Business Intelligence in iGaming

Show: Revenue Optimization with StatsDrone
Episode: Business Intelligence in iGaming
Host: John Wright
Published: Tue, 18 Mar 2025 02:46:00 +0000
Duration: 37:15

Speaker labels are a two-speaker split from pauses (host vs guest). Timestamps are `[HH:MM:SS]` from the episode audio. Light ASR; names and brands may need a pass.

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[00:00:00] John Wright: Also to build a small element of self-service BI, I give you access as it actually requires a small training. And what we've seen is though we see training, it's not easy to call people for a training.

[00:00:14] John Wright: So a small training, even one hour or two hours would go a long way in making sure these dashboards are used well.

[00:00:21] John Wright: We'll actually adapt out to self-service BI. There could be some metric or some idea, build it yourself in a self-service BI in a few hours or a day or two, makes sense. And then if it makes sense, you start publishing it and at some point in time it can get embedded into an existing dashboard.

[00:00:39] John Wright: I'm John Wright, and you're listening to Affiliate BI, a business intelligence and affiliate marketing podcast brought to you by Snapstrom.

[00:00:48] John Wright: Welcome to the Affiliate BI podcast. Today we're chatting with Rakhul Jain, who is the delivery head of business intelligence and analytics at Biz Acuity, which does BI analytics in the financial, real estate, events, gaming, and telecom industries. Rakhul, welcome to the show.

[00:01:06] John Wright: Hi, John. Thank you. Thank you for inviting me to the show. Yeah. So I've looked up your background in LinkedIn and I know you've got a bunch of analyst roles, but I wanted to ask, how did you land in the role you have currently? How did you get into analytics and business intelligence? What led you down this path?

[00:01:25] John Wright: Oh, great. So I was a software engineer. I started my career programming, but having started with a parallel database, just automatically moved towards databases.

[00:01:36] John Wright: My first switch, we happened to a company, which is into iGaming and I was getting the database there.

[00:01:43] John Wright: And from then it has always been data and could see how that helps massively. And most of the iGaming industry have seen being online, the amount of data that gets generated is massive. And one of the most dynamic industries, the usage of data in multiple streams is very high and it just grew along.

[00:02:02] John Wright: And so I've just then rendered the horizon of the streams of data integration, visualization, or now artificial intelligence, moving forward more of the Gen I as well, I think what answers your question?

[00:02:17] John Wright: Mostly we're definitely going to dig it and have lots of rapid holes of discussion. One thing that I have in this opinion is when I see people come from outside of iGaming and they come inside, they think it's like the future. And then once they arrive, they're like, wait a minute, this industry's run on spreadsheets, there's technically a lot of data, but I think there's not a lot of people doing stuff with it. And given that you work in other industries, I'm assuming you see a few things that things are probably more advanced outside of iGaming and you probably gain a lot of ideas and inspiration and projects outside that can benefit insights, what's your take on the state of iGaming?

[00:02:59] John Wright: Honestly, I would defer a little because my experience has been when it comes to spreadsheets, even in most of the, let's say non-iGaming spaces also, even if you have advanced analytics platforms, the usage of spreadsheets is still fairly common because that's a lot more easier and it's a little more bit, right? That said, what is it that I could perhaps get into non-iGaming spaces could perhaps be in the gaming? One thing I've seen is the general value of the general terms like lifetime customer value is something which has been replicated very well now.

[00:03:35] John Wright: Most of the gaming operators, a good number of them on the voluminous ones, they certainly have a prediction model which predicts customers lifetime value.

[00:03:45] John Wright: Operators who in the market for a few years, I get to catch on to that.

[00:03:49] John Wright: I'm sure they'll do it very quick.

[00:03:53] John Wright: And similar things like predicting a customer churn, predicting future behavior, cross-selling a product. Cross-selling a product is something which gaming has been trying to do, but I think the usage of data analytics on that front is something which can certainly be extended a lot more.

