It is a beautiful day out here today guys unfortunately I can't say the same about the crypto market. This weekend Bitcoin continued to inch its way lower and today has been no different as we've continued to see the lows get swept. I'm gonna be honest with you guys although we're currently sitting at this 38k support price action seems like it wants to continue sweeping the lows and retest this Fibonacci level back here. The issue with the retest of that Fibonacci level is that then we are threatening to drop way back down here which is the lows of back in May to July. Today I'm gonna show you guys how Bitcoin is threatening to go even lower this week than it did last week and where we could expect to see some buyers come back into the market and hold the prices up. We're going to be looking at its current price action, the next support levels and we of course are going to look at the order books. Then I'm going to put my theories to work and show you guys the exact trade setups that I will be using throughout this week to try to profit from these moves. So let's go ahead and dive into today's video. Alright guys so a quick recap from Friday's video I gave you guys two different trades that we were watching. The first one was a short below $40,000 if you guys were able to catch that. That was a move of around 6% using 10x leverage like we usually recommend that would have been around a 60% profit on that trade. So the second trade setup that we gave you guys is actually in the process right now so we'll talk a little bit more about that one when we get back to the trade setups that we will be watching today. Alright guys so not too much has changed from Friday right? On Saturday we saw a little relief green candle there and then yesterday once the futures market opened back up we had another two and a half move down and then today so far we're pretty much down about 1%. So if we zoom out to the weekly chart here you can see that we closed in the green in what's called an inverted hammer. So if you look the week before that we closed in a regular hammer and then last week we closed in the inverted hammer. So what exactly do these mean? So we pull up the definition here you can see that the hammer is a bullish reversal pattern which signals that a stock is nearing a bottom in a downtrend. And if we pull up the definition of an inverted hammer you see that an inverted hammer also forms in a downtrend and represents a likely trend reversal or support. So both of those are potential reversal patterns to the upside. And we've had them two weeks in a row back to back right now. However I have to remind you guys although this is good news a lot of this price action is still being driven by events and by the news going on out in the war. So you got to kind of take all of this with a grain of salt currently. If we zoom out you guys can see that the bullish structure is still currently set up and it's still in play. As long as right here we hold up a higher low than this right here then we're still technically in a bullish structure. One thing that we are keeping a close eye on is this potential head and shoulders play which could lead to a drop maybe down to this level right here. That support level there is basically a support that goes back many many years. The only time we've really fallen below it. You can see was during the 2020 pandemic and we popped right back above it. So if we are seeing and if we do see some sort of head and shoulders pattern play out here with everything going on in the war this would mean that the war has escalated even more which would then speed up this drop here and make this head and shoulders pattern probably play out. I would be guessing that at that point the bottom would be somewhere around this range and that range is basically between twenty two to twenty five thousand dollars. Now zooming back into the daily timeframe you can see the exact range that we're currently trading in. We've been basically just getting rejected over and over at these areas and then bouncing from these areas here and it looks like we're going to be retesting that for a possible third time. So that does give us a possible opportunity to look for a long position if we see that price bounces at this support and starts heading back up. We'll touch more on that in a few when we speak about the trade setups for this week. Now if we come over to the order books and try to see where the buyers are you can see there's really nothing until around thirty seven thousand and those are small by walls there if we continue down there's some more buyers at thirty six thousand if we continue lower where the biggest by walls currently are sitting is at thirty five thousand you can see it's bright and yellow that means that there's a lot of buyers currently sitting there beyond that the next important level where there's the majority of buyers is that thirty two thousand dollars and then the biggest by wall of all is currently sitting at thirty thousand dollars. So this is why I don't like the idea of retesting this support here and falling lower because there's no real big support areas there's no real areas with a lot of buyers waiting until thirty five thousand thirty two thousand and thirty thousand dollars and even though we can see those current by orders right now doesn't mean that they will stay there sometimes those by orders will be canceled they will be moved it depends on the price action what happens to those by orders sometimes more can come in but if the self-pressure is too strong the self-pressure either way is going to eat those up and is going to continue pushing prices down so when we get down to those levels we need to reevaluate what is going on and then make a new decision based on that now before I show you guys my exact trade setups that I will be watching this week to make some money on the possible moves that we will be seeing all I ask in return is that you guys smash that subscribe button if you guys are looking to make money through trading through crypto passive incomes or maybe you just want to stay up to date with the latest money making trends in crypto I upload a video almost every single day about these topics so please smash that subscribe button so that you never miss another opportunity to make money in crypto in addition to that I answer every single question that I get in my DMs on Instagram and I also answer every single comment that I get on my YouTube channel so please if you guys have any questions at all about the video drop it below all right guys so let's dive into the trade setups that I will be watching this week so the first level that I'm watching right now is at $38,000 level remember I gave you guys that level on Friday's video I told you guys that below $38,000 I like that as a short trade entry I have everyone in my group right now taking this exact trade and guess what they're ready over $500 in profit on the trade I'm going to continue using that level this week to look for short entries now if I see any type of reversal where the market might be going back up because of some event driven news then I might be using that level to take a long entry to the upside to try to ride that price action up it just again it depends on what happens depends on the situation depends on the context right the next level that I will be looking at this week if we continue lower is if we get below this Fibonacci level if I notice that we're starting to drop below it and we're not going back up I will use these levels to take a short entry below it so that level is probably going to be around $36,800 or so so if you want you can even use $37,000 because if we fall below $37,000 we're probably going to continue below $36,800 so I like to keep it simple and use the whole numbers that are nearby and I don't want to use $36,000 as a short entry because remember there's a lot of buy orders sitting at $35,000 so I'm not going to be using $36,000 as a level to play for long or short entries there you can currently see the market sweeping the lows like we spoke about in the beginning of the video it's very likely we're going to be testing these lows down here now if we do see any bounces back to the upside the level that we are watching is first again $38,000 is going to be a key level and then the next level above that will probably be $40,000 I really don't want to play anything between those two now with all that being said if you guys want to know exactly what trades I am taking every single day in real time you must join my mentorship trading group in this group I share every single trade that I take with my complete trading plan entry stop loss take profit all of it I teach you guys how to trade using leverage to maximize a capital that you have and how to use the right position size and risk so you never have to worry about blowing up your trading account again in addition to that I also post crypto passive income opportunities NFT projects plater earn projects and much much more now there is limited availability for this because there's so many people that want to get in but I can only work with so many at once so if you guys are interested make sure to click the link in the description below I'm telling you guys crypto is a once in a generation opportunity and the way that it's set up right now to make the easy money the easy profits this will not last forever so please if you guys are interested make sure to sign up in the description below if not then I strongly suggest that you watch the next two videos that I put up on the screen because those are the videos that YouTube thinks that you should watch next thank you guys so much I hope you have a great week as always peace and love


The impact of climate change on water resources can be estimated more accurately by post-processing regional climate model, R.C.M., simulations. This process involves transforming the R.C.M. output to better match observed climatological data. Three types of transformation were identified, distribution derived, parametric and non-parametric. Non-parametric transformations were found to be most effective in reducing bias in R.C.M. precipitation. This article was authored by J. E. Haugen, T. Engenskogen, J. B. Brems, and others.


