Sessions
Berlin, 5.- 6. Oktober 2022

Predictive Subscription Lifecycle Marketing at DIE ZEIT

Predictive Subscription Lifecycle Marketing at DIE ZEIT

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Summary:

Description: A newspaper subscription is defined by various critical events, ranging from the end of the trial subscription to receiving invoices. Based on predictive analyses that anticipate customer behavior during these events, we develop, test, and implement customized marketing interventions covering the whole subscription lifecycle. You will learn about modeling via a custom AutoML-pipeline and its close intertwining with marketing execution that aim to maximize subscription lifetime value at DIE ZEIT.

Attribution at Springer Nature: Understanding the Journey of Journal Submissions

Attribution at Springer Nature: Understanding the Journey of Journal Submissions

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Summary:

For a publisher it is key to understand the customer journey of a submission process i.e., which online touchpoints and marketing channels drive submission. An attribution model has been evaluated using different machine learning approaches and is introduced into the marketing organisation of Springer Nature. The combination of describing elements with a predictive component and simulation approach helped us the understand the journey, prioritize and quantify the value of marketing activities.

Boost your Customer Understanding Using Survival Analysis

Boost your Customer Understanding Using Survival Analysis

Summary:

Survival analysis, the modeling of time-to-event data, is a statistical field with a long history and great potential to marketing and analytics. In this deep dive, you will learn about the brief origins of survival analysis and applications in the field of customer retention, which is of particular importance for subscription-based growth. Learn how to grow your understanding of customer churn, and learn how to better predict your customer’s lifetime, along with monetary aspects altogether.

Six Business Skills Critical for Data Scientists

Six Business Skills Critical for Data Scientists

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Summary:

This talk will introduce the foundational business skills you’ll need to deliver business value and grow your career as an analyst. Drawing on best practices, published research, case studies and personal anecdotes from two decades of industry experience, we give an overview of foundational skills related to Company, Colleagues, Storytelling, Expectations, Results and Careers–emphasizing how each topic relates to your unique position as an analytics professional within a larger corporation.

How Eye Contact with a Robo-Advisor Shapes Investment Decisions

How Eye Contact with a Robo-Advisor Shapes Investment Decisions

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Summary:

Making eye contact is one of the most powerful ways to build relationships—whether it’s a new date or a potential business partner. But is it also true for robo-advisors which give consumers investment advice? Today many consumers do not trust robo-advisors, which are mostly text-based interfaces. In this talk, we present a new robo-advisor prototype in the form of a virtual social robot that makes eye contact and show how it impacts consumer trust and investment decisions in online experiments.

Real-time Fraud Detection: Challenges and Solutions

Sprecher:

Fawaz Ghali

Real-time Fraud Detection: Challenges and Solutions

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Summary:

Fraud can be considerably reduced via speed, scalability, and stability. Investigating fraudulent activities, using fraud detection machine learning is crucial where decisions need to be made in microseconds, not seconds or even milliseconds. This becomes more challenging when things get demanding and scaling real-time fraud detection becomes a bottleneck. The talk will address these issues and provide solutions using the Hazelcast Open Source platform.

Authentication Vulnerability Detection on Tabular Data in Black Box Setting

Sprecher:

Debasmita Das

Authentication Vulnerability Detection on Tabular Data in Black Box Setting

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Summary:

Adversarial archetypes exploit workings of any system to disrupt robustness and decision-making of the underlying algorithms. This deep dive presents AuthSHAP, a model agnostic and robust implementation of SHAP to uncover the extent to which key features are not appropriated by any model in the decision making. This ‘knowledge’ is significant information to a fraudster to design intelligent or adversarial attacks. The presentation shows that even in black-box settings, it is possible to understand the vulnerability.

