Building a world-class data org | Jessica Lachs (VP of Analytics and Data Science at DoorDash)

0:00 so you built one of the largest and most respected data teams in all of Tech for me analytics is a business impact driving function and not purely a service function not just answering the why but answering the what do we do now that we know this one of your colleagues told me that you incredibly good at defining metrics retention is a terrible thing to goal on it's almost impossible to drive in a meaningful way in a short

0:24 term ultimately you want to find a shortterm metric you can measure that drives a long-term output you mentioned the early team had felt extreme ownership yes you are a data scientist but your goal is to figure out what's happening and if that means that you're going to pick up the phone and call customers then that is what you're going to do so roll up your sleeves today my guest is Jessica LAX Jessica is Vice President of analytics

0:53 and data science at door Dash which has built one of the biggest and most impactful data teams in Tech she's been at door Das for over 10 years and was the first gmma door Dash responsible for launching new markets previously Jessica founded GS simple a social gifting startup and began her career in investment banking at Leman Brothers in our conversation we go deep on how to build and scale your data org including why a centralized org model is so

1:19 effective what to look for when hiring data people how to pick the right metrics for teams to align incentives and drive the right sorts of outcomes examples of how the data team at door Dash has helped the business make better decisions a bunch of great stories about the early days of door Dash and a ton more if you enjoy this podcast don't forget to subscribe and follow in your favorite podcasting app or YouTube it's the best way to avoid missing future

1:42 episodes that helps the podcast tremendously with that I bring you Jessica LAX Jessica thank you so much for being here and welcome to the podcast thank you so much for having me I'm very excited to be here so you built one of the largest and most respected data teams in all of Tech I've heard from a number of people that look to you for advice when they're trying to build and scale their data teams and then D Dash

2:11 in particular is an incredibly complex business there's three or maybe even four sites to the marketplace there's this operational element from the outside it just feels extremely complicated and wild imagine from the inside it's even more wild let's talk about some of the things you've learned about building and scaling the team you have a fairly contrarian perspective on how to structure data teams this is reference this was referenced when we had Elizabeth Stone on the podcast too

2:37 she approaches data the same way so I'd love to hear just your take on how to structure data teams within companies this episode is brought to you by webflow we're all friends here so let's be real for a second we all know that your website shouldn't be a static asset it should be a dynamic part of your strategy that drives conversion that's business 101 but here's a number for for you 54% of leaders say web updates take too long that's over half

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4:35 customization their SDK provides non-technical folks love Anvil's drag and drop Builder and developers love their flexible apis and easy to understand documentation build document software fast with Anvil that's use Anvil docomo that's USV il.com Lenny there's two main things that I think are important when you're structuring a team the first is I believe that analytics should have a

5:07 seat at the table just like engineering and product and and sort of the business folks The Operators for me analytics is a business impact driving function and not purely a service function I think there are analytics teams at other companies where they are answering people's questions maybe even through Jura tickets or build dashboards that's that was never really of interest to me that wasn't the team that I wanted to build for me it's about finding

5:37 opportunities about having a point of view on the decisions that we should make not just answering the why but answering the the so what so what do we do now that we know this and so that that's definitely one thing as far as my point of view on on building a a data team I think the second thing which may be a little more contrarian is I think there are there are people out there who think that analytics should be

6:03 embedded into business units I strongly disagree I I believe a central Model A Center of Excellence is superior and I'm happy to talk about why but that's something that I feel quite strongly about we we've tried it or shouldn't well we've experimented in the past with the alternative so putting it into a business unit and it's just much more problematic and I think the value you get from a central model is far greater than some of the the things that you

6:35 might lose so yeah let's definitely talk about it and just to make sure people understand when you say Central versus embedded is that in terms of reporting lines in terms of their goals it's a great question so mostly it's in terms of reporting lines because I think on the goal side that is something where we have the same goals that our partner teams have and I think that that's actually an important part of a successful set Central model so when

7:01 I say Central model it just means that instead of for marketing analytics marketing analytics is part of the broader analytics team it does not sit and report in through marketing that that just to clarify got it so the reporting functions in some companies there's like the head of marketing or some partner to the head of marketing where the data say analyst or Biz offs people or data scientists would report potentially to them and that's it and

7:25 they're not as connected to the core to like the rest of the data team the rest of the analytics team versus exactly yeah so you'd have a bunch of sort of smaller of course data teams that sit embedded within the functions and I I understand why Business Leaders like that you know you're you're embedded within the functions you're a part of team that ownership that camaraderie that comes with that I think you can solve for that but I I do understand

7:51 that that is benefit I think the other benefit of course is you know the the Business Leaders control the road maps so they get to dictate the world they know that they have help and resources in that area when they need them so that that certainty that control I I totally understand the value there but I think that those are two things that you can Sol for if you know that those are the kind of biggest issues with a with a

8:19 central team so for us we have a central analytics team but we are we're divided up into pods that map perfectly with how product engine ing operations marketing are are are structured as well and so our team de facto has these folks embedded with our partner teams even though the reporting structure is up through a central orc through through me and that helps the team to to feel

8:49 like they are one team both in terms of the analytics team feeling like it's one team but also to use the marketing example the marketing folks are one team and because the analytic shares the same goals as the marketing leaders your incentives are aligned to work on the most important things and you your success is their success and vice versa so I think that that's been really a good way to a happy medium but still preserves all the benefits of a of a

9:20 central orgon and there are there are a lot of them I want to hear about him but I think something that some people may think when you say a central org is like a silo data team that sits there and they're like this they're like a service org a little bit within the company it's like hey I need some data help and you try to convince that you hey I need some help on this thing and

9:39 that's not what you're saying oh no no no no that that job seems terrible I don't want that job no we are very much you to the earlier point we have a seat at the table we are business partners we are thought partners with our product counterparts with our engineering counterparts with our Ops counter parts and we sh again share the same goals and have the same you know initiatives that that they do and it's just our job to come at it

10:11 from a data driven Place we bring to the table insights on things that we've noticed deep Dives that we do to understand the problems that we're trying to solve better if we need to grow what are the most efficient ways to grow what are the trade-offs that we have to make wear their pockets of opportunity that that is what I expect my team to be able to to bring to that table the proverbial table that we

10:39 want to seat at and so and in order to earn their spot that's that's the deal we get the seat at the table and we need to earn it by bringing opportunities that we all can go and go after awesome so it's in a sense it is embedded they're embedded in cross functional teams across the org but they report up too Central orc to you essentially in the end yeah cool what are some of the benefits of this