[00:04:08] John Wright: And I think Gen-I is something the use cases in the iGaming are to be developed to a mature stage.

[00:04:15] John Wright: Okay. That makes sense. Now the obvious question is what does AI look like in any of these industries? Like I have one opinion, which is, I think there's a lot of companies that are chasing AI, but they could be maybe perhaps a data company instead where AI needs unique datasets. And I think it's like what we've decided to do as a company to say, do we want to create an AI division or do we want to just keep exploring the realm of data creation and unique datasets? And we've made that half in David to say, we're just going to stay a data company. And yeah, I think a lot of people aren't really understanding where and when to tap in the AI.

[00:04:56] John Wright: I think that is something I can second.

[00:05:00] John Wright: I've also come across that aspect and I consulted AI and business equity as a company get consulted on a lot of options as to explore the use cases where AI can be implemented.

[00:05:11] John Wright: Opens much wider avenues, but yes, though the standard, there are about at least 10 to 15 standard use cases where AI is a straightaway go-getter, understanding customer behavior based on their own past patterns or replicating their persona, mapping onto some existing players persona and look at similarities and make predictions. We've seen cases where outputs or predictions from AI engines are integrated with your CS customer service where some of the front desk agents are while interacting with, let's say, the VIP players are actually getting real live recommendations from AI.

[00:05:47] John Wright: That could be, for example, when he's a player, perhaps is this step, perhaps the last step in this journey, those kinds of alerts automatically generated, which help a lot in making sure that, for example, a player who's just lost a big bet.

[00:06:03] John Wright: And if that player has been doing this for a while, and it would certainly have been critical, some of the CS agents would then give him, let's say, a free product of bonus.

[00:06:14] John Wright: Moving on, I always like to ask this question. It's not a straightforward answer is what could happen in iGaming or any industries you work with in terms of would you be able to double the industry over if you had the power to just take on everyone as a client and you have the capacity to do it? Like how much do you think there is for either reducing like revenue leak or increasing revenue, doing a bit of both? Like how much further could we be along if we, if everyone just said, okay, we're going to step on the gas. We're going to go all in on analytics, business intelligence, AI, all of it. What do you think?

[00:06:51] John Wright: Okay. I think the first thing has to be a wide digital transformation, which in the case of iGaming is certainly I would say at an advanced level, but in addition to being iGaming where let's say iGaming gets players logging on to an iGaming platform, but the back ends are for the type of officers, the way they operate the administration, all of these. So the digital transformation on that front is yet to pick up even in iGaming. So that digital transformation, if I can use it on perhaps end to end digital transformation is I would say the first step that should, you know, any industry look towards doubling revenue sooner.

[00:07:30] John Wright: Now, apart from that, very specific, if I was now looking into AI capabilities into how data can be used more by existing teams or consuming data, also spreading data usage into various teams, focusing on data driven decisions as much as possible, these would certainly give you a lot of traction for that acceptance of this because data driven culture, how like you use the example of spreadsheet being used, right? So I know we have so many clients, both in gaming and non-gaming, where you have a solid data platform used heavily by quite a few teams, but

[00:08:08] John Wright: there are a lot of others who still do not have access to that or don't have awareness. So awareness of data availability, perhaps extending the data platform towards data catalogs, having a data governance framework. These are the next steps, which make sure that data is now spread across and is driving the organization towards doubling the revenue.

[00:08:33] John Wright: I want to pivot and talk about how people get into BI and analytics, you know, in my case, it's, I didn't even realize we were creating a company that was going to actually focus on business intelligence. What I see, it's not like you go to school and say, okay, I'm going to do four year degree in analytics. Like you can become an analyst and I think there's lots of paths you take. But I think now there's an explosion of these kind of like fast track courses to maybe in six to nine months or even less, you get like this diploma. What are you seeing for, how do people get involved in this? Because in my opinion, I think a lot of people in iGaming and other industries,

[00:09:11] John Wright: they want to level up their skills and they're like, do I go back to school for a year or longer or do I get diplomas? What interests you or how would someone try to get a job at your company to say, Hey, I'm really passionate about this. Where do they start?

