EP157 - Bain & Company Partner Cesar Brea
Ceasar Brea (@cesarbrea) is a partner at Bain & Company, focused in the Advanced Analytics and Marketing practices. We cover a variety of topics related to the disruption and future of commerce.
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Episode 157 of the Jason & Scot show was recorded on Monday, November 19th 2018.
Jason: [0:25] Welcome to the Jason and Scott show this episode being recorded on Monday November 19th 2018 I'm your host Jason retailgeek Goldberg and as usual I'm here with your clothes Scott Wingo.
Scot & Cesar: [0:38] Hey Jason and welcome back Jason Scott show listeners Jason we have a really exciting guest on tonight show our good mutual friend Rob Schmaltz said hey have you guys ever thought of having Caesar Brea on the show and we said who is that you said you need to get him out of there ASAP and when Rob talks we listen so we're real excited to have Caesar on the show Cesar is a partner at Bain & Company where he is in the advanced analytics and marketing marketing practice welcome to the show Cesar.
[1:12] Thanks for inviting me guys a pleasure to do it.
Jason: [1:15] We are thrilled to have you in a Caesar if you've heard the show before you know we always like to start off by having guests give us a little bit of their background and how they sort of came into their current role so could you give us that the recap of your trip.
Scot & Cesar: [1:30] Sure so I am a. Several time vain person actually the last time I was here was in the mid-late 90s I was doing a lot of work in high-tech and and software and I left to help build a couple different software companies. Ended up at one point helping to run sales and marketing at razorfish and then later on I built a marketing analytics consulting firm. And a couple years ago I got invited to come back to beIN and I've been back in a couple of years and I'm really enjoying this this latest iteration.
Jason: [2:08] That's so you're basically a boomerang.
Scot & Cesar: [2:11] Yes I am kind of I guess one way of putting that as I can't hold a job very well but but I'm really glad to be back at the firm.
Jason: [2:20] And you mentioned that one of your previous roles was at at my current employer razorfish which book make me super excited but it's also kind of sad because I feel like that's a a storybrand and name in our industry that is a falling under decreasing use as a all the agencies in the Pacific group sort of merge together.
Scot & Cesar: [2:43] Yeah it's true I I I'm a proud razorfish Alum it was a privilege to work there I got a chance to work with some incredibly talented people Bob Lord is now at IBM as an old friend than a and a former boss and he originally asked me to come help out there and. Got a chance to work with really some incredible people that to this day I started following and keep track of intimate touch with. I still learn a ton from so it's I feel the same way about it and it was really amazing place and but that's the way a lot of things work out so.
Jason: [3:20] Indeed add a fun fact on Bob I run into him occasionally at industry events and my my favorite thing is to for those that don't know Bob is that the Chief digital officer for IBM answer my favorite question to ask him is why IBM needs a chief digital officer I always am I who's the chief digital officer at Facebook or Google.
Scot & Cesar: [3:43] Yeah I think the premier that's a thinly-veiled excuse to to have Bob Lord so they're lucky to have him and whatever whatever will make sense doesn't matter so that's why I look at things like.
Jason: [3:54] No I I totally agree to it until I agree but it's it's fun to needle in a little bit as he also was my former boss though it's and it's safer now.
Scot & Cesar: [4:05] Cesar what is so analytics and marketing are near and dear to our heart muscle bit more about what that entails.
[4:13] So the simplest explanation that I have for for folks who say what the hell do you do is I tell people I help big companies use big data to spend really big ad budgets better. How's that is that! Yeah yeah I like a I like to do so big around budgets especially in terms of what we, get involved with here it really is what I've been doing it ranges pretty widely it said everything from. Turn reduction programs to assortment optimization to media mix optimization. Really just a demand forecasting really just a whole range of things that were that were getting involved in to help our clients do better, ghost sounds like someone in your company is engaging with a company and they they need an analytic ninja to come solve some really hard problem and they call Caesar what happens well I would ya like but I guess that's part of it more broadly typically the the work that we do involves sort of tackling in a bigger issue for which analytics is kind of one part of the overall solution so I think that's you know to distinguish it from situations where you might just hire say a modeling firm to build you a model or you know her or something like that.
