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Craigins

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oh i see now. the survey results are different from the forum thread. I did not manually compile the forum thread into the survey results. We've circulated the google survey throughout fb/reddit/forums for people to fill out. Some people fill out the forum poll and the survey, some just the forum poll, others just the survey.
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SteveInBend

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Unfortunately, I don't think the survey results are very reliable. Are there really 7 folks with R1S orders with LE and Max Pack who have received expected delivery windows in 2022? I thought that Rivian hadn't announced the Max Pack for the R1S. Has Rivian even announced any expected dates for the R1T with Max Pack?

It seems like some respondents have entered the dates they expect/want versus official notification from Rivian.
 

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Unfortunately, I don't think the survey results are very reliable. Are there really 7 folks with R1S orders with LE and Max Pack who have received expected delivery windows in 2022? I thought that Rivian hadn't announced the Max Pack for the R1S. Has Rivian even announced any expected dates for the R1T with Max Pack?

It seems like some respondents have entered the dates they expect/want versus official notification from Rivian.
There are clearly a few people who either selected the wrong trim or battery, or just don't know how to read. However, that's only about 2ish percent of respondents.
 

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There are clearly a few people who either selected the wrong trim or battery, or just don't know how to read. However, that's only about 2ish percent of respondents.
I didn't remember the difference between MaxPack and Large Pack. and chose the wrong one. There is no way to go back and edit the entry and the person who set this up doesn't correct it if a change is requested.
 

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Just so everything doesn't get lost in the mega thread with the poll, I compiled the links to the survey, the spreadsheet results, the dashboard results, and a geojson here:

https://rivian.craiginsdev.com/

I manually generate the geojson so it won't be in sync with the spreadsheet and dashboard.

1637730964244.png


Enjoy!

Just a quick edit: https://rivian.craiginsdev.com/index2.html has the clustering enabled if you want to drill down into specific areas
Not sure where you'd like to receive the data.... but thanks for doing this!
Ordered April 2019, R1S LE, LA Silver, Ocean Coast, 22"s, delivery to Ashland Oregon= May-June 2022
 
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Craigins

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Unfortunately, I don't think the survey results are very reliable. Are there really 7 folks with R1S orders with LE and Max Pack who have received expected delivery windows in 2022? I thought that Rivian hadn't announced the Max Pack for the R1S. Has Rivian even announced any expected dates for the R1T with Max Pack?

It seems like some respondents have entered the dates they expect/want versus official notification from Rivian.
The only dataset that will be perfect is the dataset that Rivian has that they use to generate the delivery schedules.

All surveys contain noise. The point isn't to pick out individual records of a dataset, but to observe trends in the whole dataset. Clearly this means pruning outliers, things like you have pointed out, when doing analysis.
 

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Started looking at the data in the spreadsheet a bit more closely. While there is data that seems to have errors attached (LE AND MaxPack), some duplicates, and some dates typed incorrectly, there is enough to extract trends. I tried to remove most of the suspect entries and made a series of wild assumptions to see what could be learnt from the few hundred remaining data-sets. So far I focused on LE R1T data only. My thoughts so far:

1. The strongest correlation to Projected Delivery Date seems to be the Pre-Order date (Black line in Fig).
2. Within the contiguous 48 states location does not seem to play a significant role
3. Outside the Contig-48 and for Canada the delay seems to be 3-4 quarters (red line below).
4. The Exterior Color, Wheels, Spare, Off-road-Upgrade, and Accessories do not seem to play a significant role
5. Interior Color DOES seem to influence delivery dates. Black Mtn seems to be prioritized. Ocean Coast seems to have a 1-8 week delay, and Forest Edge seems to have the longest delay (4-10 weeks).

Not sure if Excel Figs are legible in this forum, but just in case they are - one of the Figs. I generated is included below. With more data - and more QC of the data - quite a few conclusions could be extracted. Even with the data in hand there is more that could be done to look at non-LE orders, and look at all the R1S data.

survey_1-png.png
 

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Started looking at the data in the spreadsheet a bit more closely. While there is data that seems to have errors attached (LE AND MaxPack), some duplicates, and some dates typed incorrectly, there is enough to extract trends. I tried to remove most of the suspect entries and made a series of wild assumptions to see what could be learnt from the few hundred remaining data-sets. So far I focused on LE R1T data only. My thoughts so far:

1. The strongest correlation to Projected Delivery Date seems to be the Pre-Order date (Black line in Fig).
2. Within the contiguous 48 states location does not seem to play a significant role
3. Outside the Contig-48 and for Canada the delay seems to be 3-4 quarters (red line below).
4. The Exterior Color, Wheels, Spare, Off-road-Upgrade, and Accessories do not seem to play a significant role
5. Interior Color DOES seem to influence delivery dates. Black Mtn seems to be prioritized. Ocean Coast seems to have a 1-8 week delay, and Forest Edge seems to have the longest delay (4-10 weeks).

Not sure if Excel Figs are legible in this forum, but just in case they are - one of the Figs. I generated is included below. With more data - and more QC of the data - quite a few conclusions could be extracted. Even with the data in hand there is more that could be done to look at non-LE orders, and look at all the R1S data.

View attachment 12237
nice job!
 

Kialoa

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Since the last Fig was readable - thought I'd share the estimated impact of the interior color selection. The Black Mtn data is not shown again (was on last Fig) - and is only indicated by black line here. Forest Edge data is in Green with Red Text - clearly right-shifted (delayed). On closer inspection Ocean Coast data (light blue, Black Text) is more complex. Almost seems like a bimodal distribution - SOME of the orders are minimally (0-1 week) delayed wrt Black Mtn. Anther set of orders almost overlap with Forest edge delays. As always, there are some outliers...

Survey_2.png


survey_2-png.png
 

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Started looking at the data in the spreadsheet a bit more closely. While there is data that seems to have errors attached (LE AND MaxPack), some duplicates, and some dates typed incorrectly, there is enough to extract trends. I tried to remove most of the suspect entries and made a series of wild assumptions to see what could be learnt from the few hundred remaining data-sets. So far I focused on LE R1T data only. My thoughts so far:

1. The strongest correlation to Projected Delivery Date seems to be the Pre-Order date (Black line in Fig).
2. Within the contiguous 48 states location does not seem to play a significant role
3. Outside the Contig-48 and for Canada the delay seems to be 3-4 quarters (red line below).
4. The Exterior Color, Wheels, Spare, Off-road-Upgrade, and Accessories do not seem to play a significant role
5. Interior Color DOES seem to influence delivery dates. Black Mtn seems to be prioritized. Ocean Coast seems to have a 1-8 week delay, and Forest Edge seems to have the longest delay (4-10 weeks).

Not sure if Excel Figs are legible in this forum, but just in case they are - one of the Figs. I generated is included below. With more data - and more QC of the data - quite a few conclusions could be extracted. Even with the data in hand there is more that could be done to look at non-LE orders, and look at all the R1S data.

View attachment 12237
How did you pick the estimated delivery date for each R1T from among the two month window given?
 

Kialoa

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How did you pick the estimated delivery date for each R1T from among the two month window given?
One of the many assumptions I had to build in. I scaled this based on the order date, assuming the data we have are a normal distribution - which it is not. So those that ordered earliest I put near the beginning of the relevant delivery window, and those that ordered latest near the end of that delivery window. Certainly imperfect - but since this seems to be the driving prioritization variable, maybe not too far off. Of course this is most error prone for the delivery windows with fewest respondents (least normal population).
 
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