Podcast 1: Understanding and Implementing Effective Merchandising Strategies

hello my name is Helen com and I'm the product director at Leia and I am Jeff schart I am the founding partner of merchandising metrics here in the USA and today we are going to discuss uh uh the current Retail Landscape the essential merchandising metrics the data driven strategies the open to buy merchandising and how to implement all this uh into uh like the current processes of the retail so let's dive in it okay let's go today we are going to discuss rather important and interesting topics uh and um um currently uh our main aim is to understand the current state of the Retail Landscape some essential merchandising metrics the data driven strategies uh of course open to BU merchandising and and uh practical

implementation uh of uh the merchandising metrics uh merchandising metrics and strategies based on this metrics uh and the implementation of the open to buy if you don't mind of course course okay great so my first question will be regarding the latest retail Trends uh so to start let's talk about the current Retail Landscape so Jeff can you please share your insights on the major trends of the mod on retail how do you see that well there's um of course so many moving parts to the business right now the way we see um data and Tech and um you know so much digital integration into the business that just wasn't there a short time ago and you know we're

still recovering from the pandemic and all the supply chain issues that that gave us um but but now that that's all settling down a little bit I I guess my observation would be that um there are still too many retailers that don't seem to have been able to get a sufficient handle on their own data so that they plan their flow and their their season ending inventories in a manner that is optimal for profitability so even with all the talk about data and digital integration of all the tools that seem to be available so a lot of retailers and and I I speak mostly from the context of apparel retailing so there's a lot of apparel retailers that don't seem yet to be able to sufficiently plan and execute flow so

that by the end of the season they're really where they want to be which is minimal inventory from the prior season and really well positioned for the forward season a lot of progress that can still be made there okay uh and how do you think what kind of solutions or what kind of decisions can be made to help them to overcome these issues with the data and the lack of digital integration well to my observation when I when I look at retailers um at the end of the season and again I'm looking at stores I'm not looking at data I'm not looking at Excel spreadsheets I'm looking at stores in real life um and to my eye what I see is that the stores end typically with way too much what I would classify as fashion or Trend um inventory and you know it it and it's

not simply because they they didn't get the fashion right um again to my mind it's because they they probably fell in love with what they did early in the season they bought too much of too many skus and they simply couldn't chew through it all so you know I I I think that that the process needs to be indeed not just make the speech but but make the whole process more datadriven and use use the the information that the end of the season is is saying as much as in season meaning when you study what didn't sell and any given location it's kind of silly to go back and repeat that mistake time and time again and I I think what I like about um

well for instance the lefo approach is that you know you use a word in in your your process that do TS perfectly to my way of thinking and that is clusters you talk about clusters of product and stores I talk about peers of R and shelf life and that's really the same thing when when you try to aggregate product into clusters of similarity and aggregate stores into clusters of similarity and and look at what then doesn't sell by the end of the season it ought to be pretty simple to go back and not repeat that mistake the next year but too many retailers seem to be making that mistake season after season year after year so I I just think there's a huge room for improvement there um with with just

embracing a more datadriven point of view which is not being critical of the design it it's it's meaning that they they've got a a much better opportunity to do better editing better curation if you will thank you uh and um uh it is a very nice example uh the clusterization of stores and ask use and let's take this example and uh talk about that a little bit more uh so Alia we are using the machine learning for making this clusterization as uh it can seem rather simple to just divide all the range and the stores into clusters according to their similarity or according to the same demand Behavior Uh or According to some other factors that can be relevant in these cases but in real life it stands out that it is not so simple and not so obvious and we are covering that with the help of the ml so what from

your point of view what is the role of uh Ai and machine learning in modern retail in terms of uh retail operations in terms of Supply chains management and some other um spheres maybe where it can be relevant is it from your standpoint is it rather a hype or a situation that is obvious for everybody that yes we will probably increase our profits margin and Etc kpis uh while using the machine learning or AI in general right well I I think that um yeah I think part of the key is to not over complicate it in the very beginning is to is to take it a layer at a time for instance um when it comes to clusterz stores that if you simply make at the beginning of the process a differentiation between the Northern

