Sending Google the Wrong Data, On Purpose - Inderpaul Rai

In this episode of Google Ads Unleashed, host Jeremy is joined by Inderpaul Rai, Director of Paid Media at WeDiscover, to dive deep into some of the smartest, most unconventional strategies in performance marketing. 

From feeding Google ‘wrong’ data to improve bidding accuracy, to overcoming the limitations of PMax and attribution windows with predictive modelling. Learn how AI and data science are being used to solve complex problems like underperforming feeds, inflated conversion values, and long purchase cycles. 

Find Indi here: https://www.linkedin.com/in/inderpaul-rai/

Get your free 30 minute strategy session with Jeremy here: https://www.younganddigital.marketing/

Scale your store with 1:1 coaching: https://www.younganddigital.marketing/1-2-1-coaching

Transcript

So hello everyone, and welcome back to Google ads. Unleashed another episode. I'm finally back from my honeymoon from the US. So you've had a few recycled episodes and a few pre recorded ones. So you might be wondering what the hell is going on, because I missed on out of GML, for instance, I think I'm the only one who has but I'm gonna make up for it with you, because finally, we've been able to make it happen. A couple months ago, I told you about my experience at hero conf, and one or the other might remember I mentioned one or two talks at the conference which were really, really interesting, and from which I've actually drawn a couple of insights already. So I had to speak to the man who sort of inspired me to do so, and which is why I'm super excited to have in the Paul Rai on the podcast indie I take Indy is better, right? Yeah, easy. It's an absolute pleasure to meet you. You are the director of rediscover, a London based performance marketing agency with more or less a bit of a more of a twist on data science and on sort of leveraging its own data. But you are the best person to obviously introduce yourself and tell me a little bit more about what we discover is about how you came what how you came to, to to the business, etc, etc. So welcome. Really appreciate it.
Thank you for having me. Yes, I'm indie. I'm director of pay media. We discover. I've been at we discover for about 18 months now. And I've been in industry and paid search since 2012, and 13, so quite a long time, and I started, started agency side. So I worked for an independent agency for about two and a half years, moved up to a network agency, and was there for about year and a half. So like a big like media agency, which looked at not just PPC, but and not just performance marketing, but offline, like TV, radio, and how that works for a big brand alongside paid marketing, which is quite an interesting experience. And then I was there for a year and a half, as I said, and realized actually in order to do great work, especially with paid search, you really need to speak to the teams that are responsible for the conversion journey after you've driven the traffic to the website. And typically, when you work for an agency, you don't, or certainly back then, you didn't, often speak to those teams. So analytics teams at brands or like SEO teams, product teams even, even like merchandising product teams in terms of not just the digital product in terms of website, but the actual physical products as well. Brands like that,
I feel that's an unsolved problem forever, right? I feel, especially the bigger businesses get, and all agencies or whatever, there's always going to be this issue. I don't think I've ever been at one business which has solved this problem forever. I don't think that's ever existed.