[5:39] Yeah seems like you're solving acute problems with data and and getting to Solutions certainly are it's a lot of fun it's a great time to be in the business. How much is there is a pie chart of kind of the verticals that you company vertical see you interact with how much of that pie chart would be what we would think of is retail and how much is something like I don't know the travel industry you're the finance industry. Yeah so you know that's that's a varied a lot over over my career I've actually worked in lots of different retail settings big and small here I would say that. You're probably about a quarter to a third of what I do is retail get involved in cpg a lot and then the rest of berries could be everything from Telecommunications. Finance any number of different categories but retail is certainly now and then certainly over the course of the years I've been doing this. A big piece of it principally because that's where a lot of the action is right it's generally speaking a less-regulated place. People that the margins are spinner so being good at data and put it in a lytx is more existential for four people the other you know that the people that can do that tend to survive in the people that can. Don't so so it's it's always been a part of what part of what I've been up to over the years.
[6:57] Call will just go ahead and go to the big elephant that's always in the retail and increasingly other Industries rooms is the Amazon elephant what do you have you put any thought against Amazon and how retailers can either inoculate themselves or protect themselves even just plain survive in a world where Amazon has become so first of all the first thing to observe is is it really is amazing how. How they are beginning to go into places where you know historically you you didn't think of historically thought of Amazon is okay you know I go and buy stuff online but now when you think about it there. They're moving into customer experiences into a physical retail into into social of kind of formats and everything. And also on the back and on the product side you know that what they've done in terms of beginning to take over product categories with you know what their private labels. That's that's really extraordinary so it's interesting you know Baynes done a lot of research into how. What what Amazon is doing and how how to try to in a build a strategy that that.
[8:09] It will let me not be Amazon proof it actually gives you a better shot of competing with them no one at one of the things that. That you think about is you kind of have a couple of choices when it is you know do I. Do I try to find a place within their orbit where I can actually. You know through some form of coopertition kind of you know coexist with them and the other is you know can I can I try to build some ability to. Distinguish myself or at least you know you have a business in places that are that are sort of less susceptible to the Bezos flywheel.
[8:45] The examples of the former would be. Things like you know best by deciding to sort of carry Amazon Fire TVs or Kohl's deciding to accept Amazon returns because it brings people into the store and then they can sell them other stuff that they sell at Kohl's right those are those are kinds of kind of examples of of people trying to coexist and then on the other side you know there's the question of well you know and this is kind of been the subject of some some research we've done.
[9:17] About how do you how do you actually in a carve out a space where where you can survive so for example you know you if. At one way to do it is through exclusive things that they don't otherwise selling Amazon right and historically I would have said Apple was an example but it's all recently now that they're you know more and more there their they're actually beginning to do. First-party distribution through Amazon I guess that the new iPhone x are is going to go through there now it's all an announcement against in the last week on that. There there are if you're big enough within a category you can actually be cost-competitive good example is you know tonight. If you go on Walmart.com you can buy the Viva paper towels 12 pack for like under ten bucks in the same things on sale at Amazon for 15. We're close to 16 actually so that you know if you're if you're a player like Walmart that buys a lot of paper towels or stay Home Depot that buys a lot of you know stuff 240i wires.
[10:22] Chances are you you know you can you can compete on cost but it but that's that's going to be tough another example of a company that I think. It is that's really interesting to me is Wayfair here locally in Boston they I think do a really good job on analytics on actually helping people discover what products should have go with which products you know in the long tail of things that they have in their product offering and doing a really good job of certain Fina putting together rooms and kind of cross-selling different products to people and so. You got to find some way. And if you think of the Beezus flywheel is kind of Fino selection and cost and experience you've got to find some way to think okay how am I going to. And run what they're doing in one of those places at least. Abacus you can't if you if you if you just try to sort us a while I'll just try to keep up you know you're you're going to get crushed so that's I think a productive way to sort of unpack that problem and think about maybe what your strategic alternatives are.
Jason: [11:30] Yeah interesting and obviously everyone has to ask her to find a different vector to compete with them I'm curious you mentioned up front that a lot of your analytics work goes towards helping people optimize their their big advertising spends and you mentioned you work with cpg so it's in that that segments it's interesting because it seems like, the cpgs are both having to compete with their advertising spends against Amazon who I think is the largest spender on Google for example and then increasingly Amazon isn't it. An important advertising platform that cpgs are spending on so I like how do you how do you think about that and are you saying budgets shift to Amazon and and you know what how do you think that's all going to play out.