The topic of my paper presentation is an online stroke patient with tertiary health care syndrome. Stroke is a fine assist syndrome of an aptly developed and ended-sized clinical sign of pupil disturbances of cerebral function, last week I went home for hours or leading to death and it's a beauty. So the stroke is a primary to achieve death and disability. The incidence of stroke in India is 1.5 per 1 lakh person in our country, 1.24 per 1 lakh person in rural. Stroke are said to be an infarction primary, non-traumatic sobriety, ischemic stroke is a common step. Stroke accounted for 80-90% of all cases. Stroke results in a central adverse infarct tissue, some of the peripheral region of the standard cells, that is called the penamins. CT has been a modality widely used in evaluating. However, MR has a greater sensitivity and specificity for detecting early infarction in the nurse's city. MR has become a primary modality for evaluating stroke patients. Stroke MR protocol consists of T1, T2, player, D2Y, ADC, MR, angiography, and MR spectroscopy. Most infarct can be used in conventional MR sequences. Equationally, MR is sensitive for detection of hyperecutisemia. GRE for the hemorrhages, status of vessels can be assessed by MR angiography and status of various metabolites can be evaluated by MR spectroscopy. They aim and object to the role of MR in evaluation of stroke to determine the territory of involved blood vessels to MR spectroscopy finding in machine stroke. Material methods, the study was conducted in the Ontario Diagnosis Department of Pacific Academy Medical College. It was implemented by U Precinct and after that weighed a percentage of the study in comparison to the study in the Jean Metsch, the study was conducted with model of the stroke... the studies were carried out with patients line of phone. The standard of supply is polarized head point. These are the case one. Here we can see the acute left to... This is the DWI image... this is the corresponding ADC image showing hyperecord density and this is the same patient MR spectroscopy... had aero конечно, and the patient MR spectroscopy shows the lactate peak in the same area. This is the case two is the axial image showing the hyperintensity in the PCA directory. This is the case three showing lacuna and what is the DWI image? We are showing lacuna part on the left for the nucleus. And this is the MR angiolactic 3D duct image which is showing occlusion of the left MCA. This is the case five image which was shown in subarconic harm in the right cell infusion, which is shown as hyperintensities in the left cell infusion. This is the case five which shows the GRE. This is the GRE image axial section, which shows a blooming in the right hyperatomic region. This is the same as morning DWI image. This is the same patient showing high pain test in the same region. This is our discussion. This is the case distribution among the stroke patient. 80% were from the park. Cerebral hemorrhage was present in 10%. Cerebral hemorrhage was present in 5%. Cerebral hemorrhage was present in 1.2%. The inter-presentation of the stroke patients were hemophlegia and 49%. Maximum was facial palsy. It gave disturbances that headache and vertigo, visual disturbances and vomiting. This factor is associated with stroke as we have come across that hypertension was present in around 28%. Diabetes was present in around 52%. Spoken was present in around 38%. Heaviness in lightness and hypertension was present in around 36%. Hyperplostrilineal was present in around 32% and cardiovascular diseases in 9% of cases. In an extreme stroke, the population of 55% patients, 8 to 40% were from the infarct. 31% were from acute ischemia and 18% were from chronic infarct and 5% were from acute and chronic. The most commonly effective treatment was the MCA which accounted for 45 to 46% of all. In fact, it was for the observed at the left MCA. It was more commonly involved. PCA was also involved in 6 patients. Among them, 4 were patrolled on the right side. 3 patients were from A.C.E. and 2 were from the right side A.C.E. Involving the basilar artery to artery without involvement of PCA was observed in 3 patients. Human changes in white matter were observed in 6 patients. Among the 6 patients, 3 stood very well due to hyposintensities and 3 patients suffered from hypereintensities and subcortices. In fact, Shafakai was observed in 4 patients. In these white matter, hypentensities and dnds. 3 patients in a study population suffered from multiple impact. Among the study population, 2 patients suffered from water-shed impact between the right and PCA right. M.C.E. and P.H.E. So, we are finding an impact that we came through. The series of hypereintensities in 24 hours, the T1 iso, T2 pair iso to hyper. D.W.A.Increase signal, ADC is now 3crease signal. On acute one to 3 days after the insult, T1, T1 is hypo, on T2 it is hyper. D.W.A.Increase signal, ADC is decrease signal on early patients and subcortices that is 07 days, T1 hypo, T2 pair and hyper. D.W.A.Increase signal and upper end ADC is decreased. On late subcise 273 days, T1 hypo, T2 pair, hypentensity which不到 increase signal, ADC, EDC, they came in normalizing the signal to just know as the strength. On chronic, there's hypo intense due to the volume loss in sublimal Asia without that involvement. And T2, it is surrounding hypo intensity with surrounding hypo intensities, which are because of sublimal Asia with gaseous and DWW, there's normalization, EDC, it is normalized. On comparison, among the study population, 31 patients have a acute altered characteristic problem in the acute patient, both in the convention, T2, it can be devised. This is a student who has shown the situation in DWI without altered characteristics in the conventional environment. T2 was observant of the patient, hemorrhagic stroke, 6 patients in our study population suffered from hemorrhagic stroke, which was 2 to 10% in a study that's available. Hemorrhagic and gaseous anemia was observed in three patients. One patient suffered from lower involvement and one patient suffered from cerebral involvement and one from halogen involvement. Scoping mixed with five patients among the study, three patients suffered from cerebral illness from versus and two patients who suffered from hemorrhage. These are the stroke parameters. The MR finding was Markovoxel MR was performed at one patient with a student path in Australia and showed decreased na concentrations of all 31 patients compared with the contralateral cell and the lactate was observed increased in all the 31 patients. Voxel immediately adjacent to the area of diffusion which has normal signal on the MRM dish, so preserved na concentration, however, the lactate was observed. So this is the salvageable area. On conclusion, MR is anonymous in mortality, it is noticed that finalization radiation has that certain lesions kind of identified in MRI, you could see clearly why that is the revolution. Early detection of infarct and MRI maybe as we will do is ability to pick up altered water signals. EWR showed that altered signal characteristics in acute infarct earlier as compared in conventional K2. VWR showed deputant hypochloric acute infarct makes it one of the primary sequence that can aid in treating the patient in golden age. EWR can differentiate up to platelet infarct with white medicine which is responsible for patient symptoms. And EWR helped the patients have acute infarct issues to try and prove from acute and chronic. While the Voxel MR was performed patient that we do have, we also have a decrease in innate infarctations and the impact it could have all the patients when compared to contralateral site. These are my differences. Thank you.