Yeah, exactly. So, yeah, that took me to a FTSE 100 called footsie, 100 company called RS components. So I was there four and a half years in numerous roles. So I think my role changed every single year through various restructures. But, yeah, started managing their PC. They did everything in house. So building a big in house team to actually, I'll tell you, when I first started there, that the accounts were very nascent, so I've joined them in 2017 but I'd say the accounts were very much like 2010 1011 like all manual, like bidding and no automation or anything like that. So that's quite a good experience, like using my 86 expertise to build their capabilities, both from people and then also technology. So then my role changed naturally because of that. So I then headed up there all the ad tech, not just across PC, but display how they do affiliates, that sort of stuff. Then moved into martech role where, like the head of the analytics team left after a number of years, and they basically joined his tech team with my tech team to form one marketing technology ecosystem, essentially for the group, which is quite interesting, because the analytics team had built all their infrastructure on Adobe technology and the Adobe stack, whereas I built everything on the Google stack. So that was one of the reasons, actually bringing it all together and having an aligned approach just made it easier for the business to actually, you know, join up marketing with performance marketing, essentially. So that was quite an interesting time. And then I left them for a startup, an early stage startup called you furnish.com which is a comparison website for furniture. So I was there for about two years. Yeah, let's say like 20. I went from a company of 6000 employees to 20 where, like, everything was on me with regards to performance, marketing analytics, and they built that stack and and their marketing function, essentially. So that was quite an interesting time as well. And then, yeah, say 18 months ago or two years ago, rather, yeah, the CEO of we discover really. Out to me is actually one of the talks I did, one of the first talks I did in 2022 which sparked the conversation, and at the time, hadn't heard of weeks cover, so we discover, started about five years ago. So literally, in the week of the pandemic, the first lockdown, which is probably the worst time to start
an agency, or the best time,
yeah, where you look at it, but yeah, so Byron CEO, he founded we discover with another chap called Adam, who's our CTO, and they both used to work together at Deliveroo. So they came from an in house background, which was quite an interesting twist, like an agency being started by people who'd never worked agency side. So that immediately kind of catched my attention around. Having been connected with Byron for a while, I'd seen some of the cool stuff that they were doing, particularly with a tech focus, which is something throughout my career, like ever since, like, Google Ad scripts became a thing over 10 years ago. Like, that's the sort of area I've always naturally veered towards. So outcome
just always had, like, a natural interest in it, or just like, sort of overall, sort of background previous to that as
well. Yeah, and I've always, I've always been more excited about, how can we make things better, how can we do things faster? How can we free up people's time and actually spend the time doing more valuable work? So that's why my interest has always veered towards that. Like, before I even got into marketing, I'd always been interested in computers. Like, originally, my career plan was to be a programmer, and then I realized, actually, that's not really where my skill set lies. So kind of veered more towards marketing, but still had that, I guess, technical sort of aspect and view of marketing as a result of that, which is where not many people did when, when I first joined the industry. So yeah, it naturally led me into situations and roles that, you know, leverage both of those aspects, both technical and marketing. So yeah, I hadn't planned to come back agency side, but the the opportunity that we discover, and the type of agency that had been built and definitely captured my attention. So I mentioned there's two co founders, Byron and Adam. Adam is he manages the technology function that we discover. So the agency has been built. I'd say about 60% performance marketers, 40% data and technology. So think like engineers, data scientists, analysts, etc. So we have a very cohesive approach when it comes to running performance marketing campaigns with our clients. It's not just running their PPC activity or supporting their in house team with strategy. It's actually utilizing technology from the very beginning to solve some of the challenges that you may have. And there's been a lot of interesting challenges. Certainly as when we discover started and and typically now, we still attract a lot of these clients. Is a lot of clients that have that very long purchase cycle of, you know, we work with car well, for example, who you know, from the very first click to someone actually buying a car could take two years,
which obviously, with even just traditional in platform, attribution is impossible, right? 90 days. So
you have, like, your max of like 90 days attribution window within Google Ads itself. So how do you actually optimize and and understand the activity that click you're driving today actually results in a sale in two years
time. Well, night men, you basically can't. You have to push the buttons, wait for two years and then, yeah, no, that actually worked or didn't work, and then make changes.
So yeah, like, like that for that example, like we've created things like predictive algorithms. So like, for this click that we're driving today, we estimate it's going to drive X amount of revenue, and in time, like the model will see, did that actually happen or not? And then fine tune so, and then plugging that information to Google straight away, then allows Google to optimize today, rather than having to wait. It can't wait two years. It can only wait 90 days before it makes a decision. Decision.