Scot & Cesar: [12:21] Yeah definitely it it's.
[12:25] You know being being on Amazon if you're if you're a cpg you're being a frankly if you're in the other consumer Products company that with products to be sold there is now got to be a part of the of the strategy I insert when I said be on Amazon being being there from an advertising perspective earlier this year acquired a, digital agency that would work with for many years from called forward out in frwd out in Minneapolis and that that has a lot of experience in these areas and that's you know helping clients figure out. How powder. How to make that work is now a big part of what we're doing in our marketing practice and and the other things I think their mind therapy people talk about you know analytics but in this case. News limited history right so a lot of what we end up getting involved with his actually testing this stuff and setting up tests programs to you know to to figure out what was actually going to work.
Jason: [13:24] Yeah and I I guess I'm curious about that like does Analytics. Mean a lot of sort of attribution modeling and figuring out. You know how to spend the next s dollar and immediate mixes and those sorts of things or is it more Predictive Analytics and and soda programmatic AI based bidding type stuff or both.
Scot & Cesar: [13:47] I think I think the answer is I think the answer is both but but I think I think. One way I break it down in terms of thinking what you're getting at I think for my for my perspective is actually thinking both macro and micro and end here here's here's a point of view on this that might be useful. You know a lot of people a lot of marketing organizations and up doing a lot of wonderful sort of micro optimization whether they do it themselves or they do it with Partners you know they'll figure out like you know how can I tune my by search budget or how how can I how can I figure out a way to get lift over control on my I might display budgets with programmatic and then there's you know tmp's and cdp's and everything they're using to do all that stuff with.
[14:36] But what's what's interesting about that is if they typically are missing big opportunities at the macro level that they tend to sort of get down once a year and say okay our overall Investments going to be actually going to split it roughly this way across the channels and then we tend to sort of your ossify during the course of the Year about about no power going to sort of allocate that money across across different channels a lot of cases if if they're using TV for example to just go out and say all right you know The Weeknd car. This amount of money and we're going to go buy it as cheaply as possible at the upfront and then we're just going to go run the campaign for the year and will report it each week as we go but there's not any meaningful you know sort of movement of budgets are testing or anything like that the top.
[15:19] And so you know a lot of people see historically this kind of. Down media mix modeling approach in the bottom of attribution approaches kind of In conflict and I actually see them as as you know. Pate yin and yang of of of of what we're trying to do in marketing where it is very important to be doing kind of within a channel specific optimization certainly want to take advantage of those opportunities for example you know when search let's say you know D average in your spend and maybe doing things by day week or by day part or across your keywords whatever but but equally important is actually to have this macro view where you say you know like at any given point in time is my bottleneck in my business you know attract engage convert or retain and how should I be kind of disproportionately Shifting my attention and my resources to solve things you know at that bottle neck and in the latest month in the latest quarter and once I saw that there. Then I can throw to move on to the next bottle and I can figure out where my where my attention out of be as opposed to just sort of saying okay we're in 6 channels let's be as sophisticated as possible in each of them and optimized to a fare-thee-well at the micro-level miss the big. Mr. big pictures.
Jason: [16:38] Yeah so I can definitely see that and I'm particularly interested in that sort of macroview why do you Tennessee clients. Getting more sophisticated about how they do the macro View and I mean to me it feels like the media mix modeling is several decades old now and it seems like that's still the the predominant and I'm just it's hard to believe that that still the best the best approach.
Scot & Cesar: [17:03] Yeah you know so I think I think we need to distinguish between the analytics and the politics so. There's nothing that you know media mix modeling course is only as good as the data goes that goes into it if you don't have any variation you know in your date if you just keep doing the same plan all the time you really never going to have a useful model because it's not going to tell you much. If you do have some of that there's there's certainly lessons that you can draw from the data I think what. What happens though is that a lot of organizations are in was that old expression about culture reading strategy for breakfast the if you have a. A way of doing things that has led to the creation of a certain sort of an organizational structure and collection of Partners and agencies and so forth those things all have a certain momentum associated with them. And I think actually you know the well there are certainly opportunities to improve media mix models through creating. You know tests and creating and just artificially creating more variation your data to help you you know that would sort of the statistical significance of what you're looking at I think they're much more important thing for people to really look at it to try to get people on the same page about.
