PAW FOR BUSINESS BERLIN PROGRAMM 2022

05 - 06 Oktober 2022 | Berlin

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 take a hands-on perspective on what really helped companies to sustainably turn data into value.

Decision-making is about leveraging data & AI to manage volatility in the crazy world we  live in. Forward-looking decisions leveraging AI-based forecasts are oftentimes seen as a “black box”, which slows down utilization. Prof. Dr. Frauke Schleer-van Gellecom will demonstrate how the most complex trade off between accuracy and explainability can be handled and how the black box can be transformed into a grey box.

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.

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.

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.

In recent years, there have been various efforts to product thinking and decentralized data loading using data meshes. However in deep learning, data loading is still challenging to master at scale. In this talk, we present our decentralized data loading solution and show why flexibility and collaboration is key to enable novel ML use cases. We hope to make large-scale model training accessible to a wider community and move towards more sustainable ML.

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.

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.

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.

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.

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.

Relying solely on coding data scientists to handle your company’s predictive analytics projects is a recipe for failure. No company in the
world can hire enough data scientists to support all their use cases, and leaving domain experts out of the process doesn’t just overload your analytics teams—it also ignores critical business context. 

Join RapidMiner Account Executive Friedrich Rath to learn: 

·        How your organization can leverage the right technology and approach to get non-coding domain experts contributing to AI projects 

·        Why upskilling your people is the key to executing faster and building truly differentiated data-driven solutions 

·        How data scientists and domain experts can work together to bring more models into production throughout the business and scale the impact of AI enterprise-wide 

RapidMiner was started by PhD data scientists who understood that the power of AI shouldn’t be reserved for…well, PhD data scientists. RapidMiner is a code-optional data science platform that can support
anyone in your organization across the full analytics lifecycle—everything from making sense of your data to building a model with it to ultimately deploying an AI-powered app to drive better decision-making. 

Immobilienscout24 went from outsourcing Marketing PPC activities to creating their own Marketing and Data capabilities. The data lake enables cross channel performance dashboards, predict sales and spending volume, create automated campaign alerts and outlier detection. In this case study, we will outline the lessons learned and the impact it had on both marketing and data teams.

Classification is one of the most frequently solved problems using machine learning. Unfortunately, it cannot handle a case with a number of classes, varying over time, and require all the data to be labelled. There is another approach, designed to solve cases when we can’t perform full data annotation and/or would like to dynamically modify the number of classes. Similarity learning is capable of solving such problems even with extreme classification. We’re going to show how to use such models in production.

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%.

AI algorithms deteriorate and fail silently over time impacting the business’ bottom line. The talk is focused on learning how you should be monitoring machine learning in production. It is a conceptual and informative talk addressed to data scientists & machine learning engineers. We’ll learn about the types of failures, how to detect and address them.

Sensing customer demand accurately is a crucial factor to optimize the inventory levels in the retail sector. Using sophisticated black box machine learning algorithms provides our client, a major Turkish retailer, accurate forecasts. However, it makes it impossible to understand the effect of each feature for model outputs. Our solution for Explainable AI (xAI) solved this problem by providing transparency on the feature level and enabled the retailer team to trigger actions for managing the customer demand in the market effectively.

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.

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.

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.”