The days of cash boxes and rolodexes are long gone. Technology is radically changing the sports industry and giving us access to more data than ever before, from more sources than we could have imagined twenty years ago. Without automation and machine learning, more isn’t always better.
Sports and entertainment organizations are at different levels of maturity in terms of how well they are able to interpret and action their fan data. With data growing so quickly and the value being in actioning it rather than simply assembling it, we find there is often times a gap in technology and resourcing. The days of simply storing data and spending valuable and finite resources to assemble and quality test can and should be problems of the past.
“Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing. Advances in AI now mean product developers can create innovative and leading-edge products and services that, until recently, would not have been within reach of the average marketing budget.” – Vance Reavie, Forbes
Whichever camp your organization fits in, the question always comes back to: How can our organization get the most out of our data? How well do we understand our fans? That’s where machine learning and artificial intelligence comes in.
What is Machine Learning?
Machine learning is a set of algorithms that a software application uses to automate analytical model building. It is part of the larger world of artificial intelligence which is based on a system’s ability to learn from the data sets provided and continually improve and get better. For sports and entertainment organizations, it can enable them to understand how and when to engage with fans to drive engagement and revenues.
An example of this is using machine learning to identify patterns and drop offs in customer journey mapping and then using those insights to provide revenue generation opportunities for an organization. This means that as customers click on an ad, visit your team website, buy tickets, or open an email, their profiles are continuously being updated and insights are being uncovered. Organizations use those insights to understand product propensities, channel affinity, and other valuable information for sales and marketing teams to action against. What would be time consuming, near-impossible and in no way scalable for a human analyst to tackle can be swiftly delivered into valuable insight by a machine.
A few questions that organizations are answering with the help of AI and machine learning are:
How do customers naturally group and what common attributes tie them together
Who are the best leads to target for a home game
Which package accounts are at risk of not renewing next season
Which fan behaviors indicate future engagement or dwindling interest
Which messages are most likely to resonate with which types or groupings of customers
Which channel is most effective for conversion of a specific segment
The Value of Adding AI to your Analytics Efforts
Regardless of where you are in your analytics journey, your organization can benefit tremendously from automating data processes and adding machine learning to your arsenal. The ability to deanonymize web interactions throughout a customer’s journey allows organizations to better understand touchpoint effectiveness (clicks on online ads, visits to your website, interactions on social media) and which interactions were most influential in turning them into a known customer (via their first purchase, newsletter sign up, survey, or other type of form). This enables the discovery of the underlying value of the hundreds of attributes associated with each fan, from the vast array of data sources and produce insights which can be actioned for all stages along a fan’s journey.
“The future for analysts is much less dystopian than the headlines suggest. The advancements in AI look a lot like having efficient assistants rather than replacements.” – David Crawford, Venture Beat.
The availability of real-time information about distressed inventory for an upcoming game, past purchase history, and product propensity, for example, help get your sales team in front of the right fans at the right time to fill seats and increase engagement. Additional value is found in segmenting and identifying look-a-like audiences for event-specific marketing, increasing marketing reach to new customers primed to become fans.
AI and machine learning are becoming tools that forward-thinking sports & entertainment organizations are adding to their technology toolkit and it’s more accessible than ever. Sales, Marketing, and Analytics departments are all taking advantage of these new possibilities – ultimately using these insights to drive customer lifetime value and affinity.
Our Customer Data Platform is helping teams maximize the potential of their data via machine learning, that leverages behavioral data points to segment audiences and identify the why behind customer engagement and conversion. Our Customer Data Platform allows organizations to grow the affinity and lifetime value of their customers in an automated way, so teams get the answers to their questions immediately, when they need them most.
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