Essentially, that's really amazing. So, you know, you know what I often think about Google, a little bit like, if any, any kind of plaque could be meta, could be anything. Is we all just cook with water, right? We don't. We all have the same tools. It's like everyone who plays music has 12 notes right in a couple of octaves. That's it, and you can put them together in a right, in a certain combination. But there's not unlimited ways of using what's at your disposal. So what fascinates me, and that's why I want to have a chat with you, is that that yet, with a limited tool set, if you like, you can still be so creative to get the machine to what you want it to do, and to also get useful insights and useful data out of it. And I feel like too few people really. Really think about that when they start a business, when they look after a client, when they try to optimize their own business. So I find that super fascinating. So the two talks that I've seen from you, one was, I'll be perfectly honest, I can't quite remember the title, but basically it was about you were, I think it was leads for a for, I think it might have been cars as well as some sort of very high average order value business. And you manipulated the conversion value data to eradicate outliers, to keep the algorithm more stable, is that kind of, is that a fair description of what the talk was about? Yeah, yeah,
bang on. Yeah, exactly that. So yeah, I think the title of that talk was nice and click bait when it's right saying Google adds the wrong data.
Yeah. I mean, which, in essence, is what you just described as well with that predictive model, right? It's fake data realistically, right? It's not real. So you just, but you kind of, it's still useful, right? So it's a super interesting way of thinking about solving problems like that, which I'll be honest, is a little bit above my capability. I think I told you this before we started hit the record button, but yeah. So tell me about maybe that case that was super interesting and kind of what is your approach when you guys look at things like, what's your How does your brain work in a case like this? To find solutions like that. Yeah. So
as I said, like, because of the team that we have, like the technical team, as well as the performance marketing team, like we're we're working as one team together to solve these problems, and we have people that have had the experience and the education of things that are outside of marketing, right? So like data science, practices, analytics, etc, etc, whereas previously, in my previous agency experience, 10 years ago, it was all on the performance marketing team or PPC team to try and figure out these solutions where it's like, you don't know what you don't know. So everything, we approach each client very individually. So yeah, we approach every client individually and when they have these unique problems, typically, a client has enlisted us because they have unique problems that they've not been able to solve themselves within Google ads, or agencies that they've worked with haven't been able to solve within Google ads as well. So typically, we would find a problem if we would work on it, and if we find a solution, which is great, we are able to replicate that across others. So like the value outlier one you mentioned, that was the first time we'd encountered that sold for it. It's now something we look at for other clients that have big ticket, high order value bookings, or orders like that's one of the first things we look at. Now we don't have to go through the whole process again.
Yeah. So, for instance, recently, we had a client selling engagement rings, and what had happened was that we regularly had extremely high average order value purchases, because naturally, some engagement well, most were in the few 100 quid, right to maybe one or two grand, which is fine. And then some were, of course, if you get some sort of baller who purchases, like a 50 grand ring or something like that, I would completely mess up, like, not only reporting, but lots of other things, right? But sometimes you'd get fake orders as well. Which, of course, would be, yeah, which, which are not, not optimal.
Yeah. We used to, when I worked at Rs, components we used to find in our Adobe analytics, like a million pound order come in, which is just impossible, and it would obviously be a fake order, and we'd be able to remove that from, from Adobe amp, from Google ads, to stop Google ads, or data exclusion, or whichever way you want to do it, yeah, but yeah, it's not just fake orders. There are real orders that can basically throw out your bidding. And actually it's more beneficial not telling Google not not to optimize those conversions, or don't even show them to Google in the first place, because we know that it's not going to give Google the right signal to optimize to like the example, the case study that I presented, that you saw, we found that it wasn't Google finding these high value bookings and optimizing to it. If it was, it was Google finding them and not trying to Great. We'll