Tier of of stores versus the Southern Tier of the stores and in the USA we we have a a little bit bigger climate um difference between northernmost and southernmost so that especially when you're planning fall and winter you you've got to be very aware that the needs of an a store in Boston in October are very different than a sea store in Miami in Boston so um that might take a whole lot of machine learning to do but but the simple fact that you've clustered the stores in a very simple manner but then then there can be all the gradations in between Boston and Miami that that can break down and refine how the Clusters operate so I I think it's a question of using using the

data in a manner that starts with simple breakdowns and and then gets more uh more in-depth and more complicated as you get more comfortable with with different levels of data but at least start with with some level of clustering and understand that um you know there's nothing that can be done with a cookie cutter approach anymore uh I agree totally but if talking about the uh category level not the overall company or the overall stores but in terms of category sometimes we see that um it when we start making the clusterization when we want to drill down into the category levels we understand that the demand Behavior can be really different and it stands out that we have hundreds of hundreds of clusters that it is not

possible to manage them manually uh or with the help of the AEL spreadsheet so how do you see the influence on the qu quantity of C categories to the complexity of the using of the cluster analysis well I I think here I'll I'll introduce what I call um the scorecard thinking a scorecard is um a template if you will that is based on prior Seasons data that takes into account all of the variables that you're talking about which could be hundreds of clusters and it it understands that there are ABC stores in the north and ABC stores in the South and there are Suburban stores and City stores and you know what actually sells in each of these different clusters is different enough that they

have to be fine-tuned in that manner so so just like Ai and machine learning has to be has to be fed with with data the scorecard has to be fed with enough historical data that going forward AI can help populate that scorecard and and give direction to what each of the different clusters will be optimized with so I I think there's there's just a lot of um efficiencies and Time Savings that AI can lend to the process but like any other AI model it's got to be given the right the right template to begin with absolutely that's what I call the scorecard absolutely yes I agree okay uh and uh the next Trend or the next uh how to say um observation um that we are currently uh

having in retail sectors is the increase in the uh e-commerce and slight decrease in Brick and Mortals uh so in uh uh 2017 the new term was introduced like the retail apocalypse so what do you think about this trend uh what do you think about the death of classical bricken mortal retail and how many years do we have until it will die if it will die of course I think retail apocalypse was a um mechanism by which to grab headlines and make everybody crazy um which is not to say we haven't experienced an enormous amount of change and the the simple fact that Ecommerce has siphoned an enormous amount of sales dollars out of physical

retail um is is not an apocalypse it it it's a it's it's an evolutionary moment that you know we just need a reality check okay I I'm not going to get the same the same sales per square foot per store that I used to you know by the way in some markets that means some stores are going to close that that's just the hard realities of the math of the business right now but at the same time I I think there's this pretty huge recognition that physical retail is is where the customer really develops their bond with any given brand or retailer it's what makes it real and it's physical retail that gives the customer the confidence to go shop and buy online so you know once I develop a a

favorite retailer or a favorite brand and I really know product and the quality and the fit and the value well my comfort level for then buying online is huge and therefore I don't return a whole lot but if I ever decide to get experimental and you know try a brand where I really don't know the quality or the fit to the then you know I I think that's where there's some hit and miss factors going on that's not an apocalypse that that that's a simple evolutionary moment in the business it's just that that moment is going to last for several years and I I I don't know where the pendulum will come to rest on what percentage physical versus what percentage e-commerce and I don't think that number is even important I I I think that you know right now I think most people think that that that physical retail is

essential to again giving people the confidence to shop online so it's a question of of the brand of the retailer executing you know with with that whole kind of unified Commerce if I can use that term um as as their operating model it's not one versus the other it's it's all of the above but it's not an apocalypse I agree and uh uh it is everything about the customer experience that the customer can get while for example trying something on in the physical store and afterwards maybe choosing the same thing or another color thing that was absent in the store in the physical store online and vice versa uh but regarding the customer experience now so the uh increase in e-commerce increase in Social Commerce that is

really increasing Trend when people are purchasing WEA the social networks uh the very crucial thing here is the delivery and especially Logistics and delivery and especially the same day delivery Trend that uh we see uh the huge increase in the necessity of the same d delivery throughout the whole world in Europe in America in the United States in Asia even in Africa so what do you think about the Same Day deliveries how it is impacting the business as it really increases the cost uh and uh What uh what is the future of that I think same day delivery um kind of revolutionized the way both retailers and customers think about um shopping and buying and and um but

that doesn't mean I think it's all together a healthy thing I I think same day delivery uh or even next day delivery um is really the mechanism by which I'll just say Amazon kind of or now any e-commerce uh individual you know establish themselves as the goto place where in the beginning next day delivery was unheard of but if you pay a subscription fee then all of a sudden next day delivery is free and even though you paid a subscription price so the delivery isn't really free the word free began to be used so freely um but it developed this mindset of oh I just go to my computer and I get it next day for free well not really

um but it changed the attitude of how people think about you know their all their shopping habits um you know and I think the other lesson we're learning now from um some of the newer um e-commerce businesses is that they offer very inexpensive product direct from the factories but it takes two or three weeks and and they're proving that people are very willing to wait those two to three weeks in exchange for this this incredible value price so I think everybody's learning that you know pre next day delivery was huge well now all of a sudden two to three weeks was okay if the price is right and you know the bottom line of it was was all that delivery was never free the return rate on some of that business

