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Snapshot Reporting

John Jakovich
$organization

Hello BI Group!  As you probably saw in the January Tessitura Network newsletter, we’ve launched a Business Intelligence initiative.  We’re currently in a “discovery” phase in which I’m soliciting feedback from licensees about the current state of Tessitura BI.

In these early conversations a few themes are emerging, one of which is the desire to perform “snapshot” or “as-of” analysis.  For instance, "how many members did we have as-of July 1st, 2013?"  I would appreciate your feedback, experiences or examples of “as-of reporting”.  Have any of you attempted snapshot reporting?  If so, how did you approach it?  Were you successful?

Additionally, if any one would like to chat with me about BI individually, please email me directly at jjakovich@tessituranetwork.com.

Thank you!
John 

 

 

 

 

  • $organization

    So I will jump in with a few examples:

    1. How many members of each level do we have on every day of the last 5 year so that we can graph this over time. We want a one page visual understanding of what is going on with memberships over time.
    2. In FY2013, FY2014 and FY2015, how many members bought tickets to our Spring Season prior to the first performance.  How many bought during the run of the run of the show. Group this by their membership level at that time.
    3. For all of the shows in the last 3 spring seasons.  How many holds of different kinds did we have on each show 15 days prior to curtain.  Did the Seat eventually Sell?
    4. Over the past 3 years where were subscribers living.  Provide the results by Zip code at the time, the ticket was placed in an order.  (Note younger folks in NYC move around.  Their current Zip code might be some where in NJ by now. And we are not seeing them a lot of tickets any more.  However when they lived in BAM's zipcode 2 years ago they were a subscriber and we kept the order open for them for the season.  They might have moved mid subscription during the period of the order.)  Where where they actually when they decided to buy that ticket?  (Not where do they live now.)

    We have successfully done #1 and spoke about this at conference in 2014.  For this work we were not able to tell exactly what level folks had if there was an upgrade to their membership.  We reported all days of the membership as if they were at the final level.  So there was a bit of historical data shifting that occurred day to day when you run this data set.  (This was due to a few customer upgrades during the year.)  We also did not properly account for early suspensions and cancellations which do not update expiration date, this just changes status.  We reported as if all membership ran the full course init_dt to experation_dt.  We did not count Lapsed periods in which a member might get member benefits, when looking at how many folks could use benefits.

    For the rest we have tried, and by in large we have failed.

    We are going to make another try at something like number #3 next week using T_ORDER_SEAT_HISTORY data.

    Folks please chime in.  We need to help John in his new roll understand our challenges so that he can help us get better data sets so that we can do fantastic Diagnostic and Predictive reporting.

  • $organization

    John,

    Just had another conversation with a BAM staff person asking a great diagnostic question that I cannot currently answer with the data I get out of T_Stats, RMA, or frankly the database currently.

    Here is the question:

    “Does the general availability of “good seats” (Price Zones 1 and 2 not in holds) at the on sale date have a correlation with BAM’s ability to “make goal for the performance” (Performance Sales $ (at curtain) >= performance budget $)?

    Here is what is missing.

    1.       I do not know how to do a true se Seat Inventory that describes how many seats we have not blacked out not in a hold codes at On Sale Date.  (Frankly This question could and should be asked about any time along the sales cycle 10 days before curtain, 30 days before curtain… Or any set of availability conditions not just certain holds…)

    2.       In a Seat inventory we don’t know what zones seats were in at the time of On Sale or any other time other that as it is today.

    3.       Here at BAM we do not do a good job at recording an On-Sale Date in t_perf.def_onsale_dt. (Bad on us. We could fix this.)

    What do we already have to answer such a question.

    1.       The T-Stats data warehouse currently tracks Performance Sales data at the seat level as of today.  However, one cannot determine the sales status “as of” say 30 days before performance 10 days….  We could use a sales transaction data set for data analysis.”

     

    2.       The T-Stats Data warehouse currently tracks budget numbers by performance.

     

  • Former Member
    Former Member $organization
    We also have member pre sales a few days before each on sale. It would be nice if it were simpler to correlate how many new donations resulted from a desire to buy early for a particular artist. It can be done, but it is cumbersome, and not really worth calculating for most shows. The pre sale timing fluctuates depending on the show so we would probably want it based on MOS and new donation or old donation plus number of tickets, ticket value and donation level.