let it do it, and we'll take the instability of CPCs while it's trying to find these people, but it was these bookings are being inflated by the client themselves, because they're offering or actually not offering. They were they had very low supply in very specific geographic areas, and so they whacked up their prices massively for that short period of time. And then some people would still place that booking anyway and prepared to pay for that. So it wasn't something Google could influence like they had no idea about that, and it couldn't it. Influence towards it. There was no benefit to sending Google based orders, because it's going to try to find people that doesn't really exist. Essentially, yeah, it's it was a problem. Obviously, we didn't notice it straight away. It's something like, we kept seeing, we kept seeing this pattern every and it wasn't even a pattern, because it was just happening randomly, like every, yeah,
it took quite a few sort of, because, yeah, no one's like, a magician, right? Like, you can't, like, instantly spot stuff like that. It has to happen a couple of times for, for, this is strange. Every time. I don't know. CPC is, like, ramp up when this happens. Yeah, yeah, exactly. Mega interesting. So the talk then that kind of pushed me over the edge to reach out to you and to walk up, because is was, in the end, the talk that you then give at brightness, SEO slash hero conf, I think, two months ago or something. And you talked about because e commerce is obviously the game that we love most here on the pod, you basically looked at a business and they always struggled with, how do I set up my ad account for optimal success? Big challenge lots of people have all the time. I've done plenty of sort of tutorials as well on this pod for like the most common businesses that we encounter. But this one was very special. So as this is a case study, I think, publicly available, I'm sure you can probably lay out what, what was
happening then. Yeah, so this one was, again, a very similar situation, like with many of our clients, is they had a problem they couldn't solve, and for them, they just, they've been trying to say it's an E commerce client. They've been utilizing performance Max feed only, which typically would work for many businesses, but they couldn't get it to work, and certainly not to the same standard that of success they were having with standard Google Shopping. Um, previously, so they, they'd spent a good 12 to 18 months trying to figure this out in house, and they couldn't. Then they
utilized with a team as well. So they did have, like, one or two PPC guides, yep. Okay. Then
they utilized a PPC tool, e commerce platform that was out there that specifically optimizes to p max to solve their problems. Didn't work as well. So then they came to us about it, and again, we probably spent a good couple of months trying to figure out, why is this not working for them? No matter how they segmented their campaigns, were they segmented by category? Were they segmented by ROI? They were getting through Google Ads price points, even just nothing was working for them. So what we eventually came down to was, again, utilizing our tech and analytics team to try and figure out, okay, under the hood, what is happening here, and why is it not working? Was that we found that there were two things going on. So one, Google, it would find 10 to 15% of products that were working, and then only push those products and it would ignore the rest. This client had over 300,000 products. So okay, you got quite a few. You got 20, 30,000 products that work, and the rest just getting pretty much no visibility. And then what the client is trying to do is they're trying to push spend across the other 80% but the problem is not all of them going to work with Google, and you're going to waste quite a lot of money doing that. That's what they're finding. So they ramp up, then scale back down, because they're not getting profitable return off the back of it. So so one Google's being lazy, going after the easy winners, and not not testing out the other products to see what works and what doesn't, or certainly not doing it in quick enough time to scale up this client's activity and then to Google just doesn't know what other products gonna work. That's probably why it wasn't testing them. So what we did was we looked at the back end. So we looked at their products, the attributes of the ones that are being successful within Google ads, and then looked at the products that they get in traffic outside of Google ads. So, you know, through SEO, through direct to website, looked at the different combinations of like. These are the products that are driving, sales driving, you know, people are adding them to basket. And then looked at what the common features of the successful products, so price point, delivery times, reviews on the on the website like
to do like, just do like, sort of large scale, sort of like a qualitative analysis, way with just so, oh, you know, these are the features we've seen here on this part of this and this and this. Just let it run through chat, GBT even, know something like that and see what, what, what do all these products have in common? Or what was the process there? Yeah,