was astronomical and absolutely profit killing so even now Amazon has introduced you know very modest return free fees but it's their way of saying oh wait a minute everybody we we we can't keep supporting the level of returns that you're giving us and even to the point where sometimes they'll say you know what just keep it it it's it's less expensive for you to keep the item than for us to take it back and reprocess it so I think delivery and and how retailers whether they're brick and mortar or e-commerce and how they offer it and manage it is is going through this its evolutionary movement of its own where what used to be the world's best customer acquisition tool turned out to

be a great tool but at a huge cost and that huge cost isn't sustainable so you know the pendulum is coming back now where where the the whole value equation of the price paid for the item plus the deliver equation is is going to reach some new equilibrium it it's been uh you know we we we've learned the new expectation is next day delivery for free or today with some modest you know delivery charge and that's not that's not going to go away we're just you know going to continue the evolutionary process as we go along here uh if not consider considering so huge players like Amazon uh for the smaller retailers uh the cost the logistic cost for the same day delivery

can be very significant if to compare it with the total logistic cost let's say how it is influencing how this trend or other words habits of the customers that are uh supposing to receive the same day delivery is influencing SMB business well I I I think you know Amazon has the benefit of um a whole different business their Amazon web services division which which makes so much money that Amazon can afford to lose a little bit of money yes exactly delivery mechanisms most businesses don't have that luxury they can't they just can't yes so this this this imposition of the charge that they do is is really just an attempt to to play a role W with the customer to

compete but but they can't lose money in the process they don't have the same luxury the same efficiencies that Amazon does so um and and like I said earlier even Amazon is now starting to charge you know some some very modest fees so again I I I think it's moving from being a customer acquisition tool to now the playing field is leveling and everybody's trying to figure out well how do I be in the delivery business and and be part of the customers's consideration for their shopping but I have to charge for it I I I have to at least break even break even on the delivery part of it and make some money on the product part of it very tough equation yeah okay so um if you don't mind I will summarize the first block of uh today's our talk about the

retail Trend and the Retail Landscape uh I think that we probably can talk about that for the next couple of hours but we are limited on time that is why I will summarize so we were talking about the digital integration and transformation of the retail businesses and not all the businesses unfortunately now are on the curve of the using of the data in a proper manner gaining collecting this data uh and uh building their decisions based on the data uh secondly uh like an example we were discussing the cluster analysis like the um analyses that can help retailers to be more data driven and data oriented um in terms of their decisions especially in terms of inventory management how not to uh have this huge inventories by the end of the season or by the end of the expiration

date or by the end of the um some particular period of time depending on the retail vertical uh also um we were talking about the customer experience and uh the role of brick and Mortal retail uh e-commerce retail the trends uh the percentages of uh the shares that are gaining from e-commerce and uh uh offline retail and as a conclusion I can say that it is like the normal state of the development of the market so uh of course it was influenced by covid a lot I mean the development of the e-commerce but at the same time it was uh um go in the same way uh the more people are using their mobile devices internet the more of course they tend to make the online purchases but at the same time they still receive this uh customer

experience in Brick and Mortal retail and uh we don't see how um the e-commerce um will uh substitute the brick and Mortal stores okay so uh the next block uh uh the next topic uh I want to uh arise now is the matrics for merchandising uh so how do you think uh what kind of metrics are crucial for Effective merchandising um could you please explain the key metrics from your experience um yeah sure um I guess first let me um give you my definition of of merchandising um I think most simply said merchandising is a fusing of planning and design it's it's certainly not pure number crunching it's certainly not pure

design it is informed design um it's designed with consideration for both great storytelling um but also profitability so so I I I I think it's really important to understand that merchandising is not just you know going into the store and making it look nice it it it's the it's the statistical analysis of past performance and what that would suggest for the future but it's also from a pure design and creativity point of view what the designers see as forward Trends never mind find the historical because you know Trends come and go and it it becomes the Judgment of the design team about what trends are diminishing and what trends are incoming and historical

numbers won't tell you that but so there there end ends up having to be a fusion of both the planning and the design process and that's not so much about specific metrics as understanding just what what level of teamwork is involved to be able to make that happen um but then to jump into metrics um I I immediately go to um well I'll I'll use your term clusters and and the establishment of clusters of product that reflect levels of risk or levels of fashion so that you start with Basics and then key items and then fashion and then Trend and you know breaking apparel content down into those four levels and treating each of those levels separately

in in terms of just how much breadth and how much depth they'll each get um and and then of course the further refinement of that is north and south and the further refinement of that is ABC Stores so it it and and the management of all that is what creates the scorecard that I was talking about earlier so it's it's a um it it's building this this um conglomeration if you will of clusters and how that the Clusters get populated that that formulate the merchandising process uh but in terms of the key performance indicators uh despite the fact we are managing the Clusters uh when we are dealing with four different level of risks uh by by your definition

and for example different levels of clusters in terms of stores which creates the metrix for some amount of clusters uh dependent on the risk and stores but at the same time what kind of kpis what kind of metrics for efficiency will give uh the top managers uh commercial directors um the understanding whether they are going uh forward in towards to their aims or not I think the key specific metric then is u weekly sell through because each of the four clusters of risk is probably going to have a different expected rate of sell through Basics will sell through low slower than key items they're going to sell through slower than fashion or Trend so you plan fashion and