11:04 approach oh there's so many okay so the first thing is a consistent and high Talent bar I think this is this is something I saw when we would have some sort of pockets of of analytics folks embedded is the having a consistent bar for talent in terms of what we're looking for what are the technical skills what are the soft skills and being able to kind of evaluate candidate with that same bar using sort of our same rubric just you just

11:36 get more consistent and higher higher talent in my opinion I think that's number one number two is actually growth opportunities so if you're siloed you may be the most senior data person within keep picking on marketing but you might be the the most senior sort of data scientist within marketing where do you go from there I think when you have the central or you're able to see if there are growth opportunities in other areas within the company and so that

12:05 really helps folks to stay engaged because they can look at new problems if the kind of problems they've been working on for a few several years or getting maybe boring and they want something new There's an opportunity move from marketing over to Merchant analytics and then I think similarly if there isn't a promotion or Room to Grow if you want to be a people manager there just isn't a people management role kind of within your functional area

12:34 well you've got 10 other ones to look at and maybe there is that opportunity so I think it helps with the growth opportunities for the team which helps to retain Talent so that's a second thing the third thing is just consistency of methodologies and metrics so you don't have sales that was as defined by one team and sales as defined by another team you just have sales and everybody is using kind of the same metrics the same the same methodologies

13:05 and you're able to improve your methodologies with input from you know more people and rather than kind of recreating the wheel doing the same building the same churn prediction model on six different teams you can instead build one and have the input of six different teams I think that's a definitely another benefit also helps you to scale because you start to see the same problems across teams and so you're like oh this is an issue that we need to get ahead of this is

13:32 something we need to automate or this is something that we need to to improve upon or a problem that is going to grow as our business as our team scales so I think it helps you see around corners a little bit more and then just lastly there's The A Team culture brand I think that's really important not just externally for recruiting top talent but you know the team is really proud to be members of the analytics team we have a

13:59 a unique culture you know of learning of sharing you have someone you can go to to talk about your challenges you have someone who can peer review your work I think just having that that team culture that we have is really important and it's a lot harder to get when you have the you know individual individual silos particularly in an earlier stage when it's a smaller team you just don't have as many people around so everybody

14:26 wants to have friends at work and we're creating an environment where they can find like-minded kind of data nerds it makes me think about airb B's First Data team I don't know if you know Riley Newman well but he built airbnb's First Data team and it was actually an analytics team they called it themselves The A Team on the point of culture and that that always felt a lot of fun and and they loved being part of that

14:48 team yeah we have we have the same thing but now I feel a lot less special for being you know coming up with that name so oh you called it a team also yeah got the A Team yeah and then I think they moved away from it when there was a push now we're data scientists we're not anal an analytics or analysts and that was like a I don't know 10 year ago like hey data science we're data science we will

15:09 always be the A Team there's like so many threads I want to follow here one that's kind of a tangent but something that I think a lot of people struggle with is you talked about how you want your data team your analytics team to be proactive to find Opportunities to give you ideas to help you figure out what to build not just answer questions at the same time there are many questions that teams need to get answered do you have

15:30 any advice for just how to set up a team where they both find time to explore dig show opportunities and come up with big ideas and also hey we just need to figure out the funnel conversion on this thing or hey what do you think what's happening in China right now thoughts there yeah I it'san such a good question I think it's something that never gets easier you have to be very intentional to carve out time for exploratory work

15:55 for deep Dives because as you mentioned there are always more questions and more work to be done than hours in the day and so I think being intentional about it and setting goals for your team around finding this these insights through self-directed work is an important mechanism for holding ourselves accountable to that goal because it tends to be the first thing that goes when you get you know a lot of inbounds you're

16:26 like all right well this deep dive on something that I don't know if it's really something you know the the could be high Roi could be low Roi I don't know so the expected value is is lower than this known thing that I can deliver and make someone happy and so I think to prevent that time from just slipping away you really have to be intentional we would do hackathons for our team to carve out days to just go and look into

16:55 these really interesting things and find Opportunities and I think we have the support of our business partners because so many great insights have come from these deep Dives and it really has been some of the work that driv future road maps so they're they're always really great at allowing us to have this time and actually encourage us often to have this time for for some self-directed work to go find the next big opportunity so if there's no answer that comes

17:24 to mind that's totally cool but is there an example of one of these insights that someone on the data team came up with that led to something big for door Dash that you're able to share so one interesting example was from a hackathon we did a couple years ago where we were looking at referral as a channel for consumer acquisition and when you compared that channel to others it was below average in terms of the engagement you'd see from consumers who

17:55 came through that channel and the payback period and we rather than just lowering spend on referrals and moving right along we really wanted to understand what was happening and so during the hackathon we did a deep dive into into referral we actually tried referring each other we tried committing referral fraud creating new accounts to get around rules and we uncovered a lot of fraudulent Behavior through this deep dive we ordered so many cupcakes to the

18:27 office I remember using referral credits to because you had to place an order to be able to get the referral bonus so we would create the account place the orders and we just kept ordering cupcakes and we what we noticed was that referral as a channel was a bit misleading when you would look at the average in terms of payback and that it was really a bodal distribution and you had one group of really great consumers who were referring other

18:57 really great consumers and the payback on that on those consumers was was really strong in fact if you if that's all you saw you would spend a lot more on that channel and then what was happening was you had this other group of consumers that were not as good people who were posting referral codes online and you know getting people who were just in it to get free free discounts and credits and we had at that

19:26 point in time pretty Lacks fraud rules and we didn't have caps on these things all of which came about from this deep dive where we found that this group of consumers was really a drag on the efficiency of this marketing channel and so I think that's an example of a a few things that we we we like to do at at door dash one being these deep Dives and taking the time to really understand the problem and then ultimately make a

19:57 bunch of recommendations for what we should do including better fraud checks caps on referrals etc etc but also sort of how this a the average can be incredibly misleading and so looking at distributions and trying to kind of break down what you're seeing to find ways that you can optimize and ways that you can you know gain and efficiencies that's an awesome story great great memory to come up with that one so this is a really good example of a way to

20:27 carve out time for the data team to think longterm think look for opportunities find Big Ideas so the hackathon is one idea imagine many data people are struggling often to push back on asks that are just like oh we need to know we just need this one thing here's a question just just answer this one question for do you have any advice to data people to get better at pushing back sounds like a bit of like cultural

