A weekly podcast with the latest e-commerce news and events. Episode 105 is a hot take on Stitch Fix IPO filling, and a deep dive into the subscription commerce model.
This episode is a hot take of the Stitch Fix IPO Filing:
- How IPO’s Work / Jobs Act
- $1B Exits in E-Commerce
- Zappos – $850m 2009
- Quidsi/diapers – $545m -2010
- Kiva – $775b 2012
- Trunk Club – $350m 2014
- Jet.com – $4b 8/16
- Dollar Shave club – $1b 7/16
- Chewy.com – $3b 4/17
- Zulily – went public with $2.7b
- Stitch Fix Background
- Financing History
- Stitch Fix financial performance
- Stitch Fix Customer Value / Churn
- Personalization and Machine Learning
- Company size and roles
Don’t forget to like our facebook page, and if you enjoyed this episode please write us a review on itunes.
Episode 105 of the Jason & Scot show was recorded on Sunday, October 22nd 2017.
New beta feature – Google Automated Transcription of the show:
[0:25] Welcome to the Jason and Scott show this is episode 105 being recorded on Sunday October 22nd 2017 I’m your host Jason retailgeek Goldberg and as usual I’m here with your co-host Scot Wingo.
[0:40] Hey Jason and welcome back Jason Scott show Sanders,
we started working on a little new show this week and as we got into it real realize that the big news that is dominating the retail and e-commerce world is one event.
Stitch fixes S14 their IPO so as we got into it.
And started working on this week we realized that the Stitch fix IPO is really a platform that we can use to talk about some of our favorite topics here on Jason Scott show,
it’s a little bit of everything Jason it’s got,
Ikea’s venture capital and exit e-commerce subscription Commerce which we talked about one of your favorite topics personalization machine learning and AI.
There’s an Amazon undertone where you know this is one of the few companies that’s made it out hopefully knock on wood and then Amazon dominated world how are they doing that,
and for all our e-commerce retail us there’s this really interesting KP eyes are key performance indicators here like the cost to acquire customers at lifetime value turn,
and one of our other favorite topics is private label and digital native vertical Branch so Stitch fix IPO covers everything.
[1:52] It’s like our last hundred and four episodes all rolled into one it’s amazing.
[1:56] Yes clearly Katrina over there with says must be a big lesson her because she’s kind of wrapped it all into one company which we appreciate.
[2:06] To a lot of the distance, interesting stories so we were at code Commerce now we reported this on the podcast for those either that follow this so in March there was shot talk and Jason Delray the Commerce and had the founder of Stitch fix Katrina up there and,
she kind of baited her and said that his sources are saying that there are over 500 million in Revenue side I think a lot of people in the street didn’t really believe there are that large,
and then she said I can’t talk about it but we aren’t a billion dollars yet so that was really interesting cuz she.
Not only was a denial about 500 it actually kind of put a bracket on it that simply said.
I’m not going to deny 500 I’m going to say we’re less than a billion so then it gave us kind of the sliding scale of somewhere between 500 million and 2 billion is kind of where they were so speculation was running rampant with that and then they hired a.
CFO of the Hennessey low change and y La here we go boom the you know they’re actually.
977 Million Dollar business this year which is.
Pretty darn impressive there you’re just as runs August to August I believe which is why they can talk about 2017 it’s not over yet.
So you know I think it’s really interesting that here is this.
Pretty big company like I’m in the billion-dollar Revenue Club here and then another thing that’s interesting as it’s pretty Capital efficient so it’s profitable which is good and then also they raise between 40 and $59 in venture capital in a lot of these other billion dollar companies have raised hundreds of millions of dollars of capital so.
[3:41] Really interesting case study they also talked about at the code conference that,
you know they’re there watching and other categories so they’ve launched men in that business in six months is where it took three and a half years for women and they watch plus and it’s already doing it more in its first month,
students first year so we had a lot of nice kind of little data points from that conference and then,
you know the the s-1 launching has been pretty exciting to read through that but Jason I’ve read through it with a fine-tooth comb and are.
Job in this hot take / deep dive is to pick up that you see parts for you guys and walk you through it.
[4:22] For sure and we’re super lucky as a regular listeners will know Scott is the financial markets Guru amongst the two of us,
partly because I’m completely inapt and partly because of you you actually took your own company successfully public and presumably learned a few things along the way so I’m hoping you can get things started by giving us all a primer in the IPO process,
and I’m going to start you off with a question and I may have misread this but I had had to pick up a couple places that they may have filed.
Earlier in the year confidentially and then there’s all this talk this month about this them doing the s-1 filing was that a red herring is this in a new filing or are we just seeing what they filed back in.
In July or August.
[5:13] Yeah the.
[5:16] So what happened is the way IPOs worked before 2012 was you filed your S1 and everyone could see it and the super annoying because.
That’s ones go through usually like 10 or 20 drafts so you submit it and then the SEC is the government body that regulates these things will come back to you and I’ll say,
Jason what did you mean by that sending you don’t answer and then I’ll be like okay it while you need to tell potential investors that so there’s this like back and forth also.
You may not you may not know it’s really the kind of a.
Nonlinear risk point where you decide to file this one because you’ve really hung yourself out there and maybe have a bad court maybe you’re talking to the SEC for 6 months it usually takes and yeah the back corner in there or,
markets turn South so to help companies go public in 2012 they passed the jobs act which.
Which does Stanford jobs but it actually stands for Jumpstart our business startups and what that allows you to do is date they separate the the filing so.
As a startup and they’re certain definitions around this you can choose to have a confidential filing so.
We did ours we were totally confidential but I think Stitch fix action now it’s just that they had filed confidentially was just a signal.
The car says it was their choice you can you can do that or not there’s probably some reason they decided to do it.
[6:44] So in July they announced that they had filed confidentially so would that allowed them to do is to work with their Bankers work with SEC get a quarter kind of under their belts and,
then you expand I also let you see how other IPOs so in that time frame they were able to see how Blue Apron did for example or United Snapchat had gone public by then but they,
I could see kind of how it worked so that that kind of.
It’s really nice because it gives you the ability if you want to you can actually kind of yank the filing and not go public financing of the kind of put themselves out there but it does help with this whole process so that’s what that was all about.
So yes it so we went public at Channel visor in 2013 did this whole process we did the confidential filing work with SEC and,
actually use the same Bankers in the same banking team that song stitches could just,
sent them a note and they said yep we’re working on Stitch excite I know exactly the kind of hell what’s going to happen there it’s going public is a very very exciting kind of a thing that sucks with lots of stress kind of power concert.
Pretty interesting times and excited for for this company to get out we we haven’t had a lot of IPOs in the market and in quite a while.
[8:05] You know this is just one of the most-watched IPOs in a long time because we really haven’t had a lot of e-commerce IPOs and then I posed that we’ve had kind of a dud larger digital world,
how can I put two out there Snapchat they went public at a $30 price point in its now 15 and a blue apron when,
public at 10 and is now five those are not really successful IPOs so so it should have come.