Hello, everyone. My name is Hayden Blousfern. I'm a software engineer on Google's open source security team. And today, we'll be talking about the intersection between privacy law and transparency logs. So at a high level, transparency logs are immutable and append-only data structures, meaning what goes into the log can't be mutated and can't be removed. And we'll talk a bit more about how these logs are constructed and applications for them later on. One thing I've been thinking about recently is, what if I want to remove data from an immutable log? Well, one answer to this might be that I can't. The contract of the log states that what goes into it is immutable and can't be removed. So maybe this isn't a valid answer. But when considering privacy law, I'm not certain this is an acceptable answer, technically. If privacy law states that you should be required to delete something from a source where data is collected, I'd like to have some technical solutions to be able to solve this problem, to actually delete from an immutable log. So that's what we're primarily going to talk about today. We'll start by discussing Merkle trees, which are the underlying data structures for transparency logs. We'll talk a little bit about logs and their applications. We'll have a brief aside discussing privacy law and the bits of privacy law that are relevant to transparency logs. And then I'll conclude with some technical approaches for implementing a mechanism we'll call redaction, which is the ability to remove something from a log while not breaking the cryptographic properties that the log guarantees. So let's first talk about Merkle trees. And before we do that, I want to briefly discuss cryptographic hash functions. Because the properties of cryptographic hash functions give us some intuition about why Merkle trees are able to provide certain properties. So a cryptographic hash function is typically a function that is described as one way, meaning given some message, I'll pass it through some hash function and then get a digest. And because it's one way, there's no un-hash function, no way to take a digest and get the message that was used to create that digest. And typical hash functions include things like Shah or Blake or MD5. There are three properties that are worth bringing up with cryptographic hash functions. The first is what's called pre-image resistance. This is given y, some digest. It's hard to find some value x, a message, such that the hash of x equals y. This is effectively formally defining a one way function. Given some digest, I can't find the underlying message. And I'll note here that when I say hard, I mean improbable or infeasible. So for example, if a digest is 256 bits long and there's to the 256 possible values for a digest, it's not going to be possible to enumerate all digests to find some matching message. Another property is second pre-image resistance. So whereas in the previous example, we fixed the digest, in this case, we fixed the message. So given m some message, it's hard to find a different m, m prime, such that their digests are equal, hash of m equals hash of m prime. And closely related to this is collision resistance. This states that it's hard to find any two arbitrary messages, m1 and m2, such that their digests are equal. And it's worth noting here that collision resistance implies second pre-image resistance. Meaning if I can't find any two arbitrary messages that are equal, I won't be able to find two messages that are equal when I actually fix one of those values. So let's talk about Merkle trees and keep these cryptographic hash function properties in mind as we talk about them, because we'll get some intuition about why these Merkle trees can work the way they do. A Merkle tree is typically represented as a binary tree whose leaf nodes are going to be some hashes of input. In this case, it's d1 through d4. And every non-leaf node is the cryptographic hash of its children. So for example, looking at the left side of this tree, in order to build hd1, d2, I'm going to concatenate its children, h of d1 and h of d2, and then hash it. And then going up one level to build the root hash, it's the same thing. I'll concatenate the two children and hash that value to get the root hash value. And this property, this structure for the Merkle tree allows us to calculate what's called inclusion proofs. And this is a set of hashes that allow us to prove that some value is actually in a tree. So before we talk about what specifically can go into an inclusion proof, let's create a brute force solution. So if I want to prove that some value is in a tree, first I'm going to need to effectively pin the root hash. So when we consider things like verifying a chain of certificates, you have some leaf certificate, and then it chains up to some root certificate. And this is some certificate that you've been given or you implicitly trust. Maybe it comes with your operating system. And this is similar here. The goal of calculating a proof that proves that something's in a tree is that we'll eventually be able to calculate that root hash. And thinking about the properties of these cryptographic hash functions, because they're one way, I can't take a root hash and effectively figure out some values that would eventually calculate to that root hash in any probable way or any feasible way. So one way to calculate inclusion might be to give you every single leaf node in a tree. And you've also been given the root hash in some way. And then I can recalculate that root hash by taking the pairs of nodes, calculating the parent, and so on and so forth up to the root hash. And if I can calculate that same root hash value, I know that some element was in a tree. Well, the problem with this solution is having to give you every single leaf node. In this example, there's just four, so that's doable. But let's say there's a million or two million or a billion leaf nodes in the tree. That doesn't really make sense to have to calculate inclusion for every single node if you only care about one. One thing I'll note here is that in order to provide this root hash offline, typically one way that this is done is that the transparency log will end up having some signing key and then creating a signature over that root hash. So you don't actually have to fetch a root hash out of bands. The log can give you its root hash and as long as you have the verification key of the log provided out of bands, then you know you can trust a root hash. So let's take a look at now calculating an inclusion proof with just a subset of nodes. So like we said, the goal of calculating the inclusion proof is to verify the root hash. So I need its two children, which in this case is h of d1, d2, and h of d3, d4. And as part of that subset of nodes, I also need to have the hash of the element I actually want to check inclusion. In this case, it's d3, so I have h of d3. And what you'll see in this example is I don't actually need every single node in the tree to verify the root hash. I only need that root hash as two children and then looking down the right side, I don't actually need h of d3, d4 because I can use h of d3 and h of d4 in order to calculate that parent node. And what ends up happening is you only need the sibling of a node at each level in the tree and you only need the number of levels in the tree, number of nodes. So looking at this inclusion proof, all you need is h of d4 and h of d1, d2. And because you already have the root hash provided out of bands and you have d3, meaning you know it's hash, this is all you need for an inclusion proof. And once again, keeping in mind these cryptographic hash properties we talked about, there's effectively no way to forge some set of hashes given some initial hash, d3's hash, and the root hash. It's infeasible to be able to calculate some other chain. So let's not talk about transparency logs. Like I mentioned early on, transparency logs are built on top of Merkle trees. You can see the structure looks very similar but the primary difference is in the leaf nodes. Transparency logs are typically meant to be publicly audible, meaning those who are putting entries into the log or consuming them will likely want to know values that were recorded in this log. So typically transparency logs aren't just going to have hashes in their leaf nodes. They'll also have the values used to calculate those hashes so that the logs can be searchable. Because transparency logs are built on top of Merkle trees, it allows us to calculate inclusion proofs so we can prove that any element's in a tree. And there's actually some types of Merkle tree constructions that allow you to calculate non-inclusion proofs that allow you to prove that something's not in a tree. Just to give you an example, there's a type of Merkle tree called a sparse Merkle tree that allows you to calculate these types of non-inclusion proofs. Now, Merkle trees are not inherently immutable or append only. If you want to remove an element from a Merkle tree, that's fine as long as you recalculate all of the nodes, all of its parent nodes up to the root hash. So in order to have this property that transparency logs are immutable and append only, we're going to need to calculate what's called a consistency proof. Let's take a look at that example. So effectively, a consistency proof is showing that from some state where I've already verified consistency or maybe there's some initial state, if I go look at a new state, entries have only been, and no entries have been mutated. And the way this is done is effectively by verifying that there are some subset of hashes that allow us to calculate some root hash where we verified consistency previously. And those set of hashes exist also in the right side of the proof where we're verifying some new root hash. So if we look at this