20:51 like we have time we need to work on these bigger things but just any advice for data leaders or data IC to find time for these sorts of things yeah I mean saying no to someone is never fun I you know I think you know as a as a self-proclaimed people pleaser you don't want to say no especially when it's something you can do and you know that you can very easily with maybe an hour's work make someone happy I think

21:14 it's really important for to establish a culture and to for leadership to really sort of establish the rules of working and the that operating model so that some of the junior folks aren't forced to always have to say no and I think one of the ways we do that is through our goaling so because our goals are the same as our business partners we're able to pretty easily say hey we've got a limited amount of time these are our

21:41 goals what are the most important things that we are going to work on this week or this month in order for both of us to hit our goals and so when something comes up to be able to say hey is this you know data pull that you want me to do is this more important than these other three things that I was going to be working on yes or no and I think when you sometimes people don't necessarily

22:06 realize the tradeoffs and when you make them apparent and you put them front and center they realize that oh actually you know what that that asset's not important that can wait H so I think that that's definitely something I would recommend which is always share the tradeoffs don't kind of suffer in silence with how am I going to do all four of these things bring it up and say hey this is what I was planning to do if

22:28 you want me to do this extra new thing then one of these other things is going to have to drop and I I personally don't think that your ask is more important than these three things but maybe there's new information maybe there's context I don't have so let's talk about it rather than just being like no I won't do that I don't think that's that's not a great approach either I think having the conversation and constantly re-evaluating your

22:52 prioritization to make sure you're working on the most important things or your team is working on the most important things is is good hygiene to have with your business partner so some teams do that through a weekly kind of standup of like here's what we're going to do this week do we like this prioritization do we not some folks do it less formally than that I think you you got to figure out what works for you

23:13 but to the earlier point it's a conversation with your engineering partner your product partner your Ops partner you're all on the same team you're all trying to achieve the same goals and you're all incentivized to have your Analytics team working on the most impactful things this advice is great for any role basically and the like if I were to summarize it to a couple words it's just like prioritize and communicate what your priorities are and then align on the tradeoffs that

23:44 shifting your parities every once in a while you just kind of throw one over and say you know what this is quick I'll do it at least I do I think you know sometimes just knock it out build some Goodwill I think that that's also important but usually it's not a something something you can do in five minutes and in that case it's that ruthless prioritization for sure and then there's also the side that you talked about of just show that you can

24:08 provide value doing these things that are longer term like prove your worth hey look at all these opportunities I found for a team over time like I should keep spending time on these other areas versus the on fire stuff when you're hiring people for your team I'm curious what you look for and you think is incredibly important that maybe other people aren't this prioritizing as much what do you what do you focus on when you're hiring yeah I

24:32 mean so everybody needs to have a certain set of technical skills I think that's sort of a a non-starter we have a technical bar we do a technical screen so I think that's table Stakes there's some really unique characteristics that I've noticed when I look at some of the top talent that I've I've had on the team or have on the team I think the first thing is just curiosity you you can't teach curiosity or at least I I

24:57 haven't found way to do it if somebody else knows how please let me know somebody who is just self-motivated to pull on the threads when they find them so they don't just answer a question they're like hm this thing seems a little odd I'm going to dig in and look even though I could say I'm done I answered the question I did the thing I was going to do the the the person that has that Curiosity something something

25:23 seems off something doesn't really make sense and goes and proactively looks into what that is like that that is just so valuable so I I really look for that Curiosity and that self motivation to do it without being told how do you test for that how do you do that in an interview and get a sense of if they're good at that one way you can do it through the questions you ask is have something that is not quite right within

25:50 the case that you're presenting and see if people notice first and foremost and even if they don't if you point it out right like where did they go with that I think that that's something that you can you can test for I think you can also ask for examples that for these folks typically will highlight this they'll talk about I you know I noticed this thing and so we decided to investigate so I think that you there there are ways that you can

26:20 get it get that signal through through the interview process but it's really hard I think you know testing for for hard skills is a lot easier than testing for soft skills and I think you know in some of the questions we ask we'll ask a question with the idea that we're assessing something separate than what the question is necessarily asking and I think that this is a one one example of what where that really works you said that you give them a case what

26:48 does that look like what is the actual kind of approach to how you do this interview our interview process has in the early stages a a coding exercise so we do our technical screen and a shortened version of a business case so real world problem solving typically it's something actually from door Dash history like a real problem that we had to see how people can problem solve on the Fly I think that that's a an

27:18 important skill to be able to have which is how do you take a problem break it down talk through it a little bit like some of those Consulting cases that you you know hear about but something that's really rooted in in real problems and I think you can learn a lot from those types of cases where yes you get to see how people handle ambiguity and structured problem solving But ultimately most people get something kind of wrong right they make an

27:48 assumption that's wrong because well I would hope that the interviewer knows the business better than the interviewee and seeing how people react to being told they're wrong is is an really important signal in my opinion seeing how people respond how they're able to take new information and kind of pivot how they're able to make a decision so that's another thing that I like to see in cases where hey you may not know the right the re the real right decision you

28:20 might say hey I could see I could see it going one way I could see it going the other way but I always push people to say if you had to make a call right now what would it be so are people able to have a point of view without full information because that's that's life sometimes you have to just pick a pick a direction and make a decision even though you don't have perfect information so I like to see some of

28:43 these some of these softer skills and how they manifest throughout a case interview even if it's not specifically what I'm asking with the you know the literal problem we're solving in the case kind of along these lines but sort of in a different direction you don't actually have a deep data science data background before you got into this stuff you I know you had some kind of art background you had like art you had an art portfolio back in school and I

29:10 think a lot of people wouldn't imagine that for someone being head of analytics for a company like door Dash I don't exactly know the question but I guess is there anything there that you think would be interesting for people to know or here yeah it's funny I sort of joke that I have a job I'd never be hired for because I don't have a traditional data science background and I know that Elizabeth Stone on her her podcast with

29:34 you talked a lot about her sort non-traditional background for a CTO so hey maybe there's something to it but I I became a data scientist out of necessity I completely self-taught in terms of SQL and Python and I I did it because there was a need at door dash for someone to help figure out what the right goals goal were how we set those goals how we were performing different markets kind of early in in in the door Dash

30:06 story so 10 years ago at this point and I just had a I think I just gravit gravitated towards that type of work and Tony Tony recognized that superpower in me even though I don't have that formal training so yeah I'm a bit of a an artist for fun but a I guess a data science scientist in in practice or for career but I think that that non-traditional background has been a great thing because I'm able to