we used a few data science techniques around it, like utilizing Python to help the analysts to figure this out and the relationships between those attributes. And then, yeah, we built a system based on those attributes, those common attributes. Okay, so you. These are our best sellers. These are our mid tier products. These are the ones that they are profitable, but they're not existing the same attributes of the successful products. So if we could sell these great but if not, we're not going to push you so hard, because we know that the chances of success, in terms of conversion rates, are quite low compared to these other ones. So off the back of that, we probably were able to get about 30% 40% of the feed, where we say, actually, we can put as much spend behind these as we like, within our target ROAs premises, and they are going to be largely successful within Google ads from a conversion rate sector. So we don't want to waste our money there. And then we have a set of products where it's like, okay, we'll keep testing Google. We'll put spend behind it, not as much as the other products, but we'll keep doing that until it reaches those parameters where they've been added to basket X amount of times or converted X amount of times, where they can then move into the top tier or mid tier, and if not, then they stay there as a seed, essentially. And then we have a list of products that we just consider junk, like there's just no point advertising on it whatsoever. And what this client did, actually, is they worked with a CSS who are based based in an affiliate CSS, where they they pay for the media spend themselves so they only make money if
so many who pay commission per sale, risky rents or something like, yeah, yeah. So
completely risk free from that. They know they can't, they can't afford to bid on these products ourselves, like, give it to them. If they can make sales on it, okay, we'll pay for pay for that. But if not, fine. So that's the approach we took and it, yeah, it was super successful, as you saw from there. Yeah,
that. That's insane. So maybe one, one question I have for you, potentially, what your views on do you think Google? Because obviously I'm not smart enough to know how Google so an algorithm works, or just see the same pattern, which is why we often create sort of, you know, like zombie or or sort of underperformer campaigns with different targets just to give those products a chance anyway. Do you think Google, like perpetuates the same 1015, 20% of the product base, because it's some form of path dependency, because, based on recent performance, those are the only products we can give ad spend to like, sort of assure some sort of return on ad spend goal, or what's, what's the reason behind that? That because you think Google would be at some point, got to be smart enough to see, you know what this product that hasn't had any visibility has got to be put to the test at some point so that we can spend isn't Google like sort of shooting itself in the knee by doing that. I've always wondered that, because very often I get asked, why does it work? And I just know it works, but I can't explain why it works. Does that make sense? Because you think Google would figure that out themselves, that you wouldn't have to force them to. If that makes any sense, what I said,
Yeah, it's a good question. I think it's partly Google, but it's also partly ourselves. So we kind of you go into a bit of vicious cycle. It's like, you need to make the activity more profitable, so you tighten up the target ROAs, and the tighter you put those limits, the lazier Google's gonna be. So it's not gonna it's gonna like, basically that that net, it's gonna bring it back towards what's gonna work and what isn't. So the looser you make the targets, the wider that net is going to get cast by Google across your products. But on the flip side, you can't afford to do that, because, you know, 300,000 products if you cast out net too wide and spend too much, is is not come going to come into the return on ad spend that you need to be profitable as business. So you kind of in a catch 20. See there, as is Google.
Yeah, I'm gonna, for lack of other words, I'm going to ask, like a, I say provocative because it's not going to provoke you. Or rather, rather, the, rather, the software that I like to perpetuate quite often, that is profit metrics. You're very well aware, probably, of profit metrics. What does that lack, compared to, for instance, an approach that you, you guys put because, for instance, we've often solved that kind of issue, or just various profitability levels, right? Per order, per product, people sometimes order him a lot little, or there's sometimes quantity discounts, which, of course, gives you bigger margin than a large or etc, with profit metrics, right? Which can do a lot of that, I'd say 90% of the work. Why is that not good enough? You think so?
