Analytics has become a buzzword for professional sports in the last few years. Teams and fans are familiar with how analytics are applied to the performance of athletes, but a Calgary-based company is applying analytics to how teams connect with their fans.
StellarAlgo’s Customer Data Platform uses machine-learning and predictive technology to enable live audience organizations like sports teams to gain valuable insights and thorough understanding of their fan universe. Founded by Vince Ircandia, who previously led business intelligence teams in front offices of sports organizations, StellarAlgo is already working with teams in 4 of the 5 major leagues, and dozens of teams in the minor leagues.
“For us, it’s all about building and reinforcing the fan experience: matching fans with the products that they would want and tailoring the marketing to them,” said Sean Fynn, CTO of StellarAlgo. “Doing this well is hard, and this is where AI comes in. We use machine learning technology to make inferences on how best to do this based on data.”
StellarAlgo works with data that sports teams have to help them learn how to engage their fanbase on a more personalized level: making inferences from marketing touchpoints, channels, and sentiments, to purchases, surveys, and even personal conversations that staff have with fans. Depending on the organization, the sports franchises may work with expansive data sets from a wide array of systems such as:
- Primary and secondary ticketing transactions, including attendance
- CRM
- Email marketing campaigns
- Digital (website interactions and digital advertising)
- Surveys
- Geographic data such as weather and socioeconomics
- Team statistics
- Social media
- Demographics of fans engaging with marketing materials (Are they families? Do they tend to live close to the venue?)
- Success of product offerings depending on price levels, time of the season, time of game, the opposing team, etc.
Even with the above data sets, the StellarAlgo Customer Data Platform is not a one-size-fits-all solution. The StellarAlgo team also considers the different industries of the businesses that they work with, and the resulting nuances. This can include:
- Major vs. minor league
- Length of season and number of home games
- Games vs. tournaments
- Jurisdictional and geographic differences
StellarAlgo’s technology applies algorithms and processes to drive retention, and their propensity models help teams determine the suitability for product offerings. For fans, this means that they receive offers that they are more likely to want and be interested in. For teams, they can have more specific feedback on their product mix (products and services) and make adjustments so they can offer more of what the fans want and truly grow their fanbase.
“We look at the majority of what we do as machine learning: we’re taking a specific set of algorithms to train on a set of data and make predictions to optimize certain business processes,” said Fynn. “We solve discrete problems like retention, or reducing churn of certain buyers, looking at propensity models, and even suitability of products and events for certain buyers.”
Organizations in any industry know that AI can differentiate your company, and that the insights from AI are highly valuable. The StellarAlgo Customer Data Platform provides predictive insights in a really accessible way for organizations to execute on, while also bringing value to teams by showing them what they are already doing well. The platform also shows where there are some blind spots or missed opportunities for better or new practices, channels, touchpoints and systems that they can invest in. One such client was the Vancouver Canucks. Using the StellarAlgo Customer Data Platform, the team was able to create a 360° view of their customers: aligning data from nine different systems, creating a master customer record from which they were able to make data-driven decisions.
The StellarAlgo Customer Data Platform’s segmentation tools and campaign feedback loop help organizations manage their campaigns to a tee. They are able to help teams easily identify segments so that they can reduce blast-type marketing campaigns in favour of more targeted ones, reducing spend while getting a better conversion or response rate. In one client for example, they went from getting 1-1.5% engagement on a campaign up to 6%.
The StellarAlgo team is proudly based in Calgary (with some team members in Ottawa), and Fynn says that they have made a conscious effort to bring in personnel whose backgrounds and skills cover a wide span of capabilities. The data science team is composed of computer scientists, software developers, engineers, MBAs, and database developers and pipeline managers. Statistical, data engineering, and coding skills cannot always be found in one person, so the all-Canadian team’s expertise varies by design.
“I think that we are also able to attract and retain talent because our team feels like they are able to solve tangible problems and receive immediate feedback from our clients,” says Fynn.
StellarAlgo works closely with their clients in the further development of their platform, and integrates client input, aligning their goals and investments into the product roadmap. In the almost three years since StellarAlgo first launched, they have amassed an impressive client list with over 35 live audience organizations, and developed technology that has received great feedback. They are preparing to roll out a new dynamic pricing feature in the fall, which will take trends from current sales, product inventory, and secondary market data to inform pricing strategies.
“There are many directions that we could go with this technology,” said Fynn. “We will have to be selective, and pick focus areas, continuing to understand fans and expand our models that power our customer data platform.”
Names and titles of team members in photo, from left to right:
Vincent Ircandia (Founder & CEO), Nivedita Baliga (Data Engineer), Megan Kurcwal (Head of Sales & Marketing), Joseph King (Head of Product), Sean Fynn (CTO), Mudit Saxena (Data Scientist)