Bad Dana Point out there then we have this company that I’m surprised everyone with the scale that it’s at and there’s this kind of.
Waiting group of e-commerce and digital companies that are not be watching this and really closely and if this IPO can go off not only price well but staying well for for a year or two I think it means good things for this does not cohort of companies that are.
Are probably ready to go so in there are the ones that that I kind of think about our you have wish which is the marketplace with largely Chinese Goods box Pinterest house Flipkart stripe.
Fanatics instacart Warby Parker in Casper and Kendra Scott Kendra Scott’s like more old school but I thought I’d throw it in there because it’s kind of interesting.
Most of these are unicorns which means they have received a billion-dollar private company valuation and you know any kind of thinks through the scale that they have to be at to do that.
[9:27] Your bus is companies can have Revenue that are are very much north of a hundred million if not kind of closing in on 500 million in a billion dollars so they’re definitely in that kind of class of companies that have the scale the growth brand to be able to go public.
Also it’s it’s interesting cuz we don’t have a lot of data on public e-commerce,
that’s because a lot of the ones that get ready to go public get snapped up by Amazon that’s actually you know not out of the question that maybe Citrix doesn’t actually make it public there’s there still this is kind of the.
[9:58] The about halfway point of that six-month process imagine before the end of the year though price and go out.
[10:04] But a lot of times he’s S1 stimulate buyers to come out kind of say this is my one time I have to buy this before it becomes public.
[10:13] Can I take a new bladder so so why keep an eye on that.
The the public companies that are out there there’s only three so you have CafePress and Overstock and those are kind of.
Micro Capstar kind of sub billion dollars the most successful public e-commerce company so around is Wayfair it has a six billion dollar market cap that’s about two times its revenues I think.
If you were going to hold my feet to the fire on Stitch fix at a billion dollar Revenue.
Growing 30% I think it probably is what’s a 325 x multiple so I think we’re going to see a a market cap.
You know it does three to five billion range so much better multiple because it is much more subscription kind of recurring Revenue than you Seattle airfare which kind of has to sell.
Everything each time so she Furniture you know I don’t think you know in your life when you need furniture and then then you can out of the furniture business for a while.
[11:13] Yeah yeah they’re definitely you know just a different model but yet a lot lot better gross margins and net margins.
And another thing I look at when I see these s ones from IPS perspective is what is the banking Syndicate the the blues to Blue Chip Banks are Goldman Sachs and Morgan,
and what you do is when you look at the page and it actually put a digital copy of it even in the PDF or on the s-1 over with sec.
There’s different positions they mean different things the lead Banker gets this position is a larger font. There’s all this kind of History around this that we can’t going to but it’s pretty interesting and.
You guys look at the left first and the largest upper left is called lead left is Goldman Sachs and this example so Goldman Sachs is the The Bluest of Blue Chips.
Yeah you know Jim Cramer calls them golden sacks slacks and another good company is they rarely do things with with Morgan Stanley those two kind of go head-to-head it’s kind of like.
[12:13] Oh I don’t know Canton gun LG your two sports teams that are bitter rival State they look they usually don’t do well together.
[12:23] There you go there you and then.
So you don’t have Morgan Stanley on this week if you have JP Morgan which is very good bank and then you have Barclays RBC Stiefel Piper Jeffrey and William Blair and what will you do here is your thinking short and long-term sign,
to the bank’s you pick you want a great firm that’s going to help you sell your IPO so they have relationships with the buyers of IPOs which are institutional buyers which tend to be hedge funds and mutual funds and.
All these banks have that and they will do a great job selling this company.
But then the secondary consideration is longer-term what you’re trying to do is get a great internet analyst that are are great analyst this is called a cell site analyst dick in.
Advanced buy-side analyst that your company is awesome and in public about it and you’re in the show we talked a lot about you know these analyst we’ve had several on the show talking about the things that they report on and.
Goldman Sachs you have all these guys have really good analyst and,
many of them may be familiar with socks on the show so it will be interesting to see so he’s Terry is the big guy e-commerce guy over Goldman Sachs I imagine that’s who will cover it and,
all down the line there there’s some really good unless she wants to go public there’s this waiting. And you have the analyst cover it and and it’s good as a company to have.
People that really understand your business out there banging the drum so that that’s kind of what you do when you do the banking process last couple little points on the public market.
Thinks there’s ticker symbol is going to be S fix and they’re going to raise $100 this is really just a placeholder what you do is you put out this initial draft and then you start to get reaction from.
[14:04] From buyers are early reaction and then,
as you see how the markets going you raise more and then you come up with your pricing and that kind of thing they’re using their guitars to public market so you go on the New York Stock Exchange that’s what we did at Chow visor they have chosen to go with the NASDAQ it’s kind of a,
six to one half dozen the other I do like the another aspect of an IPO it’s a raising money kind of a thing and then it’s a pyramid.
And I do like the pr aspect of the New York Stock Exchange you get on CNBC get to ring the bell you’re right there New York NASDAQ you just go and press a button at the NASDAQ Market Center in Times Square if so that’s exciting in in grandiose,
New York Stock Exchange.
22 is what we’re going to run to hear is called the prospectus and that’s the s-1 which is to the technical number given to these documents by the SEC.
And it’s only one.
People read these aren’t familiar with them they get really bogged down at the top the first 50 pages of an s-1 are really cya it’s a bunch of lawyer stuff to keep people from suing so.
Pass that stuff and don’t get wrinkled up and it feels like this kind of effort lawyers called a parade of Horrors it’s like literally a list of all the things,
the wrong it is a really weird way to collect unit tell people about your company but it’s just kind of the way it’s done so.
You know it’s like everything that could possibly go wrong with your company and then you’re like an end here’s here’s why we’re so excited,
it’s really strange strange way to do it but it’s done to reduce risk of litigation so skip to that and go right to the management discussion and usually there’s a letter from the CEO so.
[15:39] Yeah we’ll put a link to this over on the SEC in the show notes or or like to download the PDF and use your fine function and go right to management discussion.
[15:50] Awesome tip let the record show channel advisor got to wake or ticker symbol then then the Stitch fix it.
[15:59] Xperia S fix SF 49959,
another thing that is good about this is we haven’t had a lot of Billy dollar exits and e-commerce so if if my math right you again this could be hopefully north of Two And in that 325 range depending on how it prices.
[16:25] There hasn’t been a lot of VC investment in the e-commerce industry because we haven’t had a lot of exits ovc dollars chase the exits,
and exits are commonly referred to as liquidity events at the two most popular are acquisition or m&a and an IPO so just,
quick history here.
If some of the bigger one so we had in 2009 we had Zappos at i850 million Quincy diapers.com it 545 million that’s Mark Laurie 1.0,
and then we had Keva at 775 I don’t know if I can count that as e-commerce but but I know this guy saw us let’s talk about it 2012 Trunk Club which is very relevant to this one was acquired by Nordstrom for 350 million in 2014,
that’s not in the billion-dollar kind of close to Club but I thought I’d include it because of the proximity to stitch fix,
and then. Mark Lori 2.0 soljet to Walmart for 4 billion on August 16th that guy had like a five five billion and just suck the last 4 years.