How Data Literacy Drives Innovation – The Digital Academy Approach at EWE

Sprecher:

Hauke Thaden

How Data Literacy Drives Innovation – The Digital Academy Approach at EWE

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Summary:

Since 2017, Hauke is a Data Scientist at the innovation department of EWE AG in Oldenburg. Before that, he studied mathematics and worked as a researcher in the field of applied statistics. At EWE, Hauke is responsible for turning data into business value through data science projects in diverse application areas of the energy sector. Additionally, he focusses on spreading data literacy and data culture to enable colleagues to work with data and generate new ideas for data-driven solutions.

Leveraging Zero-trust Architecture Principles to Achieve World-class Enterprise Data Governance

Sprecher:

Anna Kramer

Leveraging Zero-trust Architecture Principles to Achieve World-class Enterprise Data Governance

Summary:

Global enterprises are increasingly relying on data analytics for decision making. To process data, firms leverage cloud-based data warehouses. As more on-prem data is moved to the cloud, the need for robust data governance controls to ensure data integrity, security, and regulatory adherence is mounting; however, existing governance processes are lagging. Here we present a zero-trust approach that can augment existing governance models and reduce exposure of sensitive data like PII.

Efficient Data-Driven Marketing: Machine Learning at Major Telecom and Banking Companies

Efficient Data-Driven Marketing: Machine Learning at Major Telecom and Banking Companies

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Summary:

Proper utilization of Machine Learning and Predictive Modeling allow companies to increase profit, gain competitive advantages, grow, and win market completion. This is illustrated by results achieved across Major Telecom and Banking Companies. Efficient Proactive Retention, Revenue Stimulation, and Second-best Offer approaches require powerful Churn and Propensity models. There is a big number of techniques that can be applied to any modelling task, but it is almost impossible to know at the outset which will be most effective. Also, there are many ways to interconnect IT and Business. One way to address these topics is through advanced analytical base tables that proved to be highly efficient in all the illustrated cases.

AI Optimization of the Markdown Process: How Benetton Raised Revenues with a Prescriptive Approach

AI Optimization of the Markdown Process: How Benetton Raised Revenues with a Prescriptive Approach

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Summary:

Markdowns allow retailers to get rid of dead inventory and ensure turnover. Traditionally, even AI tools use a rule-based approach that constrains optimization and hurts revenues. Fashion retailer Benetton partnered with Evo to explore a new prescriptive approach to optimize markdown performance through complex process mapping. The resulting algorithm relied on a formal AI forecasting model feeding a price optimization model—a clear improvement that ultimately increased revenues by more than 5%.

Building Pricing Agents Starting from Scratch

Sprecher:

Taras Firman

Building Pricing Agents Starting from Scratch

Summary:

 As far as people moved to sell and buy online because of covid-19, the importance of making right pricing decisions increased dramatically. Market is extremely competitive, supply chain is very complex. That’s why being top seller is much more complicated than it was before. This deep dive session will show how to build pricing agents that will react on different changes in a market and will keep your products on top of sellers.

Dealing With the New Artificial Intelligence Act: How to Build Compliant and Risk-proof AI

Sprecher:

Ayush Patel

Dealing With the New Artificial Intelligence Act: How to Build Compliant and Risk-proof AI

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During this session, we will discuss the different risk-based categories of AI laid out by the EU’s Artificial Intelligence Act and find out how to become more admissible as per the Act. Thereafter, we will walk through the concrete steps, tools, and practices such as monitoring, explainability, model fairness, and compliance that are instrumental in achieving Responsible AI and building more risk-proof and market-friendly solutions.

An Approach to Optimize Personalized Treatments in CRM-Campaigns at PAYBACK

An Approach to Optimize Personalized Treatments in CRM-Campaigns at PAYBACK

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Summary:

Evaluation and optimization of the effect of personalized treatments plays an important role in marketing campaigns and CRM strategies at PAYBACK. However, optimization goals are highly variable due to the wide range of applications and constraints imposed by the industry. Here we compare the use of optimization tools to overcome problems encountered in various business settings, such as cost/profit optimization with restrictions enforced by the CRM objectives.