Trend flow based on the fact that you have an expectation of anywhere between I would say 8 to 12 to 15% a week sell through as opposed to Basics and key items which might only have four to six% per week sell through so understanding that that each of the Clusters has very different expectations for sell through rates is is part of what drives it they also are going to have very different expected rates of of maintained margin Basics sell a lot slower but they don't have any clearance markdowns so they're going to have a higher maintained margin rate fashion and Trend sell through at a much higher rate but they also have a much higher markdown rate so as a group fashion and Trend are probably going to have a lower maintained margin and it it's being able

to establish the different metrics for the different clusters that that that will drive the inseason management of of how slow selling product is is dealt with for instance if you have something in the fashion and Trend area that's only selling through at 2% a week you got to mark it down now you can't wait until the end of the season um if you've got a basic selling through at 8 to 10% a week you've got to increase production now because you weren't planning on that level of of sell through rate yes so it it's it's really understanding how different the metrics can be by cluster level that that that brings Focus to the whole process uh so it is like one one point of vision when we are talking about the

sales through and the margin it is more about the category management about the commercial things uh more like even the snop process but on other hand we have the costs the logistics costs the inventory cost so what kind of kpis are um relatively important in terms of their tracking in terms of clusters or in terms of General situation um in the company or in terms of categories how do you think um well I I think that there there always needs to be overall um inventory level considerations that there needs to be a cap of some kind that um you know it all goes to the open to buy process but I think that the single biggest consideration

is from a very macro point of view how the open to buy process is managed from establishing a macro expectation you know at the top of the pyramid is the business expected to grow 2% or 20% or are we in the middle of a pandemic and the business is going to shrink 20% um because all of the other factors need to be derived from that that that reasonably predictable macro expectation so I think it all starts with um this the setting of you know for the entire business whether it's a small dep specialty store or you know a giant uh Mall anchor department store it it it's keeping the bound iies on on the open to buy in inventory

levels that that that have to be done to keep the business in control uh so we will definitely talk about the open to buy uh a little bit further in the fifth if I'm not mistaken part of our interview uh but now um what about so um some of uh the experts uh I I talking to um some of them do believe in GM Roy and they are measuring and they understand that like we need definitely to understand GM Roy um overall in the business on the category level so and they are uh driving the business based on that and some of them do not believe in GM Roy because uh uh like other factors are more important for from their perspective to manage the retail business what is your point of view uh from the standpoint of the GM Roy do you

believe yes I do um and really the the metrics I mentioned earlier of weekly sell through rates um very quickly translate into gmroi um you know if if you've got the weekly sell through rate that you expect to get then your gmroi is going to be satisfactory but if you're if you're not meeting that weekly sellr rate then you're gonna have a problem at the end and the problem will only get bigger if you wait to take the markdowns so in in the very beginning of the process and throughout the process the weekly sell through rate is telling you what to do if if the weekly sell through rate is meeting expectations then you're on target to hit the gmroi you want to

hit if it's exceeding expectations you're going to do better if it's not meeting expectations you simply have to start taking markdowns and unfortunately you're not going to hit the gmroi expectation that you had but waiting to take the markdowns is not going to change that so that goes back to what I said earlier about the end of season inventory problem that if you know it's like retailers Were Somehow waiting and hoping that things would get better well they don't the C the customer is voting every day every week and in a bad situation you get the best gmroi possible by taking the markdown as early as the customer is telling you to and not ending the season with all

that excess inventory because if you still own it then then you're G to be selling it at 60 to 70 to 80% off sell it at 30% off sell it at 40% off that's how gmroi is maximized in a difficult scenario I see uh and what is your advice to the retailers uh in terms of um understanding or analyzing the period of data when they need to decide that already Yes we already we really need to make some clearance sales or markdowns because uh like to maximize the GM Roi um what are your thoughts here well I I I I guess I would say again is you know the customer is talking to the retailer every day every week and if

