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(upbeat instrumental music)
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This is our fourth year
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of, um, running a sustainability report
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or a carbon emissions report, um,
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that looks at corporates, about 2,000 of them.
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I'll go through the details in a second.
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We do it in partnership with CO2 AI, um, and I'll let Dmitry perhaps introduce CO2 AI a little bit
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before we get into the details of the report.
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So, ah, CO2 AI is a carbon management emission platform
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that helps you to compute your emissions.
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Actually, you did a great job explaining what we do.
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We focus, not on the specific problem like you,
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but we're rather trying to help big corporations
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to measure their emissions across their - all scopes,
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across all their, uh, data, and to then drive the carbonization,
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try to find, uh, best levers that can help you
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to reduce your emissions.
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Ah, we're a very young startup that was originated inside BCG
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and was created by BCG originally.
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Now, we're an independent startup,
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but we already have quite cool logos,
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that's companies that's working with us.
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Ah, you know some of them - some of them are less maybe public known,
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but they're huge corporations, like Cisco, Reckitt,
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which has challenges especially in the scope 3.1
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with a lot of a lot of supplies that you need to help
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to decarbonize.
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Um, the carbon emission survey, ah, this year
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roughly covered, um, 2000 organizations.
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Um, I think the interesting, um, factor here,
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it's about 40% of global emissions.
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So the data we're seeing is representative, ah,
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of what's out there.
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And then you can see the spread across geographies
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and industries.
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Um, if you have an interest in geography
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there's some really interesting insights about countries
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that are actually leading on, um, measurement and reduction.
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and it is not the countries you expect, um, which I think is an interesting, um, set of, ah, results anyway, um...
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Ah, it's a bit of a sad picture... so, um,
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and I think this is where the power
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of the year-on-year comes.
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Um, only 9% of organizations
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are comprehensively measuring scope one, two,
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and three, right?
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That is scope three of stream and scope three downstream.
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But that's eroding.
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Um, that's not the one that scares me the most.
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Ah, the middle and the last one are perhaps the problem.
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Um, targets... so about 3% of organizations walk back their targets.
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Um, we see this, right?
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Many of our clients have come to us and said,
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the target's too difficult, I can't reach it.
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Um, if I keep growing the way I do,
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it's, I'm really gonna struggle.
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And so they're walking back their targets.
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Um, so I think it's an unfortunate, ah, state of play
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and then, um, only 11% are reducing in line
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with their ambitions, right?
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So that's... I think where, ah, hopefully technology,
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as we'll talk a little later, green technology can help us.
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Now, the bright spot... there are companies
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that actually are decarbonizing. Great.
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And the leaders of them, so 25% of the companies
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that are decarbonizing are getting 7% or more
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of their revenue and financial benefits -
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and I'll say net financial benefits,
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so after their investments.
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So we're talking about 200 million bucks to the bottom line.
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Okay, finally, a sigh of relief.
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Like you can actually make money
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and decarbonize at the same time.
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So it's not just, you know, moral high grounds.
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Um, the main way they do it is they save cost at the same time as they decarbonize.
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And and many of us will hear this with our clients,
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whether you swap, um, old boilers for new ones,
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whether you reduce your footprint.
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Um, a lot of it, and we see this in the MACC curves.
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I don't know if all of you know what a MACC curve is,
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but we generally see the first 20 to 30%
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of initiatives you have be cost - cost negative.
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Um, so they are really, really impactful.
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Um, so this data just essentially prove
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that, that it's happening on mass scale, um,
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and we are hoping more folks hear this message
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and more leaders hear this message, um, so they understand
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that there's a really positive ROI on the money
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being put forward to decarbonize.
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Um, I'll hand over to Dmitry in a second,
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but what are companies that lead do?
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Um, and this is where you'll see Sylvain's famous number
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that he, he suggested earlier.
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They do a bunch of basic things, right?
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Like, okay, you need to measure, um,
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you should report and have a target.
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But where things get really interesting
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is if you're gonna lead in this space, you need a plan.
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Um, and, and this comes up more and more now
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and more of the statistically significant data indicates
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the climate transition plans and their importance.
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You need product-level emissions
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'cause actually sentiment of what you buy in any,
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whether it's grocery store or vehicle
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or whatever, really matters.
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So you knowing what the carbon footprint of something is
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and then making an informed consumer decision
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makes all the difference.
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So that's the four times.
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So those that lead are four times more likely
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to measure product carbon footprints.
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It is far more difficult... I think Dmitry tried to code it... He'll tell you all about it if you wanted the break.
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Um, and then they leverage AI.
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Um, we gotta get some shortcuts in, right?
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People are trying and waiting for data to be perfect
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and they want the perfect measurement
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and, you know, it just doesn't exist.
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And AI helps them get there a bit faster
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and it helps them get going, um, and and that's a really big unlock.
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Um, and with that, I'll hand over to Dmitry
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to take us through why.
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So essentially, you saw on previous slide,
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there are two levers that helped a lot companies
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to decarbonize faster.
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It's AI and product carbon footprint.
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Essentially, the reason why AI is so helpful here,
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companies have millions of activities,
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you called it what you will multiply
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and impacts before... abservations... we call it activities, but it's essentially the same thing.
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And usually big corporation have millions of them.
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Oh, I bought this amount of collaborative electricity
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in this day and this month, or I purchased these items
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or I resolved these like thousands of items.
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And you can imagine, especially for retailers,
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it's very, very hard.
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They sell hundreds of thousands of items every day.
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Ah, they're different.
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And you need to understand where the hotspots are,
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where the problem is.
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You don't need ... you need to know where to focus.
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And AI gives you an ability
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to quickly match those activities, observations
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to emission factors, impact factors
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to understand your CO2 emissions.
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After that you can go into more deeper, you can try
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to maybe estimate a bit more accurate your hotspot,
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but you need to know where you need to estimate.
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That's probably the biggest unlock.
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Without it, you really cannot say what to do next.
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So you got your total emissions, but what to do with it?
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You really need to understand,
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oh, this item is my biggest hotspot.
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I want to work with supply of this item to decarbonize it.
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(Dmitry faintly speaking)
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And second thing is product carbon footprint.
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Essentially, you... there are different levers why it's important.
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For like producers of product, of course, it's important
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to understand what's coming inside of it.
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What can I reduce?
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What can I swap maybe potentially
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for less carbon-emissive item.
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And for example, you're, I don't know, a beer producer,
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you want to do it at scale.
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You have 20, 30 types of different base, maybe hundreds.
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So you want to have some methodology
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that helps you to do it at scale.
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So you blend AI and matching with different emission factors
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together with some structure on how to aggregate this data
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and to show transparency as you were presenting before, ah,
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to your customers in order to convince them
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that you actually have really great products
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in terms of the carbon.
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Um, can I go next, please?
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And one of the examples we worked with repeat-recently
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on, first, estimating their emissions
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and, ah, helping them... um... reduce them... ah... over time.
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So the problem is that they have 25,000 products
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and there is way more ingredients coming into creation
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as of those products.
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You need to estimate them to see where your hotspots are
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and create a plan that can be executed across the company.
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The last bit is actually also very, very complex
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because it's a big corporation.
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You have hundreds of users internally,
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which can affect, like, uh, uh, this equation.
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So you need a way to people - for people to communicate,
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to set targets, to involve your supply chain into it -
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to potentially, ah, talk with your echo design teams
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how to change this in the future.
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So all of this is a very, very complex part
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with a lot of data to play with.
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And it's data that, it's more granular than financial
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because you need to actual splits
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of what is inside your items.
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That is very hard to make, very easy surfaceable
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for also non-very experienced users.