[18:22] Where are the opportunities might lie and and what they could be doing about that and and not try to get fixed on Unser to some holy war between you know one analytic techniques versus another.
Jason: [18:34] No that's that seems like great advice. Speaking of Holy Wars I want to transition to a buzzword that seems like it comes up most often especially when you used Big Data three times in the same sentence and that's a artificial intelligence and in particular machine learning and. You know you go to any of our industry events now and you know you'll see a hundred vendors claiming that there in ml base solution like including the custodial Services seem like they're machine learning based. And that feels like a little bit of hype to me but at the same time it seems like they're there really is something there I'm curious how you think about Ai and machine learning and is it is it really being embraced particular by Rita.
Scot & Cesar: [19:20] Well a couple of thoughts first of all. IU know that movie Fight Club right in the first rule of Fight Club is we don't talk about Fight Club we we have a saying around here which is the first rule of advanced analytics is we don't talk about Advanced analytics we talk about results and. For me all this stuff you have any conversation that you have about AI or machine learning whatever has to start not with well you know. Do you have a squad of phds and are you using tensorflow and you know yada yada but but really.
[19:56] Is the Baseline performance of the business process and the statistical metric associated with that business process that you're trying to improve off of and what progress have you made in the last you know three six months whatever on both of those things. And so I don't care whether you get there with a simple algorithm or a or a you know neural-net or a three eyed pigeon. Yeah that you keep feds underneath your desk I think the important thing is that these conversations have to shift from from talking about the thing to talking about the result. The second thing that people need their kind of Bear in mind when they think about AI is that AI isn't a tool so much as it's a process right you need to think in terms of you know picking the right question making sure you have the right data for it you can't do real sort of.
[20:46] AI without really big data and you have to sort of maintain a data platform be able to do that you know and then and then you kind of got to make sure you can do something about it right so if you have some great insight, if you don't have the you know the marketing infrastructure let's say to a sort of act and we'll talk later by personalization but you know if if you can.
[21:05] If you discover that you know you can turn it down to an individual level and distinguish people's preferences if you don't have the sort of digital asset management system of the content management system is so far to be able to handle Communications about level granularity you're really you're really kind of you know not getting anywhere and so I think I think we see a lot, is is people pulling together components of of an AI or an ml solution but not thinking about the full system it so they don't get the full value of it, I'm familiar with one company that you had one group that actually went out and bought a DMP but they hadn't really hired the people who new kind of what to do with something like that so basically sat on the shelf for about a year until you managed to come together and actually help them apply at that you know to something does something useful get a result and then actually get some enthusiasm for investing and all the pieces they need to do to take advantage of that and that's it that's a good example having said that.
[22:06] You know there's there's exciting stuff happening with with AI in the world of RetailMeNot you know one example there's nobody like playing around on tracks that you're probably familiar with you know that that basically use image recognition to help you kind of keep your your you know your shelves kind of the way they need to be and and then and then help you tune that and that's, no that's that's actually a you know there's there's applications like that that I think have enormous potential obviously to the kind of reshape the category but it all starts with having a clear idea what problem you're trying to solve it supposed to just for the talking kind of you know breathlessly about Ai and how in all the intergalactically wonderful things that you can be able to do with it.
Jason: [22:49] Yeah I know for sure I doubt that the the company you mentioned that that invest in a DMP with no plans for using it was alone by the way in that.
Scot & Cesar: [23:00] Don't know what happens all the time right it's it's just you know and I think I think it's a symptom of this idea that. We we have confused the means for the ends where people are pursuing these things as you know things to be bought initiatives to be you know undertaken as opposed to sort of viewing it from a results and performance perspective and saying you know. How well am I how efficiently and effectively am I out there you know attracting engaging converting customers and to what degree does a DMP powered solution actually create some sort of lift Over Control. You know over what I had before. You know and at what point do I get diminishing returns so I don't need to worry as much about the tack and need to worry more about say the content I have or the offer that I'm making or something like that right.
Jason: [23:51] Yeah I know for sure and I mean we on the show we talked about a lot is sort of the the shiny bauble problem that you know some some board member goes to a conference and then come back and sent a note to the VP of e-commerce what are we doing in machine learning and 3 months later they've got this cool data Lake that's doing propensity modeling with you know zero plan to act on that or to change any customer to experience as a result of it.