It’s pretty good. Shave club was acquired by Unilever for a billion and then Chewy was recently acquired by PetSmart for 3 billion,
Zulily was an interesting one that kind of got the the double whammy so they went public I had about a three billion dollar valuation and then work wired that IPO didn’t do well over time that’s fatigue with our customer base,
hot and then it was acquired by QVC for 2 and 1/2 2.4 billion in August of 2015.
Seems like a lot when I say it like that but but since 2009 we’ve really had like 9 kind of exits 6 or so that are over that billion dollars.
[18:03] And three of them were in the last 18 months this is an industry we really need a lot more of these kind of exits to keep venture capitalist investing so this is really important for industry I think we all are all need to be great for this to do really well and in kind of.
Bring people back to the e-commerce fold-in Amazon his cast of pretty dark shadow when you talk to people that I know that are trying to raise money,
you know they say it’s Amazon question that really stops am at you every BC wants to know how is your five or ten million dollar company to go to survive in an Amazon world than now if this does well people and say well so I’m sure we can.
That’s some of the implications at a macro level.
Jason why don’t you would kind of gone a pretty long way without actually saying what’s just fixed us why don’t you bring people to speed on that.
[18:53] Yeah for sure so Stitch fix is.
You can think of is an apparel retailer they were founded in 2011 and they had what.
I believe it was a novel concept back in 2011.
They would sure ate a box of items for a customer and initially this was targeted just at women and so you would do a subscription and you can in that subscription you would get a box.
Of 5 items of apparel and accessories and you could.
[19:25] Cheap all some or none of the items in that box so essentially you paid $20 up front.
Which was that sort of a styling fee the first time you use the service you fill out a survey so that the The Stylist can get your preferences they Stitch fix picks five items they think you’ll like and want to keep,
they send them to you if you like him you pay for him if you keep all five you get a 25% discount if you just want to keep some of them you pay for him and send back what you don’t want,
if you like none of them you can send the whole box back and you’re just out the $20 styling fee and I should mention the styling fee is waived if you keep any of the items.
[20:04] I’m so back in 2011 this is the founder of Katrina Lake like literally.
Getting customers to pay her for a box she would go shopping at Nordstroms by things know what the return policy was at Nordstrom’s.
Send them to the customer and the customer and keep them she would return them to the the retailers that she bought them from so she’s.
She’s managing all these sort of return she’s almost like a personal concierge,
for the Shoppers and she turn this into a very significant Automated Business so over time that that business model is sort of evolved.
Initially it was subscription-only and you could kind of pic.
The frequency of the subscription you can get a box every month every other month every six months you know I’m a different set of periods.
[20:54] Shifted to a model where you can still can have that subscription but you can also just order a fix on demand so you since you don’t have the pressure of a box showing up when you don’t need one and whenever you feel like you just need to refresh your wardrobe.
I want something new to you you can go online hit the fix button then and I’ll send you a new box.
Originally they were all selling other people’s products,
and they they started to develop their own Brands what they they call it exclusive Brands and so now portion of the,
the products in the Box are coming from Stitch fix which will talk more about it later they also added men’s much more recently in Ascot mentioned the men’s products scaled-up much more rapidly they’ve also offered plus size boxes,
and I think the newest offering is maternity boxes and so all of this from a CEO Katrina Lake who’s now.
34 years old which is pretty impressive.
You know we’re talking about the rare of 1 billion dollar e-commerce exits in the the relatively small number of of e-commerce companies that successfully doing lipo when you talk about those companies that are led by a woman CEO.
It’s it’s like even extremely more rare which is I think exciting and and pretty awesome so you.
If you were to read her letter in the s-1 she kind of highlights.
[22:26] The Three core principles of the business right the first one is that they’re always customer-centric that they’re always focusing first on the needs of their customer.
personalization is the future we’ll be talking a lot about that and number three they think they have this unique combinations of humans and data and they have made some very substantial investments in AI which will be talking about and they think that unique combination of humans and data are better together than either.
Human stylist or artificial intelligence is by itself so that.
In a nutshell is the business order effects get these byproducts keep what you want.
Send back what you don’t and I would argue that it spawned a large industry of similar competitors.
In the same category as in an other categories like Children’s Apparel for example before we go too much further,
do you want to dive into how they they were funded by once they got beontra Tina’s original Nordstrom’s credit card.
[23:32] Yeah yeah and she used to work at Poly where I don’t know if you ever met her back when she was there at the podium for founder is an ex eBay guy that I’ve met several times and so she was she was kind of early on in this this whole industry to start with c.
Pretty pretty neat that sheep spun out of that and it’s.
Effectively lap them I think at this point so I share your enthusiasm for female Founders and see is I think it’s great the only other guy was kind of what he said that the only one I could think of.
Was Meg Whitman at eBay I can’t think of another you know kind of a the CEO female CEO kind of in our industry.
[24:10] Yeah the IPO level so they are capital efficient and you.
The sky saying they only raise 45 million you know it is interesting because 45 million is no no that’s not chump change but you know it takes a lot of capital to build a business like this and I think.
How many billion dollar businesses have soaked up your I said it before but 100 200 300 million to build a good almost take.
500 million pop service is very impressive and so the funding history.
In 2011 Lightspeed Ventures did a seed round.
[24:52] 2013 two headed around from Baseline and then very quickly on top of that and and,
so I was in February 13th and then in October 13th at a 12-9 Darby with Benchmark and then Benchmark is the company is one of the Blue Chip VC’s in the Bay Area,
girly a bill girly is on their board from their heat that that’s one of the firms that did eBay and Yahoo in the early days,
I also an outspoken Uber investor and then they did a series C.
In the sea 24 in 14th 6,
teen ceduna 14 and then dated a top off kind of in 2017 of 12 million and,
I just called a mezzanine round so ABC and mezzanine for those who that haven’t raised Venture Capital with the way it works is in an IPO the same way you.
You issue new shares so each time that kind of value the company at a pretty money you added this Capital you get a post money and then you get diluted I mention this because I saw a lot of conversations on Twitter when you look at the ownership.
You end up with Baseline at 28% Benchmark 25% light speed at 11% and then Katrina Lake the founder at 16%,
there’s obviously a case there that says that’s not fair Katrina should own 80% of this as a founder of you,
we are doing is kind of making this bet on your is Venture Capital you get you get more than just Capital but just kind of keep it to that conversation you’re making this.
[26:27] To you when I take this 45 million and give up you know 85% of the company there should be a bigger outcome then if I didn’t do that and.
You’re clearly these kind of cases you take her 16% you multiply it by that that 3 billion you get like 450 million kind of evaluation of her ownership,
I probably the right choice but you don’t you never know the other side of the outcome you know maybe if she’d bootstrapped this and waited 5 more years it would actually she could own 80% of it and have just a bigot as an outcome in fast-moving markets where you have,
companies like Amazon swimming around its speed that is definitely something that that takes is probably a good choice to raise capital for.
