Personalization in Action: Maximizing Impact with What You’ve Got

NOTE: This is a raw transcript and contains grammatical errors. The curated transcript will be uploaded soon.

Vipul Bansal: Hello, everyone. Welcome to Convex 2024, the annual virtual summit by VWO. Thousands of brands across the globe use VWO to optimize their customer experience by gathering insights. Running experiments and personalizing their purchase journey.

I’m excited to invite Jay Lansdown on stage with me here, who is an experimentation lead at New Look. Hi Jay. How are you?

Jay Lansdown: Hi, I’m doing well. Thank you.

Vipul Bansal: Awesome. Awesome. I feel very excited to have you on stage today and talk about the most pressing topic under the umbrella of, of, of optimization. Which is personalization.

So would you like to, you know, maybe give a very, very short sneak peek into what this presentation of yours is going to be all about?

Jay Lansdown: Yeah, of course. My presentation is called personalization in action, maximizing what we’ve got. And what that really means is just making the use of what you have. And that’s what we’ll talk about today.

Vipul Bansal: That sounds really exciting. And of course, uh, folks, there are going to be lots of examples from New Look and also from the personal experience of Jay. So there are going to be lots of takeaways in this entire presentation. So keep your notepads and your pens ready.

Thank you. It’s going to be a really insightful session. And with that, uh, Jay, I hand over the mic to you.

Jay Lansdown: Thank you. So as I just said, this is really about making the most of what you have. I work for New Look, which is a fashion retail company. It was founded by Tom Singh back in 1969 and it’s been going strong ever since.

Now New Look’s biggest audience is 18 to 44 year old women. But it’s also popular with women and girls older and younger than that and we sell children’s wear and men’s wear too so there’s a very big audience. It’s one of the most popular retail fashion brands in the UK with 418 brands, sorry, stores here plus international stores. We still sell more offline than online.

But we’re looking to change that, and that’s partly why I’m here. I’m the experimentation lead at New Look, and my job is to take experimentation and personalization from something being owned by a few people and democratize it. So it’s the way that New Look does stuff digitally, and the way we just release things. Experimentation starting off with two teams.

Product and trade and optimization. Product owns all net new commerce, e commerce areas, both for the website and for the app, they look after the creation of new features. That might be, for example, a new layout of a PTP or changes the payment methods offered to users. We have a product team for each area of the website, but one of the biggest challenges of product, of releasing new features is one that this report from Pendo back in 2019 highlights.

That’s that 80 percent of software features that are released are rarely or never used. And back in 2019, that equated to a waste of 29.5 billion dollars. I can only imagine how much that equates to now.

But that means that only 20 percent of the products that were released were used regularly. Think of all the hard work, the stress, the money poured into efforts that were completely wasted. It’s quite sobering, isn’t it? There’s no point in releasing 10 features, 10 products.

If four were useful, and drove a benefit for the customer, helping them find and buy what they wanted. But four, actively harmed the experience and stopped them from finding, from getting in for inspiration, from converting. And two, were just a bit meh, just didn’t really do anything, didn’t improve the journey, didn’t harm it, but were just a kind of wasted effort. But if that happens, then overall there’s been no benefit, no moving the needle.

Any gains have been counteracted elsewhere by losses. Experimentation is really important for us, so we can identify and release just those positive features, so we can ship the things that matter. And help both our customers and us. I’m Mr.

Long this journey. We have a long way to go until we can make certain we’re releasing only positive features. But I wanted today to talk to you about trade and optimization. Our trade team focus on making sure the right products, the ones with most demand or ones you want to push are put in front of customers so we can sell more of them.

The optimization team, on the other hand, are primarily responsible for on site marketing. It’s important to make sure that we know what kind of products are selling and what kind of messaging and online experience resonates best. Experimentation is a great way to learn this. But experimentation isn’t what I want to talk about today.

I want to talk about data. Personalization. Now, I don’t know about you, but I feel like I’ve been hearing a massive buzz about personalization for years. This is the year of personalization.

Hyper personalization is going to change the world this year. One to one AI personalization will change e commerce forever. Back in 2012, Mike McNamara, Tesco’s CIO at the time, was saying that personalization is the next big thing in retail. It’s not even there anymore.