30:38 hire people who have the technical skills that I don't have the folks with phds and statistics and the the data scientists machine learning and otherwise you know I'm able to hire those folks and yet keep them really focused on driving business impact because my background was in the on the finance side and so I've always been a you know pragmatist and for me the purpose of our team is to drive business impact and so the mix

31:08 between the technical skills of the smarter people that I've hired the smarter than myself and my kind of grounding in driving business impact has been a really great great partnership that's a quite an inspiring story for someone that is just starting out and doesn't necessarily have a lot of experience in data but also just generally like I think this is a really cool example you could be successful in a field that you don't have a ton of

31:36 background in I'm curious what you think it was in you that allowed you to succeed in this and get to where you're are today like what do you think you did right or what are some habits or ways of thinking that you think helped you achieve that first off I I have impostor syndrome like everybody else so it's not like I have this crazy sense of confidence of like I can do anything I I definitely have the same doubts and

32:05 that that that others have I think part of it was probably not even realizing what I was doing you know when you're at a startup and things are moving quickly and you see a problem and I've always liked solving problems so I was like all right how do I solve this problem I was like oh well I need to I need access to the data I don't have access to the data all right I'll ask an engineer to get me

32:24 the data well this isn't going to scale I can't always father you know an engineer so how do I figure out how to get the data myself right well Let's Learn Python so I think it kind of came it happened organically and I don't think I realized at the time what I was even doing and then I think if you think about things from first principles about what you need right now in front of you to unblock yourself or solve a problem

32:51 and you just focus on that instead of thinking about like you know a global or that you're trying to build and you know I think that that helps so for me it was always about solving the problem in front of me the best way I could and if that meant I needed to hire an engineer to report into me through the finance org then that was what we were going to do and nobody was going to tell me I

33:14 couldn't do it so I think you know it's it's a belief in yourself and ultimately it's just my desire to solve problems and figure out what has to get done is I think ultimately how it came about I love I love that so much there's so many elements there that I think a lot of people can learn from I feel like there's also this underlying current of you're just motivated for this to work like you need you wanted door Dash to

33:41 succeed and you're just like I will do what I need to do to make this happen like I need to solve these problems I'm not going to overthink do I have the skills necessarily to do these things yet yeah I think I'm competitive I think that's a trait that you find in a lot of sort early door Dash folks and current door Dash folks to be honest just being really wanting to win and being willing to do you know whatever you need

34:02 to to win so roll up your sleeves do something that's not your job I think back to you know early days of taking out the garbage on Saturday nights because it needed to get done right I think that that kind that that was something that is ingrained in our culture from Tony Shu from our founder and CEO and I think the that really resonated with me and I feel like I've always sort of operated that way as well and I think that that

34:32 helped to help me in my career to be able to do what I've done without really thinking about it too much are there any other memories or stories of the early days of door Dash that would be fun to share something that sticks with you like wild I can't believe that's what it was like oh man there's so many including so many mistakes that we've made but I think something that really stands out to me is

34:58 before I moved to the analytics area I was actually a GM I was the first GM at door Dash and I was in Boston in 2014 launching this the city of Boston when nobody knew who we were and we would wake up early in the morning 5:00 a.m. and we would go out in the in it was the winter of of 2014 we'd go out and we'd hand out promo codes to to

35:28 Consumers outside of the tea in Boston and these promo cards would be attached to kind bars so people would take them and the whole the whole team it was a small team there were four of us but the whole team would go out in the morning to do this and I I think back to our sales our sales guy shout out to to Joey G so Joe graio is our sales guy in Boston and he was gold on signing

35:53 merchants on the platform that was how he was gold his compensation was tied to that and yet in the morning when we would go out he was with us handing out promo codes because he was part of the team because he wanted to win you know we wanted to grow the business and I think that that is a just a great example of kind of the culture that that Tony and the you know the early employees and you know Stanley and Andy

36:20 other co-founders really instilled in all of us early in those days so I think that that that ownership that extreme dream ownership of the outcome is is definitely one of the things I think the other is just being very customer first and I say customer I mean consumers Dashers and Merchants as as all being our customers and the first time I ever went to the the office headquarters in Palo Alto which at the time was in an

36:49 animal hospital the first time I went there there was a huge sight outage and the whole company it's like 20 people at the time you know the whole company jumped online to do customer support to answer the phones to make sure that folks were getting refunds for orders that weren't going through make sure the orders that were out there were getting delivered just dropped everything and and and hopped on to do support and I was brand new didn't

37:17 really know how to use tools and so was like how can I be useful and so back in those days we used to order dinner to the office using door Dash and so in order to preserve about three Dashers who would have had to deliver food to us I was like I'm going to go out go out dashing go get everyone pizza so that we could kind of feed the masses doing credits and refunds and you know do what we had

37:42 to to make sure that we were serving our customers well and I think that that night was one of the largest refunds like as a percent of our bank account that we that we had ever given given out and I think Tony's talked about some of there were sort of two examples that he's talked about where we just gave a lot of money back to customers because it was the right thing to do because you know our service failed and we wanted to

38:06 do right by them so I think that those those are sort of two stories that stick out in my mind and really highlight culturally what makes Door Dash unique and what what I think has been a really important part of our success reminds me of the story that Tony and all the early employees and I imagine you did this just like we Dashers like it's like aot where you dash for a while right is that part of the culture yeah so we have a

38:31 program a we Dash program and Keith yendell who's our our chief business officer did your podcast last year and he talked about this but four times a year all the employees go out and go dashing or do customer support and it's part of our culture that I love I actually go pair dashing so I go together with with one of my one of my colleagues we've done it for years now and it's sort of a fun fun thing

39:01 that we do together four times a year actually usually more than that but and it's it's important because you get to use the product you get to you build empathy with all the audiences I mean I think all of us order door Dash a lot so we we've built empathy with the with consumers but being able to go and understand what it's like to go out dashing and when you're in the restaurants going and talking with

39:25 merchants and seeing The Experience from their point of view I think it's just incredibly important and and of course we find a lot of bugs like this doesn't work the way it should let me report this so I think it's also just great for for catching catching bugs in the product this episode is brought to you by ATO a radically new type of CRM there's a world where your CRM is powerful easily configured and deeply intuitive ATO makes that a reality ATO

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40:28 level head to ato.com Lenny and you'll get 15% off your first year that's ATT io.com Lenny I want to come back to a thread that something you mentioned where you and a lot of the early team had felt extreme ownership over the company and that's why a lot of this stuff happened for people that like every founder every product team they're going to like yes we need that let's make sure everyone on the team feels extreme ownership is

