Data is the new king of the internet and every company is competing for your data. But what they do with your data is the most interesting thing. They create track your activities, create a profile of you and then target you with personalized ads. All this happens in the background without any knowledge to you. And to interpret that data in various ways, there is something called Analytics.

In this week's talk, Amit and Rinat talk about Analytics, what is it, what companies use, how do they use it and a lot more!

Hi, everyone. Welcome to Tech Talk, a podcast where Amit and I talk about various technology related topics. Today we're going to be talking about analytics. It's, it's a very exciting topic and covers a lot of things including data science and all the other insights or analytics related products that we see in the market. So I am very excited to talk about it. I don't have any like commercial experience on it, but I am a very avid user use many analytics including Google and Tableau and a few others in what or so yeah, analytics. Let's talk about analytics. What's your thought on it? Amit?

So thank you so much Rinat for the introduction. I think analytics is quite important these days. The whole world is now being driven by data, or most of the companies want people's data and most of the transactions now are being done online. So earlier when we used to go to a brick and mortar shop, so say you go to a shop to buy groceries or you buy clothes, or even electronics. You used to go to a physical shop and people could monitor how many people are entering how many people are buying etc. Now, if you want to do the same thing online, how do you do that? How do you track everything? And that is what analytics is all about. So it's about the data about you and the kind of interactions that you're doing online. And based on that data, you do some analysis and then predict what will happen that is basically an analytics.

Yes, absolutely. Yeah, data is very closely tied together with analytics. And you know, how this data is collected is a big part of analytics in a way but where is this data store? You know, we are seeing analytics, we're seeing some visual representation, but where this data come from and most of the quality of it, so yeah, we can't talk about analytics without talking about data. So we're definitely going to be you know, talking about them both. But yeah, I mean, it's such a powerful tool because you can have a lot of raw data. But without accurate representation, you could, or anyone, audience could go away with a completely wrong assumption. And they would be very confident in that assumption, thinking that oh, I've seen some bar charts or I've seen some representation with some clear dates. It's good information source but it Couldn't be completely misled with, you know, I wouldn't say always malicious but sometimes malicious and sometimes naive representation of data could lead to a lot of wrong decisions. So yeah, I mean, I want to I mean, for me, one of the interesting part of this whole thing is how this data is sourced and a lot of the times we think that we know, you know where people source data for analytics, like surveys and stuff Bucha like in your face, like you know that your data is being collected, but so many other ways data is collected that you haven't even thought for example, one thing that a lot of people aren't aware of nowadays is cookies, you know, and now we have to give consent that they weren't you know, they can take with cookies, but did you know that if you go to any conferences, and you know, exhibitions, etc, you are given a badge with a barcode and you know, a lot of the people comes to you in the exhibition and just scans your barcode, and you just think oh! they just want to know who visited, but they actually collect that data. And when you signed up for that exhibition, you also gave them a lot of data, like your salary information or range or buying capacity and that kind of stuff. And those all of those information paints that picture about you. And all of these people who are collecting these data, they can use it to target particular products to you, which is quite powerful. And this is just one way cookies, then exhibitions. You know, there are so many other ways you know, organizations are collecting data we know about Google and Facebook, and that's like everyone knows that. But there are many other smaller yet powerful companies who are collecting data and using it quite manipulatively There's always manipulative or they are telling what they're doing. But yeah, it's always that,

