{"id":9187,"date":"2026-11-02T09:23:11","date_gmt":"2026-11-02T14:23:11","guid":{"rendered":"https:\/\/numea.ca\/?p=9187"},"modified":"2026-11-02T09:23:15","modified_gmt":"2026-11-02T14:23:15","slug":"what-hockey-can-teach-us-about-customer-segmentation-2026-edition","status":"publish","type":"post","link":"https:\/\/numea.ca\/en\/what-hockey-can-teach-us-about-customer-segmentation-2026-edition\/","title":{"rendered":"What Hockey Can Teach Us About Customer Segmentation: 2026 Edition"},"content":{"rendered":"\n
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The start of the Canadiens\u2019 season always brings a certain buzz to the Numea office. Last year, we built a segmentation of NHL players, which means grouping players into typical profiles based on their stats, just like we do for our clients. We did it for fun first, but also to show what a segmentation can actually be used for.\r\n\r\nRead the first edition here!<\/u><\/a><\/p>\n\n\n\n

Given the interest the project got last year, we decided to rework the segmentation model more seriously. We asked our in-house \u201cexperts\u201d for their input on choosing and defining the variables, and our model is now even more robust than last year\u2019s.<\/p>\n\n\n\n

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First, the data<\/strong><\/h2>\n\n\n\n

The key to a good segmentation, and to any AI project for that matter, is data. First, you need access to clean, centralized data. In this case, we used 2025-2026 stats for 828 NHL players (source: MoneyPuck.com<\/a>).<\/p>\n\n\n\n

Next, as anyone who has worked on a segmentation project with us knows, the first step is to choose the key concepts that will group similar players together.<\/p>\n\n\n\n

In our first edition, the variables were used as is. For example, we looked at a player\u2019s total goals for the season. This year, we measured performance while the player is on the ice (per minute played), while still taking his total ice time into account. That way, a fourth-liner isn\u2019t at a disadvantage against a star who plays 22 minutes a game.<\/p>\n\n\n\n

We also added new concepts to better tell players apart. These indicators cover both offensive production and defensive playing style.<\/p>\n\n\n\n

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Here are the model\u2019s variables, in order of importance:<\/strong><\/h3>\n\n\n\n
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The segments<\/strong><\/h2>\n\n\n\n

Once the variables were chosen, we grouped together the players who are most alike. The result: six segments, split into three groups: forwards, defensemen and fill-in players. Each segment plays a different role on the team, just like each type of customer in your business.<\/p>\n\n\n\n

\"\"Forwards<\/strong><\/mark><\/h3>\n\n\n\n
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Offensive stars<\/mark><\/h4>\n\n\n\n

(14% of players)<\/mark><\/h4>\n<\/div>\n\n\n\n
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The offensive leaders. Close to a point per game and about 19 minutes of ice time a night. Almost all of them are forwards.<\/p>\n\n\n\n

On the Canadiens: 14 Nick Suzuki<\/strong><\/mark><\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Supporting forwards<\/mark><\/h4>\n\n\n\n

(28% of players)<\/mark><\/h4>\n<\/div>\n\n\n\n
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The largest segment in the league. Second- and third-line players: about a point every two games and 15 minutes of ice time a night.<\/p>\n\n\n\n

On the Canadiens: 91 Oliver Kapanen<\/strong><\/mark><\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Depth forwards<\/mark><\/h4>\n\n\n\n

(16% of players)<\/mark><\/h4>\n<\/div>\n\n\n\n
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Fourth-liners. About 11 minutes of ice time per game and few points (0.2 per game).<\/p>\n\n\n\n

On the Canadiens: 17 Josh Anderson<\/strong><\/mark><\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\" Defensemen<\/mark><\/strong><\/h3>\n\n\n\n
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Offensive defensemen<\/mark><\/h4>\n\n\n\n

(13% of players)<\/mark><\/h4>\n<\/div>\n\n\n\n
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The defensemen who play the most: over 21 minutes a game, the highest ice time of any segment. They produce about a point every two games, as much as supporting forwards.<\/p>\n\n\n\n

On the Canadiens: 48 Lane Hutson<\/strong><\/mark><\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Stay-at-home defensemen<\/mark><\/h4>\n\n\n\n

(17% of players)<\/mark><\/h4>\n<\/div>\n\n\n\n
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Defensemen who play regularly (16 to 17 minutes a game), but contribute little offensively (0.2 points per game).<\/p>\n\n\n\n

On the Canadiens: 72 Arber Xhekaj<\/strong><\/mark><\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\" Fill-in players<\/strong><\/mark><\/h3>\n\n\n\n
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Fill-in players<\/mark><\/h4>\n\n\n\n

(12% of players)<\/mark><\/h4>\n<\/div>\n\n\n\n
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Call-ups, injured players or players getting a tryout. Fewer than 12 games played on average, little ice time and very few points.<\/p>\n\n\n\n

On the Canadiens: 62 Owen Beck<\/strong><\/mark><\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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The Demidov case<\/strong><\/h3>\n\n\n\n

Last year, Ivan Demidov was our example of a young Canadiens rookie. This year, with 62 points, he\u2019s one of the offensive stars. Just like a customer, a player can move from one segment to another. That\u2019s why it matters to track these movements over time!<\/p>\n\n\n\n

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Ice time, points and segment size<\/h2>\n\n\n\n

The chart below places each segment on two criteria: average ice time per game and points per game. Bubble size represents the number of players in each segment (the bigger the bubble, the larger the segment). Offensive stars clearly stand out at the top, while offensive defensemen stand out for their ice time.<\/p>\n\n\n\n

Note: depth forwards and fill-in players look alike on these two criteria. What really sets them apart is the number of games played.<\/p>\n\n\n\n

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A portrait of the league<\/strong><\/h2>\n\n\n\n

Not all segments carry the same weight. Offensive stars make up only 14% of players, yet they collect 35% of the league\u2019s points. Add supporting forwards, and 42% of players account for 68% of points.<\/p>\n\n\n\n

Your customers work the same way: a minority often generates most of your revenue. You just need to know who they are!<\/p>\n\n\n\n

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Every team, a different strategy<\/strong><\/h3>\n\n\n\n

Looking at each team\u2019s makeup, you can see very different strategies.<\/p>\n\n\n\n