And then sink that covers.
Big pieces so we don’t want to get too bogged down in the financial stuff but Jason do you want to hit some of their revenue highlights.
[27:23] Yeah so they’ve had an ice hockey stick which is I think one of the things that that has caught a lot of folks attention 2014,
they they reported 73 million dollars in Revenue,
2015 the ramped up to three hundred forty-two million dollars in Revenue 2016 they they doubled at 2 730 million dollars in revenue and in their fiscal year 2017 which is over as you mentioned they were just under a billion dollars at 7977 million dollars which.
Parenthetically has to has to kill them that they didn’t quite get over that.
That be so so it’s been a pretty good ramp up and,
several of those years were profitable it looks like they they ramped up some expenses in 2017 and maybe weren’t as profitable.
[28:17] Yeah and then the growth rates to just look at the growth rate between 14 and 15 like almost 400% growth so crazy but that was exciting time to be there and then from 15 to 1613 per cent growth death definitely Torrid but not as crazy as 400%,
and then between 16 and 1734 per cent and in this is where you know what I’m imagining happened is that kind of said.
Yeah should we go raise a $59 in turn around or should we just slow the growth rate get profitable and prove the model.
This is interesting decision because what most pundits would tell you is while she loves growth so if they could have.
I have gone public at 100% growth rate that probably would have been a different outcome than 34% but you know I think in hindsight it may actually.
[29:09] Better that they’re growing a little bit slower and more profitable because with the.
I mentioned it the the Snapchat problems and questions around their ability to get profitable and then Blue Apron kind of hitting the skids.
I think this is this ends up being a nice balance between growth and profitability of so so it will have to kind of see how it prices and then you know.
What I’m engine is if they.
Delray’s over north of $100 that gives you a quite a bit of jet fuel to get that that engine going back up so I bet very quickly they’ll try to get back to triple-digit growth building unnoticed looking at some of the numbers they don’t.
[29:47] The NEP now they don’t specifically breakout sales and marketing or art effectively,
marketing but I do kind of wrap it up into a number that has gnats GM and that is actually growing a good bit faster than Revenue so,
between in 2016 840 per cent versus Revenue at 1:13 and then in 2017 and grew 55% versus 34% in.
What you will you see inside a subscription models is in the early days you know it’s you can you find your early adopters and it’s pretty inexpensive too.
Get to them but then as you grow your having spend more and more and more on the acquisition of of customers are the metric commonly known as cat that cost to acquire customer.
Did you see any other metrics around that Jason.
[30:35] Yeah it was like I was the one of the really interesting things is are they.
Capturing repeat customers and what’s the lifetime value of those those customers,
so they they did share a couple of things to give us some insight into that they they reported what they called this repeat rate which is.
The percentage of customers from the previous year that purchase in the subsequent year and so they’re sitting in in.
[31:05] 2016 that was 83% and in 2017 that was 86% which sound pretty good,
they also did this kind of convoluted cohort analysis that I’m going to rely on you to try to decode if anyone is cuz I I frankly didn’t follow it it didn’t seem quite as an.
[31:28] As straightforward as I might have expected on one hand but on the flip side I guess I was pleasantly surprised that they tried to get some disability to that at all.
[31:39] Yeah and what you’re trying to do coordinate a Caesar are very confusing because,
we’re trying to do think of it like a graduating class so teach your graduating class let’s say you had a bunch of seniors that graduated in 2017 from high school,
and then you followed him through college and the rest your life and you kind of saw what happened to those people that’s a cohort analysis secret you lock in time this group of customers acquired from a certain. And you see what happens to them.
The first thing to do in the cohort analysis is they they look at a 2014 cohort and they show the value from that Court was 639.
[32:19] And then the value of its dollar so than the value of a 2015 cohort with 718 so I think it is a fault this 14 people.
[32:28] From 14 15 16 17 and they said those guys generated 639 / user / that life.
[32:36] And they followed him and they said that.
That actually went up pretty nicely you know about I will see what is that 10% in so that’s good that shows inside of that cohort what you have is a lot of factors you have to learn so it’s people that say.
I tried this I’m no longer going to use it.
It’s more complicated in these models that do you have the on-demand like when does someone turn maybe they’re on an annual plan you have to wait a whole year to see if they’ve turned maybe they’re there every two years they want to get a fix or no.
If someone moves from a monthly to accordingly that’s not really churn so you.
It gets really hard to measure turn so inside of that 10% increase you have some customers they’re leaving but then you also have some customers that are buying more.
So what their kind of saying here is the customers that end up buying more.
Hope you’re over Road by about 10% economically.
The factors of turnt that’s what’s the story they’re trying to tell I’d it’s interesting I bet you know we don’t have privy to this but I bet if we looked at the initial as when they filed this wasn’t here and this is a reaction to Blue Nile to Napoli now but Blue Apron.
Yeah I just felt like my at yeah it felt very much like a oh crap we have to really kind of figure out explain to people what’s going on here.
Then if you take that data point then they kind of looks and looks like the 16 cohort came down a bit and then they start looking at some of the first half’s and what you see there and they had a little blurb in their hair that said.
[34:07] The call in first half of a year so it’s kinda like the six months.
[34:13] Piece of the second six months they show you some of that and it’s really fun and loaded so what happens is people by a fair amount in the first six months and then it kind of declines there,
they talk about it as an opportunity it’s also kind of weakness but it’s not fair to do for them to get better with the data science this mirrors personal my wife.
That was a stitch fix user had it for about four or five months and you have by the end of their had had.
[34:41] Acquired enough clothes in it was kind of burned out by the processor forgetting to return it and getting fees and all this kind of stuff so hopefully something a little bit of yellow flag something they need to work on when I do my mask.
[34:54] They give you just enough kind of figure this out so this is the first half of 2016 is 3:35 but then the total was like an essay.
5061 FM 506 so that when you do the math in the second half is 154 if so.
[35:10] Literally dropped by half over at the pier to be here so let’s see what that be 2/3 would be in the front half and then a third on the back half so interesting kind of.
Trend air it’s not clear how much that Stern and I got two people saying I don’t want to box it all or how much is you filled up their wardrobe in their closet they’re good to go.
[35:31] Yep and I I guess I should have mentioned another potential way to think about this is we did not mention the growth interactive customer base but,
the back in 2014 when they did 73 million and sales they had 261,000 active customers with their defining as.
Someone that bought a box in the latter the received the box in the last 12 months and if you look at their growth of active customers.
[35:56] At the end of 2017 they are like almost 2.2 million active customers so the the growth has been.
Year-over-year it is always the same order of magnitude as their revenue growth but it it has been slower.
Then the revenue growth so that the the fact that they’re the revenue is growing faster than active customers.
[36:21] The week like on the surface looks like a good thing because it that that implies that they’re they’re driving greater Revenue per customer as as they get a a bigger and more mature customer base.