I think, I think, I mean, you've painted a very dark picture. I just want to look at the positive side. So suppose you're running a company and you're running an online company and you're trying to sell products. Say you're trying to sell mobile phones, and you've created a website and people come to your website and they start buying mobile phones. Some people buy certain mobile phones and some people buy some other mobile phones. So there is a preference for brands. Plus, there is a time of the year where people start buying more mobile phones, say when they get their salary, or when there is a festival or when there is a sales offer going on. So that's the time when they buy products. So you have these different types of information. About the people's buying habits, okay? And it's not just that it's about okay, so suppose you're selling a phone for 50,000 pounds, and then you're selling a phone for 10,000 pounds. And you want to see okay, which phone is selling more which phone is selling less? And if some phone is selling more, you want to maybe increase the inventory for that particular phone and if some phone is selling less, you want to reduce the inventory. So you don't lose money on maintaining that inventory because that's dead. Dead money for you right you have not sold it. So things like that are very important to take decisions based on people's buying habits. And that is what is driving the analytics now. So it's actually about collecting information about people's behavior online. And predicting what they will buy or what the what they want to buy something like that. Okay, and it could be like a telecom company. So you've heard of Vodafone. Vodafone has a pay as you go plan, and similarly plan, mobile phone plan. And based on these plans, what they can do is they can start selling all these plans online. And after a few months, if you go to Vodafone's website, you will see that the plans have changed. Now how have the plans change? And why have a change? That is being driven by analytics. That is of course data being stored there is business intelligence, that data warehousing, etc. But the basic information is some people go to a website, they buy a product, they start using it and that information is sent to the companies and that's how they use it. Now, on the website, you have Google Analytics, which is one of the biggest analytics tools available for companies. You can put Google Analytics for every part of the website for clicking for opening the page for scrolling down scrolling up for clicking a drop down for adding products to a basket for checking out for dropping right at the time of checkout for browsing,etc. And for every single event. You can check, where in the journey are customers actually dropping? Where are the journey or how long of the journey are the customers progressing? etc. So it is very, very powerful. To grow your business. It's not just about collecting information to sell you more things. Of course it is that but I am running a business and I want to sell you things. How do I do that by analyzing what is your buying habits so yeah,

yeah, absolutely. So let's let me bring the audience back to the dark side again. So there is this story. Walmart suddenly started showing a teenage girl products which are pregnancy related. The teenage girl, she's going to school and stuff and she's and her parents saw that she's getting suggested products that are related to pregnancy and stuff like that. And then they kind of you know, wanted to look into probably complained that Why is she being shown or being subjected to this, this product and then they realize that there was an accidental pregnancy but WallMart with the data analytics predicted that before the girl even knew that she could be pregnant. So that's how powerful analytics could be using data. So looking at her shopping habits and other habits in daily life, they were able to predict that not that they did it intentionally they have an algorithm which predicts any payment and so yeah, I mean, I'm not saying all of it is malicious, but it does certainly feel intrusive a lot of the times but yeah, going out outside of that delivery of privacy concern. Analytics. Let's step back a little bit in terms of history. of analytics. We obviously started to know about analytics in the digital world because it became more and more prevalent with, you know, the big giant tech companies collecting our data and everything. But Analytics has been around for much earlier than the digital or the computing age. Even before that, individual businesses would try to collect data on customers. I mean, you know, those of you guys who are you know, have this experience of, you know, like, before the digital age, you know, even if you sort of wanted to register for something, or open an account somewhere you had to fill out this long forms with a lot of questions which you might have thought that why are these necessary but they were trying to build a pattern based on your answers. And it wasn't pen and paper before the digital age but the data collection was happening and the Analytics was happening in the background, in their sort of business. meetings where they had sort of found patterns and decided made business decisions based on not individual, not individual information, but a pattern of data of their customer base. So yeah, analytics has been quite powerful and will remain powerful, because it shows data in a meaningful way that helps businesses make decisions, make the right decisions. So yeah, I mean, while there is a dark side, but I think there's a lot of positive side and you can use it in a really powerful way. So it brings positive changes in the world. For example, you could look at analyze the data of various charities and see which charities are more effective in terms of bringing people out of poverty. And what are their specific actions that brought us the biggest impact and then others could replicate that. So yeah, there is. I mean, it's a powerful tool, how it's being used and who it's being used by. That's, that's a different story. So yeah, or lightside is probably bound to that, but yeah, it's a very powerful tool. And humans are very suggestive towards, you know, visual representation of charts, bar graphs and stuff like that, and gives them a feel of getting more information.