[00:09:29] John Wright: So to be sure when this question, and I give you a more focused answer, you're asking me if I have to hire somebody, let's say. That would be one example. Yeah. Okay. And even if apart from that, if I have to make more people in my team data ready,

[00:09:46] John Wright: that would be another case. So how do I process?

[00:09:49] John Wright: Yeah. But from a point of view of that, you're not the company that does analytics. You're like another company that needs this help or, or rather you're someone that says, Hey, I've been watching this BI space for years. I see other people getting promoted or getting it. It's an interesting career.

[00:10:06] John Wright: Okay. I think I get your question now. Okay. All right. Let's sorry. I'm not a data company. I'm not any other organization. There's no data platform or this availability or the type is less. Now, if I have to make that team, that company more data ready, I would, first of all, I would start small because I can create a beautiful vision. Try selling it. It'll take years. Okay. And even if it gets sold, it'll get sold only executives, but when we're actually supposed to be the operational users of data, perhaps we're not using. So rather start small.

[00:10:43] John Wright: I would possibly look at one year where there's a lot of processes, cleansing of data, too many, too many Excel formulas being applied then passed out to you. You're also applying some for the pivoting, et cetera. I would identify one such manual process and build up a very small POC around it, saying, how can I automate data process on it? So fetching data from one single simple source, all the data, cleanse, extractions, transformations, a visual of that. And once I'm able to convince you on how much time and effort of yours and everybody else,

[00:11:20] John Wright: this alone can save, then you are sold on that. And you could perhaps then become at least the initial sponsor for a slight next set of POC and that's how I would take it forward. So now that would not only save me time, but also show how much cost it would have saved otherwise, because now your time go on a lot of initiatives.

[00:11:41] John Wright: Cool. Without revealing confidential info of your clients, what would you say some of the most profound impacts you've had in actually working with a customer where you've unlocked not just insane insights, but been able to deliver real value? I'm not saying this is another example of doubling a company over, but what have you seen that just said, wow, this is really powerful stuff.

[00:12:04] John Wright: Okay. There are many, but should I speak? Share an example specific to iGaming or it can be any industry. It's as long as you think it's insightful. And again, it's like, whether it's a customer you can talk about without sharing confidential data or rather leave the customer out and just say, this is what we're able to achieve.

[00:12:22] John Wright: Okay. So perhaps what I can do is I'll not name the customer, but I just go small insight into the customer. I'm just trying to think which would be okay.

[00:12:32] John Wright: All right. So probably give you a couple of examples. And if you think we have time, I'll cover one example on iGaming as well. Sure. So one is a semi focused, it's a bank. It's a digital bank based in Europe, which is focused on a semi lending. So now as part of their data transformation, one additional aspect was to identify which is the point in which, which is the point in the loan journey where customers drop off.

[00:12:57] John Wright: What is the reason? So there was a lot of data that was not being collected because a lot of the process was manual brokers, king in details. If at all the broker would have qualified the customer.

[00:13:09] John Wright: So if a broker manually on his or her own description has not qualified a customer, a potential customer, the bank does not have those details.

[00:13:19] John Wright: So first thing that we did was to, so like I say, when I say data,

[00:13:25] John Wright: what essentially building a data platform, it's a lot more right from actually collecting the data.

[00:13:31] John Wright: So in this case, we started recommending that you need to collect these data. So now build up a portal where the brokers would actually in any potential borrowers in their details, and then it moves into the workflow.

[00:13:48] John Wright: So now filtration or qualification or disqualification of a borrower would even flow through the workflow. But even if a customer has been disqualified today, the details, contact details of all those customers is on the platform. The moment we have any new loan product, which perhaps caters to them or for which they're eligible, they would directly be a result via a digital campaign. That's one. And a lot of, because this particular bank, the reporting of credit committee pack that is creating and all of these was fully manual.