example, let's say that on the left side, we have some root hash where we've already verified consistency. And on the right, this is a new state where we've added some elements to our tree and we want to verify consistency from it. On the left, we need its two children, which is HD1D2 and HFD3. And on the right, we need the two children of that root hash, which is HD1D2 and HFD3D4. But once again, we don't actually need every single node in the tree, we just need these siblings. And we need that same set of nodes that was on the left. We need a subset, that subset in the set of nodes on the right. And once again, keep in mind these properties of a cryptographic hash function for why this works. Because if anything was changed in the tree, I would have ended up changing the root hash so I wouldn't have been able to verify what's on the left, that root hash where I verified consistency from previously. One thing I'll mention here is that transparency logs are not equivalent to blockchain. So if you're familiar with blockchain, blockchain is effectively built on top of Merkel trees and has these similar properties of being immutable and append only. But there's a difference in how we enforce consistency that they're immutable. Blockchain relies on things like proof of work and stake, where it's computationally expensive or maybe you need to put up some money in order to verify transactions on a chain. Transparency logs rely on a different mechanism, which is called gossiping. And we'll talk about that in a moment. So let's first build up this gossiping protocol for keeping logs consistent. In order to do that, first we're going to need what's called a witness. So like I mentioned before, transparency logs typically sign their root hash so they have some key that's known and in order to trust that root hash, I just need the logs key. Well, as a client, if I want to verify consistency, I can do it myself or I can rely on a third-party witness to check the consistency. So much like a client might persist some root hash to verify consistency from a witness can do the same and they create what's called a counter-signature over a root hash. So in this case here, we effectively have two signatures over the same value where the second one on the right here is a witness saying that I have verified consistency from this point. What witnesses primarily prevent is what's called a split view attack where the log effectively forks and presents different views to different clients. So let's imagine we've inserted two values, D1, D2 and into the transparency log and for whatever reason, maybe the logs malicious or maybe the logs misconfigured, it chooses to manipulate the second value D2 in certain cases and this results in an effectively a different tree. And if I have multiple witnesses who are asking the log for their latest root hash and verifying consistency but the log chooses to fork for certain clients, what you'll end up seeing is that most of our witnesses are counter-signing the same root hash, but one witness ends up counter-signing a different root hash and maybe to that witness it looks consistent because it's always getting this different view of the log. So as a client or as another witness, how do I know if a split view attack is happening? Well, one way is that as a client, I can ask all of the witnesses that I trust for these counter signatures and let's say I have the verification keys for all the witnesses, I can verify these counter signatures and then compare the root hashes and if I see any root hash that differs from some root hash that I've persisted, let's say from an inclusion proof, then I know that there is some split view attack and I can alert the ecosystem to this problem. Well, as witnesses, let's come up with some protocol that allows these witnesses to find out if other witnesses are having a split view attack. And that protocol is effectively called gossiping. And I'll mention that gossiping is a very active area of research within the transparency log space. So I won't talk too much about it, but roughly you can imagine that we can come up with some protocol where the witnesses share their counter signatures with other witnesses. And if they see differing root hashes, they can know that there is some split view attack or some misconfiguration. And once again, alert the ecosystem to this problem. As a client, beyond just monitoring the cryptographic integrity of the log, I also might care about what's in the log. I mentioned that logs are meant to be publicly auditable. And so as part of this, I might want to periodically query a log to find out are there relevant entries in the log that I care about? And this could include things like identities that go into logs or certificates or verification keys or artifacts. And we'll take a look at this in a moment as we look at the applications of transparency logs. So the first one I wanna mention is certificate transparency. Before certificate transparency came about, it was possible that certificate authorities that were issuing public TLS certificates could be misconfigured or maybe even malicious and issue certificates when a domain owner didn't actually request that certificate. And there was no way to basically hold certificate authorities accountable. And this is the goal of certificate transparency that the actions of certificate authorities can become publicly auditable. So as a domain owner, let's say I wanna fetch a certificate for example.com. I'll request a certificate from some certificate authority or CA and present some proof that I'm the domain owner. And then the certificate authority will write that certificate to a log. And those logs can be audited by monitors, both for cryptographic integrity that nothing's being deleted from the log and from the domain owner to see if this example.com shows up in the log when I didn't request the certificate. The log returns back a proof. This doesn't have to necessarily be an inclusion proof. This can be some sort of a sign value from the log that says I promise that I will commit something to a log. As a client or as a browser, I will then check this proof comes alongside a certificate. This is effectively an optimization so that a client doesn't always have to contact the log and ask for some proof of inclusion, though that's also acceptable here. And the idea that is that if I can present this proof, I know that the certificate's actually in a log and therefore the domain owner can actually verify all instances in the log. Basically it forces any sort of malicious behavior to be in the open. The next example that I wanna bring up that's particularly relevant for supply chain security is binary transparency. So instead of putting certificates in a log, I'm going to upload artifacts. So let's say I have some artifact that I maintain, I'm going to sign it and upload it to a log. And the benefit of this system is that it's going to ensure that all consumers receive the same untampered artifact because as a client, I will go request an inclusion proof every time I see some signed artifact that I wanna use and check that it's actually in the log. And there is no way that I'll be able to locally tamper with this because I'll check that it's actually in the log. And as a malicious actor, if I were to upload a malicious artifact to the log with my own signature, that client wouldn't trust it. We can even go one step further and say, let's say that the signing key of the artifact owner was compromised and someone was able to upload a malicious artifact that was correctly signed to the log. Well, once again as an artifact owner, I can monitor these public logs to see where my signing key is used. And if I see any cases that were unexpected, I can know that there was some compromise. And once again, you can have public monitors that are verifying the cryptographic properties of the log. One example of binary transparency is SIGSTOR, a project that I work on. SIGSTOR aims to simplify the creation of digital signatures for artifacts and containers. And it does so by providing a client to simplify this generation. And also some services. One of these services is a transparency log. This is represented in the middle here as a recor. And this transparency log allows you to upload binary artifacts and their signatures. One other goal of SIGSTOR is to simplify key management. So instead of having to manage your own keys, we're going to associate some ephemeral signing key to a long-lived identity. So effectively what this changes is your verification policy. Instead of verifying some public key, I verify some identity. And as a maintainer, I don't need to hold on to a signing key. I just need to make sure I'm in control of my identity. And this association's done with a certificate authority. That's at the top here, Fulcio. So the typical flow is as an artifact owner. I'm going to request some certificate from the Fulcio CA. And I'll note that this certificate also gets written to a certificate transparency log. So that as the owner of an identity, I can monitor this log to make sure that the certificate authority or maybe the identity provider is not misconfigured. The artifact owner will sign the artifact and then upload that certificate and artifact to record the transparency log, which can be publicly monitored. And then we'll staple all of that together, the artifact, the certificate, and some proof that comes back from the log. And then that can be uploaded to wherever a client's going to pull that artifact from. In this example I show, for example, a package repository like Maven or RubyGems. And as a client, I will then verify that proof to make sure that the artifact's actually in the log so that I know that the artifact owner can be monitoring the public log to see where that artifact shows up. And we can also imagine here in the bottom right of this example that the client might also ask the monitors or the witnesses for their counter signatures in order to mitigate split view attacks. The last example I wanna show is key transparency. And this is where four keys that are managed by key owners, they can upload to some public log associating their key with some identity. Effectively what this enables is the discoverability of user's verification keys in a way that presents the same view to all verifiers. This is the same with binary transparency except we're presenting keys. One other benefit of this system is that it presents a permanent log of all key changes. So let's say I need to rotate my key, maybe I've lost my key, maybe for some reason my key's expired. And I want to rotate it. Well, as a verifier I can look in the log and see this change happen. And then I can, for example, go to key owner and confirm that this key change was expected. And as a key owner, I can verify every place where my identity shows up. And if I see places where my identity is mapped to some key that I don't expect, I can know there was some compromise. And vice versa, maybe I see my key showing up associated with identities that I'm not in possession of. So the takeaway really from all of these examples is just that transparency logs force everything to be out in the open and present a permanent record of changes that occur. So let's switch gears and talk a little bit about privacy law.  