if there might be some obstacles to taking markdowns um you know it used to be it used to be that somebody would walk around with a little sticker gun and literally put red stickers on price tags on individual garments okay now what the retailer wants to do is they want to put a sign up on a fixture that says 40% off everything on the fixture um and I guess my advice goes back to somehow some way that that markdowns have to be taken at the most surgical level possible that there might be three colors in a style that are performing great and one color that is performing horribly just mark down the one color don't put a a sign on the whole fixture um that's a problem in physical retail on websites I see this all the

time where there might be a whole series of like items and there might be nine of them nine plaid shirts but only two of them are marked down well that's because the customer has spoken and the retailer can go onto a website and just mark down the two of them and that's easy so the implementation of taking surgical markdowns in a physical store that's really difficult but but somehow it's got to be done otherwise the retailer is just throwing away margin dollars uh I agree here and furthermore um we with our Solutions are currently even helping our customers to uh I liked your expression surge ly uh um identify uh which particular esus need to be marked down and which particular SS if it is possible of course need to be

withdrawn from the range because they are not performing well and they need to be changed by another asuse of course depending on the retail vertical for example in grocery it is much more easier to make this uh um decisions based on the data because you clearly understand the expiration date you clearly understand the trend of the and the Dynamics of sales you clearly understand and can build the demand forecast and by comparing uh like the days of coverage and the expiration date you clearly understand that you probably will not sell uh all the quantities for the particular Asus during the remaining shelf life of the particular particular es so it is a bit easier but at the same time uh in appal retail you we also have the expiration soal expiration date the end of the season so it's pretty easy to

uh build the expectations whether it will be possible or not to sell this ASU uh and we may need to make the managerial decisions whether to relocate to uh make the markdowns to um if it is possible to make uh the like the uh some kind of clearance sales or the returns to the suppliers as earlier as possible I agree totally with you here okay uh thank you very much uh and our next uh block and segment will be about the metric based merchandising strategies uh so um um we were discussing uh like the different metrics the metrics about the sales through metrics about about that the sales through needs to be managed on different levels in terms of clusters in

terms of risk levels depending on uh the the business uh but when we're talking about the strategies um what are your suggestions can you please share your experience about the particular strategies uh that are based on metrics that are based on data uh from your experience um yeah of course um here's where I would go back and revisit what I spoke about a little bit earlier um the scorecard um the scorecard is is is a process whereby um all kinds of of analysis has has been done on historical selling information in all the Clusters that we've talked about North and South ABC Stores Suburban city um and the

scorecard by my definition is is meant to be um kind of an assignment if you will that is given by the planning organization to the design organization um by the merchants the the merchants are kind of bridging this whole process and it's really the planner saying hey you know I've crunched all the numbers and and this is what seems to make sense to me in terms of what the forward-looking um expectations are and the designers can come back and say well yeah but you know that c huh we want more right right well to that point um the scorecard is meant to give the design team some boundaries and guidelines because designers love to design and you

know they they they they tend to just you know churn out you know one great thing after another but the scorecard tries to help everybody understand how much is enough of a good thing in other words the scorecard helps assemble the portfolio of product that that goes into making up the total what percentage of the business can be Basics what percentage of the business should be key items and then in fashion and Trend the other way of asking it is how much risk do I really want to take on and in all my years in the business um I never looked at Fashion often enough as risk everybody got into the design process and the editing process and all the fashion was wonderful and great and fabulous and terrific and everybody loved it and the

mistake you make in that emotional process is over assorting into high risk product so the scorecard is meant to say oh wait a minute we we over assorted last season now let's understand that even in our best stores there was only X level of appetite for this higher risk product so we have to build a portfolio of risk levels that understands the way customers out there actually think can behave because customers think can behave differently than we do in the design room so that's real life that's what everybody's prepared to buy let's let the scorecard give us some guidance about you know how much is enough of a good thing and yes designers

do find that um frustrating because they will typically want to design more than the scorecard calls for but it also um it also helps everybody just focus on the combination of great storytelling and great profitability uh so you're describing rather a classical um how to say um conflict between the designers and plannings between the plannings and category managers between people who are focused and motivated on different things and it creates some kind of conflicts between them uh from your experience how do you see how to overcome such kind of conflicts uh who um what title or what kind of person uh does the company need to overcome these

conflicts and to um make everybody happy to align these two things the production and or the designing or the sales with the planning uh people who are responsible for cost mostly well I think that boils down to Simply the head Merchant's job is that that the head Merchant their whole job is to I I'll use the word fuse again is is to fuse the planning and creative processes I I express it very simply as you know data plus design but um most planners know that the plan alone isn't going to win the day and I won't say most but some designers know that it's not as simple as just designing wonderful stuff and putting it out there