So I think just for the benefit of everyone who's listening, we are talking a lot about data and we are talking a lot about analytics. But there is a difference. Data is the stuff that the websites collect. Analytics is the stuff that happens over the data. And that happens in tools like Google Analytics and other tools. So whenever you do something on a website, you browse something, you shop something that's data. It has got no analysis, nothing, it just collecting raw information. It has got no meaning there is no pattern, no one can understand what's happening. They just know someone added some product in a basket. And at the time of checking out they dropped. That's it. That's data when they go into Google Analytics they now see okay, for today, how many people checked out how many people added products to the basket, but didn't check out? How many people browsed didn't add anything in the basket? How many people came to the first page did not go to the next page? How many people change the basket multiple times? How many people added products of different categories, etc. Now that you see it Google Analytics and that gives you a picture about what's happening. And based on that you take decisions so you have data, then you do some analysis. You come up with some solutions, or some predictions and then you act on it. Now, it is I gave you an example of just checking out because that's the most popular thing you can also use analytics to tell whether the site is up or down. So a lot of times, if I mean before the advent of cloud computing and many other things, you have to physically go and check that if your site is up or not, or you send a ping to your say Ping google.com. You run the command in the terminal, and you can get the status whether the site is up or not. Now, the site, you can ask the site to send you information. I'm down Okay, so the server is sending you the information so you don't have to go look, the analysis is the data is coming to you. And then you can see okay, over a period of 24 hours, how many how many times does my site go down? When it becomes unresponsive where people are not able to come to my shop? Because remember every minute that your site is down, you're not making a sale. You're not open to the internet. You're not open to the public. So that again is a problem because you want to sell more stuff. You want people to come to your website to read more information, etcetera, etcetera. So it is very crucial that you analyze the data and for that you need to collect the data. So there are different mechanisms in which you collect the data. So you have data, you have analytics and you collect the data to analyze the data. I hope that makes some sense.

Yeah, it makes it makes sense to me And absolutely, you know, for especially ecommerce business, data collection and using that data effectively is very important. For example, as you said, like you know, you collect data on what at which stage of the equal whole ecommerce experience do customer stroke the probably top and just before checkout because maybe in on the on the homepage, you have all you said, oh, there's gonna be a 20% off. But you know, you don't have that 20% of activated in the checkout page too. So they get discouraged and then you need to take actions based on that. And that's just one example. You know, there could be so many things that you can investigate based on the correct data collection. And, you know, whatever. We talked about Google Analytics, we kind of intuitively just go to Google Analytics, but I want to explore some other analytics tools that are out there as well. So there are I mean, if you are, you know, doing website designs and stuff, there are quite a few data analytics tools that are available through WordPress, there WooCommerce and then Jetpack, there are a few analytics software's that are available which are all pretty good. Google is also good. It's not bad at all. And one of the things that I do with Google is sometimes when I'm bored, look at trend analytics, like, for example, the song Last Christmas, starts getting more hits, just when the December or just about the end of November, I need it. You know, I see that trend and I feel like it's Christmas already. So, but you don't have to stop there. But you could kind of see which is kind of just about to trend right now. And yeah, you could use those information for, you know, to give discounts in your ecommerce shop. I mean, we keep going back to ecommerce but yeah, I mean, any

But there are many other many other things. So I covered ecommerce because it's important for businesses. But now let's come to social platforms. So suppose you are on Facebook or your Instagram, and you are now browsing and you are looking at certain Instagram, you're looking at a picture and you like it. Okay, so that's an analytic. So Instagram has now collected data about what you liked. And that picture has certain tags, so it knows that you like a picture of a certain type with a certain tag. It also has location tag to it. It also is actually uploaded by your friend or it is uploaded by someone you follow or it is uploaded. by someone you don't follow. All that information is now being collected. That data is now collected. It is stored by Instagram. And now based on the algorithm that you said. So you write an algorithm and that algorithm makes some predictions and now it personalizes the experience. So now what happens is when you go to Instagram again, based on what you like, it starts showing you pictures similar to it. So if you like a lot of motorcycle pictures, you will find that you will you will not in your feed is what where you will see stuff from people that you follow, but outside the feed when you go to under such. So you will you'll find that you're getting a lot of motorcycle pictures. Suppose you take part in a lot of events. You will find that now you're getting recommended a lot of events which in which you can participate. So Instagram now has data on how to show pictures. So it's a personalized experience. Now coming to Facebook, Facebook will see what you like and based on that they will show advertisements because on your feed, they cannot show you people I mean, they can show you some stuff from other people if they themselves are trying to advertise. So based on what you like, you will see certain types of advertisements. So as you mentioned if I'm pregnant, I'm looking for say some advice. Okay, I want to I want to look for classes. I want to look for some stuff for my baby who's on the way, things like that. So based on that, Facebook will say okay, this girl, she's pregnant. And she's looking for these items. So I should start sewing this data. That is one type of analytics that is social. Now we'll come to OTT platforms. So Netflix, Disney plus, whenever you open Netflix, Netflix is also collecting data and they are trying to see what are you watching more frequently and based on that they are trying to recommend you more titles. Also. They have different images for different films. So suppose you watch the movie Matrix. I watched matrix , Rinat Watches matrix. But the photo that I get on Netflix for watching matrix is different from the photo that Rinat gets. That's a way of selling or telling you watch. Watch this film. Okay, so they're using all this data to see how the user reacts. And based on the user reaction, they try to personalize the experience and try to make predictions and then they take it, they say they implemented and then they see the user reaction again. So that's how you see a lot of recommendations. So all these I mean, so we saw ecommerce, we saw social, we saw OTT platforms. And if you look at Uber or say Google Maps, Google Maps now knows Okay, where are you located? Normally, what are you searching for? If you don't delete your cookies, they have the recent history based on the recent history they can predict. Are you looking for this or are you looking for that if you go to delivery, or if you go to UberEATS? So that's OTT. So and then you if you go to delivery, say