[00:14:22] John Wright: Automation of this via important data points brought in a lot of ease in for the executives to understand how the business is moving.

[00:14:30] John Wright: And I'm sure accuracy of the data. You give people a manual input. It's either not getting done or not getting done. Absolutely. And obviously it involves a rigorous testing. Every time a small new data element gets added, a rigorous testing of that data element is also taken.

[00:14:49] John Wright: So I hope you also are convinced that this is something of what calling out use case.

[00:14:54] John Wright: There's another, this is for a company which is into event technology management. So this company manages the audio, visual audio, videos, supplies for all grand events.

[00:15:08] John Wright: Now in this case, so wherever there is any event happening across their geography, they reach out pitch to host the entire audio visuals.

[00:15:18] John Wright: How are there quite a few opportunities which were being lost?

[00:15:22] John Wright: Yeah. So as part of the data workflow, there was a lot of data points where manually people were king in, but people on the ground would not get a lot of data.

[00:15:35] John Wright: Now, when the digital transformation primarily focused on data started in this company, one point that was made sure was that even if an opportunity is being lost, you key in what was the potential reason that opportunity was lost.

[00:15:49] John Wright: And using this, identify what are the common reasons for which a lot of opportunities are being lost.

[00:15:56] John Wright: That brought in some changes in how the operational efficiency was being measured. Some efficiencies brought in, and then these were continuously reviewed.

[00:16:06] John Wright: And today, for example, this, this company being obviously event technology source spread out across a wide geography. They're one of the biggest users of one of the reporting platforms, because now everybody, not just at the exec, not just at the offices, but on the operation floor level, are now using the reports, using the dashboards to understand which are those geographies, which are those opportunities where they have to go, which are those were the last one opportunity because of explicit reasons how they work on it.

[00:16:39] John Wright: Would you say that there's a lot of companies that they're just, they have an opportunity to get data, but that's just one of the things they're not doing because in the previous example, you just said, here's a funnel where they weren't even collecting a lot of data. And that was the first thing you did. This sounds like it's pretty common. I see this in affiliate marketing all the time.

[00:16:58] John Wright: Yes. And to be honest, if you would ask me this question, I would perhaps know it's not common because for some reason I did not encounter these many use cases, but I started encountering a lot of these environments, perhaps post COVID. And I saw that, oh, there's a huge, huge gap here. There's no usage of data. There are a lot of manual processes still running across industries. And because you brought about affiliates, right? So one thing I've seen in iGaming is when I talk to a lot of affiliates, we go to so many conferences, we have so many calls, right? Now operators, trust me, operators are very tech though they may not come out, but at least the back-ends, back-end is really tech.

[00:17:35] John Wright: VM developers are obviously again tech, but affiliates, the moment we say tech for some reason, they're like, okay, it was nice meeting you.

[00:17:45] John Wright: So when affiliates for some reason, the conversations surrounding technology or data have not really moved forward too much.

[00:17:54] John Wright: I would rather, I would want to understand your insight on this as well. Yeah, that's a good question. Cause it's the example that you mentioned before where this one company wasn't actually doing surveying of data. I'm like, like I'll just give you a couple of really basic examples. The SEO space has been changing for affiliate marketers where Google's giving them less traffic. And what I'm starting to notice is that one of the signals that Google is looking for affiliate sites is that you have traffic outside of Google. That's coming back. It's like a signal that says you're real. What's one way of doing it? Creating a newsletter. What's a newsletter? Creating either a signup form or a prompt that says, Hey, uh, I'd like you to join the newsletter.