40:55 there anything you think that the early team to create that or is it hiring just pick people that will have that feeling already or is it something or is cultural I think it's both I mean it's definitely cultural I think it comes from from the top and I think that Tony exhibits this extreme ownership and and looks for it in others so I think that it that that helps but I think even today I expect of my team that same kind

41:24 of extreme ownership over the outcomes and so I'm more interested in our team figuring out how to solve a problem than sort of the box that someone fits in like I am a data scientist so I only do these things right it's like no yes I mean yes you are a data scientist but you your goal is to figure out what's happening and if that means that you're going to pick up the phone and call customers then that is what you're going

41:50 to do and I think that expecting that and setting that as the norm for the team this sort of ownership of the outcome is something that we continue to to do at door Dash and and and instill in everyone whether you were you know early or just joined last month is there an example that that comes to mind of someone practicing extreme ownership like a data scientist calling someone or something on those lines yeah so I

42:16 actually had a meeting yesterday morning with the team that's working on some of our affordability initiatives and we had chipped something that we expected to work and it didn't and you know instead of you can dig into the data to understand the segments of consumers that you would expect it to work with and those that it wouldn't of course we did that but ultimately it's like I don't know why right and that's where qualitative research is superior

42:45 to to quantitative research right is that asking for the context actually talking to people to figure out what was the motivation what worked what didn't for them and so the team data scientists included just sat and made phone calls and so they they they were talking about what they found in from those phone calls and that's going to inform kind of future decisions and I think rather than saying well that's what the the qualitative research team is

43:11 supposed to do it's like no no no that is what our team anyone's team is supposed to do because that's what's needed to unblock us from this next test that we want to run because we need to know what we what we're testing so I think that that that it happens it happens every day I think I I really love when I see team members go outside the sort of traditional bounds of what a data science role might be and you do

43:37 some product management work right do some engineering work I think that that's that's part of what keeps the job interesting I think it's part of what makes our team special is that that is not only you know allowed it's encouraged which is and and probably also reason why we've had folks whove gone from my team to the product org and to the Ops org and to the finance org is because they get to do and experience parts of

44:07 that job and get a good sense for what that's like and then realize that something that they love so I think it's it's definitely a a something we encourage at at door bash I love that I want to move in a slightly different direction one of your colleagues told me that you were incredibly good at defining metrics which is so important to get right for a business especially when that's complex of door Dash and I hear you're especially good at finding

44:33 the right metric to drive the right incentive especially when the business is really messy and things like that so I'm just curious what you've learned about how to pick good metrics and align incentives well I've learned a lot of things about metrics mostly from Bad metrics I actually think you learn a lot from picking the wrong metric ultimately you want to find a shortterm metric you can measure that drives a long-term output so people always talk about oh we

45:01 want to drive an improvement in retention retention is a terrible thing to goal on because it's like it it it's almost impossible to to drive in a meaningful way in a in the short term and yet you want to be able to experiment and iterate quickly so what are the what are the things that drive retention what are the inputs so I think it's it's really important to find the right inputs and then through experimentation test whether or not

45:26 those short-term inputs are driving the long-term output that you're looking for I think that's one thing I think keeping things simple is another thing I've learned over the years maybe it's data scientists but they tend to love these like composite metrics like you know with a coefficient we're going to wait this input you know at X and this input at X plus two and and and then you end up with like a a metric that nobody

45:52 really understands that like doesn't actually mean anything and you're like I don't know if a 0.1 increase is that is a lot is it good is bad so they're just hard to work with and so I always encourage folks just pick something simple even if it's not perfect and your composite would be more perfect if people understand it if they have an intuition around it if it's something that people can talk about across the company it's going to be a

46:22 much better metric in terms of driving real outcomes than you're made up composite score that nobody understands so I think keeping things simple is also really important and then I think the last thing I'll I'll say is it's important to understand how metrics across the company equate to one another and so we spend a lot of time quantifying things in terms of a common currency so for

46:53 example if I were to lower price by a dollar what would I get in terms of we'll say volume well what if I lower delivery times by a minute what do I get for that in terms of volume and so now you can make tradeoffs between maybe your marketing team and your Logistics team because you have this common currency that everyone can talk talk about and so we've done that we've tried to quantify all of the

47:21 levers of our business price selection quality in common terms so that if we have say a dollar to spend we know what we get depending on where we put it over what time frame and I think that that helps us make decisions more quickly because we we sort of know what our options are we know the we we have our inventory of things that we can do short-term long-term and what we get for

47:51 it so it it it definitely helps us to make decisions more quickly and hopefully better decisions these are so awesome I definitely want to follow up on some of this this is so good so maybe on this last one which we did at Airbnb also just like what how does everything translate into into nights booked and booking like every decision we make what is the actual nights booked impact and so I imagine your case you don't I don't know

48:14 if you want to talk about these things imagine it's like transactions or purchases or gmv or something like that as I'm guessing is the final metric I don't know is that something you talk about or or we don't talk about that so I mean we we we measure things in terms of of gov so gross order value and and also volume got it okay so basically every other metric that people are gold on as much as you can can translate

48:40 there's a model that translates that into gross order value and volume awesome so when a team is saying like Hey we're going to change the onboarding flow and impact conversion here and I don't know I guess what's yeah what are some examples of other metrics on teams that potentially translate into gov and and volume just to make it even more real yeah so everything from the the example that you started with with it which is like an improvement in the

49:07 login flow right how many more you know consumers are getting onto the app and ultimately placing orders and so you can translate that to of course orders and gov but then something is interesting is you know selling a a Thai restaurant in Sacramento right we we're able to say what do we think that that gets us in terms of gov from the consumer by selling that Thai restaurant so it's it's every area of the business it's

49:38 mobilizing more Dashers on the road what does that do to our quality metrics in terms of delivery times how does that translate and so because of that we're able to figure out if we want to spend you know spend the dollar or spend the time the team's time on improving conversion or spending more money in marketing or onboarding more more Dashers we're signing more restaurants we're adding more grocery

50:08 stores right so we we were're able to look kind of across the whole business and figure out what is what is the right mix of actions to take to achieve our goals I could see as you talk about this why this is so important at a in a Marketplace especially a multi-sided marketplace where there's all these trade-off decisions between Supply investment and demand growth and Dasher growth I don't even know my brain would explode trying to think about all these