[36:31] Yeah yeah yeah I agree in,
I have a feeling that as they do their Roadshow so wanting to keep an eye out for if if this is topics interesting for you,
when you do your road show you actually have to record it and it’s part of the SEC rules that anyone can watch the road show so it’s on Retail Road show if you go to Retail Road show.com you will find that,
don’t be a window of time in any sings expire pretty quickly so but Jason I will treat when it’s up in what you have there probably is Katrina and probably the CF oh and maybe someone else maybe the cool actually walking you through the Roadshow and I.
Bats that they have to peel out a little bit more information cuz I think investors are going to be very keenly tied into this and trying to understand really what I think.
I think that’s the one piece missing hearing and people don’t want to know that so it’s me an option to see if they have to disclose that.
[37:28] When are there fun tidbits when you were talking about this this sales and marketing spend they did mention in the ass one that they actually hired miller-brown to do this aided awareness study so essentially in like May of are in December 2016,
they went out and interviewed a bunch of women that were in their target market which are women are making over $50,000 a year that live in us and said,
are you familiar with Stitch fix and 28% of the women that they surveyed said yes in,
in December of 2016 so then in May of 2017 after they sort of double that adds fan that aided awareness went up to 41%.
[38:11] Like I would take it away Ernest with a pretty large grain of salt.
Cuz you’re you’re asking someone if they remember if they’re from they were something in a lot of people will just frankly lie because they don’t want to say,
they’re not friendly with something but if it’s true that that 41% of their target market are now from there with them.
Like that implies that the the next big tranche of growth is probably harder to achieve than the.
The last one was cuz it’s it’s a heck of a lot easier to go from 20% to 41% then it is to go from 41% to 75%.
[38:50] Absolutely yeah yeah and then I Delray had an interesting article about talking about how you know it’s really kind of a non Coastal audience I don’t know,
is data that really supported that but I think when you get too many people you have to kind of be spreading out to the Midwest and what not so interesting.
[39:06] Yeah and I think part of it is just that their price points are like these are not like,
super premium price points and you know in general these are not Designer level Apparel in so it’s,
you know it’s it’s meant for sort of a more modest consumers and I think there was even I can’t remember was in the interview or something that Katrina said recently but she talked about that they at one point had a pretty bad.
Inventory glitch where they weigh over bought and the,
the root cause of over buying the wrong inventory was it they were buying sort of on-trend stylish stuff and their customers were we’re responding that they didn’t keep any of the items because they were inappropriate to wear at the PTA meeting for example or that you know,
the the the sort of everyday occasions that their customers were we’re hoping to use the products for it so I think that that helped Define the.
The Target in the use case for Katrina.
[40:08] Yeah that and that’s a really good kind of transition to the AI machine learning in the personalization it’s this is kind of a it’s really interesting weed from that perspective I’ve never,
you seen anything quite like it so and I know you spend some time on it so it should take us to that.
[40:23] Yeah yeah it so it’s it’s almost hard to talk about machine learning and personalization separately Katrina and her in her letter talked about those.
Tubing Big premises personalization is super important and then machine learning plus humans you know being the secret sauce,
and the reason it’s hard to talk about separately is because largely what you’re doing with machine learning is.
[40:47] More personalizing the the offer in case the actual products to each customer.
[40:55] So I do want to start by talking a little bit about this how they use AI overall,
so you fill out a 60 question survey and then they want to pick the five items that you are most likely to keep and they said they don’t have a standard starter box so it’s not like they’re sending the same box to everyone.
Everyone’s box is going to be different based on current trends.
Seasons what they have in inventory right now and the the answers to the 60 Questions that they know about you and so one way to do that is have a stylus that.
Read your 60 questions and then have him or her go pick the five items in another way to do it is to to use some sort of algorithm to pick those items in so initially,
the the model at Stitch fix was let’s establish a computer algorithm to pick those items and then lets it let the stylist.
[41:54] Override it so we know what will pull up a list of candidate items for The Stylist and maybe you know that has eight items in it and you let the stylus pick the final five or maybe that the algorithm shows the first.
5 in the stylus can say yay or nay but interesting Lee.
Early on they hire this guy Eric Olsen to be their Chief algorithm officer and build this Audrey them to figure out what you you send in that first box based on the answers to your survey and.
Eric is an interesting guy because he was literally the VP of data science at Netflix which we all use as one of the best examples of.
AI driven businesses I think he was also a data scientist a Yahoo to a super credible guy that’s been working at Stitch fix on the this interesting answer to this question.
How do I pick the five right things to send to this first customer so that sticky so that she buy some of them so that you’re she’s profitable but also said that she keeps using the service,
cuz it does first five items are wrong your your odds of getting another chance or dramatically lower.
So then they’re also going to use a I once you.
[43:06] Pick some of those first items and don’t pick some of those first items they’re going to use that data to refine the items they send you in subsequent boxes and that’s where they start getting this really valuable contextual data that’s both implicit and explicit like they,
implicitly know you return something and they can make inferences about why you returned it but there’s also an option for customers to tell.
The Stylist why they didn’t like something until they get this explicit information the him was too long it didn’t fit me well.
All all of these sorts of things and so very early on situation was a believer in leveraging deep learning.
As the merchant instead of heading human sort of dictate what styles customers would get exposed to which Tamiya super interesting.
But then in more recent times it actually taking it to the next level so we mentioned.
That they started watching their own products and I’m not sure we said this but if it sounds like about 20% of all their sales are from what they call Exclusive Brands which are predominantly.
Brands that they created and they’re actually using AI to design the products they offer and so what they’ll do is they’ll say hey.
We have a big segment of customers that don’t like a neckline lower than.
8 cm and the majority of product we buy from third parties have this 10cm neckline and so we’re going to design your own product and it’s going to have a 7cm neckline and said they’re actually using their they broke each.
[44:45] Each piece of apparel into 60 different attributes and they’re using a guy to define the attributes that their customers would want that might not exist in that Marketplace in so they’re using that too to dictate what what new products.
[44:59] The build which is super cool they had not that I have seen disclose any.
Hard data about how successful that AI is or how successful that AI versus a human is but another in RF event there the interest x on it in San Diego this year and one of the speakers was this woman Megan Rose,
and Megan is the founder of a a smaller company that in some ways is Stitch fix for jewelry it’s called Rockbox and.
Very similar to stitch fix you get a box of five pieces of jewelry to keep what you want you buy it.
You return what you don’t want the others extra model where you can kind of rent The Jewelry by just keeping it for as long as you want until you want a new piece,
but they also are leveraging aai’s their stylist and what I found interesting is Megan shared some of the statistics that when they transitioned,
from Human curators to machine learning the purchase rate on the first box increase by 300% so that that computer was.
3 times more likely to pick items that that customer would keep they were able to improve their inventory efficiency by 85% when they went to the the AI BAE Systems and they they still cheap stylist but they have the.
The way I am.