Scot & Cesar: [24:17] Now that's that's that's true story, night you know you got it only seems you got a Target better and Market better right so if you only do the Target that are part and you don't have the ant the engine to kind of do the market better part you're you're not going to get there.
Jason: [24:32] Yeah I'm just the one example you you gave was I sort of think of is back-of-house optimization sort of improving inventory and and shelf management I've heard a couple people theorize that the in the short term that the biggest opportunities for machine learning to make really you know practical impact on on retail are those kinds of things that it's it's inventory optimization and cost avoidance in those things more so than necessary necessarily dramatically do new or different customer experiences.
Scot & Cesar: [25:08] Yeah I I I think. Prefer not to generalize too much about it I like to find itches to be scratched right so in a 1-1 company that I'm familiar with you know looked at it from the perspective of having a chronic problem with over ordering for you know for the sales they had never variety reasons why this happened you know a demand forecast it wasn't as accurate as it needed to be they had kind of a hard to learn ordering application they had organizational structures that a grown up the you know to compensate for that that introduce a lot of bias into the system and and so in that case you know we. You know what we we were able to help him basically reduce the forecast error that they had improved the order management interface and actually, what kind of change some of the organization and operating practices that kind of wrapped around all that and and what's what's interesting about that is is that it's for me all these things come up from very specific use cases II I would say. I just prefer generally not to you know not to sort of right off one. One category or another every conversation that we have in our tries to start with tell me tell me specifically kind of what.
[26:37] You know your date is telling you about where the problems are in your business and and then through work up from something specific that we can get our arms around that that's proven to be kind of a.
[26:48] You're generally more more successful way instead of tackling the application these kinds of Technologies.
Jason: [26:54] Know that that seems I totally fair and wise and I hundred percent agree the three-eyed pigeon under Scott's desk has way too much open to buy and is definitely over spending.
Scot & Cesar: [27:04] It's one thing I kind of. What is machine learning stuff it feels like as a startup guy kind of like the next Network effect right so you're you're getting more day that you're getting smarter that creates this nonlinear advantage over competitors and then I started looking well then is it true that companies with the most data win so so then I kind of come to this place where no one's and have as much transactional data as the big guys like. Ecommerce side Alibaba Amazon yeah babe didn't even on the ad networks you know we all thought these ad networks would create this huge democratization of had platforms, but now they're really just kind of Ogle opoly with Sprite there's two of them exactly so so does it mean kind of game over because those guys have all the add data and the car or stay there or is there hope if I am a smaller independent company that could mean you'd like in a Best Buy in this this world were talking about yeah. Help me understand that it is kind of an outsider of how you're thinking about them yeah so.
[28:20] One way to a take to process all this is there's no there's no question that. The types of sophisticated machine learning algorithms things like in a deep learning and neural net approaches and things like that. Those really begin to shine when they have a lot of data to work with you don't you know a lot of people misunderstand that that unless you have a lot of data in general the performance of one of those will you know.
[28:51] May not even be as good as what you get with and it was some of the you know some of the more conventional machine learning approaches things like you know. Gradient boosted trees and things like that so what I would say is though is that. It isn't just about how much data you have it really it's really back to this idea that you want to think systemically you want to be performance-oriented been think systemically about about what you're doing and in terms of you know being aligned and where the opportunity is at any given moment being at you having the access to the data to work with it but then also having the the kind of the operational flexibility to act on it I actually think that the people that are winning and winning less because they have big data and more because they actually just have cultures that are data-driven that are Nimble that are better to and and and that you know frankly are just you know they're wired tube to move in a more agile way then then their traditional folks are that and in the proof of that pudding actually is just so you know if you look in if you look in sort of the cpg world for example and you look at where all the growth is Ben it really is coming from these insurgents that are so much smaller then.
[30:12] You know than the than the traditional than the traditional players in the categories that they happen to plan but they just move faster and there you know they are more, analytic by Nature even if they don't have access to the massive datasets some of the you know some of the bigger players you know. The gravis if they had the inclination to do it.
[30:33] Cool so let's set some kind of best practices of The Cutting Edge to backtrack a little bit you've got a long history of seeing this what are some common pitfalls folks fall on when they when they kind of think about.