Typically privacy law is about protecting a consumer from data collectors and giving the consumers certain rights. Some of these might include transparency around how the collected data is shared and accessed by the data collector. This could include also access to the data and portability so that I can take my data to other data collectors if I wanna switch services. This also might include the objection to data collection. We've all seen things like cookie pop-ups, for example. And also security requirements around that collected data, for example, encryption at rest and transit. The one I wanna focus on is around a right to deletion that's as a consumer, sorry, as a producer of data, I should be able to delete my data from a data collector. Typically privacy law is just about individuals but in this example, I wanna mention corporations too because I think it's worth calling out that there might be secrets that a corporation wants removed for some reason. When we talk about the information that gets deleted for individuals, typically we're talking about PII, personally identifiable information. This could include things like your name, email address, physical address, phone number or any other sense of information. For corporations, this might include business secrets. So thinking about the example with certificates, this might include some domain that's associated with an unlaunched product. Maybe the business didn't mean to issue a publicly trusted certificate and now that they have their domain is out in the public. Like I've mentioned many times, transparency logs are immutable and append only, which means what goes into them inherently can't be deleted. I think it's also worth noting that logs can be mirrored too because these are public. Maybe they're just logged for performance reasons. For example, to put the log into a different region so that it's more performant to contact that log to get inclusion proofs. So there is a question here around if I wanna delete data from one log, how do I also delete it from all the mirrors? Another interesting question is, do logs provide a benefit such that they should be able to opt out of a right to deletion? And there is a precedence for this already with some privacy law. For example, governments might be able to opt out of privacy law or healthcare. And there's some options to opt out for statistical or historical collection purposes. I don't have an answer to this. I don't know if they should be able to opt out but I think it's worth considering this as privacy laws continue to grow and evolve. But I wanna assume that we'll need some technical solution for this. And so that's where we come to building a redaction mechanism. A way of being able to remove data from a log when requested while not breaking the cryptographic integrity of the log. And these are the primary guiding principles for our solutions. And as we'll see in a minute, not all the solutions follow all of these. So the first guiding principle here is usability that the log must remain verifiable. So we cannot break the cryptographic properties of a mutable and append only for the log. Another one is I don't want the log to be overly trusted. I wanna make sure that we can hold the log accountable for redactions and make sure one, that it actually redacts the information and two, that it can't redact too much information. So the first two examples, I'm going to call destructive approaches and we'll see that these technical solutions don't work for keeping the log verifiable. The first might be, let's say I wanna delete D4 from the log, I simply remove that leaf node. In the example on the left, I just delete a leaf node. On the example on the right, I delete the leaf node but also recalculates all the parent nodes up to the root hash. Well, the example on the right fundamentally breaks consistency proofs as we talked about how those are calculated. For the example on the left, this ends up breaking when somebody goes to audit the entire log and they request every single leaf node from the log to verify that all the internal nodes are still consistent. And what they'll find is because there's no D4, you can't verify that parent node. So the solution doesn't work. Another option is simply removing a log. Now, this is technically an option, you could, but it's gonna be very disruptive to clients. To just have to remove a log for just one entry. Now one thing to note is that logs are typically sharded by year. This is a performance optimization to prevent the log from indefinitely growing and having to have a lot of storage for storing the log. So maybe if we keep our sharding window down very small, this might be a possible solution. But ideally I'd like to avoid this approach because it's somewhat of a scorched earth strategy. It is very disruptive to clients. The next two approaches I call prevention mechanisms. They don't actually delete PII from the logs, they aim to keep it out. And to give some examples here, let's say that we are uploading certificates to a log. Well, the first thing might be, well let's just not misuse certificate transparency and recognize that what goes into a certificate is public. So one example might be add some enforcement mechanism to make sure that you're not publishing private domains and requesting search for private domains. Another example might be avoiding putting PII into custom X509 extensions. And maybe we ask certificate authorities to do some sort of cursory check to make sure that PII doesn't wind up in extensions. Uploading artifacts, it's the same thing. Let's make sure that we're not publishing private artifacts. But obviously with both these approaches, this, these are just prevention mechanisms. Things still slip through the cracks. On the identity side of things, one option would be relying on pseudonymity. Having some mapping between some pseudonymous identifier and some public identifier, or sorry, some private identifier like an email. So maybe when I'm uploading some mapping between an identity and a key to a log, I'll use my pseudonymous identifier. And as a verifier, I'll be given some private mapping somehow. And I'm somewhat vague here because that verification policy to me is the primary challenge here. If I still need that private identity to verify things, how do I create that mapping? How do I distribute that in such a way that my identity remains private? Also, this solution only works for identities. It doesn't work for all examples. And once again, this doesn't actually remove anything from the log, it's just a prevention mechanism. One thing I'll mention here is there is some very interesting research around zero-knowledge proofs that might give us some ways of presenting pseudonymous identifiers in logs. But that's very active research. So the next two examples I think are most promising. So one example is that we build some log front end in front of the transparency log. And we have it be responsible for holding what's redacted. But the data actually doesn't get deleted from the log. So for example, if I wanna request D4 from this log but I wanna redact it, the front end would return some error. There's actually an example of this. The NFT marketplace OpenSea I found was the best example for respecting copyright takedown requests. So if some copyrighted artwork winds up in an NFT and it's requested that it be taken down, the OpenSea front end removes access to that NFT. But obviously that NFC still exists on the Ethereum blockchain. So you can just go view it there. There's also a lot of centralization in this example that we rely on the log to do the right thing. And as witnesses, we still need to verify the cryptographic integrity of the log. So we still need access to all the leaf nodes, meaning witnesses still have access to the PII. So now we come to the last solution which I think is the most promising. If we talked, as I talked about previously with Merkel trees, typically the data actually wasn't in the log. So what if we had that same approach here? That we move the data out to some database, or for example, a content addressable store where data can be referenced by some digest. And we have pointers to that data. And whenever somebody goes to request some entry from the log, the log resolves that pointer, looks in the database and then returns that with the proof. And the example where I want to redact some data, the