I've not personally used profit metrics, so I can't speak to that specifically, but having used a few different tools that do similar things, and the tool that this client used previously to try and scale their performance Max and why that didn't work. What we've done differently is we've not just looked at Google ads. We've looked beyond that. We've looked at what the attributes that make these products appealing and successful for the business and for customers of those products and Google. Allows, there's only one part of that to be able to understand that. So there are various things so that obviously, a lot of these tools and things that we've done, or I've done in previous jobs, is we focused on, okay, obviously, if the margin is great on these products, push those more and you're going to drive margin and profit margin, rather than just focusing on return, on ad spend. That's just one layer to it. So there's other things that make products economical and profitable to the business, like, you know, as I said, like that, just that lead, delivery, lead time being three days rather than 10 days for this class specifically made such a big difference to conversion rate. And Google Ads isn't necessarily going to know that information. It will know which products are more successful versus the others based on that conversion rate. Specifically, it doesn't know why. So if we're able to build in the why into the actual I guess algorithm and the AI that we've used to analyze which attributes, when you combine them, make these super successful and super likely to convert very i and provide that to Google, say, put spend behind those. That's where we're probably different to these other tools that are looking purely at the Google Ads data specifically and it might take into things like profit margin et cetera, which are kind of like cookie cutter for every business, like every business looks at margin, but there are very bespoke things that work for certain clients that aren't going to work for others, and vice versa, and information that we might not have, because, like, every business is different, has different models as well.
Do you guys use, like, a stack of tools where, if anyone were interested or nifty enough, could have a have a sniff and have a go, like, build in something like this? Or is your like, do you guys be like, obviously, after you said, before, already certain things that you then find out, this is a problem that another business faces, that then comes first, boom, you've got the tool built. But do you have, like, own built tools, or do you use sort of, you know, like a certain, certain softwares, or something like that for that,
or Yeah, so so for this specific case, and it's the case with clients, when we're working a problem that's new, we will build something that's like, very sort of bespoke and very kind of like, very quick, in a sense, like this is like a very quick data analysis using data science. So there wasn't any tools behind it when we did this with this specific client case study that you saw. But since then, since we've been taking this approach with other clients, we've built a whole product behind it. So we've utilized so we typically use GCP, so Google Cloud infrastructure to build a lot of our tools. We've layered on AI on top of that, using things like vertex AI, which is through Google as well, and actually put more, I guess, AI into this is not just a simple data science technique, but for that client case study, specifically, I mentioned a lot about historical data, or I've inferred that it's based on historical data that doesn't necessarily take into account products you don't have any data on whatsoever. So it works really it worked really well for products where we've seen data come through SEO, direct, etc. But the model that we built for that, specifically the quick and dirty model, as I'd call it, it didn't take into account where you don't have any data at all. So those are you just have to test those, as I mentioned, like in your seed bucket. Now, what we've been able to do now we've laid on AI on top of it is actually reveal actual predictive models where it's actually okay. It has very similar attributes to these other products. The titles semantically are quite similar. So therefore we can infer as a cluster of products, these should have certain conversion rates rather than just looking at every product individually. And what that allows us to do is actually build seasonality prediction into it as well. So you know, if a product has been successful, historically great. But what about clients that basically refresh their catalogs every single year? It's like fashion retailers like they don't typically have products that last on the website more than a year. So how can you predict seasonal trends on historical data when it's a brand new product entirely? So we now need to build predictive models to actually assess future trends, rather than just the historical data of the catalog. So
I generally have one print on demand client who has, like, of course, huge seasonal jumps and in different areas as well. So French Mother's Day is on a different day than British mother day Mother's Day, and vice versa, and and that's something we've struggled with every year. Maybe we'll have to have a chat afterwards as well on this, but because that's super interesting. But of course, it's very difficult to do. So maybe something that I find interesting. What kind of problems do you regularly run into, where people have already come to we discover, and you've seen this before. Four. So we've got this, this one thing that you already have, which, by the way, I guess you can get, like, tons of other insights from this as well, once you find out, let's say, the lead time for that one case study you've just mentioned that could have, like, even CRO implications, right? Like, sort of, if a product takes longer, you can just put a widget on there saying, you know, don't worry, you still get it by whatever. I don't know this was maybe stupid example, but yeah, I mean, to sort of reduce that conversion friction, you actually know what is working well and why, you should roll that out to products that don't work well to a certain degree.
Yeah, even things like where you place your products on the category pages. It's like the order you like, what do you put on page one? What do you put on page two? Like, you can use this exact same analysis. Say, Okay, if we know these are the buckets of products that are going to be more likely to successful, we can order landing pages in the same way, rather than utilizing a CRA tool to do that. That's amazing.