you want to make an order some food delivery now knows where the order frequently from and based on that, they'll show the most frequent restaurants that you order from and they also can suggest you most frequent items that you order from that restaurant, and they can then now recommend so you don't have to do the work. They are recommending you and you order from them. Also same with Google Maps. So if you go to Google Maps, they know where which are the locations you normally search for, or at this time of the day, what are you looking for, and based on your location, what you would ideally be looking for if you're saying a jungle, you would ideally look for a place where you want to go to a main road. If you're at a main road you bite ideally looking for a taxi or you might be idly looking for a bus or you might be looking for the nearest train station. If you're already in a very in a very hip and happening place. In a shopping complex. You might be looking for places to eat, places to drink or places to buy stuff. So based on all these things, they can do a lot of predictions. I also I just wanted to summarise that it's not just ecommerce websites, analytics is on every single application that you use. It could be a gaming app, it could be a shopping app. It could be social app, it could be an OTT app. It could be even a Maps app. So it could be anything a search app. So everywhere you have analytics and it is basically I mean, companies how they sell it is that they are trying to improve the experience of a user. That's how they sell it. They want to make sure that whatever, Whenever you come to their website, you get a very nice experience and a very personalized experience. That's what they're aiming for. Of course, the problem is you can fool the algorithm. You can skew the algorithm and you can, you can start playing with it. And then the recommendations are just here and there. So, so those are the plus and minus sites. But it's not just ecommerce website, you have analytics across various other platforms and other categories.

Absolutely, absolutely. So yeah, the way you were saying, you know, how different companies gives you suggestions? I mean, if you are to blindly believe that their suggestion is the best question and a lot of the times this is I would say most of the time, it is actually what they're recommending is what I would actually want. So I a lot of the times I knowingly blindly just go with the decision. But again, this kind of creates this thing called echo chamber that you know, the algorithm keeps bringing you stuff the same things. So you are kind of always surrounded in a bubble. And there is this theory I what I saw it in a TED Talk that everything that we do in life, every single action can be categorized in either in two ways. It's you're either exploring or exploiting. You're either exploring something new, or you're exploiting information that you already gathered. For example, if you go to a restaurant, that you've been there before, and you know it to be a good restaurant, and you know that their food is good, then you're exploiting the information from the first time when you went there. But if you're going to a new restaurant, which you don't know and you're kind of taking a risk, but you feel a little bit better today and might and you know, go and find out and you find out that whether it's good or bad if it's bad, it still you know, next time you're not gonna go there. So you're still exploiting that information from the exploration that you've already done. Now, with these, these recommendations, you're exploring less so one of the things I personally do is that I would ask me, no, I would face to our to our audience to do that as well. that whenever I'm doing something online, I try to keep at least 10 to 20 20% of anything that I'm doing. For example, if I'm doing grocery shopping I do my usual shopping and I looked at their recommendation etc as well. But then I also go and buy something completely random that I don't even know what it is. Sometimes I found really vegetables that I really like, or some products that I've never thought of but actually I found but a lot of the times those were wasted because I actually didn't like them or whatever. But through this exercise, you're still making yourself explore new things that you probably would have done if you weren't kind of echogenic chambered in this bubble of algorithms. So that's one of the things that I would advise our audience to do as well. Not just going grocery shopping, but anywhere like every few days make an impulsive decision, which you wouldn't usually make. You never know where it would lead.