log remains intact. It remains cryptographically verifiable because all of the nodes are still in the log. But the underlying database where the data actually is, where the PII is, has been deleted. And so if we try to resolve that pointer, there'll be no data there. And so the log can return some error that it's been redacted. Now like with our previous solution, this does put additional trust in the log. It does require that the log, for example, can't redact all elements. So in this example here, the log could just choose to delete D1, D2, D3, D4. And there's no mechanism to prevent that because those are not a part of the tree. So they don't have that same property of immutability. So potentially, and maybe this is a bit of a turtles all the way down solution, we create more transparency logs. And every time a redaction occurs, a transparency log publishes that redaction to this transparency log. Obviously that log also has to be monitored, but maybe it could be operated by some other party that doesn't operate the transparency log. And monitors can verify that there aren't entries that are redacted that don't wind up in that redaction log and vice versa. So wrapping up, it might be clear that there's really no straightforward answer here. And I think that's kind of an interesting part of this problem that I think the last approach is probably the most promising and if we needed to do something very soon, that's what I would recommend. But this is an area where there's an opportunity for more research and seeing if there is some other potential solution. But like I said, if we needed to do something immediately, I'd probably recommend some solution in which we move the data out of the log and have some way of monitoring when redactions occur. Thank you very much. I think we have some time for questions. Yeah, so the question is, what about being able to revert some transaction in the log? So in this case, I'd imagine that because we're mutating some state in the log, we're effectively removing an entry and so that's going to break one of the promises of being append-only. So are you thinking, yeah, so I think with that approach, the problem would be that something still exists in the log and is still publicly auditable. So even though we can say no longer trust this and for what it's worth, that would be a good mechanism for example, building revocation. So where we don't care about that there is PI in the log but we just wanna say no longer trust this for some reason. But I think if we want a redaction mechanism in which something no longer shows up in the log, that wouldn't be completely sufficient since we'd still have the data actually in the log. What are those? Yeah, so the question is in the diagrams and I'll go to one example, we talk about monitors. So we'll go to this example. So a monitor in this case, I think can play two roles. Either it's a witness, which is verifying the cryptographic integrity of the log or a monitor can be inspecting the entries themselves. So technically the artifact owner could also be one of these monitors or maybe it's verifying other properties. For example, in the case of certificates, maybe it's looking for weak keys in the log. So those are I think the two primary things, either verifying cryptographic integrity where you don't actually care what's in the entry just that the log itself remains consistent or doing introspection on the individual entries. Yeah, so the question is, does SIGSTOR have automated monitors? And that's actually a very timely and great question. So SIGSTOR is actively working towards a more general availability where we're gonna offer stronger guarantees on around SLO for example. And one of the things also is gonna be making sure that we have monitors too. So something I've been recently working on is a monitor that's based on GitHub actions. So it's very easy for anybody to set up and run to start verifying consistency. And then we'll need to figure out some mechanism for being able to notify people we could use things like issues. There's also some other work. So I'll mention here that our transparency log is built on top of a framework called Trillion. It's effectively a scalable Merkle tree. And there's a very interesting repository called Trillion Examples that has an example of a witness called OmniWitness that is effectively an easy to run witness that does these consistency proofs and then uploads those counter signatures to GitHub. So that as a client, I could then query GitHub or maybe we could also extend it to upload these counter signatures to somewhere like GCI. That's a really great question. Awesome, well thank you very much.


In this episode, we'll be talking about the challenges related to designing customer experience in a high-stress environment. We'll talk about how can you make strategy, high-level strategy work in day-to-day operations, and finally, how can you use customer feedback to change employee behavior. Here's the guest for this episode. Let the show begin. I'm Stine and this is the Service Design Show. I'm Mark and welcome to the Service Design Show. This show is all about helping you to design services that have a positive impact on people's lives and are good for business. My guest in this episode is Stine Ringwig-Marsal. Stine is the Service Excellence Director at the Copenhagen Airport, where she's responsible for driving the efforts to improve the overall customer experience. In the next 30 minutes or so, Stine and I will be talking about the challenges related to designing customer experience in a high-stress environment. We'll talk about how to make high-level strategy work in day-to-day operations, and finally, we'll talk about how to use customer feedback as a tool to change employee behavior. We post new videos here on this channel every week, so if you don't want to miss anything, be sure to subscribe and click that bell icon so you'll be notified when new videos are out. So that's it for the introduction and now let's quickly jump into the interview with Stine. Welcome to the show, Stine. Thank you. Awesome to have you on. I really like to have sort of the practitioners or maybe the clients of service design on the show because it's really easy to find the agencies, the consultants, the designers themselves, but you're a person who's actually consuming service design, so I'm looking forward to our conversation. Trying to use it, yes. Exactly. I'm going to ask you the question that I ask all my guests on the show, which we start with, and that is, what is your first memory of service design? It was, my first memory was when I worked as a user experience consultant in a small agency, and our clients started to ask us, okay, so now you've designed our webpage and we would like to design the experience that people get when they enter our call center or enter our store and to get the expert on that, my colleagues contacted the guru on service design here in Denmark, old son, Big Mac, and if I wasn't married to my wife, this guy would be the love of my life. He literally sort of sent me down this path that I was trying so inspirational in ways. Yes. Oh, wow. We need to get them on the show next probably. Stina, you've sent me three super interesting topics.  We're going to improvise to some interview, Jess, are you ready? I am ready. Okay. I'm going to pick the first topic, and it's really cryptic, so I'm really curious what you'll make out of this, and can you pick a question starter? This topic is called customer feedback. Do we have a question starter that goes along with this topic? My question would be, why use customer feedback? And obviously, when you want to do a design process in any way, you need to sort of know who you are designing for. Know their innermost emotions, or almost, and for that, obviously customer feedback is very valuable. Now, customer feedback is a lot of things. For me, what is important is not necessary to get the percentages of who said what, right, but to get a more qualitative insight in what is top of mind and the emotional most important thing for this person passing through a given touch point. And one of the things that I use a lot is the net promoter score, but sort of twisted a little bit, because for instance, it's very difficult to ask people, would you recommend your colleagues or friends or family to go through a security check? But so we twist the question in a way, but I still use the numbers in the sense where I look at who answered zero to six to see what should we try to get away from, and then who answered nine and 10 to be able to sort of praise the employees for whatever behavior that the customers are praising them for, trying to make that our standard. So to make, to sell to employees and an organization, the idea of why should we improve anything here or why should we work with this? It's super powerful to have the voice of the customer saying, we actually see you like this and be able to use that both as a as a carrot in a way where you say, when your business is love you, this is what you do. And that's awesome. And then to invite a work group in and say, all right, so this is the criticism that we get. How can we counter that without it being costly, without it being tiresome in the everyday operations? And so yes, customer feedback is super important. So we've had the topic of customer feedback come up quite a lot on the show, also related to airports. And one of the topics back then was, how do you gather that feedback? And this is like a two-stage question. First, of course, how do you get the feedback? And are you also looking at feedback that is outside of the doors of the airport? How do you take that into account? How do you mean feedback outside of the airport? Well, before somebody enters the airport or after they have left, is that also something you look at? But