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#44: 4 Data Points to Know During COVID

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Manage episode 281107175 series 2843765
コンテンツは Gym Ops Radio によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Gym Ops Radio またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Data Point #1: Active Membership by Location

  • Why it’s important to differentiate between total membership, active membership, and paid membership to create an accurate financial forecast.
  • SCENARIO 1: Active membership goes down, total membership remains the same (OKAY)
  • SCENARIO 2: Active membership goes down, total membership goes down (PROBLEM)
  • Why per location matters?
  • Each one has unique selling points, is there a service failure at one but not the others?
  • It helps to keep focused on the macro, ie. “we’re okay”

LISTENER TAKE AWAY: Start calculating your weekly and monthly holds percentage, can you identify intrinsic or extrinsic forces that cause a positive or negative swing?

Data Point #2: Micro Attendance Metrics

  • Average class size + total visits in a month. Each one per location and as a company following the same concept of macro vs. micro. Both are important.
  • Many scenarios throughout this:
  • ALL OUTDOORS, HALF INDOORS, ALL INDOORS. So we’ve needed to know exactly who is going where and why.
  • Emergency Scenarios: Adjust programming

LISTENER TAKE AWAY: Start tracking and see where correlation arises, then use it to forecast when/if it happens again.

Data Point #3: Front End Interest (ie Leads)

  • Total number of names each week into our membership waitlist is indicative of two things.
  • 1. How we are doing as a company in our service offerings (referrals)
  • 2. How much trepidation does the marketplace have with COVID (state of industry)
  • Conversion is important but it’s not the whole story, for example conversion is garbage right now because of the holidays and COVID, but interest is still there which means emotionally people are still very interested.

LISTENER TAKE AWAY: What metrics do you have in place to measure interest?

Data Point #4: Avg. Membership Value

  • Basic equation that tells you how much revenue you’re generating in each class, used to go hand in hand with data point #2.
  • Why it matters especially in COVID, less people are able to come to the gym each day.
  • For example, right now we have a problem. Our AMV is the same, yet our class capacity is cut by 40% which means our revenue opportunity is reduced by 40%.
  • Prices have to go up with capacity going from a surplus to a premium.
  • 18 x $113 = $2,034 per class
  • 12 x $113 = $1,356 per class
  • = $678 less per class
  • That’s 1-2 jobs
  • Very similar to the revenue per table in the restaurant industry that’s so crucial to support operations.
  • If we can get our AMV up over time, then it means we don’t need to rely on capacity, so knowing it is absolutely non-negotiable.

LISTENER TAKE AWAY: How can you increase your AMV without compromising experience?

  continue reading

57 つのエピソード

Artwork
iconシェア
 
Manage episode 281107175 series 2843765
コンテンツは Gym Ops Radio によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Gym Ops Radio またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Data Point #1: Active Membership by Location

  • Why it’s important to differentiate between total membership, active membership, and paid membership to create an accurate financial forecast.
  • SCENARIO 1: Active membership goes down, total membership remains the same (OKAY)
  • SCENARIO 2: Active membership goes down, total membership goes down (PROBLEM)
  • Why per location matters?
  • Each one has unique selling points, is there a service failure at one but not the others?
  • It helps to keep focused on the macro, ie. “we’re okay”

LISTENER TAKE AWAY: Start calculating your weekly and monthly holds percentage, can you identify intrinsic or extrinsic forces that cause a positive or negative swing?

Data Point #2: Micro Attendance Metrics

  • Average class size + total visits in a month. Each one per location and as a company following the same concept of macro vs. micro. Both are important.
  • Many scenarios throughout this:
  • ALL OUTDOORS, HALF INDOORS, ALL INDOORS. So we’ve needed to know exactly who is going where and why.
  • Emergency Scenarios: Adjust programming

LISTENER TAKE AWAY: Start tracking and see where correlation arises, then use it to forecast when/if it happens again.

Data Point #3: Front End Interest (ie Leads)

  • Total number of names each week into our membership waitlist is indicative of two things.
  • 1. How we are doing as a company in our service offerings (referrals)
  • 2. How much trepidation does the marketplace have with COVID (state of industry)
  • Conversion is important but it’s not the whole story, for example conversion is garbage right now because of the holidays and COVID, but interest is still there which means emotionally people are still very interested.

LISTENER TAKE AWAY: What metrics do you have in place to measure interest?

Data Point #4: Avg. Membership Value

  • Basic equation that tells you how much revenue you’re generating in each class, used to go hand in hand with data point #2.
  • Why it matters especially in COVID, less people are able to come to the gym each day.
  • For example, right now we have a problem. Our AMV is the same, yet our class capacity is cut by 40% which means our revenue opportunity is reduced by 40%.
  • Prices have to go up with capacity going from a surplus to a premium.
  • 18 x $113 = $2,034 per class
  • 12 x $113 = $1,356 per class
  • = $678 less per class
  • That’s 1-2 jobs
  • Very similar to the revenue per table in the restaurant industry that’s so crucial to support operations.
  • If we can get our AMV up over time, then it means we don’t need to rely on capacity, so knowing it is absolutely non-negotiable.

LISTENER TAKE AWAY: How can you increase your AMV without compromising experience?

  continue reading

57 つのエピソード

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