    Thanks!
    Nicole

    Sent from Outlook Mobile




    On Fri, Feb 19, 2016 at 2:37 PM -0800, "Tom Brown" <bounce-tombrown3568@tessituranetwork.com> wrote:

    So I will jump in with a few examples:

    1. How many members of each level do we have on every day of the last 5 year so that we can graph this over time. We want a one page visual understanding of what is going on with memberships over time.
    2. In FY2013, FY2014 and FY2015, how many members bought tickets to our Spring Season prior to the first performance.  How many bought during the run of the run of the show. Group this by their membership level at that time.
    3. For all of the shows in the last 3 spring seasons.  How many holds of different kinds did we have on each show 15 days prior to curtain.  Did the Seat eventually Sell?
    4. Over the past 3 years where were subscribers living.  Provide the results by Zip code at the time, the ticket was placed in an order.  (Note younger folks in NYC move around.  Their current Zip code might be some where in NJ by now. And we are not seeing them a lot of tickets any more.  However when they lived in BAM's zipcode 2 years ago they were a subscriber and we kept the order open for them for the season.  They might have moved mid subscription during the period of the order.)  Where where they actually when they decided to buy that ticket?  (Not where do they live now.)

    We have successfully done #1 and spoke about this at conference in 2014.  For this work we were not able to tell exactly what level folks had if there was an upgrade to their membership.  We reported all days of the membership as if they were at the final level.  So there was a bit of historical data shifting that occurred day to day when you run this data set.  (This was due to a few customer upgrades during the year.)  We also did not properly account for early suspensions and cancellations which do not update expiration date, this just changes status.  We reported as if all membership ran the full course init_dt to experation_dt.  We did not count Lapsed periods in which a member might get member benefits, when looking at how many folks could use benefits.

    For the rest we have tried, and by in large we have failed.

    We are going to make another try at something like number #3 next week using T_ORDER_SEAT_HISTORY data.

    Folks please chime in.  We need to help John in his new roll understand our challenges so that he can help us get better data sets so that we can do fantastic Diagnostic and Predictive reporting.

    From: John Jakovich <bounce-johnjakovich8396@tessituranetwork.com>
    Sent: 2/19/2016 1:29:34 PM

    Hello BI Group!  As you probably saw in the January Tessitura Network newsletter, we’ve launched a Business Intelligence initiative.  We’re currently in a “discovery” phase in which I’m soliciting feedback from licensees about the current state of Tessitura BI.

    In these early conversations a few themes are emerging, one of which is the desire to perform “snapshot” or “as-of” analysis.  For instance, "how many members did we have as-of July 1st, 2013?"  I would appreciate your feedback, experiences or examples of “as-of reporting”.  Have any of you attempted snapshot reporting?  If so, how did you approach it?  Were you successful?

    Additionally, if any one would like to chat with me about BI individually, please email me directly at jjakovich@tessituranetwork.com.

    Thank you!
    John 

     

     

     

     




  • $organization
    Tom,
    We have a custom report for our Daily Sales Report that gets to this question for us- mostly. Maybe not in as much detail as this person may be asking. It shows the current sales, current capacity and the amount we'd have to sell each day in order to make our goal. We have created many custom content types in order to facilitate this information and seems to do the trick for us. Basically we can look at remaining capacity and kind of eye whether or not we have enough to sell in order to make goal or calculate it quickly. 

    I agree that a standard report in Tessitura that does this would be even better. 

    Janna

    Sent from my iPhone

    On Feb 24, 2016, at 3:14 PM, Tom Brown <bounce-tombrown3568@tessituranetwork.com> wrote:

    John,

    Just had another conversation with a BAM staff person asking a great diagnostic question that I cannot currently answer with the data I get out of T_Stats, RMA, or frankly the database currently.

    Here is the question:

    “Does the general availability of “good seats” (Price Zones 1 and 2 not in holds) at the on sale date have a correlation with BAM’s ability to “make goal for the performance” (Performance Sales $ (at curtain) >= performance budget $)?

    Here is what is missing.

    1.       I do not know how to do a true se Seat Inventory that describes how many seats we have not blacked out not in a hold codes at On Sale Date.  (Frankly This question could and should be asked about any time along the sales cycle 10 days before curtain, 30 days before curtain… Or any set of availability conditions not just certain holds…)

    2.       In a Seat inventory we don’t know what zones seats were in at the time of On Sale or any other time other that as it is today.