they kind of know that that they need need each other's talents to to succeed in the long run um and you know to me the very simple mechanism is um you get everybody together on a weekly basis it's the weekly bestseller meeting and you put everybody in the same room with 11 racks of samples and the data and you review it and by the time you throw all the best sellers up on one wall and all the work sellers up on another wall and just Hawk it through everybody can sit back and go oh okay um because you know designers don't sit down and say I think today I'm gonna do the worst Sellers and today I'm gonna do the best sellers it just it it has to unfold over time so the the Monday best seller

meeting process however the retailer manages it is is really just crucially fundamental to the whole to the whole process because it brings everybody together they all look at the same product and the same data at the same time and they can talk about why why was that a bestseller why was that a worst seller and you know it's out of that that that comes the ammunition that lets the planners go back and refine the numbers that lets the designers go back and design into the bestselling criteria and as frustrating as it might be they have to understand that the worst sellers well they thought it was good stuff but it didn't work so okay um and you know weekly the process just

moves along but it's it's jugular to the whole the whole process I see okay uh and the thing that uh can be relevant during this discussion uh about the strategies the merchandising strategies is the particular business model so if to simplify that uh we have the uh meat and premium price segment and we have meat uh and uh low price segment so for the premium segment let's say uh they they are more focused on the margin and uh they're less focus on the inventory turnover on the cost of stocks and Etc and on other hand when we are talking about the Discounters and especially High Discounters all of their focus is on the cost of course the sales is important as for each and every every retailer um but due to the low margin

they are more focused on the activities that can decrease the uh cost of logistics and like the general cost operational cost and everything else so from the perspective of different these different retail business models how it will influence the implementing or like the understanding how it is better to work with the merchandising strategy well I I I think the beauty of um both my model and your model is that the clustering of the business works at both extremes and in the middle um you know every retailer whether they're operate whether whether their opening price point lower margin or Upper price point higher margin everybody wants make money so it's the managing of the Clusters that gives

everybody the best opportunity to do that it it it's just that the the lower tier of the business the lower uh the opening price point retailers are probably going to be more risk averse because they have lower margins to begin with so they're not going to get very adventurous at the Fashion end of the spectum the upper end of the market to your point has the built-in higher margin opportunity and therefore just has more room take markdowns at the end of the process and at the upper end of the spectrum customers have an expectation that they're going to be kind of wowed in the process of of shopping there you know there's a whole different level of expectation between shopping for apparel at Walmart than there is at nean

Marcus there's just no comparison so you know the the higher end with the higher margins they they simply have to take the higher level of risk because that differentiation is is indeed what separates them from the rest of the market and I always say that that actually you know for all that I talk about risk I never talk about eliminating risk I talk about managing it risk is a Brand's best friend because it's a differentiator so you don't want to eliminate risk you want to put in just the right amount of risk at the right time at the right margin so that as a brand you're different differentiating yourself from the rest of the market and that's much more difficult at the lower

end of the market than it is at the upper end of the market but then so are the expectations at the customer level they just don't expect as much at the lower end of the market okay so um as a conclusion we were talking in this part of our discussion we were talking about the merchandising strategies uh and strategies based on some uh kpis and metrics so we were talking about the scorecards and the alignment uh between the planners and designers or planners and category managers in terms of uh non aeral retail uh and the their alignment with the help of their uh equal understanding sorry equal understanding of the same processes of the same data of the same operations uh and uh uh with the help of that uh everybody can

understand uh whether we are um getting our goal whether we are going straightforward to our aim or not and if not we need to uh make some adjustments into our strategy but Based on data based on St cards okay so uh our next uh topic will be the open to buy in merchandising so uh can you please explain what is the open to buy uh and uh uh why you choose this uh approach in Fashion retail well we we we mentioned it a little bit ago um yeah open to by I think is really the last step of kind of fitting all of this great product development into a very

specific um grid if you will and I I said earlier that you know the open to buy had to be predicated on what is truly realistically predictable what kind of expectations do half of the business um three to six to even 12 months out because the the the time and action calendar for a parallel product development can be anywhere from weeks to sometimes a year out so you've got to have an expectation for right now we're sitting in July well I need to have an expectation for what's realistic for me to expect in the summer of 2025 for me to be able to develop the right level of product and then specifically buy the right amount of product so the the open to buy at the

macro level and then how it gets backed down into the category and classification level and then down the SKU level um is really Paramount when it comes to not not over buying um it's got to be the I just always come back to it's got to be realistically predictable to be to be um to be manageable um it it it it takes the whole process of of kind of open-ended development the scorecard gives it boundaries and ultimately the open debond gives it specific purchases at the SKU level so it it it it's just this ongoing process of of getting more and more finite uh and how to ensure that the uh

