Becoming Data-driven - Machine Learning @ XING Marketing Solutions

This slidedeck contains a sketch of how we at XING Marketing Solution are trying to become truly data-driven.

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 2

werben.xing.com
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 3
This talk is about

werben.xing.com
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 4
Create ads @ www.xing.com/xas/
Native Advertising
• Different placements
• Multiple Ad Types
• Events
• Groups
• Jobs
• User
• BusinessPages
• Websites
• Video
• …

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 5
AdManager
From Heuristics to Algorithms

Second Bid Auction
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 6
We focus on
• Relevance
• Predict expected
Clickrate = eCTR
• Revenue
• Predict expected
Revenue = eRPI
• Features
• Targeting
• Ad-related data
• User-related data
• Time
• Channel
• …

Heuristics – Naïve Bayes (not quite)
Why?
• Easy to implement
• No theoretic background needed
• No additional toolstack
• Can be implemented by Software Engineers
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 7
Why not?
• Hard to optimize
• No theoretic guarantuees
• Toolstack limited w.r.t advanced methods
• Cannot be re-used by Data Scientist
Imprecise predictions lead to suboptimal business decisions
“Visible” costs are low “Invisible” costs are higher

Algorithms – Collaborative Filtering
Why not?
• Significant implementation effort
• Complex theory
• New and unknown toolstack requires training
and learning time
• Cannot be implemented by Software Engineers
alone
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 8
Why?
• Allows for ongoing optimization
• Theoretic guarantuees are a prerequisite for
reasoning, proper evaluation and testing
• Modern tooling enables learning from much
more data
• Additional Data Science and Engineering skills
enhance the team capabilities in many ways
Short term savings
Long term benefits

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 9
Advanced Delivery Pipeline
Separation of Concerns

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 10
Collaborative Filtering for Recommendations
Predict approximate scores
for empty spots based on
similarities between users
and items

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 11
Matrix Factorization

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 12
Short term savings versus long term benefits
0
20
40
60
80
100
120
140
Revenue per Impression [RPI]
Before ADP Start of ADP ADP today
+11%
+30%
• Development
time 4 month
(small team)
• In production
for 4 months
now and more
to come

Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 13
AdManager
More to come

Algorithms – The Next Level
More Data = New Features
• Natural Language Processing - Matching user
interests and ad descriptions
• Social Network Analysis - Recommendations
based on interactions in the user’s network
• Interaction with other content
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 14
New Methods = Better Predictions
• Multiple methods in parallel (Multi-Armed
Bandit)
• Multiple theoretical approaches (LogReg, Tree-
based)
• Ensembles
New data dimensions
require Big Data solution
New algorithmic dimensions
require powerful distributed
computing system

Algorithms – The Next Level
Dr. Stefan Kühn - Becoming Data-driven: Machine Learning @ XING Marketing Solutions 15
New data dimensions
require Big Data solution
New algorithmic dimensions
require powerful distributed
computing system
We are already prepared for that!

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Everyone is talking about Data Mesh architectures already - assuming that there is already a full-fledged self-service data platform in place. A reality check reveals, that most (data) platforms are not really working that well, and fail to deliver value at scale. And in contrast to the business notion of a platform, where network effects make platforms even more valuable, the more users and products are there, this does not hold true for data platforms in particular (at least I haven't seen a proof so far). So where to start, when data-transforming an organization? One approach, inspired by the Lean framework, is outlined in this talk. It all starts with what is actually working - identify some (data) products that drive value already. These are the ones you can build a platform for. It's a myth that you just need to build a solid platform, and then everyone will come and build amazing data products. They will never come. But starting with what already works is a reasonable first step. Step two is about creating flow, supporting the value stream end-to-end. Co-creation is your main tool here, fostering collaboration and ownership. Then you can think of platformizing what is really, really needed, avoiding the "waste" that modern data systems / platforms / architectures tend to pile up. In the end, the "right" architecture for your organization will emerge, you cannot simply copy-paste "solutions" that are not addressing your specific challenges. Long story short, there is a path to success, but it's not easy, it's not copying others, it's finding your own way. And as in all good strategies, you can specify the "qualities" you'd like to see in the end. And the concrete solutions need to emerge from the hard work of the motivated people, that are already driving value for your organization now.

data2day2022_SKuehn_DataValueChain.pdf

Data teams are contributing to a variety of value streams, as they are delivering value to a variety of stakeholders. The value streams are often not well-supported and the involved teams are facing constant challenges like Data Quality and Data Ownership. Also, data products often rely on the same data points for building the product and for measuring its success - so a lack of data quality leads to poor product quality and weak measurability at the same time. These challenges become exponentially harder, the larger the organization has grown. We propose a way of conceptualizing and visualizing the process of building data products, using the concept of the data value chain. Applying the Five Principles of Lean, especially Defining Value and Mapping out Data Value Streams, to the way build data products and operate data systems at scale, we create a framework that allows to focus on value delivery, avoids "waste" and supports ownership.

Talk at MCubed London about Manifold Learning and Applications

Data Science - Cargo Cult - Organizational Change

The document discusses challenges that large organizations face in integrating AI/machine learning due to misalignments between their organizational structure and data flows. Specifically, it notes that hierarchical structures optimize top-down decision making rather than collaboration and data quality. It suggests organizations need to align structure with horizontal data flows, allow know-how to move more freely across product teams, and ensure management has sufficient data skills to effectively leverage AI. The solution involves more holistic product development, flexible expert teams, and representation of data scientists in top management.

Interactive Dashboards with R

Talk at PyData Berlin about Manifold Learning and Applications

Bridging the gap

The Machinery behind Deep Learning

Manifold Learning and Data Visualization

Learning To Rank data2day 2017

The document discusses learning to rank, which involves using machine learning techniques to generate ordered rankings based on training data. It describes ranking as a supervised learning problem that deals with ordinal labels rather than categorical or real-valued labels. The document outlines different approaches to learning to rank, including pointwise, pairwise, and listwise methods. It also provides examples of applications like search engine rankings and personalized ad recommendations.

Deep Learning and Optimization Methods

Visualizing and Communicating High-dimensional Data

Slides from my talk at Data Natives, starting with the different Modes of Perception, the components of Visualization and Graphics and how to transport Information efficiently, then giving examples of how modern approximation techniques - manifold learning, principal curves - and visualization techniques - pair plots, correlation plots, parallel coordinates, grand tour - can be used in order to approach complex multi-dimensional data.

Data quality - The True Big Data Challenge

The document discusses data quality challenges, especially with big data. It notes that data quality starts at data creation and production, and that both data producers and consumers play a role. With big data, quality issues like redundancy, lack of resolution, and noise are exacerbated due to diverse sources of data, lack of documentation and standards, and increasing volumes of data. The document recommends treating data as a product and implementing quality standards, detection of problems, and root cause analysis to improve quality rather than just collecting more raw data. A shared responsibility approach between business and IT is needed to develop a common understanding of data.

Data Visualization at codetalks 2016

SKuehn_MachineLearningAndOptimization_2015

SKuehn_Talk_FootballAnalytics_data2day2015