Fanalists - Bringing Data Marketing to Sports and Entertainment Organizations of All Sizes
Name: Thierry de Reus
Title: Head of Tech & Data
Country: Netherlands
Organization: Fanalists
Nowadays personalized experiences are the norm. Personal attention, or the lack of it, can make or break your business. Fanalists supports organizations in sports, media, and entertainment to get to know their fans and to get a better grasp of their business. Fanalists breaks data silos, centralizes and enriches data, creates rich fan profiles, and makes them available to analyze and communicate with. Fanalists works for event and festival organizers, sports federations like the Dutch hockey federation (KNHB), media companies such as The Walt Disney Company, and sports organizations like cycling team Team Jumbo-Visma and football club Anderlecht.
Awards Categories:
- Value at Scale
- Most Impactful Transformation Story
- Partner Acceleration
Business Challenge:
The world of entertainment and sports is inspiring. Talented event organizers create the most creative live concepts, and popular sports clubs create stimulating experiences for their fans. But really getting a grip on the actual individual fan? Nah. Data is generally not their comfort zone. Let alone understanding data science concepts like predictive modeling to understand their fans and customers even better.
It turned out to be hard to truly get a grasp on the power of data, customer segmentation, and personalization by explaining the theory behind it. Something was missing. Something that makes it transparent, visible, and usable for creative marketers. Without that missing link, organizations would never outgrow bulk marketing campaigns and generic strategies. And that would be a shame: the interplay between creativity and data results in such a powerful combination.
Moreover, Fanalists wants to support organizations in sports and entertainment in a scalable manner. Creating ad hoc analysis and customized data models for more than a handful of organizations is not feasible. On the other hand, there will always be elements fully tailored to the needs of the individual organization.
How can we create a clear and scalable workflow for our own data analysts and experts? How can we achieve that while making it understandable for the talented marketers and campaign managers on the other end of the table? How can we use Dataiku to make great use of the best features of the platform? And how can we split project-specific details and configuration from the models in Dataiku so our data flows won't grow into non-transferable messes over time?
Business Solution:
We created a framework that ensures that our in-house data analysts make use of a data flow that is as standardized as possible while enabling the individual data analyst to create changes or data models specifically for an individual project. In short: we created an extensive ETL flow, divided the flow into four phases, and started managing project-specific settings and definitions externally as much as possible. And we launched an interface that brings transparency to the marketers and specialists at the other end of the table: Fanalists Terminal.
Firstly, the Integration phase consists of connecting to data sources, databases, and external platforms. To keep Dataiku as clean and accurate as possible, this extraction and initial cleaning happen outside of Dataiku DSS on a separate server.
When the data is ready to be processed further, we enter the second phase: Context and Settings. Building and managing business rules, definitions, and metadata is possible for both Fanalists' data analysts and marketers on the client side through the Fanalists Terminal.
All the created settings and definitions are pushed to Dataiku DSS to add the unique context to the data model. We developed a Dataiku plugin to make managing and developing it a breeze. This way, every project can be unique without making concessions to the standard flow while making the data model a perfect fit for Fanalists' clients.
After this hard work, we enter the third phase: Grouping and Modeling. In this phase all data is combined to create 360-profiles.
After this phase, everyone involved in the project can analyze and act on rich fan profiles. Moreover, within this phase our data analysts can add prediction and clustering models.
Lastly, in the fourth phase, Segmentation, we retrieve and apply additional business rules from the Fanalists Terminal through the Dataiku plugin.
Business Area: Marketing/Sales/Customer Relationship Management
Use Case Stage: In Production
Value Generated:
With a clear data infrastructure, Fanalists is able to support organizations in sports and entertainment. By making it as transparent and comprehensible as possible, everything clicks into place while also giving them the tools to play with their data.
Through this combination, our clients can get the most out of their data driven marketing strategy. It gives them the opportunity to focus entirely on their marketing, creating a great fan experience and exceptional personalization. That's where they excel. Fanalists helps them with this focus without needing an in-house data team or immense budgets. In short: data-driven marketing and decision-making within reach.
Together with our clients, we focus primarily on making fan experiences relevant and interesting while keeping business value in mind. This goes hand in hand: the fans of a sports club, an artist, or a brand are getting a much better and personalized experience through multiple communication channels (e.g., e-mail, messaging like Whatsapp or SMS, mobile apps) while the business flourishes because of more loyal fans, higher lifetime revenues, and lower marketing costs.
Let's make it specific. What sounds better: sending out a generic e-mail announcing a newly launched festival to the total newsletter base? Or sending out multiple segmented e-mails announcing a newly launched festival with their personal favorite as the sender? The latter is possible through the solution Fanalists offers, while keeping the impact and workload on the marketers as low as possible. And it results in higher open rates, better click rates, and, most importantly, higher conversion rates: an increase of more than 200% is more the rule than the exception.
We'll be busy with the technical side of data while marketers on the other end of the table think about the perfect use of it. Real democratization of data in the sports and entertainment industry.
Value Brought by Dataiku:
In the sports and entertainment industry, striking a balance between proper flexibility and a cost-efficient way of working is always a challenge. While working with creative minds, it can be hard to restrict them in their options, but on the other hand, giving them ultimate freedom will result in marketing plans that are way too expensive to execute.
We believe we found the ideal balance between the two: creating an interface (Fanalists Terminal) that lets marketers think and build within an interface with evident restriction while also giving them creative freedom.
Dataiku DSS facilitates us in finding the ideal balance. Because we defined a clear framework consisting of different phases, we were able to find out the ideal setup for our data models over time. We started out building custom data models for every new project fully in Dataiku DSS, but we currently have clear boundaries regarding what is running where and why.
Looking at our data flows in Dataiku DSS, one could notice that it's divided into different stages and components. Some components are running outside of Dataiku DSS, while some are running 100% in Dataiku DSS with low code recipes. And some could be labeled as a hybrid, such as the plugin we developed to connect our interface Fanalists Terminal to Dataiku DSS. The plugin functions as the bridge between managinproject-specificic information and definitions and building transparent data models in Dataiku DSS.
Our clients' marketers can focus on their business, and Fanalists' data analysts can focus on creating robust anwell-performingng data pipelines and models. And when our clients wish to see further details about what's going on in the data models, it's simple to present because of the visual presentation in Dataiku DSS without being overly complex.
Value Type:
- Improve customer/employee satisfaction
- Increase revenue
- Reduce cost
- Save time
- Increase trust
Value Range: Hundreds of thousands of $