you have mentioned that it must be predictable and it must be manageable that is why it is important to have the open to buy but how to ensure um the how to say the predictability accuracy or forecasting accuracy uh when we are talking about the open to by approach from your experience of course well that's actually one of the the best questions of all um so again back to the cluster level um Basics key items fashion and Trend the predictability of Basics and key items is huge that this year's data on basic and key item products is excellent to very good in predicting next year's demand because again we're sometimes talking you know six to 12 months out but at the SKU

level this year's selling data on this year's skus is not going to be very helpful in predicting ing the performance of next year's fashion at the SKU level so so at the open to buy um in the open to buy process what what you end up having to do is is to say well I I might not have predictability at the skew level but I think I've got pretty good good predictability at the category level at the cluster level so I'm going to give this cluster 12% of the open to buy understanding that any given SKU in there you know I'm not GNA have much predictability on but at the basic and key item level I am GNA have good predictability

so basically what I'm saying is that just like product has shelf life so does data data has shelf life this year's data on Basics and key items is good for predicting next year's performance in fashion you might have a couple of weeks to a couple of months of shelf life to the data so if if you've got last week's best sellers and you can replenish them from the factory in eight weeks well that's fabulous you know you can replenish in season you don't have to predict the year out you can manage the business in season not many apparel businesses can do that so it's recognizing the cluster level and the

predictability the quality of the data at the cluster level and what kind of predictability it really gives you at the cluster level so this whole cluster level of thinking is is just it's so important in in how the business sets its expectations and manages its forward look uh you have noted a very important thing that uh not all the retailers not all the apperal retailers uh are able to make the Fulfillment during the season so usually they are preparing uh in advance before the season starts and afterwards uh you don't have an opportunity to make great adjustments you can like make the internal replenishment of course but at the same time it is costly uh but to make the standard process of the replenishment from from the central warehouse or from the uh external supplier uh is not

always possible so how do you think um is what kind of advice can we uh give such kind of retailers how to switch to the opportunity to make these faster decisions uh to make the changes and to make the fulfillments and the deliveries on a on a more frequent way to ensure that even if our level of predictability is low at the same time we have an opportunity to make this adjustments based on the real data that becomes a very pure conversation on Supply Chain management and it's all about the level of partnership that the retailer is prepared to enter into at the factory level um so I'm I'm really yeah it's at the factory level I'm not talking so much about Transit and DC okay but it's the ability of a Design and

Merchandising team to come up with a new idea imple minute test it get it manufactured in a timely basis to get it on the floor and then respond so whether it comes by boat or by airplane there's a difference there but rather than operate on six to nine months of time and action calendar if you can operate on four to eight weeks of time and action calendar that's huge and I have found that that what that takes is it it means that again you've got to go back to the design team and say look the design process can't be just totally open-ended and you design whatever you want that if we take the design process and focus it on these core

Fabrications then the factory can stockpile those Fabrications and trims and all the other necessary you know parts of of of the the Garment and they rather than start from scratch every time we get selling information they tap into existing inventories of ready-made Fabrics now not every category can do that knitwear is especially flexible because they can stockpile Yarns and the manufacturing process can very quickly turn Yarns into knitwear into garments so it's a process I refer to as platform merchandising because the platform is the raw materials so you start with the raw material end of it versus starting with the design end of it it's a very different way of looking

at the whole thing but it can dramatically um alter the way that a factory responds to a Retailer's rate of sale and from this perspective uh uh we all know a great example of inditex when uh they are managing this very frequently very flexibly so what is their secret I I think the secret is that um they kind of built this process up from the ground level from day one as I understand it uh they took a look at you know the basic apparel time and action calendar at the very beginning and said easy how can you operate an everchanging business on a six to 12 month calendar and you know they they built some of their own manufacturing

facilities and you know they have deep deep deep relationships with all of the factories they do business with and my understanding is that they fly an enormous amount of their production from factories to stores so they've made it their mission from day one to to operate on on the the just knowing that it's crazy to try to ICT especially women's fashion on a 6 to 12 month calendar um you know there there was a there was an American Retailer that that operated on this basis back in the 80s and 90s it was The Limited um you know they they were unbelievably customer driven in that regard um but as as different retailers got bigger and bigger factories just

said well I need more time and more time I've gotta I've got to get raw materials I've got to get trim I've gotta you know factories want predictability factories live and die on efficiency they just want to have a smooth run of product going through their building day in day out they can't have idle time and they can really only go so fast so it's not like they can speed up and slow down they just want this nice steady stream of production going on so the way index approached it was a huge ask of themselves when they built their own factories and of the factories that they ultimately partnered with but they did it and they succeeded I think fairly hugely in the process yes yes I agree so if to make a conclusion