A Simple Approach to Simultaneously Optimize Models and Business at EnBW

A Simple Approach to Simultaneously Optimize Models and Business at EnBW

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Summary:

To evaluate the goodness of models we have a whole lot of KPI with a set of underlying ideas of “what goodness is”. However, the impact on the real-world (when the model is applied there) is very rarely taken systematically into account when the goodness of models is evaluated. Here we show a simple idea to assess the expected improvement (win) an a real-word situation for any classification problem at the example of campaign optimization and credit rating. Of course, this also allows to compare the real-world impact on win of different classifiers.

The Data-2-Value Transformation

Sprecher:

Norbert Wirth

The Data-2-Value Transformation

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Summary:

Transforming data into value is a strategic focus for companies across industries. With the gap between leading data driven businesses and late movers growing rapidly, decision makers are wondering what secret ingredient helps companies to successfully leapfrog roadblocks on their value journey. Since this is evidently not a one-dimensional challenge, we’ll de-mystify some unavoidable buzzwords and take a hands-on perspective on what really helped companies to sustainably turn data into value.

Continuous Integration for Machine Learning Applications – A Practical Example

Continuous Integration for Machine Learning Applications – A Practical Example

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Machine learning models are becoming obsolete and must be retrained – this is the current widespread tenor. Is this actually true? And if yes, which components does a CI/CD pipeline for machine learning really need – and which are optional? How can the whole thing be implemented without building a complete Machine Learning Platform team? And which challenges are still difficult to solve at present? A field report including (mis)decisions, which will help to choose the right path for your own challenges.

Using Matrix Factorization for Real-time Personalization of Volkswagen Websites

Using Matrix Factorization for Real-time Personalization of Volkswagen Websites

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Summary:

AI technology allows improving the UX significantly by showing the most relevant personalized content in real-time depending on the user’s browsing activity. By using Matrix Factorization with the Implicit Alternating Least Squares method we can calculate individual ratings for every user. Those ratings can be used to rank all the contents available on the website, and only the most relevant content is shown to each single user. The result: increased engagement and a lead rate uplift of 42%.

Designing Geo-Experiments at Google: A Privacy-friendly Tool to Measure Advertising Incrementality

Designing Geo-Experiments at Google: A Privacy-friendly Tool to Measure Advertising Incrementality

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Summary:

Geo-experiments – advertising experiments where the treatment and control groups are chosen based on users‘ locations – provide a privacy-friendly alternative to cookie-based online experiments that can also be used to measure offline effects of online advertising. This session discusses the algorithms we use at Google to design the experiment regions based on geographical user behavior, and the rigorous statistical methods to analyze randomized experiments based on these regions.

Causal Geographical Experimentation in Marketing Made Easy

Causal Geographical Experimentation in Marketing Made Easy

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The changes in the ads ecosystem have led marketers to lean on existing aggregate experimentation tools that assume a predetermined treatment effect. Choosing the treatment group to ensure you have high chances of detecting an effect is non-trivial. Built by Meta Open Source, GeoLift solves this problem by building well powered geographical experiments. Join us to go over why geographical experiments are necessary and their implications in the marketing industry, along with a demo of GeoLift.

Predicting Wall Street Using Artificial Intelligence and New Alternative Data Sources

Sprecher:

Anasse Bari

Predicting Wall Street Using Artificial Intelligence and New Alternative Data Sources

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Summary:

The latest formula for making sound investment decisions involves mining new alternative data sources, using predictive analytics, swarm intelligence, reinforcement learning, and high-performance computing. In this talk, Prof. Anasse Bari explains how those components are driving value in the world of finance and how new Artificial Intelligence algorithms are reinventing Wall Street. He will filter fact from fiction, and outline successful use cases that he has recently led (e.g. how social performance and consumer reviews could be used as predictive features, how to derive actionable insights from geospatial images.). Prof. Bari will also present an overview that can help you design an AI strategy and implement viable solutions to generate a “predictive analytics-based investment thesis.”

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