[46:20] Inform the stylist exactly like Stitch fix is doing and that enabled them to reduce their stylist cost by 30% so.
Stitch fix is getting anything like those results that’s super substantial.
[46:34] Improvement via this machine learning and what’s terrifying about it and cool at the same time is.
[46:42] If you had a great stylist a great person picking all these products,
and she kept doing it and should get better over time and the first time she reads a survey she gets it you know I’m kind of right but by the,
thousand times she’s read a survey she’s much better at it right like this the person wouldn’t learn over time and her hit rate would keep getting better but then when you hire the next person.
[47:04] They would start at zero just like the first person did right and the magic thing about this that this machine learning algorithm is.
[47:13] It has learned from all two point,
two million customers of Stitch fix and it keeps getting better and better and so it it’s scales much better and we worms much faster than a human can come in so you don’t potentially the more customers in the more time in service all these things get in the better of the algorithms get,
the the the profitability metrics on this business potentially keep going up.
Much faster because the conversion rate just gets better over time whereas a lot of other things we do tend to regress to this mean and you kind of keep the same.
Same conversion rate over time so it’s going to be super interesting to see you know if the actual performance of the company kind of bear out.
Does hypothesis is but for sure a hypotheses I always say that wrong for sure.
Ate a significant angle of Stitch fix is.
Personalizing the offer based on this machine learning I think they said they have over 75 data scientist on staff now.
We used to joke because every time Katrina would speaking an event the number of data scientist she claimed,
had that double then it it almost didn’t sound credible but now that we see the the.
Numbers behind the business it it turns out that we probably should have been joking cuz it seems like they’re all sort of credible number isn’t in line with the the revenue growth that they’ve they’ve been experiencing.
[48:44] Yeah one of those things I thought was interesting as they also have a section in there that talks about.
Their usage of data science and the obvious one is you went through all this The Styling algorithm,
and then they also talked about nustyle development and then what you covered another one is so they have something like how many was it was 3,400 Stylistics.
[49:08] Yeah there’s a human stylist so,
actually have the kannada matchmaking algorithm and so this data science will actually kind of say you know maybe,
maybe some The Stylist our new moms and I’ll map you up with other new moms so I don’t know what day they’re looking at but that that’s kind of cool and then these 3400 Silas,
many of them are part-time so I don’t know how the interface works I’ve seen Amazon. Do this with customer care,
you do the thing where you can kind of check-in check-out and and then there’s an online your face where you can kind of do whatever style posting things they do did they talk about an application in the s-1 about,
I thought that was interesting kind of a matchmaking is how to use data science that use a lot of demand forecasting so you know.
[49:56] This is is interesting because they send all these products out right so the return rate is pretty important and it’s not entirely clear to me what happens to all the stuff.
The comes back out of it goes in other people’s boxes or what happens but there’s some demand forecasting that has to happen there,
and then there’s merchandising optimization which is.
Understanding how to order what size color and style kind of information and even talked about they use a lot of data science in the filming centers in a used one example they have five fulfillment centers so there’s a matching of,
which people go to which data which fulfillment center and then also they optimize inside the Fulfillment center using the data science for pick path optimization so I thought it was interesting that they’ve,
this YouTube Don’t this engine and they’re using it in like I bought this at like 7 or 8 different,
parts of the business so there’s really good scale from those 75 data scientist.
[50:53] Yep and we should mention I think they filed a number of patents as a result of all this right like they have something like eight eight pending patent application.
[51:01] Yeah I also thought it’s interesting day they love data science but they also talk about there’s a human kind of check elements I guess you know.
I guess maybe something has arrived at these things sometimes like it want everyone thinks they need purple socks or something that don’t have humans to catch them.
[51:19] Yeah I interpret that is twofold like that there is sort of the final check but I also think that they have decided that customers respond better.
To a human interaction so I think,
the reason that that one of those core principles is AI plus humans is you know there’s a lot of businesses where they would just try to get the AI really right and have a very impersonal experience,
and you know just have to let the customer know the computer is selecting these items for you I think the Stitch fix model is.
That they would like you to build a relationship with that stylist and rely on that stylist as a person,
and if you’re going to fight or Stitch fix I think they want you to feel like you’re firing your friend Susan who’s your stylist not just fire firing some.
[52:06] Some computer that’s that using math to pick out that’s for you and so I think the human element both has a practical element but I also think it has a strong marketing branding element for them as well.
[52:19] Yet they get this really interesting case study and then we can move on from machine learning they said one example or Delila embroidery neckline knit top is purchased 52% of the time,
and then what’s interesting is are algorithms,
I can determine How likely a client is up to 80% to purchase the item if we include it in that’s in her specific fix them so they can kind of show the power of the you know if you just blast it out to everyone you get 52% but if you can like use the machine learning.
Machine engine you get like a order of magnitude higher conversion rate which is pretty neat to your point on the,
what they’re saying about the machine learning stuff is it used to be in that venture capitalist would look for your eyes looking for a company that has a bit of an unfair advantage and that unfair Advantage used to be Network effects,
you like marketplaces are the kings of this like eBay or buyers Springs more sellers is this network effect LinkedIn the more people social.
[53:19] That works out this too but now it’s interesting is those that data on 2 million clients and think about all the.
The transactional data there’s there’s probably I don’t know zillions of Dana Point’s there.
Any company even an Amazon that has to compute these guys that they’re going to have to climb that mountain so it makes it really really hard for a startup to catch up,
you pretty quickly dwindle down the number of Cups companies that,
eat here too but maybe three or four you can have maybe a Macy’s and end their advantage would be they have more customers so they can get to that two million pretty quickly so.
Pretty interesting application of machine learning and I think this will be the first machine learning IPO that I’ve I’m aware of so that’ll be another kind of neat thing and that it’s also in our space of e-commerce.
[54:06] Until I mean two things I would just highlight there that.
[54:11] I think they’re trying to generate you know a version of a virtuous cycle here or an Amazon flywheel that they.
[54:19] Significantly invested in their own machine learning Tech and so that they have that capability that we just covered but they also have a business model that just gets them more.
Valuable data right so if you think about it and most apparel manufacturers are totally disintermediated from the customer so they get.
No data from their actual customers and even if you’re a retailer or even if you’re a vertically integrated retailer your the Gap and you make all this stuff and you sell it through your stores once it leaves your store for the most part it’s gone and you don’t you have a return rate you wanted to be as low as possible,
but you really you know this this try-before-you-buy send them five things get back what they don’t love.
Get you a much more valuable data source so the fact that they both.
Have this more valuable data and then they have proprietary technology to act on that that data is a potential flywheel for them.
[55:19] Oh, I still think it’s interesting and somewhat controversial the amount of investment they made in the the.
[55:29] The core machine learning technology right like so I could imagine when they they say.
Started this in 2011 and I assume that machine learning came in a couple years after that 2013 you could look at it the state of what was out in the market and say if I’m going to be good at this have to build it myself and if I wanted to be a core competency I need to.