[00:18:33] John Wright: So how many affiliate sites don't have newsletters? There's quite a bit. And then once you have a newsletter, how many of them are not segmenting further? I'm just learning about some of the newsletter tools that are out there for actually going, Hey, now that you've signed up before I send you an offer in five minutes, I can actually put survey tools inside of this. I just think there's not a lot done there. I know for a fact on my side with 2000 affiliate accounts, I don't have affiliate managers giving me these survey tools to ask me for a little bit more time, which I probably gladly do. And that's just like one of a few examples. And I know for what we do as a company, it's a tool to try to help affiliates

[00:19:12] John Wright: get better with collecting their own data. And we have a really good insight of what affiliates do and don't do. I think it's the same experience. We think it's okay. And then we get more experience and we're like, no, there's a lot of work to do.

[00:19:29] John Wright: And that also something we just went out of my mind. So I also told perhaps cover one more use case now, eye gaming. So one of the use cases, perhaps may not work very well with affiliates, for example, to be honest is with one of one operator, what we figured out is we worked with them in building an AI driven predictive model, which predicts our players' future lifetime value.

[00:19:52] John Wright: It was a good customer base. It wasn't like just a few tens of thousands or hundred thousands. It was a much bigger customer base.

[00:19:59] John Wright: And the insights that were coming out of it were actually initially a little counterintuitive to the users and then dug in further into the data and validated it that yes, this seems to be true how much of a counterintuitive it may seem on the face.

[00:20:14] John Wright: And what came out was there were a few affiliates who had good deals, very good deals in terms of the ref here, et cetera.

[00:20:22] John Wright: But most of the players that were coming from these affiliates were actually on the lower value side. Yeah. The operator actually went back to that affiliate, worked on negotiating that deal, to be honest, I don't know what was eventually agree, but these are some insights which come out from data.

[00:20:39] John Wright: And I'm sure which would work even for an affiliate to understand which are those operators. Yeah. Now that's important. The affiliate should hear if their player values are higher or lower. It's very rare that they actually get this feedback, but I think even knowing that it's, you know what, operators should be doing this. If the player values are low, it's you can't justify just paying it. There's a lot of affiliates that would gladly just take the money from operators and say, whatever. But I think we need to see a change in operator behavior where they just can't give up these deals blindly. And I think one of the reasons why this is a reality is because affiliate managers aren't analysts. Like what trading do we have for both affiliate management and then

[00:21:18] John Wright: affiliate management to say, Oh, here's how we're going to help you analyze the data. Like that's one big opportunity right there.

[00:21:25] John Wright: Yes. And like you said, like a lot of the affiliate managers are not analysts, but one thing I've seen is, and this is not, this one is specific, not specific to iGaming alone across a few other sectors as well as analytics. How much of a different machine once you get in. It pulls you in and you automatically grow.

[00:21:45] John Wright: Develop interest, the learning curve, unless you go a little too technical, right? Let's say if you want to become a database expert focusing on snowflake, that's a different learning curve, but somebody who's let's say hands off technology, still the data learning curve is not too steep, not long.

[00:22:01] John Wright: I've seen a lot of people coming from, not from a data background, but quickly adapting and becoming data analysts or business analysts.

[00:22:11] John Wright: Yeah. Okay. I've got another question. I need like maybe a minute to formulate this one. Let's see.

[00:22:18] John Wright: Actually I'll have to come back to it. So I'm just leaving a little mental note here. So is there a way of analyzing databases that contain sensitive data using AI tools to extract insights? So I'm going to give you an example. Let's say we're dealing with potentially our customers at Stastro. We don't look at their data, but is there a way in which we could apply some sort of AI model that would analyze the data, put it into a central database, anonymize it. I'm certain these tools exist. So I just want to know what it looks like from your side in terms of consulting a customer to say, how do you make these things a reality?

[00:22:52] John Wright: Okay.