[30:47] Using data and analytics to solve a problem well I think the Alpena kalpana scenario you see a lot which is company X hires firm why they give him all their data the guys go off site they build models they come back they present an answer and nobody understands the answer and so they don't believe in so they don't do anything about it right the biggest the biggest so what what's the so what out of that the biggest so what is that there is an enormous opportunity to get more out of your modeling efforts by making the process of understanding the data that's going into it something that's much more sort of shared there's famous a famous statistician named John tukey who invented of a field called exploratory data analysis and one of the things that we're very keen on is kind of exploratory data analysis for the masses and so what do we mean by that right so like. What that means is rather than let's take the in the media mix modeling context rather than sort of waiting for the firm to come back and tell you that the marginal Roi of searches you know is Aksum that of TV is why. Let's just go through some basic line charts up on the wall.
[32:07] And look at what happens when you spend more in TV to do searches go up dude you know dude site visits go up to conversions go up and just begin to have a conversation as business people about what we're seeing actually in the data before we actually turn it over to the modeling firms to actually go process that and crunch it and come back and tell us you know what it all what it all meant if died of an aggregate in a statistical measure perspective because I think that, you doing that really empowers marketers it did kind of takes analyst and marketers you know who typically you're kind of at this passive-aggressive relationship and turn them into collectively analytic marketers and that. That part of the process I think it's highly underrated as as a really valuable. You know part of the whole machine learning process that that the companies are trying to take advantage of.
Jason: [33:01] I'm sensing a trend that it almost seems like in general it's wise for for businesses to start to have a practical well-grounded macro strategy before they jump right into crazy tactic.
Scot & Cesar: [33:15] I think I think it just certainly I think what I see a lot of is companies that a fact. Couple things I've seen this week basically we're people have kind of a product out report out kind of way of interacting with their data and decision-making where they say all right you know we we have Project X it's week, you know end of the year compared this week with last week and you know in the context of the overall media plan we change the creative this week. Either they're basically just thinking insert a very static kind of. You know we already are just reporting on what they're doing as opposed to saying you know.
[34:01] What is what is the bottleneck in our business if you ask that question you say okay where is the bottleneck and what are we doing about it that that. Drives you to go you know explore the data in different ways and if you're just basically saying you know how did this week compared with last week or how did this quarter compared to last quarter a year-on-year whatever comparison you're trying to make and that that we find is a. You know healthy lb access it's really important is it it's an accessible way of thinking about the problem.
[34:33] Which is which is important in a world where even though obviously data and analytics are more important there's a lot of you know marketers retailers e-commerce errors out there that that. They didn't grow up that way and then or just coming to this.
Jason: [34:49] For sure and speaking of not growing up that way and having having to evolve the question we get asked on the show super frequently is about omni-channel attribution right and I'm I'm curious if you have any sort of thoughts or best practices and you know if folks are starting to break the silos in.
Scot & Cesar: [35:10] I'll tell you I'll tell you what not to do and then I'll back into what may be some some things to do work what doesn't work is the classic okay let's gather up all our data let's throw it into one big you know repository and then try to big one one big honking attribution model out of it even if that's down at the granular level what you're saying okay you know. Idx saw this ad you know 30 days ago and you know came back and and so we'll assume that that at work. That is.
[35:40] That kind of like throw it all into one big pot kind of approach I think cuz has been most people that sort of realized they know that that. That doesn't work in the work that I've done that had the opportunity to work some really you know of strong people in this.
[35:58] In this category give me example. The guys are visual like you were my first landlord back in the day when I had my old company in those guys are pros and they they know what they're doing.
[36:11] When we work together one of the things that we did was we tried to First Look at the kind of macro categories of lab results and spending and so forth and figure out okay which are the dominant channels that we need to optimize against each other in this overall mix and then just focus on just getting like one one pair of in a couple of channels working together productively right so so if their mix had you know say. TV and search and then and then you know from there though obviously the conversion through the through the vine Channel we try to just. And I try to get DBA search Optimus together if it was Search and say I wish this play in Search and you're just trying to basically say okay to what degree does display spending Drive such a subsequent search behavior let's let's get you know let's get bad kind of taken care of and and so the the smart approach was in a sequence to it was picking you know, prioritizing the channels that mattered getting those two working together you know well seeing what kind of lift you got in terms of the results there and then recycling both the results in the lessons Into The Next Step at you take as opposed to this kind of Dino throw it up throw it all into one. Big pot and I hope the best.