To build it myself and for sure you need your own experts but.
[55:52] The last five years have seen such a huge Improvement and evolution of the off-the-shelf tools that it almost certainly has to be the case that.
These guys have spent a bunch of money building their own machine learning tools that are frankly probably inferior to the the version of tensorflow the Google gives you for free today and so it.
It is they may have been a little early in the curve having expertise about their data and about the the.
Applying machine learning models to their data and having a unique data set seems like a huge competitive Advantage I imagine some smart people could debate about how valuable their their investment in their own.
[56:40] Machine learning technology was versus leveraging some of the the amazing technology that’s coming on the market now but but I’m not sure whatever know the real answer there.
[56:49] Yeah, tell if a competitor can get there with a lot less and catch up then it was worth it get a couple of anything else on machinery.
[57:04] A couple other, miscellaneous little tidbits they talk a lot about being a good brand partner in this one so they they talk about they have over 700 brand partners and some of those brand selected to provide some exclusives in in the Stitch fix this and then as Jason mentioned they do have their own private label and they call that exclusive brands,
I am Jason Howard debating my reed was 20% of fish Stitch fix his exclusive Brands were were privately,
20% of everything was their own private label but you kind of red it is 20% could be kind of including those non Stitch fix brand Partners exclusive thanks.
[57:44] Yeah they did mention that that some third-party Brands give them exclusive products and so like I’m quite aware that 20% of stuff that Stitch fix design or a combination of stuff that’s only sold by Stitch fix.
[57:56] Yeah and this reminds me of our Amazon private label discussion where where.
Part of Amazon’s private label strategy is there their data science is saying look we need a widget like this and no one’s doing it you know we need batteries that come.
24 to a box and not in a packaging that you can open and quantity 8 so interesting to see that.
Another little tidbit is so they talked about Outsourcing the manufacturing of that private label called exclusive brands,
but in 2017 they actually acquired a pretty large thing as 20,000 square-foot facility that’s actually an apparel making.
The equipment and & Company in Pennsylvania somewhere so it it felt like they were going to go all the way over to clean the grading and start actually making their own things and United States which is pretty interesting.
[58:45] Yeah although I do think in the s-1 they they made it very clear that the right you should not expect them like to actually fabricate in the US that they wanted some capability in the US for experimenting purposes but the like.
[58:59] You should not invest in them based on the premise that they were going to become a US manufacturer.
[59:04] Yeah and then people wise they have.
Pretty impressive 5800 people total 86% identify as female so that it is,
pretty amazing what you put 55% of the management team to have 5 helmet centers / 1.5 million-square-foot 1,500 employees in the Fulfillment centers,
3400 Silas 200 client experience Associates million customers that’s like what does that 1 / 100 no a thousand.
Yeah so that’s good ratio there did you dream team is actually pretty small I was surprised 95 Engineers so that’s.
[59:44] Pretty lean mean for kind of scale they’re at and Sadie I guess the 75 data scientist get it closer to effectively.
150 which is closer to what I would think it would be so that’s how the people break out largest chunk is the stylist and then the Fulfillment center employees followed by.
You know the client experience Associates and then a relatively small Engineering in data science team.
[1:00:09] Yep and this was not surprising I suspect to you or I but I still talk to a lot of people that aspired to be a billion dollar e-commerce business and they still imagine that they’re doing that out of a single fulfillment center.
[1:00:24] Yeah no.
[1:00:26] And I at yeah I mean yeah.
Not very possible and I’m like this is a perfect example of what you know again at their they’re not at a billion dollars yet and there and they they have a customer-facing business where humans interacting with every customer and yet still the largest portion of their,
their workforces you know that are close to the the second largest piece of those Workforce it as all those fulfillment employees.
[1:00:51] Yeah I wanted more information on,
fulfillment centers just because again I imagine that that almost every box comes back with something so imagine the it’s the reverse supply chain that I’ll Eat You Alive on the stuff so.
[1:01:09] Reverse Logistics are much more,
challenging than I mean things are very hard to reverse Logistics are in order of magnitude harder in your right like that’s cooked into this model is there’s always going to be a high level of reverse Logistics so that that would be an interesting area to have some unique competitive advantages and if they do they they haven’t pitched them very hard.
[1:01:30] Yeah and the day of science didn’t necessarily cover that and you know,
Gillett Wisconsin to it so what cities send out of too many customers let’s say every month they send out a million boxes will probably less a900.
Thousand come back with at least one item coming back so I’m have all of them but you know that’s hard someone needs to go through there and figure out all that out you kind of know but you have to match it up happens to it. I don’t,
do the brands allow them to kind of like put it back,
or do you have to liquidate it and then does each of these fulfillment centers have an outbound peace and an inbound if they put it back on a shelf that’s like a whole it’s really super inefficient to like open a bunch of boxes and put all that stuff on shelves that doesn’t seem logical that I have a lot of kind of questions around that I bet.
probably the Harry part of this thing.
[1:02:21] And there is like so I think this is more rumor than real problems but so all of these industries are plagued with a little bit of the like.
[1:02:30] Oh wait a minute is this close stuff that already got returned from some other retailer right and that.
The fuel gets playing there several of these services and I think including stitchfix have at some point shipped products that arrived at a customer’s location with another retailers price tag on it.
[1:02:50] Right and that you know puts all kinds of questions in the in the mind of the consumer and you start wondering like waiter is this a TJ Max kind of play where they’re getting the.
The leftover stuff from some some retard where they couldn’t sell and then their there they’re selling it at at you know predominantly with price which is part of the reason I have such good margins.
And and the explanation that that Stitch fix gave and I think you know this is blown over several years ago now was no no no no we’re not getting anything.
Back from a retailer that were selling a customer but sometimes we buy something from a brand and we’ve had a brand make a mistake and send this inventory that was pre labeled.
With another retailers labels on it before and so that you know then then created that whole set of conversation.
[1:03:38] Do you feel like the brands would let them return the stuff.
[1:03:41] I think you could I thought I do think Brands would let them take returns and resell it I doubt any brands are getting them stock balancing you know you like.
[1:03:53] There’s very little stock balancing in a pair of these days where you can actually just return stuff that doesn’t sell you know they’re there often can be some sort of negotiated terms where that the inventory doesn’t turn gets.
[1:04:06] Gets tossed reduced overtime and you get some price concessions and things that way but yet no I think.
[1:04:15] That that Stitch fix probably feels like a pretty traditional retailer in,
having a match their supply to demand as well as they can and then having how to start a smart strategy for liquidating the inventory that they’re not able to sell.
So I thought you know I think the date they pay some of the same Challenges ever no spaces there I did there’s one other.
[1:04:41] I think that the s-1 reminded us up but we but we could have known before this Stitch fix is running on an Amazon web services.
[1:04:49] Yeah yeah it sucks so does Netflix and always makes me wonder like do they sleep at night we’re going to Amazon can you.
