 Uhr
Vorstellung der Ziele, Agenda und Teilnehmer

09:15 Uhr
Vortrag I: Überblick über datengetriebene Geschäftsmodelle und Prozesse sowie Einblick in die Methode des Datenstrategie-Designs und des Design Thinking-Ansatzes

10:30 Uhr – Pause

11:00 Uhr
Praxis I: Analytische Anwendungsfälle mit dem Business Model Canvas identifizieren sowie mit dem Analytik-Reifegrad Canvas priorisieren

12:30 Uhr – Mittagessen

13:30 Uhr
Praxis II: Anwender- & Kundenbedürfnisse mit dem Value Proposition Canvas konkretisieren sowie analytische Lösung mit dem Datenstrategie Canvas spezifizieren

15:00 Uhr – Pause

15:30 Uhr
Praxis III: Datenquellen und -lücken mit dem Datenlandschaft Canvas erkunden sowie Fahrplan zur Umsetzung der analytischen Lösung definieren

16:45 Uhr
Offene Feedback- & Fragerunde

17:00 Uhr – Ende



Mit seiner Strategieberatung Datentreiber unterstützt Martin Szugat Unternehmen beim digitalen Wandel zu datengetriebenen Geschäftsmodellen und -prozessen – im Rahmen von Strategie-Workshops und Seminaren. Zu seinen Kunden zählen Unternehmen wie ProSiebenSat1, Nestlé, TecAlliance, Süddeutsche Zeitung und GfK. Seine Methode des Datenstrategie-Designs wenden zahlreiche Unternehmen branchen- und fachübergreifend an, um für das eigene Unternehmen oder ihre Kunden analytische Lösungen zu konzipieren und erfolgreiche Datenstrategien zu entwickeln.

Vor Datentreiber war Martin Szugat Gesellschafter und Geschäftsführer von SnipClip, einer Agentur für Social Media Marketing & Analytics-Lösungen. Der studierte Bioinformatiker hat im Bereich Machine Learning und Data Mining geforscht sowie als freiberuflicher Fachautor und IT-Berater gearbeitet. Seit 2014 betreut er als Programmdirektor die Predictive Analytics World-Konferenzen in Deutschland..

Beispiele von Präsentationsunterlagen sowie Videoaufnahmen von Vorträgen finden Sie hier:


„Nestlé sieht in der Weiterbildung der Mitarbeiter einen wesentlichen Erfolgsfaktor für den digitalen Wandel im Unternehmen. Bei dem zweitägigen Datentreiber-Seminar „Data-Powered Marketing“ lernen unsere Mitarbeiter, wie sie Daten strategisch und operativ verwerten, um relevante Inhalte zu identifizieren, sie auf den passenden Kanälen zu verteilen und die Kampagnen optimal zu steuern. Martin Szugat überzeugte die Teilnehmer durch die vielen praktischen Übungen sowie zahlreiche anschauliche Beispiele.“

Sarah von Mitzlaff, Group Brand Manager bei Nestlé Deutschland AG

„Als Spezialisten in der Kundenanalyse unterstützen wir unsere Klienten dabei, wertvolle Insights aus Daten zu generieren. Martin Szugat hat unseren Kollegen im Rahmen eines zweitägigen Workshops seine Methoden und Modelle erläutert. Die Canvas-Methode stellt hierbei eine exzellente Ergänzung dar, um Design Thinking in unseren Beratungsansatz zu integrieren.“

Cecilia Floridi, Geschäftsführerin der DataLab. GmbH


Estrel Berlin

Sonnenallee 225,
12057 Berlin,




+49 (0) 3068310


Predictive Analytics World Business Berlin

Estrel, Sonnenallee, Berlin, Deutschland

Estrel Berlin

Need more information?

  • What is predictive analytics?

    Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.

    Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.

  • Is predictive analytics different from forecasting?

    Predictive Analytics World London often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.
    However, Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.

  • Is this a “machine learning” conference?

    Yes. Predictive analytics means the commercial deployment of machine learning (the two terms are often used synonymously). Although the term “machine learning” used to be common only within the walls of research labs, it’s now also used more and more in the context of commercial deployment. Whichever term you prefer, we are discussing technology that learns from data to predict or infer an unknown, including decision trees, logistic regression, neural networks, and many other methods.

  • Is this a “data mining” conference?

    Yes. Data mining is often used synonymously with predictive analytics, and, in any case, predictive analytics is a type of data mining.

  • Is this a “data science” conference?

    Yes. Predictive analytics is a form of data science. Moreover, it is the most actionable form. A predictive model generates a predictive score for each individual, which in turn directly informs decisions for that individual, e.g., whether to contact, extend a retention offer, approve for credit, investigate for fraud, or apply a certain medical treatment. Rather than solely providing insights, predictive analytics directly drives or informs millions of operational decisions.

  • Is this a “big data” conference?

    Yes. Predictive analytics is a key method to truly leverage big data. At the center of the big data revolution is prediction. The whole point of data is to learn from it to predict. What is the value, the function, the purpose? Predictions drive and render more effective the millions of organizational operational decisions taken every day.

  • Is this an AI conference?

    Yes. Artificial intelligence (AI) is a broad, subjective term with many possible definitions—but by any definition, it always includes machine learning (predictive modeling) as an example of AI technology/capabilities.

  • Is Predictive Analytics World run by a software vendor?

    No. Predictive Analytics World provides a balanced view of predictive analytics methods and tools across software vendors and solution providers.

  • Is Predictive Analytics World a research conference?

    No. Predictive Analytics World is focused on today’s commercial deployment of predictive analytics, rather than academic or R&D activities. Separately, there are a number of research-oriented conferences; in predictive analytics’ commercial application, we are essentially standing on the shoulders of those giants known as researchers.

  • Are you considering new speakers for Predictive Analytics World?

    For speaker information and proposal submissions, click here.

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