50:32 things so I I get exactly why this is so important to business okay and then in terms of the simple recommendation I think when people hear are like yeah keep it simple they're like yeah yeah we're going to keep it simple what are some things that point to this is not simple that tell you like no you this is way too complicated you should try to simplify this metric even though it's not ideal it's not the perfect metric

50:54 but it needs to be simpler yeah so we we had a score from from Merchant Health which we tried experimenting with which was a combination of factors that we had found would lead to a merchant being on the platform and getting in order so we wanted to make sure that the merchant was had had active hours on the platform and had images and had a full menu that was accurate and robust and a

51:25 number of different inputs and we created a composite that weighted all of these different inputs and then we were like what is our Merchant Health score right and you were like it's you know 35 it's not 35% so like what is that what is like that 35 I I don't know what it is so instead of that we said what are the most important factors in order first let's measure how many of the new Merchants are getting an order within

51:54 their first say seven days on the platform form and then let's look at how many of our Merchants are doing these things we know are important so these inputs so let's goal our team on getting like Merchant photo coverage up let's goal the team on making sure that we have open hours accurate hours right so figuring instead of yes it's someone might say it's simpler to have a composite metric but it was so hard to understand what it was and how to move

52:24 it that it it became meaningless and ultimately moving to something that was simpler to understand even if it meant having three metrics instead of one it It ultimately was better for the team because folks knew what they were trying to move and so yeah maybe we missed number four five and six on the list of things but you got one through three and that's 95% of it anyway so once we get success with that 95 then let's talk

52:54 about figuring out the other 5% it's so funny because this is exactly what we went through at Airbnb we had a we call it a healthy host I led the host quality team for a while and we came up with this healthy host metric that was six factors of a host like their cancellation rate their review rate their responsory and things like that and then we're just like cool let's move this let's make more host healthy and

53:16 then you end up like okay which one do we focus on and oh what about all these others and we ended up basically focusing on one at a time and so let's just make that the goal for now and then rotate through the different biggest lever opport to move exactly I think in hindsight for for the example you give like which of those six things are actually the most important right and if you're able to then quantify which one

53:38 matters most you work on that one first and you materially move that one and then you you know you work on the next one and you want to move them all but like being able to prioritize and know what you're going to get for a 20% Improvement in say your cancellation rate right that's that's where analytics I think can add a lot of value because yes ultimately you'll get to all of them but the way you do that and the time can

54:03 have a meaningful impact on your growth if you can Target the most problematic things first and solve those you get more bang for your buck and that compounds over time and so doing the things that matter first and most quickly like is a competitive advantage in my opinion the other thing we found along those same lines is rotating between different metrics is so not efficient cuz you get good at we're going to move this metric and your

54:29 team's like cool we totally understand this lever like cancellation rate we become really smart at cancellation rate and then three months later you need to switch to response rate and they have to learn a whole new paradigm of how to think about it and it's just super inefficient so we found basically just like keep a team on the metric until there's no more opportunities and find give another team one of these other metric yeah so many lessons Okay and the

54:54 first thing you said on how to pick a good metric about this idea of short-term metrics that have long-term impact how did you phrase that again yeah so we find proxy metrics for long-term outcomes awesome and it's simple it's s similar to the simple metric and it all comes down to again just like the metric should be something you probably you can move you can understand that's close enough to this ideal perfect metric but isn't necessarily the entire idea okay awesome

55:22 anything else along these lines of just like picking metrics working with metrics that you've learned that would be worth with metrics we're often looking at the average and I think we talked about this a little bit earlier but but making sure that you're looking at the edge cases and your fail States is also really important and so we often will set goals actually around and create metrics around those edge cases so like the disaster deliveries the ones

55:48 that go terribly wrong right so we have this concept of never delivered which is orders that are never IED were really great at naming things at door Dash and they they're very rare right and so if you were just looking at the average effect or the average consumer experience it would never come up if you were just measuring quality on based on sort of average values of delivery times and lateness and sort of those T you would these

56:18 wouldn't show up because they are so rare but they're terrible I mean they're just they're terrible experiences for consumers they leave to churn they're incredibly expensive because you're refunding an order or repurchasing food to and having to send another Dasher to deliver that repurchased food so they're very expensive they're costly from a consumer experience standpoint and I think if you're not looking for these fail States they are often missed so I think when you're picking metrics yes

56:49 you want to improve engagement and you want to improve conversion and there's a lot of things that are kind of averages overall that you want to move but it's so important to find these edge cases and these fail States and actually set concrete goals around eliminating them because it can be really powerful so the tip here is actually make that a goal like never delivered some team just keep cutting that down exactly so we have part of our

57:19 quality analytics team and we have product engineering and Ops on it as well their goal is eradicate never delivered and in order to do that you have to understand why they happen right sometimes it's human error sometimes it's fraud and then figure out ways that you can prevent them that you can kind of fix them while it's happening and and ultimately just get rid of them from from the system and you know you're never going to

57:48 completely get rid of them but you can make a meaningful impact to make them even more rare than a fraction about you know a fraction of a fraction of a percent yeah and I feel like people may be hearing this and like of course why would you not focus on terrible order experiences but I think in most companies they look at the big numbers they look at the averages as you said like oh it's almost never happens why do

58:13 we even spend any time on this and your point is you should actually spend time on these really terrible experiences even if it's a tiny portion of your business I guess maybe share why that's important is it just because that has trickle down effects on the the brand yeah I mean I think it's a couple things so just because something doesn't happen frequently doesn't mean that it's that it's not important so the the never delivered example is a great one in that

58:39 this is leading directly to churn and it's it's also costing a lot of money far more than its frequency would suggest and I think that the fact of the matter is is when you have things that cause churn you're losing all of that consumer's subsequent orders and that is not necessarily observed you're just seeing one bad experience you're not seeing all of the Lost orders because they're lost and so I think that sometimes this is an area where the data

59:08 doesn't show you the full picture and being able to to to quantify the the impact on engagement on profitability will make it stand out as something that really matters that you would you know maybe Miss if you if you weren't really looking for it and then I think the other thing is with something like login errors sometimes you don't see it in the data because people can't even get into the data if you're not able to log in

59:36 right you're not making any purchases you're not ordering and so you may not see it in the data that you're looking at and so that's also something that I think is important for data folks to think about which is what data don't we have what data might we be missing where might there be opportunities and things that we actually need to identify and fix that we may not see because in this case with login failures they're not