[00:22:54] John Wright: In fact, and as you'd expect, a lot of customers from Europe and the US, this is one of the paramount considerations. Everybody wants to be sure that GDP are compliant. So a lot of the environments also that we typically access of our clients is typically in their environment. We log into their virtual machines and access the data. Now that said, in terms of anonymizing data, there are multiple mechanisms and not complex, very simple mechanisms, which are like frameworks commonly used in a lot of data engineering.

[00:23:27] John Wright: For example, if I do use a simple example, when retrieving the data, let's say from source platforms, we read the data in an encoded way. So now, and the AI machine can be passed over just the encoded case.

[00:23:41] John Wright: So then it knows that if encoded version for John is let's say JWR, it'll not be that simple. We just call that out. It will be a much complex 16 digit XR number, but let's say JWR. So every report coming from you would be JWR. So the machine can then know that yes, JWR, this is his or her data, this is perhaps a person I can map John to and learn behavior and make predictions. So that's in fact, that's the most common, I can call it a framework to deal with the personal data. That's it. Emails, phone numbers are still some data sets, which everybody's a lot more

[00:24:20] John Wright: sensitive about, so more often than not, these are generally passed as by default or just.

[00:24:28] John Wright: Okay. Another question I have, and it's almost like a personal one to ask it's what would you say for companies that have, they have all these dashboards built and it's, it's almost too much data and too much analysis, but what I find a lot of companies can end up doing is not spending enough time looking at their own data, what would you say is the best practice? Should teams get together once a week and say, Hey guys, let's dig in. We have all these dashboards. We're not going to get any insights unless we actually go and use the tools. There must be something here that you deal with all the time.

[00:25:02] John Wright: Okay. Yes, definitely. This is something we deal with almost all the time. And my most basic recommendation on this would be something take a few steps back.

[00:25:13] John Wright: Surely start all these initiatives with a small training. Right. So I'm building dashboards. If I build dashboards, let's say a few of those would let's say address only the strategic insights for the exit committee, but then a lot of other dashboards, which are publishing, but it's easy for me as a techie. I just keep on publishing and move forward to the next dashboard. But it also makes a sense to have somebody who's spending time calling out a meeting with the users, showing them how they can, how to read each of these dashboards, how they can drill down, how they can have cascading downs with geography, if I want to look into a specific geography, how do I do?

[00:25:52] John Wright: So that that's one second is also to build a small element of small self-service back within.

[00:26:00] John Wright: I give you access as it requires a small training. And what we've seen is though we see training, it's not easy to call people for a training.

[00:26:09] John Wright: So a small training, even even one hour or two hours would go a long way in making sure these dashboards are used well, will actually adapt out to self-service.

[00:26:19] John Wright: There could be some metric or some idea of, of what you want to make sure if you want to publish it to a wider audience, build it yourself in a self-service in a few hours or a day to two, make sense. And then if it makes sense, you start publishing it. And at some point in time, it can get embedded into an existing dashboard.

[00:26:40] John Wright: And another point I would add here is, so is to audit or review, how is the dashboard being used? And this kind of takes me back to that event management, even technology management company that I was speaking of. So what they understood is over a period of time, human nature, we sometimes get complex, take a step back, go into the comfort zone. So people stopped using the dashboards and then they started using internal intuition, they would see a lapse. So we started auditing the usage of the dashboards, the management result guys start using these dashboards because these are the ones which actually help you with your efficiency. So get back there.

[00:27:18] John Wright: So this would be another way to address that. And finally, one more idea is also somebody who's owning the dashboards for them to keep reviewing these dashboards to be sure these are still.

[00:27:31] John Wright: You're giving me a ton of ideas. Well, we should do as a company, but I actually, it's the last thing you just said was super insightful. It's we can create dashboards and Tableau and Looker Studio and Power BI. But you're, I think you're actually spot on. You should actually put those dashboards back into a website, let your users use them and put maybe like a hot jar or some sort of tracking tech to go, how are they using it? Is this person spending 30 seconds on these dashboards when they should be spending 30 minutes, are they looking at the wrong things? Wow. It just sounds so trivial, but there's another example of too much data and then not having someone actually use these things.