Yeah, Ture. it is very important because if you look at elections, that's how you manipulate electorate. The Trump elections is a very good example. I mean, it's still I might say it's 100% sure what happened, but just to be the sceptic, you can say that some people on Facebook they saw some advertisements and based on that, they voted the way they did. And there was a company that was responsible, Cambridge analytical, and that was responsible for this. This manipulation, as you might call it, but it is one way where you can actually manipulate people's perception about something and you can easily skew. So you mentioned about the bubble. I think data is a very powerful tool. If I keep seeing the same thing again and again, it reinforces that yes, I want this Yes, I like it. That is what the website is showing, but this is what I want. So it reinforces certain behaviors or behaviors as well. So it's very important to go away from it. I mean, we, we are talking about analytics, but we also want to see the implications of analytics how it affects our behavior. So that's very, it's very important. I tried to clear the cookies every time I shut down my computer, I don't want to store anything. And every time I go, it's fresh.

Yes….. Absolutely. I usually try to not accept the cookie consent unless it's must needed….

Yah !!! exactly I also tried to not accept it. And even if I accept I tried to clear everything and when I close the browser, so everything is removed, and when it comes to personalized choice, I mean recommendations from Netflix what you would see is Netflix has, say 10,000 titles, but what you see on screen is not 10,000 titles, it's maybe 50 or 100 titles, the rest of the titles are hidden, and you have to actually go manually search for it. And that's the biggest problem. So normally, if you go to save Wikipedia, or say you go to an encyclopedia on a book or a website, and you can search alphabetically of what topic you want, and based on that you can read and gather information, but on a website like Netflix, you cannot do that you cannot go through titles based on the year of release based on the year of based on the category sorry you can do based on category but you cannot see all the titles alphabetically or based on the release date. You have to search

that kind of means that they have the monopoly over which might get viral or not. Yes, they could push things that you know people wouldn't know and like how when do you search for particular things like right, I mean, you know, I would go to Netflix or Amazon Prime, and I would search for something that I've heard in my life somewhere else and what how would I hear it if it's been being pushed by Netflix to others, right? Most of the times. So yeah, this is quite, quite dangerous actually. If you think about it, the power they have to make some certain things viral and certain things not. And obviously, you know, there is an interest in of conflict there. If they're producing Netflix produced shows, and they're obviously going to, you know, push those out to more people. And most of the times, all the Amazon Prime exclusive or the next Netflix exclusive shows are usually popular and I start to wonder why that is all the time. And this is this is one thing, but then, you know, going back another step what you were saying about elections, I mean, it's all fine and good with you know, one little ecommerce shop, you know, doing a bit of data analytics, or even you're using Facebook's analytics to advertise for your product, but when it changes the outcome of electoral procedure in a country that has significant moral questions that one needs to ask. So yeah, I mean, I think we've said it in many, many of our episodes, and I'll keep saying it until everyone knows, but please be aware of your data on how it's being used and you know, whenever you're being exposed to data in the form of analytics, with, you know, looking like very colorful bars and graphs, be mindful of the smaller numbers which shows the accurate representation at this point, sorry, probably saying quite a lot of things. Well, another thing I want to mention is the causation correlation. Effect of data visualizations for example on a on it's an actual statistics that in the summer, ice cream sales are increased. And also in the summer, the number of assaults are also increase. But I mean, you know, if you look at these two data, and you could think that you know, people who have more ice cream assaults, more people, but that's completely irrelevant. It's two separate data, which none, not one of them is causal to the other. It's just correlated based on the fact that it's summer so people are buying more ice creams and it's summer so people are more hot headed so they can they and there is another interesting data on the fact that more people, people who has dark chocolates are the most Nobel Prize winners, but it doesn't mean that if you start having dark chocolates you're gonna win Nobel Prize is just the fact that there are more Nobel Prize winners from Switzerland I think, and it's just happens to be dark chocolate is a very is very popular dessert in Switzerland. So it's correlated, but one is not causal to the other. So we have to remember that,