let's focus on how do you get feedback and what is the most powerful way that you've found to do that? One thing that's really important is to know that I am not the analytical department of the airport. We have an expert department on that. But for the purpose that I'm using feedback, obviously we have our overall satisfaction analysis that is an analysis where we also can compare ourselves to other European airports of the same size. But when I work with a specific touch point, what I'm interested in is to get specific feedback on that. And feedback that is not just a number per se, where we really see what is the emotional input of the customer. And can you give me an example of how you've seen that customer feedback has influenced decision making or employee behavior? Well, I mean, for instance, we have in the airport a number of staff checks, like when someone like me needs to go into the tunnels, we go through the same type of security check that a passenger does. And so in the staff check, we influence the way all of our business to business customers view the airport. Because for some of them, it is the only place where they actually encounter a CPH employee. CPH is the airport I work for Copenhagen Airlines. And so we did a survey where we actually didn't use a lot of money. We just had 10 interviewer hours. And then we asked customers on a scale from zero to 10, what was your experience of staff check today? And why? So they had to type, you know, was my experience. And we actually did the same survey towards employees and said, in your opinion, what is the customer experience going through staff check? And so we got both the feedback of employees and the feedback of customers. And based on that, I was able to facilitate workshops with employees that then help develop sort of our service principles for the staff check. And if you could dream, if you had all the money in the world, all the time in the world, what would you like to do with customer feedback? What are your dreams or ambitions regarding that aspect? Well, first of all, I don't think I need all the money in the world to obtain my dreams on this, because there's also a limit to how much data you can actually process. And that's one of the reasons I love just asking on a scale from zero to 10, what was your experience? And then why? Because then I get what, what is the first thing on that person's mind that day in that moment. And when we then have worked maybe six months with initiatives, trying to counteract the stuff that people are unhappy with and to standardize the things that people love us for, then we can measure again. And we don't need a gazillion interviews to measure. But what I look at then is, has the number one negative comment been erased from the comments from the customers? And if yes, then we have been successful. If no, I don't do my job well, if you know it. Yeah. So that's quite interesting, because in service design, we sort of have the notion to do a lot of qualitative research, dig really deep. We like to do observations. We like to do a lot of interviews and understand the complete world of passengers, patients, students, employees. And you're just saying, well, if you know the first biggest pain points, that's enough to actually start working and roll up your sleeves, right? Yeah. And I mean, you can do it in many ways. You can do it by hiring an agency, but you can also, if you need to do it cheaper, do it yourself. I mean, I could be up there, might be doing it. But right now, I'm talking from my chair. So just to say, the airport as a whole, we have a passenger department that works with behavioral design and many different departments doing different types of research. So for the field in which I work, this is the way I prefer to work when it's a touch point where we have employees involved. Obviously, observing gives you a lot of data as well. So I do some of that as well. We're entering a process where we're trying to design a new security experience. And I'm a very small part of that big, big project. But me and our behavioral designer from a different department will be sitting for quite an amount of hours and just watching what is the passenger experience here versus the passenger experience in another track where we do things slightly different. So I mean, the designing of the touch point also includes other methods than what I just described. That just happens to be my favorite. And I think adding to that, you said if you want to do it cheaper, do it yourself. And I think it's not only that it becomes cheaper, but if you do the research yourself, if you are engaged in the research process, it's just so much more powerful, right? You don't need to convince other people. You feel the pain of the passengers. Yeah, but I mean, but walking the shoes of the customers is always something that I would recommend. If you're looking at how to design an online payment sequence, try it out when you're done. But I do have respect for people working in huge organizations like myself where if we want to get sort of a representative image of how our customers experience us, I would have to be here 24-7. That would be very exhausting if I couldn't hire some amount of help for it. But for sure, you can do it. You know, you can scale it up or down depending on your budget, for sure. Let's move on to topic number two. Customer feedback. If people are interested, leave a comment on the video and we can continue their conversation there. But for now, let's move to topic number two. And I like this one already. This is the topic of high stress environments. Do you have a question starter that goes along with this one? What if you need to design an experience in a high stress environment? Like an airport. Yes, I think one of the main differences to working with custom experience in an airport environment versus, let's say amusement parks where I have worked before, I've worked in Tewoli and the Copenhagen Zoo, is that in an airport, everybody has some amount of stress. Except if you arrive really early and have lots of time. But most people are slightly stressed about going through security or am I going to make my flight... There's always that pressure, right? Am I going to be on time? There is pressure, exactly. And so one of the things I have really realized is that in many different departments of the airport, I need to design tools for the employees to be able to work with customers that have a high amount of stress. Because that's not always easy. So yes, that's not always easy. The obvious question is, can you give an example of these tools? And what do these tools do? Well, first of all, I mean, there are very sort of tangible. Example is body language. I always say to all of the staff we have that is passenger facing and passenger facing is security, but it's also our passenger service agents that are present on the floor. It's our baggage trolley employees, the ones that are making sure that the employees are in the right place because they get moved around a lot, obviously. So our cleaning staff, all of those, is that when encountering a passenger, the initial response from them often is about lowering people's stress levels. Most of our passenger contact is pleasant and fine, but a significant amount of employees come to me and tell me examples of really awful behavior, which is not because passengers in general are awful people, but they are in a situation that is immensely stressful for them for a number of reasons. So talking to them about body language training, body language, training, conflict, competences, we haven't done yet, but we are in the process of developing that. Very interestingly, I thought that security would be the place in which our employees would face the most stress, but it's actually parking. The staff that when you enter a parking house and then for some reason you are unable to get in, your car doesn't work, your ticket doesn't scan, then they will call to our service center and people are very stressed in that situation and our employees took quite a lot of, to put it politely, smoke sometimes. People would call and not even say hello or anything, just look back at that moment. So we've developed some guidelines for how do you answer a machine like that, because you're not face to face, so you can't calm them down with your body language. You've reached the operational center, they now try to say, hi, welcome, how can I help you to sort of make that, give the customer the sentiment that I will be helped. It may take some time, but they will help. So it's a little, maybe a silly example, but just to say the way you frame things, the way you say things, simple sentences can mean a lot for the customer and the other. This is super powerful and I can imagine that sort of employees have to get used to this idea, right? Because I can imagine somebody is hired to put trolleys back into the right place. He's not per se hired to deal with stress passengers, right? Yes, for sure. But I mean, I think the way you work with customer experience, go all the way back to your human resource department and who is recruited and how we frame the positions that we recruit. And I have to say that I've told a lot of our passenger-facing staff and most of them are super proud to be able to provide that service. They are super proud to be part of the airport. They are super proud to provide the best possible customer experience. And then there are some situations where someone like me goes in and does a workshop with them to counter a specific issue, a challenge, or with the customer experience. But it's not difficult to get people to work with me on customer experience because we live off of our customers. Of course, there's a team that say, why should I do this? And my argument is always, the only way people are going to perceive you as good at your job is if you give them a great feeling in their stomach. They will never know if you are the best security agent in the world. They will never know if you're the best electrician in the world. The only thing they judge you on is how you make them feel. They will never know the security legislation or whatever you need to make that ventilation system work. They will never know it. They don't have the same situation, but they will know how you make them feel. And so if we make people feel good, they will perceive you as someone who is good at your job. So everyone at an airport and maybe all in every service sector, everybody whose front line staff is sort of, part of their job is creating that experience, right? Always contributing to that experience. What is your, if you look back on the period you're at the airport, what is your biggest insight relating regarding customer experience and high stress environments?  