    3.       Here at BAM we do not do a good job at recording an On-Sale Date in t_perf.def_onsale_dt. (Bad on us. We could fix this.)

    What do we already have to answer such a question.

    1.       The T-Stats data warehouse currently tracks Performance Sales data at the seat level as of today.  However, one cannot determine the sales status “as of” say 30 days before performance 10 days….  We could use a sales transaction data set for data analysis.”

     

    2.       The T-Stats Data warehouse currently tracks budget numbers by performance.

     

    From: John Jakovich <bounce-johnjakovich8396@tessituranetwork.com>
    Sent: 2/19/2016 1:29:34 PM

    Hello BI Group!  As you probably saw in the January Tessitura Network newsletter, we’ve launched a Business Intelligence initiative.  We’re currently in a “discovery” phase in which I’m soliciting feedback from licensees about the current state of Tessitura BI.

    In these early conversations a few themes are emerging, one of which is the desire to perform “snapshot” or “as-of” analysis.  For instance, "how many members did we have as-of July 1st, 2013?"  I would appreciate your feedback, experiences or examples of “as-of reporting”.  Have any of you attempted snapshot reporting?  If so, how did you approach it?  Were you successful?

    Additionally, if any one would like to chat with me about BI individually, please email me directly at jjakovich@tessituranetwork.com.

    Thank you!
    John 

     

     

     

     




  • $organization

    Nicole,

     

    Very interesting.  So how would you like to connect the donations and sales? 

    1.       Would you want to see them in the same order? 

    2.       Or do you want to look at timing of a contribution?

    3.       Would all contributions count?

    4.       Would you have to know current “membership” status before the contribution?  (The donation could just be a renewal.)

     

    What magic are you doing today to get this data?

     

    --Tom

    …

    718.724.8135

    tbrown@BAM.org

     

    From: Self-service Business Intelligence [mailto:groups-selfservicebi@tessituranetwork.com] On Behalf Of Nicole Keating
    Sent: Thursday, March 03, 2016 11:42 AM
    To: Thomas Brown <tbrown@bam.org>
    Subject: Re: [Self-service Business Intelligence] Snapshot Reporting

     

    We also have member pre sales a few days before each on sale. It would be nice if it were simpler to correlate how many new donations resulted from a desire to buy early for a particular artist. It can be done, but it is cumbersome, and not really worth calculating for most shows. The pre sale timing fluctuates depending on the show so we would probably want it based on MOS and new donation or old donation plus number of tickets, ticket value and donation level.

     

    Thanks!

    Nicole

    Sent from Outlook Mobile

     



    On Fri, Feb 19, 2016 at 2:37 PM -0800, "Tom Brown" <bounce-tombrown3568@tessituranetwork.com> wrote:

    So I will jump in with a few examples:

    1. How many members of each level do we have on every day of the last 5 year so that we can graph this over time. We want a one page visual understanding of what is going on with memberships over time.
    2. In FY2013, FY2014 and FY2015, how many members bought tickets to our Spring Season prior to the first performance.  How many bought during the run of the run of the show. Group this by their membership level at that time.
    3. For all of the shows in the last 3 spring seasons.  How many holds of different kinds did we have on each show 15 days prior to curtain.  Did the Seat eventually Sell?
    4. Over the past 3 years where were subscribers living.  Provide the results by Zip code at the time, the ticket was placed in an order.  (Note younger folks in NYC move around.  Their current Zip code might be some where in NJ by now. And we are not seeing them a lot of tickets any more.  However when they lived in BAM's zipcode 2 years ago they were a subscriber and we kept the order open for them for the season.  They might have moved mid subscription during the period of the order.)  Where where they actually when they decided to buy that ticket?  (Not where do they live now.)

    We have successfully done #1 and spoke about this at conference in 2014.  For this work we were not able to tell exactly what level folks had if there was an upgrade to their membership.  We reported all days of the membership as if they were at the final level.  So there was a bit of historical data shifting that occurred day to day when you run this data set.  (This was due to a few customer upgrades during the year.)  We also did not properly account for early suspensions and cancellations which do not update expiration date, this just changes status.  We reported as if all membership ran the full course init_dt to experation_dt.  We did not count Lapsed periods in which a member might get member benefits, when looking at how many folks could use benefits.

    For the rest we have tried, and by in large we have failed.