[00:28:12] John Wright: That's hilarious. Yes. So the term there was somebody who called me it's as a data overkill.

[00:28:19] John Wright: But then knowing what to give this data access to and making sure that you're using the right data and actually continue to use it, having a frequent touch point, you have interactions. So that also is the role at this.

[00:28:33] John Wright: Would you say that what are some of the best tools for watching people to go, okay, I'm going to see how you use these things. The first one that comes to mind is hot jar, but is there something else that you would recommend or just hot jar as well? You may not be the best person to answer that. Okay.

[00:28:47] John Wright: We can do research on the side. I think one of the slight downsides of hot jars, a lot of people, when they put these on websites is sometimes they can slow down the website or sometimes if you're putting it on an app, it can actually slow down the app, but I think there's many alternatives to hot jar, but for me, it's the one I've known about for what? 10, 15 years. Without a doubt, I'm definitely going to take this note because we have our own BI team and I'm definitely going to share some of these insights with you.

[00:29:15] John Wright: This is amazing. Sure. And yeah, and do let me know if you, if you see some change. Yeah. We have tons of dashboards and I know personally, I'm not always on them once a week, but would you say that us as a team, we've got like a BI person, we've got a product person, and then there's me and my business partner, should we basically go, okay, once a week, let's go through all of our dashboards and go. Let's ask ourselves, what are we missing and what do we have currently and what can we change? What can we approve?

[00:29:48] John Wright: Now, the specific example that you gave, I think to start off with once a week should be fine, but over a period of week, it should be once a week.

[00:29:58] John Wright: Because by then all of you would have the whole practice would have met you. But if you ask for larger organizations, both the number of dashboards, as well as the number of changes that are coming in is much bigger, then I would recommend having somebody focused on product.

[00:30:15] John Wright: Who's looking at data, the data platform and BI as a product in itself

[00:30:20] John Wright: and building a backlog.

[00:30:24] John Wright: Any new requests from the business actually goes to this product owner who then prioritizes it.

[00:30:34] John Wright: Maybe not a super serious question, but what tools do you use if you're trying to survey users, whether it's asking clients for feedback, or if you were to put like some sort of survey inside of an app, I'm commonly using Google forgs, it works, but is there something that's a bit smarter, a bit more sexier, user friendly, et cetera? A lot of it is on Google forms, a little bit of SurveyMonkey, I'd say.

[00:30:59] John Wright: And yeah, so these are as in for smaller surveys, these work very well. But then there are also tools which kind of work for, if you're building for a much wider server.

[00:31:10] John Wright: Okay.

[00:31:11] John Wright: But yeah, as in a lot of it, SurveyMonkey and Google forms go a fairly good job, no doubt about it. Okay.

[00:31:19] John Wright: I know there's even more we can dig down in. I've already taken some notes of ones that I know when you actually collect someone's email, you can actually do deeper questioning or survey that actually pre-populates your database to just like, just load it up with insights. Have you ever tried Zoho forms, John?

[00:31:38] John Wright: I personally don't like it. It's, I remember looking at that software 10 years ago and said, I'm not touching this, but I'm sure it's great. Oh, actually I think that's grown well.

[00:31:47] John Wright: So maybe we all just, because if you're saying 10 years ago, I've worked with it as recent as about six months earlier.

[00:31:53] John Wright: That sounded good. That looked good. So you may want to just see if it kind of appeals to you now.

[00:32:00] John Wright: Yeah. No, awesome. And the last question I have is what do you see the future? I know you're not specifically in affiliate marketing, but what do you see the future of affiliate marketing as it intersects with business intelligence?