But what kind of kind of other product problems? So if someone's listening and thinking, you know, I'm working in like, I'm an in house PPC who just likes to stay on top of industry trends, we got exactly that as we have this issue, that we have this issue, what kind of common issues do you sort of solve?
I think typically, it's that transition that Google Ads has had over the past two, three years, where the old school way doing things, where you segmented everything, because there's a campaign structure to now it's actually the opposite, where you have to consolidate to actually gain the benefits of Google's machine learning through bit strategies. That's probably one of the main like, common things we see with a lot of businesses is like, how are they organizing, both in search and performance Max? Like, how they organize in their campaigns to actually leverage Google's AI as best as possible? Because that's one of the things. Like, it's a foundational thing that we do. The step that we take is like, that's the first thing we try and address before we start doing the more of the the advanced stuff. And then the other common thing is, actually, is that business actually optimizing to the metrics that are actually going to make a difference to to the business, not just marketing goal, but business goal. So typically, we would look at, okay, where does it make sense to import business information as a conversion action, and it might be more than one thing. So we're working with business at the moment where, in terms of website tagging, the only thing they can track is where someone is basically a lead. They are not currently optimizing to information of, okay, if that person has applied for something, you know, has that application been approved? And then, once it's been approved, have they then spent money? Those two things are very important. So the conversion rate from a lead to an approved application or registration to actually spending money over the business. Those are three different conversion rates, essentially, but they're only optimizing to the very first thing. So we are working with them at the moment to import the other two conversion actions, and ultimately you want to optimize to the one that's driving money. But there might not be enough conversions coming through the account to actually optimize that Google ads won't work with limited data, right? So there might be certain campaigns that you can optimize for that. And okay, if you can't, like, really generic activity, which doesn't convert very well, okay, what types of this step, where is application approved, rather than the first action, which is simply the amount of leads that you're driving. So, yeah, it's things like that. It's like understanding, do they have enough data? If not, can we consolidate campaigns to do that in this way that makes sense for their account. Again, if not, what are the different options of what we can optimize to they're actually going to drive those business leaders and business metrics that will essentially drive profitability for
them. Yeah, and that was what, one of many things. So just connecting the dots a little bit more, mega interesting. So if someone has thought now you know what I need to speak to indeed, what do they have to do?
Probably
the easiest and not badger you after, maybe not the best option.
No, no, I absolutely like conferences. So yeah, I try to attend as many of the main ones as possible. We try to speak quite a few, as you know. So yeah, ultimately, like, speak to me these events, PPC live, for those that are London based, or that actually they're doing events elsewhere in a country, like Leeds and Manchester as well. You will see me there often. So face, that's probably where you can best find me. Otherwise, LinkedIn, just reach out and, yeah, something. If there's something we can work on together or just have a chat, I'm more than happy to do that, whether it's through LinkedIn or a call. Super,
yeah, perfect, brilliant. So I guess we can land the plane there, figuratively speaking, thank you so much for coming on. Really. Appreciate these insights. I think even if someone understood maybe 30% of what was going on here, they should be alarmed. Because, I think especially with, like, using AI. So my sort of view on it is, at least in the next five years, it's going to make the people who are very good, yeah. Like, clearly, like, you know how to figure out a problem and then accelerate the solution to the end better, and it's going to push out those who are not going to be using it, and whether it potentially might even not, need that expansive knowledge that you that you and your team have, even if the challenge is smaller, you can potentially even use smaller solutions. And I think the takeaway is just to creatively find solutions as best as possible to the challenges that you're facing, right and you're using tech. So if that's you guys, then get in touch with indie, and if that's been interesting, then, of course, subscribe as always. Every Monday, we've got new episodes. You can always contact me as well. I can put you in touch with indie via my LinkedIn, via the website. You can always book a call with me. This has been Jeremy Young as every week. Thank you very much for listening, and I'll see you next week. Bye.