    We are going to make another try at something like number #3 next week using T_ORDER_SEAT_HISTORY data.

    Folks please chime in.  We need to help John in his new roll understand our challenges so that he can help us get better data sets so that we can do fantastic Diagnostic and Predictive reporting.

    From: John Jakovich <bounce-johnjakovich8396@tessituranetwork.com>
    Sent: 2/19/2016 1:29:34 PM

    Hello BI Group!  As you probably saw in the January Tessitura Network newsletter, we’ve launched a Business Intelligence initiative.  We’re currently in a “discovery” phase in which I’m soliciting feedback from licensees about the current state of Tessitura BI.

    In these early conversations a few themes are emerging, one of which is the desire to perform “snapshot” or “as-of” analysis.  For instance, "how many members did we have as-of July 1st, 2013?"  I would appreciate your feedback, experiences or examples of “as-of reporting”.  Have any of you attempted snapshot reporting?  If so, how did you approach it?  Were you successful?

    Additionally, if any one would like to chat with me about BI individually, please email me directly at jjakovich@tessituranetwork.com.

    Thank you!
    John 

     

     

     

     





  • $organization

    We have just completed some of the work for number 3 below.  We can now track daily hold codes.  We are still working on the latter half of the problem.  Knowing the sales statuses of the seats over the last 15 days.

     

    --Tom

    …

    718.724.8135

    tbrown@BAM.org

     

    From: Self-service Business Intelligence [mailto:groups-selfservicebi@tessituranetwork.com] On Behalf Of Tom Brown
    Sent: Friday, February 19, 2016 5:37 PM
    To: Thomas Brown <tbrown@bam.org>
    Subject: Re: [Self-service Business Intelligence] Snapshot Reporting

     

    So I will jump in with a few examples:

    1. How many members of each level do we have on every day of the last 5 year so that we can graph this over time. We want a one page visual understanding of what is going on with memberships over time.
    2. In FY2013, FY2014 and FY2015, how many members bought tickets to our Spring Season prior to the first performance.  How many bought during the run of the run of the show. Group this by their membership level at that time.
    3. For all of the shows in the last 3 spring seasons.  How many holds of different kinds did we have on each show 15 days prior to curtain.  Did the Seat eventually Sell?
    4. Over the past 3 years where were subscribers living.  Provide the results by Zip code at the time, the ticket was placed in an order.  (Note younger folks in NYC move around.  Their current Zip code might be some where in NJ by now. And we are not seeing them a lot of tickets any more.  However when they lived in BAM's zipcode 2 years ago they were a subscriber and we kept the order open for them for the season.  They might have moved mid subscription during the period of the order.)  Where where they actually when they decided to buy that ticket?  (Not where do they live now.)

    We have successfully done #1 and spoke about this at conference in 2014.  For this work we were not able to tell exactly what level folks had if there was an upgrade to their membership.  We reported all days of the membership as if they were at the final level.  So there was a bit of historical data shifting that occurred day to day when you run this data set.  (This was due to a few customer upgrades during the year.)  We also did not properly account for early suspensions and cancellations which do not update expiration date, this just changes status.  We reported as if all membership ran the full course init_dt to experation_dt.  We did not count Lapsed periods in which a member might get member benefits, when looking at how many folks could use benefits.

    For the rest we have tried, and by in large we have failed.

    We are going to make another try at something like number #3 next week using T_ORDER_SEAT_HISTORY data.

    Folks please chime in.  We need to help John in his new roll understand our challenges so that he can help us get better data sets so that we can do fantastic Diagnostic and Predictive reporting.

    From: John Jakovich <bounce-johnjakovich8396@tessituranetwork.com>
    Sent: 2/19/2016 1:29:34 PM

    Hello BI Group!  As you probably saw in the January Tessitura Network newsletter, we’ve launched a Business Intelligence initiative.  We’re currently in a “discovery” phase in which I’m soliciting feedback from licensees about the current state of Tessitura BI.

    In these early conversations a few themes are emerging, one of which is the desire to perform “snapshot” or “as-of” analysis.  For instance, "how many members did we have as-of July 1st, 2013?"  I would appreciate your feedback, experiences or examples of “as-of reporting”.  Have any of you attempted snapshot reporting?  If so, how did you approach it?  Were you successful?

    Additionally, if any one would like to chat with me about BI individually, please email me directly at jjakovich@tessituranetwork.com.

    Thank you!
    John