[1:04:57] I don’t think Amazon would ever do this but there’s the potential for someone to Cana,
take a little peek in there and see what’s going on under the hood so that that would it’s like one of those very very tricky situations there’s not really a great Alternatives that I have found two but you know you’re kind of your funding and your competitor and your competitor has potential access to your your secret sauce.
[1:05:20] Yeah and even if they had no access even if they’re completely aboveboard and they would never look at the data you are you’re still funding your competitor.
[1:05:30] Absolent yep so that’s Amazon wins no matter what.
[1:05:36] I would prefer the record I would say like I mean AWS is a great service there’s lots of reasons to use it it does to me feel like Microsoft with Azure in Google with Google Cloud platform like have some pretty competitive offerings these days.
[1:05:50] Yeah yeah once you kind of get married in the one who sings it’s a little bit of a roach motel it’s hard hard to check out.
[1:05:56] Degree architecture at some level that you have to do so Jason was kind of.
Land plane here with what do you think so we’ve gone through a lot of highlights and some impressive scale on Revenue growth slowed in a little bit,
can’t look like it’s going up a little bit I’ll TV hard to call with the cohort analysis looks like it’s a little challenged on the back half of the first year,
what’s your conclusion Justice IPO mean that the subscription Commerce is the future or or or what do we look like your.
[1:06:26] Yeah well said to me that’s a that’s a funny question the.
[1:06:32] Yeah we should have we should have mentioned earlier when you talked about it to some of these previous companies there there have.
[1:06:38] In the past been these tranches where there was some trendy fatty thing in a bunch of companies had an exit based on that fad right and said the most most obvious recent one would be flash sales you know everyone got up.
Advanced evaluation and a bunch of flash flash sale companies had.
Had favorable exits in the beginning and less favorable exits at the end and you know today it’s pretty clear that there’s not a very exciting market for Standalone flash sales that you don’t potentially that.
A tactic that a retailer would have but it certainly isn’t of itself a business model and so when I look at these guys if.
[1:07:17] If you’re evaluating them on the basis of subscription being the winning model.
I think subscription is more likely to be a trend like flash sales I think it’s a super valuable tactic.
That retailers are smart to use but I don’t think that the winning formula in e-commerce is just to go all in on subscriptions and part of the reason I think that is.
Most of the companies we think of as subscription model businesses have.
Why do they had to abandon their subscription model in order to be successful right and so you know Stitch fix.
Is a very Soft Cell on the subscription model like they started out subscription-only today like when you go sign up you’ll you’ll see in a giant.
Text next to all the frequency option option saying or just get one whenever you want.
[1:08:11] Right into their they’re really not hard selling the subscription and they don’t tell us what the breakdown is but I would be really curious to know what percentage of.
Obstetrics customers are on an auto replenishment program versus just ordering ad hoc because I feel like in the the actual subscription model businesses,
we we very frequently see this subscription fatigue so your wife wasn’t early Stitch fix customer before they add the ad hoc model and you mentioned she got fatigue I think that’s the fundamental problem with all the the meal kits that are subscription-only as they sort of.
[1:08:46] For economics have to be subscription-based and everyone gets.
Subscription fatigue and you eventually feel guilty that you’re not cooking the meals every week or you eventually feel like your closets full of clothes or jewelry or your kid has too much clothes or whatever the case is I think.
[1:09:02] Basing the business on Purely on subscriptions is probably a not very sustainable,
but I think if you forget the fact that Stitch fix even has a subscription model and you just look at their fundamentals and you look at the revenue growth over 5 years you look at their operating margins look at their customer acquisition cost.
You know I wouldn’t say it’s a slam dunk but like.
It it certainly to me looks like there’s a solid business they’re like is it a good investment as an IPO.
Qualified person on this podcast to say but this is a solid business that that could have largely,
grow from you know based on its own cash flows,
so again I like the business fundamentals I’m not super Amorous of the subscription service being the secret sauce.
That makes sense.
[1:09:59] Again and we’re not we don’t give a Vespa advice here on the Jason Scott she is so not going to say go by this IP or not but you know the one thing I think wall Street’s going to scratch your head a bit on is the growth rate so.
32% and a world of e-commerce growing 15 may not be exciting enough so.
I think that could be a platform for them to actually exceed expectations right so if they get priced at let’s say.
2x because of that 30% growth rate the razor $100 and you’re able to accelerate that then everyone loves a accelerator accelerating Revenue growth so that you know.
That could be interesting in an with any IPO,
you have to wait at least a year 18 months to really see how the company does as a public company so we’ll be watching that really closely here at the Jason and Scott show and Reporting after their first public order and in as part of a normal news coverage will let you know how this IPO goes.
[1:11:01] Yep and Scott question for you let’s let’s say they get a good price at the initial offering and it’s well before the year we don’t know how things are going to settle out should we all expect to see a ton of other.
Sort of a style box models like come and try to get a exit and you don’t plan on that hype even before we know whether this this really works long-term or not.
[1:11:27] I’ll say no so I don’t think this opens the door for other ones so for example when box went public could actually feel at times when the first company to goes out it kind of close the door so Dropbox couldn’t get out Xbox got out so.
You know I think what will happen though is if the IPO goes well on prices well you will see some other companies I mentioned,
maybe take a run at filing so you know the market background couldn’t be better right now we’re heading you know the Dow has hit you thousand Mark like.
20000 21 22 23 in the last year or so if anything is probably the biggest risk is that yeah there’s going to be working.
[1:12:08] Things are always go up to so there could be a correction at some point and so,
that’s kind of that I think would close the window on if Stitch fix goes out and we have some these other unicorns kind of waiting by I think everyone’s going to be rushing for the exits and I’ll be interested to see what the window closes so will Lori port on that as well.
[1:12:28] Gotcha and then I guess just just one other sort of competitive thing that’s somewhat interesting you mention trunk, being a sort of similar business model and sold the Nordstrom for 300 million,
I wonder if that comes up at all in in this offering that Nordstrom then had to take a 200 million dollar write down on that on that acquisition right like so that.
That that’s a business with a very similar model targeted a different customer base that.
Economically at least did not do well and then of course.
There are 30 or 40 competitors out there with somewhat similar models and certainly not with as much traction is digs fixed but the the competitor that that,
you don’t have to concern investors the most is that recently Amazon is of course the announcer product in this space and that’s the Amazon Prime wardrobe and Amazon has announced some new unique reverse Logistics to go is Amazon Prime wardrobe that seem like it could be a competitive Advantage for them.
[1:13:27] Yeah and you know us with 300 million customers to push this to you know that that is scary so what seek you know investors will vote with their wallets in and we’ll see how it comes out.
[1:13:40] Awesome well that is probably a great place to leave it cuz it’s happen again we have used all our allotted time,
we certainly appreciate everyone a listening in for this extended episode and hope you enjoy this deep dive in the Stitch fix let’s keep the conversation going on Facebook and as always if you enjoy today’s episode we sure would appreciate that five star review on iTunes.
[1:14:02] Thanks Everyone.
[1:14:04] Until next time happy commercing!