Jason: [37:30] That that's what I make sense it's funny when I went over to ask her about omni-channel attribution I find the. There's even dramatically different dimensions that people are thinking about like often their thinking about the the various advertising Vehicles like television versus search for example which I think is that first thing you took sometimes they're talking about the channel attribution. You know when when someone does a mobile check out in that stores that are online sale. Store sale no starts at things and sometimes that you're talking about a touch device attribution when someone browse is on that tablet and then consummates the purchase on that desktop how do we how do we do that sort of things. And the one that I'm most interested at the moment as we were right in the throes of Black Friday and it's it's going to be the most digital sort of holiday we've ever had both both online and in the stores any particular thoughts or or pitfalls or best practices you're seeing in terms of that the actual Channel attribution the that online to in-store and vice versa that kind of stuff.
Scot & Cesar: [38:46] Well what's really interesting is what I'm seeing a lot right now is people trying to jump the gun on the on Black Friday all the Black Friday deals that are now being trolled kind of you know in advance and and I've been tracking a few things just both for professional and personal interest.
[39:13] And watching the you know the prices come down and and and and seeing whether or not it's almost like we're almost watching sort of like Airline pricing happening in sort of you know retail world now where you're basically you know you have this attempts were to drop the price and see if you can actually get. People to you know to buy before Black Friday at the Black Friday price or something close to it because it's really it's really in its if you think about it it's a it's an experience nightmare right to try to cram everybody into the store at a specific time have people trampled to death and I was you as you as you go in and answer anything you can do to basically sort of smooth and and optimize the yeah. The flow of demand into your channels and your ability to fulfill that is actually going to be, it is actually going to be something that's to the benefit of the business so to me that's the most interesting thing about this particular addition of a Black Friday and Cyber Monday is to sort of watch kind of the you know the sort of.
[40:25] Sort of like the back in the old days the Oklahoma Sooners who were trying to jump out ahead of other people too kind of stake their claim and and it's not unlike. You know airline seat pricing now I think is what we're beginning to see happening in in retail yeah. So one of the big battle areas is cpg and in your sounds like you're involved in there to some degree and grocery where do you think that's going to wear seeing Walmart really kind of triple down on curbside grocery there's a lot of people experimenting with delivery of groceries and then within cpg you know you have, so what's going on with these new Challengers that are our kind of digitally native brands you got the old guys trying to react to that may be acquiring some give us some thoughts on where you see this phone well so.
[41:23] You know that the the question here is very often at what point do these insurgents you know gif. Buy the bigger players because obviously the bigger players do you know bring a lot of advantages to the the table in terms of distribution in terms of yeah I was just in terms of their ability to also on the back and provide a a supply chain to actually get things built at scale that a lot of these folks can't you know can't manage as they're trying to grow so. On the other hand all the groesten and pretty much all the growth in cpg over the last few years is Ben from these that should have been searching players that are for building these at least these are these new brands go out of authenticity and everything in them.
[42:17] What one of the things that's interesting you know his historical e in cpg.
[42:21] Yuri may be familiar with the kind of a felony in Byron sharp who basically said for a fast moving consumer goods it's really all about mental and physical availability right so it's not it's not about loyalty so much for segmentation that's about just making sure that. You're out there reaching and repeating and then that you have distribution in the stores and the basically that's how you want in that category what. What we're seeing now is sort of a a movement away from that we're certain brands developer loyal followings you do in fact segment more than you used to and I think we're bending the sea is this kind of weird middle Zone wear. You know the the the new folks and the old folks would have need each other it's kind of a symbiotic kind of thing where you know the the cpgs need these Insurgent brands. I will acquire them to to drive growth to begin their kind of expand their opportunities but at the same time be Insurgent Brands and a really need. The the the half-ton the scale in the distribution of the bus on the manufacturing side of the distribution side that a you know that is step one of these large cpgs with their big sale sources for example and I can bring to bear.
[43:33] And that it was probably that sort of interplay between you know. Those those those two kind of types of players is probably the most interesting place right now. I see you've all been particularly in a world where even as that's happening the distribution channels are evolving right everything from from you know drone delivery to you know to Amazon is an advertising channel to yes but that's. That's what I kind of Zone. There's a geographic term for that that that's not coming to my but that's that's I think where we should watch for a lot of interesting action if it was next couple years.