[00:32:12] John Wright: Oh, so can you, can you rephrase your question, please? Just come back. Yeah. Cause just taking a step back, I remember when you were saying like, what was like one operator, what did they do with their affiliates? They basically changed their deals to say, Hey, we did some analysis. That was one example of how affiliate marketing can be influenced. So it's, I know you have some experience in affiliate marketing from an indirect means of working with operators, but yeah, what would you say future of affiliate marketing from your lens of what you've seen on the BI side as it intersects with business intelligence?

[00:32:44] John Wright: Okay. To be honest, I think I'm not a hundred percent sure that I've understood your question, right, but let me try answering based on what I understood and you follow up with other questions.

[00:32:54] John Wright: So before that, I'll look one more use case where business intelligence platform for an operator product, some further points on the affiliate marketing side, which was about the duplication, a player, let's say John, right came by. So this is a multi-brand operator running multiple brands. And, and as in I gaming, the lot of mergers and physicians. So this, this operator or this brand to go multiple companies. What we see is the same genre is on this brand as well as another brand

[00:33:26] John Wright: and coming from the same affiliate. So the affiliate is actually getting paid for the same player from both the brands.

[00:33:33] John Wright: Now, so how do we address this is something that came up. The second question was for all new, all new registrations. If this is a player who's already registered on one of my own brands, then I will not pay any affiliate commission for this. So that brought up a friction between the specific affiliates and the operators that kind of onto the table, because all said and done operators also would not make, would not want to make affiliates unhappy.

[00:34:05] John Wright: So they got onto the table, discussed it now. So that's one, I just wanted to bring out another use case of data and it's in the whole affiliate slash operator relationship.

[00:34:17] John Wright: Now, but coming to affiliates, what I understand is what I understand and you would correct me if my understanding is incorrect.

[00:34:25] John Wright: They have a lot of, okay, but it's complicated. The SEO answer is it depends. I personally think affiliates, they don't always take, get enough data. I know one thing you can do is say, okay, let's take, you can do this on both sides of the facts. Let's take the top performing operators are the ones we know that have, they've made a lot of money. Same thing for the affiliate side. I know when you start looking at their websites, you start seeing richer segmentation, Adobe analytics, Google analytics and conversion rate or CRO tools, AB testing, these companies do that, which is great. And they should do that.

[00:35:04] John Wright: It's a great experiment for anyone to actually start testing going, Hey, what does casino.org do if I sign up to their newsletter? Like I don't have the answer right now. That would be a great experiment to go. Okay. Who's doing something better on the operator side and for affiliate programs. I think it's never ending. I just know for a fact that I can't remember the last time. So like an affiliate manager said, Hey, John, we want to do a survey because we want to understand you a bit better. They'll ask me the questions like what geos you have and those are standard ones, but why is this not in a form? Like he just click my way to give you accurate data, anything.

[00:35:43] John Wright: All right. So yeah, so I think based on what I hear, because like I said, so at least add to detail, even I'm not working with many affiliates.

[00:35:50] John Wright: So that's one area where I think affiliates, perhaps look at something new or something different.

[00:35:58] John Wright: Awesome. So Raghul, thank you so much for doing this. I want to pass it back to you to ask how people can get a hold of you. And I'm sure, of course, we'll see you at the conferences as well. Yeah. Same, John. Thanks for time and thanks for the interaction.

[00:36:15] John Wright: Yeah. So yeah, just quickly, how could people get a hold of you if you want to share, obviously we'll include the website and LinkedIn, anything. Okay. Sure. Sorry. I didn't get that. Yeah. So yeah, I can't believe it's out directly on LinkedIn and I'm fairly active on LinkedIn.

[00:36:28] John Wright: When I say fairly active, I typically just the posts of a lot of my connects. So I can share my LinkedIn ID, which is RAHUL.

[00:36:40] John Wright: We'll include that in the show notes. Thanks a lot for doing this. I know for a fact, I gained quite a bit out of this. So if anyone else did, and I'm super pumped. I've got a couple of pages of notes. This was amazing. Thank you. Thank you, John. Thanks for your time and thanks for this opportunity.