60:00 able to log in and so we're missing out on their they're not in the denominator and so we're missing out on on them from the data set entirely just a couple more questions there's one that I I skipped that I'm just going to come back to it's completely out of nowhere but I think it might be interesting is about global a global data org so you run a global data org you have data scientist and analyst

60:24 and business Ops people all over the world not just the US I'm curious just what the what how how is it different managing data people in different countries versus just the US whatever you what's the big difference everyone always asks about the differences what I am surprised by is how similar things are how similar people are the data scientists themselves but also you know consumers and Dashers and careers as we call them at Vault there's a lot more

60:53 similarities than differences I do think that when you built a business in the US and then you introduced new countries having different currencies and different languages adds complexity that you you know weren't necessarily familiar with I think similarly in EU countries versus non-eu countries in Europe there's different regulation so that adds a fun layer of complexity so I do think that it it adds complexity to

61:24 what your to the problem set but ultimately so many of the problems are the same it feels a little bit like going into a test with like having seen the answer key and so for me there are problems we've encountered at volt through volt analytics where I'm like oh I feel you know we've we've had a similar problem I have an instinct for what the answer might be let's still test because there could be differences

61:56 cultural or otherwise but I feel like I I I I know where we're going to end and then sometimes there are problems where you know it's new for one reason or another and it's exciting because you're like all right let's see if things are different here let's see what what ideas might work in in a vault country that you know don't work in a door Dash country and vice versa so I think I I tend to focus more on what's the same

62:23 and then I'm pleasantly surprised when I find things that are different because that keeps it keeps you on your toes and keeps things interesting I'm going to take us to AI Corner this is a segment we have in the podcast where I try to understand how people are using AI in their day-to-day and in their business I'm curious if you found some really interesting way of using AI ideally in like you can go in either one of these directions and how

62:50 you you or your team work day-to-day using AI tools to make you more efficient or integrating AI into your product making door Dash better yeah I mean I think that there are opportunities in in both I think one of the things I'm really excited about is actually so the former so in helping to make the team more productive we we do something called office hours at at door Dash the analytics team and it's something that we started eight years

63:20 ago and it was a way to Provide support for team that at the time we just didn't have the bandwidth to support so we would go we would in the early days we'd go sit in a room and we'd say come on in and we'll help you with anything you need help with we'll help teach you SQL we'll help look at some of your work we'll be a thought partner you could just come learn what we're working on whatever it was we we

63:46 would do two hours every week of office hours at different times to be friendly to different time zones and I think one of the things I'm excited about about is being able to really Empower some of the folks that are still coming to office hours for one thing or another to be able to use AI to help edit queries on their own for example to be able to say here's a query I want to make this please adjust this to our

64:15 grocery business so that I can see you know the gov at at for grocery and so working to build these tools that will help not just our team in terms of time saving and also to be honest folks folks are going to use it on our team but really to be able to empower non technical users to be able to to do things on their own and not have to take up bandwidth for for the analytics team

64:40 so essentially it's the chatbot that anyone in the company can talk to to get advice on how to write SQL queries query data and things like that yeah is there a clever name for this chatbot per chance so it's not clever it's called ask data AI and that's named for our internal slack channel that used to be the open kind of Q&A for people to ask data so it's not at all clever but again go with the

65:08 theme of very very specific naming conventions that we have at at door Dash never delivered in ask data AI I love it just clear Clarity above all else that's something I've learned from an editor that I work with Jess is there anything else that you want to share or leave listeners with for folks that are trying to build their data teams make their data teams more efficient is there any final wisdom nugget you'd want to share I think the

65:39 only thing that I I sort of want to reiterate is that you you don't necessarily need a you know formal training in whatever it is you're building and I think that also goes towards the folks that you hire onto the team and so you know I'm mentioned earlier that we've had a lot of folks go to product or go to opts from the team what I didn't mention is how many folks we've actually had join the analytics

66:05 team from partner teams so whether that was from engineering or from our Ops Team or marketing or Finance we've had a lot we've actually had a lot more import we we we are a net importer of talent as opposed to a net exporter of talent and I think that that's because I my own experience coming over from operations from being a GM and making that transition into analytics I find that I I'm drawn to other folks who

66:37 want to make a similar transition now again you have to have the technical skills and most of these folks have acquired these skills on the job you know whatever job they are doing at door Dash before they transition to the analytics team or they had maybe some formal training in school but I love seeing the folks that make that transition and actually want to join the analytics team even if that they're not a career data scientist I think it

67:06 creates a really unique environment where you have folks on the team from different backgrounds with different expertise who can teach each other things so I can teach you how to build a discounted cash flow model in Excel and I can learn how to make Kick-Ass slides you know from from someone who has a background in Consulting and I can learn about common gotas in statistics from someone who comes to us with a Masters

67:36 or PhD in statistics and we've got our econometrics folks and we've got our economists and we you we just have a group of people with different backgrounds who can all teach each other how to be better and we're not all carbon copies you know of of each other hearing is you try to optimize almost for a lot of different complimentary skills and very different backgrounds almost exactly and also people who have experience at different size companies I

68:07 think you know we I love folks from startups who have that that hustle and grit but I also love folks who've seen what scale looks like and can help us see around corners as far as what problems we will encounter as the business is growing and I think it you know it's not just about a diversity of skill and a diversity of background it's also you know diversity of sort of Prior company and Stage that can be really

68:34 a a unique way to think about structuring your team so that you get the best of both worlds amazing well just when you thought we were done we reached our very exciting lightning round are you ready I am let's do it let's do it okay first question what are two or three books that you've recommended most to other people I tend to read fiction particularly historical fiction and I love spy novels so I think my brain is

69:03 always in problem solving mode even when reading a recent book that I read that I enjoyed was the rose code by Kate Quinn and it's about women Cod Breakers in World War II and I just I really enjoyed that but rather than recommending a book I guess I did just recommend a book but rather than recommending another book I'm going to recommend the Libby app and supporting your local public library because I love the library and I love

69:33 Libby so I'll I'll I'll I'll give that as my other recommendation beautiful very un brand with sharing economy company stuff Libby cool okay next question favorite recent movie or TV show this is another one I don't actually watch a lot of TV definitely don't watch a lot of movies in fact haven't seen some of like the movie greats I get yelled at a lot by my friend I can't believe you haven't seen that I tend to rewatch things so series

70:01 from the past over and over again it's I think it's just like how I shut my brain off so I've recently rewatched the westwing which is one of my favorite shows of all time probably for like the 50th time oh my god and Alias which was like a Jennifer Garner series from like the early 2000s also spy so I'm noticing like a theme I think I really love these spy the Spy genre but yeah I watched those