The biggest lesson is probably that communicating to people in a high stress environment is difficult. For instance, we try the best we can to make people well prepared for the security process. We have a whole project team around that. I am, again, a small team and getting complex messages across, getting simple messages across is difficult. Getting complex messages across is really difficult. And so, yeah, and so the biggest lesson learned is basically, it's difficult and you don't always need it, but maybe you reach 75, 80 percent. I can so imagine that one of the design principles is you can sort of try to prepare people upfront, but eventually it comes down to the moment where it happens. Yeah, and the reason we want people to be well prepared for the security check is that if they are not, then the person behind them will stand in a queue and be unhappy. And so we try leading up to the security checkpoint, we try to communicate as much as we can with crazy colors and simple icons and all because we're communicating to everyone, all our customer groups with Chinese, Spanish, English, everybody. And so the messages have to be simple and precise. And at the same time, sometimes it's a very complex message that we try to communicate. How much liquid are you allowed to have in which size of container and how should you pack it? And, you know, it's just, it's just very difficult. High stress environments, super interesting topic that we'll sort of wrap up for now and move on to the third one. And I think this one sort of builds upon what you just ended with. And this one is called making strategy work. All right, let me see. I will then choose this. All right, how can we... How can we make a strategy work? So I would be talking obviously from making the service strategy or the customer experience strategy and given touchpoint work. And honestly, what is most important is the process of developing that strategy in a sense where I will set a given target maybe for an area or, you know, consult with the manager of an area. Do you think this is the words we would like to use? This is the place we want to be? If they agree, then take in the employees and use them as your partners in developing it. So that's one thing, when there is actual employees involved obviously. And then using customer feedback is great, but to make the strategy work, you really have to understand the everyday work routine of the employees in order to understand what might be the barriers to implement whatever you think is a great idea. An example, when I just started in the airport, I took a bus ride with one of our bus drivers. And this was a bus picking up passengers plain side. And so I had this idea in my head that it would be great if when we pick up the passengers we could say, oh, to your left you have terminal three and there's this building project going on right now and that will lead to sort of a tourist guide of the airport while driving to the terminals. Not that our rides are that long, but still just a little bit of giving them a great experience. I thought that was such an awesome idea until I had been in one of those bus and saw how much those bus drivers have to watch out for driving in our side. So if I hadn't done that, whatever strategy I would have developed would have fallen completely on the floor for safety reasons. So just an example of how involving the employees in your developing work is important, but it's also important to actually walk their shoes. So walk the shoes of the customer to understand what experience are we giving them and then walk in the shoes of the employees to see what are the barriers and what are the opportunities in terms of implementing whatever it is that needs to be implemented. And another thing I think is crucial when you want to implement strategy is to understand the manager that has to implement whatever it is that you're bringing to the table. In what way? I use a lot of time trying to see how can I best assist this manager to perform towards his manager or her manager and also to look good in the eyes of the employees because that's really what motivates people. And so I will do everything from making the presentation this person needs to use and writing the sound of the email that could be sent out to employees to make sure that that manager isn't sort of dragged down from the daily operation by all of the work that I now as the Service Excellence Director tell them they also need to do. So I'm really involved in all levels and I think to all levels of the organizational hierarchy if you want and I think it's important to provide that support and to think of yourself as the person supporting others in their performance. That's exactly sort of what I wanted to ask you is do you feel your role has become much more of a facilitator and maybe of a guide, a compass that helps people to do the right things and facilitate them to do the right things? Oh for sure. When people ask me you know what is behind the title of Service Excellence Director I said it's a director in the sense where I direct people in the right you know in the right sort of direction and on that way I try to help them as much as possible. And so and so yes I'm definitely facilitating the process of working with customer experience across the organization. I'm also I'm also really curious sort of making strategy work that is translating it into operations that's that's one way but I'm really curious if you also see it happening the other way so is strategy emerging from operations? Is it like a circle? I'm sure I mean when I talk strategy right now I'm not I'm obviously the work I do is a leg in the Copenhagen Airport overall strategy yeah but when I talk about rolling out strategy I'm not talking you know as a human resource manager trying to develop a company I'm trying I'm talking about the specific touch points that are you know related to the Service Excellence program if you will. So yeah sorry what was your question again? Well the the question I can from strategy to operations that's one way but I'm also really curious if the strategy you use comes from operation so if it goes the other way around I mean when I say that I do employee workshops to get to get that input shapes how the end strategy you know tries out yeah yeah yeah so strategy is always like a translation of it mirrors what maybe is already happening in operations and then formalizes it or takes it to the next level right? So something like that. I mean an example one specific touch point that I worked with had the major complaint not complaint but you know on a scale from zero to 10 when I looked at the zero to six comments the the most frequent comment was there's too much private chatter between employees and so what then happened was that the employees said all right we think the same thing but we just don't like to tell our colleagues to be quiet because I'm going to be working with him or her for for eight hours but so they set a sort of a zone saying okay from that door to this place if there are customers within that space we include the customers in our talk or don't talk privately and so that was a way to make it very clear you know when is private chatter between us acceptable and when is it not because it wasn't because employees were trying to give a bad customer experience that they would talk between each other there's just a big difference between when do I feel it's embarrassing to talk about what happened during our last party and when do you feel it's embarrassing you know maybe for you it's 10 meters and for me it's one so this was a way to have you know the input we got from customers and actually the input we also got from employees become part of the strategy of how do we want to manage this specific touch point super interesting to work work in that way um Sina and you also get the opportunity like all the other guests on the show to ask us a question the viewers the listeners of the show is there anything we can maybe help you with or think about you want to challenge us is there anything on your mind no I mean if if I were to to ask the viewers and listeners I would love to know what was your best airport experience ever and why and especially the why is important I guess exactly um thank you so much for sharing your thoughts like again I'm really always interested to hear stories from people consuming and using service design instead of just us designers so Sina thanks so much you're welcome so what is your biggest takeaway from this interview with Sina leave a comment and let us know and if you enjoyed this episode don't forget to click that like button and share this video with someone who might benefit from the things we've just discussed if this is your first time here on this channel click that subscribe button to be notified when new videos are out thanks so much for watching and I look forward to seeing you in the next episode