Graph Relationships – Social and Otherwise – in Professional Sport

Good ‘old-fashioned’ Customer Relationships are important in the sector that I work in – Professional Sport. Usually this focusses on the relationship between the brand / venue or club and the customer or supporter (note the flexible terminology here). Retention is core in an industry where the affiliation is often ‘for life’, and the concept of churn is interpreted as gradations or levels or involvement and not as ‘defection’ to a rival brand, so loyalty and the recognition of this by the club in relation to their customers is massively important in reinforcing the ‘tribal’ relationship the supporter feels to the club in question.

Lately, we’ve been doing a lot more analysis of relationships between supporters themselves, more along the lines of good ‘new fashioned’ social or graph relations. This has been driven partly by some of the projects we have been working on and their requirements – particularly large scale ‘once in a lifetime’ stadium migrations – and partly by the availability and access to the technologies that enable this. Examples here include the underlying technology such as SPARQL / RDF, as well as the ability to communicate or visualise this in tools such as D3 or Tableau.

What it doesn’t mean yet is ‘social media’ in terms of facebook, twitter or other mainstream consumer products. We’ve been hampered here by the continuing limitations on many club’s ability to link or identify a social media ID with a real-world ID – digital or otherwise. I’ll comment on this elsewhere in more detail.

In terms of Graph Analytics, we’ve been particularly interested in the patterns that represent groupings or shared activities that help re-reinforce the ‘real world’ relationships that are otherwise hidden to a club. Examples in the real world (bricks not clicks) would include who sits next to whom in a stadium seating plan, who arrives with whom and at what time in terms of stadium or venue access, and what we can infer regarding the nature of their real-world relationships that these patterns represent.

I’ll be updating this with examples later.

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