70:29 they're both great but not at all current perfect perfectly acceptable do you have a favorite product that you recently discovered that you really love this is a bit of a curveball so Korean sunscreens I so I burn really easily so I have to wear sunscreen and I I love Korean sunscreens was introduced to them by a friend of mine and they're just far superior to what we have in in the US so I highly recommend people give Korean

70:59 sunscreens a try particularly there's a beauty of joson branded sunscreen it's just amazing and it's delightful to wear which is important when you have to wear it every day so I've been trying to wear more sunscreen as I age and so this is a really good tip is there was that a brand you recommended or yeah so beauty of joson is the brand there's another brand isn't tree which also has a great sunscreen but I'll be honest almost

71:23 every Korean sunscreen I've tried is just is great okay I'm Googling this as soon as we get off do you have a favorite life motto that you often come back to and share and or share with family and friends even worker in life I do so there is a John Steinbeck quote which I'm not big on quotes but I like this one which is that it's a common experience that a problem difficult at night is resolved

71:53 in the morning after the Committee of sleep has worked on it I find that that's something I really live by I think first off I love sleep and I try to get as much of it as possible but the other thing is that if I'm stuck on a problem or if I am writing a response to something where like a a tense issue or an emotional issue often I find that if I put down my thoughts go to sleep

72:21 check it in the morning I end up with a better outcome so you know all of a sudden you have a New Perspective and Clarity on a problem you were stuck on or you realize that you weren't clear in the way you were communicating your thoughts because you were emotional about something and you're able to put together a much better response to to an email or or to whatever problem you're handling so sleep can solve lots of

72:45 problems I love sleep as well always telling my wife let's go to sleep like okay I'll be there soon I love that advice okay two more questions who's in inuenced you most in your career is there something that comes to mind so I think two answers M multi-art answer so I think first you know I've I've my career has been in male dominated Industries and I've worked with just some incredible women whove really influenced me when I was a

73:14 banker there was there were two senior Bankers Vanessa Roberts and Gina terrone Who at Leman Brothers where I worked and they were just so incredible they were just so good at their jobs and I found that really inspiring and then at at door Dash Tia sheringham who is our our GC and Liz jarvisen who leads comms are just like dominant in their fields and I think that that's really empowering and have been big influences on me to just see strong

73:46 powerful women kind of kicking ass and and that helps me believe that I can I can do the same so that's one answer and then the other answer sort of cliche but my parents my mom was a statistician at the UN before she got married and she actually chose to stay home and raise three children but when I so I'm the youngest and when I was in I think it was elementary school she decided to go

74:12 back to school switch careers and become a nurse and so the fact that she embarked on this completely new career in her 40s after you 15 years as a stay-at-home mom and you know my father supported this I think that that was really really influential and was probably the first time I saw that you can do whatever you put your mind to no matter your age no matter your circumstances so that was really influential and i' I don't think

74:42 I've ever told her that so hi Mom hi yeah I think that was that was influential for my career definitely it's a beautiful answer fun fact I work with Liz at Airbnb your person just mentioned in the com's team love each ass she's amazing final question so when you join door Dash imagine it wasn't obvious that it was going to work I imagine it was still like this is a crazy idea maybe it'll work maybe not is

75:08 there a moment you recall where you're like I think this is going to be a big success I think this is actually going to work out to be honest I went into door Dash because I wanted to learn for the experience I thought it was interesting problems with interesting people I never thought too much about whether it would work I of course wanted it to work and was very competitive and wanted to win I think there sort of two moments that

75:35 stand out one was when the thirdparty market share data showed that we had become the number one player after I think we started at number four or five and I think that that was really exciting to see the trajectory and to see to see us gain category share that was exciting I think I probably didn't see it until like months after it had happened CU we don't spend a ton of time focusing on it but I do remember

76:06 somebody wanted to include the graph in some presentation some sales material like oh we're number one like that that's incredible we used to be number five so I'd say that that was one the other one that stands out was I used to the first talk I gave in a lot of these like startup talks in the early days in Boston and I'd ask the audience like how many of you have used door Dash and there'd be like three people who

76:33 would rais their hand and then a it was a few years ago maybe like 20 2018 2019 and I was giving a talk and I asked the audience like how many of you have used door Dash and like almost everyone's hands went up and that was actually pretty memorable for me because in my mind we were still the sort of small startup that no one had heard of where I had to over annunciate the D's in door Dash so people didn't think I

77:02 worked for Jord Dash the 9s denim company and so that was that was pretty meaningful to me when when just so many people had used the product and or were were consumers of door Dash was pretty exciting and I still get excited I saw door Dash mentioned in a book recently that I was reading and I was like the book so those little things when you become part of the kind of cultural lingo that I think are are really really special well

77:33 I'm a very happy customer of D Dash I've never had a never deliver it's always it's always there sometimes a little late usually it's perfect thank you for everything you do go team door Dash two final questions where can folks find you online if they want to follow stuff that you do I know you've been doing more writing on LinkedIn and things like that so just help people understand where to find you and how can listeners be useful

77:54 to you you yeah so as you mentioned to find me LinkedIn I don't have a huge social presence but I am on LinkedIn and I am currently writing a series of blog posts about my experience building a Global analytics or at door Dash some of the lessons I've learned over the last 10 years so definitely check those out and as far as your second question of how listeners can be useful to me I guess read read the post

78:23 on LinkedIn and I'd love to hear what people think whether you agree with my point of view or not that being said be nice like I want honest feedback but I want kindness as well so yeah just engage with with the content and let me know what what y'all think I think I do have a broader ask which is just to encourage folks listening to to truth seek something I you know take seriously at door Dash

78:53 it's a company value but there's a lot of misinformation out there and it's often up to us as individuals to figure out what's fact and what's fiction so I have a sort of a plea for folks to do your best to search for the truth and speak the truth and I think we'll all be better off for it and of course used door Dash so course guess I had three there are three things that listeners can do your door dash.com

79:20 that was awesome I love that last Point as well in addition to VI door Dash Jessica thank you so much for being here thank you for having me it was a lot of fun same for me bye everyone thank you so much for listening if you found this valuable you can subscribe to the show on Apple podcast Spotify or your favorite podcast app also please consider giving us a rating or leaving a review as that really helps

79:44 other listeners find the podcast you can find all past episodes or learn more about the show at lenp podcast.com see you in the next episode