I think, I think that that's a very important thing. I mean, you look at the data, and then it does some analysis, but you forget that you are doing that analysis, and you have, you're trying to see it in a certain way. And that's what the tool is showing you and you can interpret the data in multiple ways. And it's up to you how you make use of it. So data is just raw. It's just facts, its objective, but when you do some analysis that can become sometimes subjective. It's based subject to interpretation. So we need to be very careful about what we are trying to understand and what we are trying to predict and how are we trying to predict it? So I think that's why it's very important. I mean, you look at all these analytic tools, at the end of the day, they are just processing information. And then you decide what kind of graphs or what kind of data you want to see and what kind of models you want to create, in order to maybe sell more, or advertise more, etc. So it's up to the public how you want to do it, but it is still a very powerful tool. I think without analytics. We wouldn't have the internet, the internet that we do, because it drives innovation as well. It focuses companies, it focuses organizations to only sell things or, or to market things that are actually more useful, beneficial, that people are actually wanting. I don't want to spend my whole energy into creating something that is not selling. That's not what people want. And if people want something that let me sell them that so in a way it's good, because it filters out all the naked all the all the stuff that's unwanted by the population. So it's good. But when you comes to social platforms, where you see these kinds of advertisements, it sometimes reinforces your beliefs, your ideologies, etc, even, I mean, my wife and we keep talking sometimes we talk to each other, and our smartphones are there and say we talk about certain topics. Say we are talking about Dua Lipa or Taylor Swift and suddenly when we open Instagram or Facebook, we are seeing advertisements for developer or Taylor Swift on Facebook. On Instagram. And it has happened. I was speaking to my personal trainer, I was talking about Mandalorian the series on Disney plus, and he saw it on his phone that he's being recommended Mandalorian and he has never seen Mandalorian he's never Googled it. It's being recommended to him as a result of me and him talking over the phone. That's it. And it is it is incredible. I mean, we think about smartphones, but they are also devices that are that can constantly listen. I mean, we don't know if they're listening but they can listen. So you have to be careful. I mean, Don't be paranoid, but you need to be aware of okay, there are devices in your house that could be capturing information, even your webcam. So we're talking now on camera. The camera is looking at me and I can I can see that the camera is on because it has a blue light. What if the blue light stops so I think the camera is off, but actually it's still recording. So those kinds of things. I mean, it's good to collect all the information but that information can be exploited. It can be made to a good use. So we just need to be aware of all these possibilities.

Absolutely. And you You came right back onto the dark side. Amit ! You're trying to be positive or no Yeah, absolutely. As we said I think we both ultimately said the same thing. It's a powerful tool. But who uses it is the is the question and we want the big companies and the big entities to use it morally and ethically. And when we are subjected to it, we want to be aware that how it's being represented to us but yeah, overall, it's very powerful. It's very positive. I mean, obviously, as Amit said, Don't be paranoid. I mean, it's difficult to do normally when you know about tech, if you guys are following us then Yeah, but I'm still saying not to be paranoid. I mean, I know how much data Google have on me, but I still go to Google, because I get the best service and, you know, It's a trade off which I've kind of accepted but at the end of the day, it's always good to be cautious. Yeah.

It's convenience over the privacy or the paranoia that you want to have. If things are convenient for you. Why would you do it? It's as simple as that. If, if, if doing something is more convenient on a Google website, why would I go somewhere else? I think I think that’s the question…..

you just have to like be aware of the ethical line. If Google is crossing that, maybe we want to do something about it. But overall I'm getting I'm getting a lot of service from Google and open to give some data.

Exactly. So it helps us do things quicker. It it's free of cost. Of course. It's not free. Of cost, it's still collecting information in you. Maybe not your name and phone number, but the sites you visit, the locations you go to the things that you like the things that you purchase, et cetera, et cetera. Recently I added a product on a website. And I close the checkout, I exited at the time of checkout. So it dropped. And I got an email saying, oh, there is an item in your basket. Are you still interested in buying it? So those kinds of things, and it nudges you to buy it, but it's also convenience you might have, you might be checking out something and suddenly you got a call, and you completely forgot about it? And the next day, you wanted to go back but you forgot what you were doing? And now you get an email and you get reminded, oh, yes, I was about to buy that. Let me go and finish the purchase. So it's a convenience. But if you're not interested, it's a nuisance as well. So you have to look at

Yeah, absolutely. there is a bit of a hack for you guys, though, for those of you who uses Etsy, see if you leave something on the shopping basket and leave dropout, basically not to everyone. It's I don't know. It's probably chosen randomly, but you might get a discount email saying that Oh, you didn't finish your checkout here’s 10% discount if you checkout right now or buy today or whatever. So yeah, I mean, that's

It’s a good point because I recently so we are recording this of zoom right now. And we…..I had a subscription for Zoom. A paid subscription. And what happened is, I spent a lot of money. So I thought let me cancel the subscription while cancelling it. It upsell. It said, Okay, if you don't want to pay, say 18 pounds a month, we can offer you a discount. So now you pay only six pounds a month and you can keep the subscription. Sometimes when you cancel you can sometimes get a better deal. But of course that six pound is just for six months. It's not for ever, but you get a good deal, right? So that's I mean, it's a way of keeping you keeping the money coming.

