Hi everyone, For a while now, I’ve been creating a lazy workaround to sales pacing charts for my 6-week festival season company by comparing sales using the days prior to production close criteria. However, this isn’t really cutting it anymore, at least in considering the grand total of sales across all four shows in our festival. What I would love is a way to create a line chart with the x-axis being the date prior to season close and the y-axis being a running sum of total revenue, so that I can compare sales 60 days out last year with 60 days out this year.Does anyone have a clever solution for this? It's not possible to hardcode a formula for the category, as far as I can tell...I have to choose an actual out-of-the-box criteria. If someone has any advice on this, it would be immensely helpful! Thanks so much.
I'm a huge fan of Christine Wingenfeld's Sales Pacing Chart
https://community.tessituranetwork.com/topical_groups/analytics-coffee/f/discussions/30795/sales-pacing-chart---inputting-goals-budgets
So much so that the video interview is here https://community.tessituranetwork.com/topical_groups/analytics-coffee/m/files/57683
For getting year on year multi-line graphs consider bucketing the years (as break by in running sums has always given me a headache). I've been without Analytics for about 9 months but I do have some YOY RSUM comparison .dash files if you want.
Hi Heath,
Thank you for this – I finally played around with Christine's awesome pacing chart and I think I have it working...but I have two follow-up questions that I hope are pretty simple.
1) If I were to filter her recommended formula by season instead of production, would that give me apples to apples comparisons by season even though the date criteria is the Production data point of order weeks prior to closing?
2) I'm getting a straight line for weeks that haven't happened yet, but I'd love to have those future weeks just not show up at all on the chart. Is there a way to make those data points blank?
Hi Anh Le
Hopefully this will be less confusing but
Christine's original is Season vs previous Seasons. So it would be fine to filter by Season - either by choosing the offset values so that it's always relevant year on year relative to a group of previous seasons OR by filtering it on distinct seasons.
The Order Weeks Prior to Closing filter in the denominator: it is Weeks due to the x-axis having a "week" level of granularity. If your x-axis was Days Prior to Closing then you could put Days prior in here as well. As Christine says what we are doing here is creating a percentage by week historical bar graph in the back ground to pace our current sales by. eg: on average at week 12 we were 56% of our total. Above the slash describes where were were on average historically at this spot on the x axis and ALL(Order weeks Prior To Closing) is to gather the final total by override the X-Axis position. The reason we create a percentage is because shows sell at a differing numbers of tickets but we are just interested in the trends.
The multiplyer then normalises it to the current season (or productions) ticket target. For Christine's example it was 5950 tickets and so it fits the curve to the current curves target. Note that if you were going to recreate this for revenue rather than ticket count you could use the budget amount in Tessitura. You can vote for ticket target numbers in performances here
(RSUM ( ( [Total Ticket Count] , [Season Fiscal Current Year Offset] ) ) )
/
( ( [Total Ticket Count] , ALL( [Order Weeks Prior to Closing] ) , [Season Fiscal Current Year Offset] ) ) * 5950
Heath Wilder did such a great job at explaining this - probably better than I could have myself!
I've also created a version of this for subscription purposes. It's the same general concept, but with a few changes to the values. I thought I'd share in case this version is more of what you're looking for with a full season comparison.
For the x-axis, I use "Weeks Since OnSale" as that lines up better when looking at the full season rather than comparing specific productions.
For the goal calculation, I use the Max Campaign Goal Amount filtered to the current season multiplied by an Index that looks at the pacing of sales (as explained above) for 3-4 previous seasons which are selected in the widget-level filters.
( [Max Campaign Goal Amount], [Current FY Package Season], all( [Order Weeks Since OnSale]))*(rsum( [Total Ticket Original Paid Amount])) / ( [Total Ticket Original Paid Amount], all( [Order Weeks Since OnSale]))
As with the ticket example, if you don't have a goal living in Tessitura, then you could just replace everything in the first half of the formula (before the *) with a number.
Since this is for packages, the values are looking at the Original Paid Amount because we want to see things based on the amount someone paid for their subscription prior to exchanges happening. Some minor changes to the values and this could be for package seat counts too.
Then the actuals line is just a running sum filtered to the current season.
RSUM(([Total Ticket Original Paid Amount], [Current FY Package Season]))
Maybe this will give you an idea too of other ways you could modify and use this formula!