Jason: [44:16] Yeah I told you I think it's going to be a really interesting category to follow cuz I feel like the disruption is is really only just getting started there at the moment I wanted to give it a little bit to another. Potentially interesting topic that comes up a lot but also has a a buzzworthy component and that is personalization so you know again lots of lots of folks get get directors from their board members to have a personal Malaysian initiative how do you feel about that and what what sort of best practices are you saying there is that a real thing.
Scot & Cesar: [44:54] So again let's not confuse the thing for the result right when people talk about this the question I have is. What degree is personalization are we talkin about right and end are there is is is everybody sufficiently different. That each person should actually have a materially different you know offer or experience presented to them in order to generate the kind of lift that over over some more aggregated approach that you know that you need to see so in general yesayya what we know they're there been their studies out there that basically say that you know compared with a plain-vanilla US offer the same thing to everybody you know that obviously up personalized targeted segmented approach is actually get Kratom lift but is it really is a question of degree there are certain.
[45:52] There's certain things that are also easier to personalize that other things so for example you know you can you know to the degree that it's legally Bob permissible you can obviously very upright at very low price relatively easily in an offer but executing creative sometimes can be you know challenging and you certainly can't necessarily just you know kind of like morph the product itself on the Fly for every individual customer maybe in some age where we have you know 3D printing in a widely-distributed you can you can kind of do that sort of thing but we basically. You know beat the other there limits to what you could do on certain dimensions and there's possibilities there's more flexibility have another another dimension. I think that the way to approach personalization is through having a really really strong program of experimentation and kind of test test results test for learning where you know you're constantly sort of testing whether or not. You know that extra sort of bit of variety actually provides enough economic lift that it's worth the incremental complexity that adding and is it said in some cases the dimension that you're burying The Experience on what is actually much more flexible than than another a number in a digital you know.
[47:14] A degree of offer a promotional discount in an email is much easier to bury than even the creative this wrapped around that. At least at the moment it may eventually be that we get to automated creative and so forth but that's we are beginning to but but for the moment it's it's.
[47:32] You know for most companies are there limits to just how how finally they can slice things cool so we're up against time but we we love to ask kind of more of an out there question we've been kind of tactical here and I've seen you guys really interesting tweets about AR VR and you just mentioned 3D printing and Jason I love to think about some this stuff sometimes just kind of get out of it the day today where do you see the future of Commerce and feel free to kind of Go Out 3 5 10 20 years would love to get your thoughts on them wow well you know. I think I think one way to think about this is is is that we in the end we buying things and consuming them as sort of a means to visit to meeting physical and emotional needs right and and so to the degree that technology involves. We will were ultimately need to think yeah we ultimately need to think in terms of how we're sort of doing you know doing those things as opposed to the products that happened to be the this word of vehicles for fulfilling those those objectives.
[48:47] Let me not not to be like super esoteric about it but you know it if if I am, let me know let's take for example clothing right you know if if if in the world of, Evo VR and so forth I can begin to sort of project an avatar out there you know then then basically you your way of sort of interacting with people may change and if your ability to sort of you know shift your shape on the Flies revolves it it means it has heard implications for the whole sort of you know fashion industry rights I don't want to. To intergalactically distance on this but I think the main point is to basically say that we, should not confuse the means for the ends we should think about the future of. Retail in retail technology as something that serving these physical and emotional needs as opposed to figuring out how to get specific product X to you you know more more quickly or or give you a different perspective on it.
Jason: [49:57] Well that's a great perspective it's going to be interesting to watch it all play out and that's going to be a great place to leave it tonight because it's happen again we've used up all our a lot of time but if listeners have any comments or questions about today show we encourage you to jump on our Facebook page and continue the dialogue there as always of this show is valuable to you we sure would appreciate it if you would jump on iTunes and give us that five star review.
Scot & Cesar: [50:23] Cesar folks want to learn more about us some of the topics that you covered in and see what you're you're talking about on social media where should they find you. Sure Caesar Brea Mall when were done both Twitter and Linkedin so I'll certainly post dust up there with some of the stuff we talked about here and and hopefully that'll be useful fucks, put a link to that in the show notes and we really appreciate you coming on the show thanks for joining us thanks very much for inviting me I really appreciate it.
Jason: [50:53] Is internally our pleasure thanks very much Caesar and until next time happy commercing.