Absolutely. A lot of companies does that. With substitution especially I think I've had the same experience with LinkedIn as well with LinkedIn gold, but yeah, I mean, definitely do with this information what you will I do encourage you to use it. Yeah, yeah. So but yeah, ultimately, analytics. You know, we've talked about how to be aware of it, how it happens in the background, but in terms of user of analytics or how in the part of the audience who are actually generating these, these analytics, I would urge you guys to always be mindful. Not everyone is being malicious, but it's very easy to also mislead other people. So I would encourage them in depth. I mean, I use analytics at my job as well, to an extent and I have some influence, I always make sure that the data that I'm representing is accurate. It's not misrepresenting or misleading. So I would ask the audience who are doing similar things to be careful, because ultimately, it would be good in the long run for your own business anyway, if you are, you know, giving better solid data, which are more reliable.

I think it's all about being transparent. So if Google is transparent about okay, they are collecting this information. I'm not scared anymore, because I know what they're collecting. And they're telling me that they're using this information to do this. Now it's up to me, whether I accept it or not, but it's something that's not hidden. It's not it's not something that they're doing behind my back. So they are very open and transparent about it. And I think as organizations as individuals, we should be responsible. So even now, when we are talking, I'm telling you what products we are using, what things we are buying, etc. So we're trying to be as transparent as possible and I'm guessing a lot of the other companies are as well. But it just being mindful of that. Like it's always good to be open and transparent. So in the end, it's for the benefit of everyone. Let them take make the choice.

Absolutely, absolutely. If you're providing good service or good products It would always be a very easy trade off like Google, you know, I know there's so much data but I'm still giving up you know, willingly because I want the good product and services that they're offering. So yeah, I mean, it's usually a good trade off for both parties. Yeah. So yeah, as long as you're now Cambridge analytical, I think. Yeah. Apart from that small subset of…..

But there will be companies like that. Rinat, I think, I think very wherever there is something positive, there is something there is an opportunity to exploit. And there will be companies that will emerge. You have laws, you have strict laws and you have penalties, you have big fines, your GDPR etc. But there will always be companies who are willing to exploit because people are willing to, to take that leap of and go towards an opportunity which will give them quick rewards. So they said that there will be companies that or individuals who would want to exploit they'll always be people. So you just have to be aware.

Absolutely, absolutely. All right, audience it's been fun talking about analytics. Hopefully you guys enjoyed our excitement, I'm sure kind of seep through to our conversations, and hopefully you guys will be more aware about the topic. And, you know, if, if we could change even 1% of your decision making. By the awareness we've kind of provided would be very happy. Thank you very much for guys who are listening and who are following us. Please do reach out to us if you have any topic that you'd like. us to cover. And if you guys have any feedback, or anything similar, our contact details are available at the end of our podcast, as well as in our YouTube channels. Amit, any last words from you?

yes. So thank you so much, guys. For listening. And thank you so much Rinat for having this conversation today on analytics. I think it's a very important topic, very relevant. Given what's happening around us. I think it's important to be aware. In the end, what we covered is a brief description about what analytics are, and its consequences and examples. We didn't cover about the tools like how Google Analytics works, how the other analytics tools work. I think that's something that you can always learn, but I think to be aware of the implications, I think that's a more crucial. So our aim through this podcast is just to make people aware, at the end of the day, there are many things in technology that you can always find online. But just to be aware of the implications and how it's being used. I think that's what we're striving to do. I hope you keep listening to our podcast, and keep giving us feedback. It's been a while since we posted we were both on our personal journeys I became a father, Rinat got married. So it's, it's a good personal journey for us. And in the next couple of weeks, we are planning to have some guests on our shows, which we did earlier. And we would like the viewers and listeners to recommend any guests they want to see on our show, or any topics that they want to hear. And once again, thank you so much for all the support and appreciation.

Thank you everyone.

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