Questions?

A data warehouse (DW) is designed primarily to support technical teams, like business intelligence and analytics departments. It creates a central data repository you can access for custom deep-dive analyses, or for general reporting. DWs are typically only accessed by technical users, as they require some level of coding ability to surface information or push to a data visualization tool (like Tableau or PowerBI) to create reports.

The technical barrier to data warehouses makes it difficult for other departments to leverage data on their own. But this data is critical for key business functions like multi-channel marketing, sales campaign orchestration, and measuring revenue attribution. This is where the StellarAlgo Data Lakehouse complements your DW. The DW feeds into the StellarAlgo Data Lakehouse. The Data Lakehouse monitors in place to identify and correct errors, de-duplicate data, and cleanse data, moving it from your DW to the single customer view in your StellarAlgo Platform. The resulting data is accurate, reliable, and easy for non-technical roles to action.

We see this a lot. Organizations find themselves with many systems above and beyond the standard Customer Relationship Management, Email Service Provider, and Ticketing. From demographic data appends and survey and contest submissions to digital campaign sources like Google Analytics and social Ad Platforms – there are many ways for properties to engage with their fans.

The challenge is to combine your fan data in a way you can understand, without layering reports or trying to pull SQL queries. That’s where we come in. The StellarAlgo Platform integrates with your existing tech stack and gives you a single customer view of each of your fans. We make it easy to understand how fans are interacting with your brand across multiple touchpoints, and show you how to unlock more engagement and revenue.

The StellarAlgo Platform was designed to make it easy for non-technical roles to understand, activate, and measure fan engagement. We assign a dedicated Customer Success (CS) expert to each of our clients, based on their objectives and experience. The CS team works closely with your team through implementation and onboarding to ensure everyone understands where StellarAlgo fits in your tech stack and how to get the most value from it. Once you’re up and running, your CS expert will help you apply industry best practices, explore new use cases, and ensure your data remains clean and useable.

Teams typically work with StellarAlgo for one of three reasons:

1) To better understand fans and how to personalize engagement
2) To optimize the fan experience throughout the customer journey
3) To proactively explore how to further grow fan engagement and revenue in an efficient and cost-effective way.

Marketing (generally Coordinators, Managers and even some Directors) use the Segment Builder and Analyzer functionalities to create targeted segments for emails and ad campaigns, push them directly into marketing tools, and measure success.

Sales (generally Managers and Directors) use the predictive lead and retention scores to ensure reps are connecting with the right people at the right time. Service leaders appreciate the ability to keep up with renewal trends on a daily basis and jump on at-riskaccounts as soon as engagement scores drop.

Data/Analytics Teams (generally Coordinators or VPs) use StellarAlgo Platform to drive more data-driven decision-making throughout their organization. StellarAlgo enables them to focus on more work by eliminating the time-consuming tasks of building and delivering lists.

Corporate Partners (advertising, streaming, etc.) use the StellarAlgo Platform to maximize ROI from partnerships with sports properties, and validate the true impact of their partnership.

We definitely create time savings. With automated data pulls and key reporting visuals, it’s easy to see progress towards you short and long-term objectives. On the list-building front, StellarAlgo has pre-built, high-value segments that can be used with a click of a button. Or, you can go in and build your own lists, incorporating attributes from multiple source systems ( ticketing, demographic, email marketing interactions, CRM, e-commerce, and more) or from StellarAlgo’s derived attributes (i.e. avidity and engagement scoring).

Whether you use one of our pre-built segments or build your own, execution time is measured in minutes – not hours or days. On average, our partners save between 5 and 15 hours per week on reporting tasks and list-building activities.

The standard frequency from most systems is nightly, but we have the ability to pull data in near-real time (less than 2-minute lag) depending on the source. While nightly refreshes of both your data and insights are perfect for nearly every use case, we understand that some objectives might require different update frequencies. We work with you to understand these objectives and make recommendations that best suit your needs.

Reporting tools like Tableau, PowerBI, or even Microsoft Excel, serve their purpose to give you a snapshot in time, but they aren’t meant to proactively help you understand the impact of fan behavior over time. Or show you how to drive more engagement

Our proprietary machine learning technology models your fans’ behavior and interactions on a daily basis to give you insights on who the best lead candidates are, which accounts might be at risk of not renewing their packages, how your marketable universe is growing, and more.

Standard implementation is 12 weeks from the start of the data request emails to the final hand-off of your platform. Depending on the number of systems we’re pulling from, it may be a few weeks longer. Our team works iteratively, to get you active in the platform, with early data-driven capabilities typically available within the first 8 weeks.

Unless you’re extremely confident in the data housed in your data warehouse, we recommend pulling historical and current data directly from source systems. It provides the most accurate set of raw data. We will then reconcile the data we pull with your internal records, flagging any discrepancies and working with you to fix them. During the implementation phase, the reconciliation process ensures we are within a 2% accuracy threshold on historical data while also completing a product mapping exercise that is then viewable in the platform.