um uh the first thing um that is really important is to manage the like the to make your business predictable and manageable and uh to make it predictable and manageable sometimes you need to somehow shorten the time of reaction and when we're talking about the like the um shorting this time of the reaction uh we are talking about the um lead times we are talking about the decision making we are talking about everything and as an example uh of index as uh the successful retailer with a very quick um reaction on the changes in the market and the decision making inside of the the company so the main things that lie behind their business is first of all

you have mentioned and I liked it they understood that they wanted to do that from the very first day of their existence secondly they have deep relationships with their Partners let's say partners with the factories with the suppliers I assume logistics companies uh and all kind of Partners they work with and they have fast uh Logistics so their aim is to have to make it as fast as possible okay so uh thank you very much uh about uh that's it I think uh in this blog and to uh finalize uh I think that it is one of my most um like the most adorable um topics it is about the implementation of the strategies so um we have talked a lot about the trends about how the retail business is changing about how to

measure the efficiency and how to build your strategy based on some kind of metrics in terms of the efficiency and now um what are the secrets uh of uh implementation of such kind of solutions because even if you are extremely intelligent and clever inside of your brain it is really extremely hard to implement everything that is inside of your BL brain uh to your company to for the people to understand why uh you do need to do that or this so what are the secrets how do you think well I think the first um observation I'll make is that um I'd have to highlight just how everybody needs to recognize How Deeply collaborative this process needs to be

um the business cannot operate in silos it is it is to me you know this amazing example of of teamwork I know that sounds like a cliche but but that's what it boils down to I I referred to earlier of you know the the number of crunching can't do it alone the design can't do it alone um the the the Monday bestseller meeting process um but the other part of the process is um I think all of these people planners designers merchandising people is getting out from behind their desk and getting into stores talking with sore people understanding at at the most intimate level Poss you know what's going on at the store level on the selling floor because I think the root of it all is is to is to

constantly try to answer the why question why does this work and why didn't that work we thought it was such a good idea when we were sitting in our in our corporate office Why didn't it work so drilling to the why um means that it just needs to be this highly collaborative get out from behind the desk visit the field talk to people um visit the competition by the way um you know the competition has smart people working for them too so what are they doing right and wrong it it it's this constant learning process that just never ends so I I I think that that once everybody understands just how collaborative it really needs to be and

if if the head Merchant is fostering that kind of environment um then I think the pieces fall into place and what about the project management appro approach sorry about the rules the standard rules of project manager management that helps to implement each and every project uh so how this approach can really help in implementing uh of the strategy well again I I think that's a um it's a process it's an ongoing process of the sharing of ideas and pressure testing those ideas um project managers in my mind are not um H um dictators if you will they're they're Learners and facilitators they

are they they're trying to glean from everybody you know everything there is to be learned about a specific project and bringing it all together and disseminating it back out again so project managers um you know really can't have an agenda other than you know other than learning um you know they've got goals they've got kpis of course and all all that but really good project managers are really good students of what's going on in the business they don't pretend to know it all they're always anxious to learn and then turn that around into teachable moments for the rest of the organization uh we had an interesting example lately uh with one of our customers uh that is like a good example of what you're talking about um so we

are responsible for um managing the promotional activities and uh for uh forecasting of the promotional activities for the grocery retailers and the promotional activity is like extremely important for as uh it influence everything it influences the customer loyalty it influences the sales it influen and the logistics cost so pretty everything uh and uh uh the preparation uh and the handling of the promotional activities uh it is a topic that touches each and every Department in the retail uh organization uh and um as they're preparing for that and uh they're preparing on the operational side the store managers who are responsible for the price tax and the layouts and everything of course the supply chain is preparing and they need to ensure these increased quantities in place of each

and every store due to the forecasted quantity uh of course the marketing team who need to prepare all all the materials and the advertisement uh even financial department of course is involved so the commercial department so pretty everybody uh and um they are involved in the preparation but unfortunately there is no process usually we don't see the process when they see it all together after the FI final day of the promotional activity and uh when they're discussing uh the problems the issues that arose D during the promotional activity how we did finished the promotional activ ity what are the issues uh regarding the availability regarding the cost of stock the days of stock and Etc and by implementing such kind of a process a lot really diff started to be different so a lot of

changes took place there because they really started to understand each other so before that it was an interesting story that commercials they were saying that the logistics is not working at all the operational Department told told that the commercial Department uh is not working at all and they were just like arguing between each other uh because uh they didn't uh um receive great results as the end of the promotional activity so I deeply agree with the Deep collaboration and uh the um situation when we do have the organizational silus and uh we need to eliminate that that's absolutely absolutely thank you very much for your time and efforts it was extremely interesting and extremely insightful uh and hope hope it was enjoyable for you as well it was Howen

thank you um I really enjoyed it and uh all the best to you and leao your whole team thank you