There is this McKinsey report back in 2019 saying that personalization would be the prime driver of marketing success within five years. Today, 2019 was five years ago. So, is personalization the prime driver of marketing success for your company? Maybe not.

And just last year, another McKinsey report said personalization would transform the way that companies do marketing. So where is it? Where’s this game changing personalization change that’s supposed to have come years ago? Are you seeing it anywhere?

I’m not seeing much personalization. Not at scale, not outside a handful of big companies. So why isn’t it happening? Should we just give in, accept it’s never going to happen, that we should just accept it?

I don’t think so. I want to talk about three reasons why I think we’re not seeing personalization happening much, and what New Look have done to get past them. I think there’s three reasons for this lack of personalization. One, it looks too hard.

Two, dev, develop a resource. And three. Buzzwords. So number one, it looks too hard. When we talk about good personalization, you often hear examples like Spotify or Amazon, although some will argue over that, or even Netflix.

You might even hear people say that Netflix out competed Blockbuster. Because unlike Blockbuster, where you go into a room and perhaps struggle to find something, or what you want was out already, so you grab something else that doesn’t really suit you and you’re not really happy, Netflix avoid that by giving you what you want. And they do that by knowing what you’ve liked in the past and serving you similar things that relate to your tastes. Or, if it doesn’t know you, it knows what people like you have bought.

What you’re likely to enjoy watching. Fantastic personalization. Great for the customer, for the consumer. But they can do this.

They can personalize like this. Because they’re sitting on a huge database of content that users either subscribe to or regular return customers of. If they don’t let their customers easily and quickly find what they want, they’re very likely to cancel. It’s strategically vital for them.

It’s also relatively easy for them to do this because with so many repeat return customers, they can pick up so many data points. They can easily see what users watch, listen to, or buy the most, and give them content that will resonate best. They can also easily see patterns. Users who look like other users who like this, but new look, it’s not Netflix or Amazon.

We don’t have subscribers, or a model where users plan to interact with our app or site every day. So seeing a pattern is more difficult. I doubt many companies have the same level of data that Netflix, Spotify, or Amazon have. But equally, New Look’s not really a furniture store either, where visitors might make a purchase every few years.

If we were, we’d know nothing about users apart from what we know now. And I imagine most companies sit in this in between zone that New Look does, knowing something, but not knowing everything. But the data that we do have is still very useful. Even when we know nothing about them, we still know loads.

We know if they’re new, so we can give them extra reassurance about who New Look are, how trustworthy we are, how good our pricing structure is. We know their source. We know what influence they’ve interacted with, or what email campaign they’ve come from. So you can reference that in a journey, or show the same products as seen already.

We know what the weather’s like, so we can propose best stops in the summer, or knitwear when it’s getting cold. We know when it’s payday for most people, so we can talk about going out clothes, or where they are, so you can talk about different clothing trends that are popular in different parts of the country. When we know a bit more, we can see even more clearly. We can see what they’ve done in that session, what they’ve done before, what kind of categories they’re most interested in, so we can badge those categories, or highlight clothes that go well with that look.

We know what they’re viewing, what they’ve added to cart, so we can start to infer what type of event they’re interested in buying for. We can sell more festival wear, for example. We can see what size and fit they’re looking for, so we can highlight relevant alternatives when that product’s out of stock, or guidance on the fit of certain categories. And if we know a lot, if we can clearly see, we’ll see that A, that’s Nadine Coyle, and you should buy Edit because it’s awesome.

And also all kinds of other useful data points. We can see if they’re linked to a known news app, or how she browses and what she’s done before and when. We can see relevant messaging about those products. We can see what loyalty scheme they are and what discounts they have.

We can see if they only buy when things discounted, or if they prefer to buy new stuff. And I don’t like discounted older content. We can see where they usually collect the clothes from, so we can give them the local information relevant to them. And this data is great.

As you saw, even when we knew nothing, we still knew a lot about the customer, which we could act on. Just because we don’t have the data that Netflix or Spotify or Amazon have, doesn’t mean we can’t do great things. But, if I learnt one thing, It’s not let great be the enemy of good. Use what you have.

I’m sure you can do a lot with what you have, just as we have already. Looking too hard shouldn’t put you off. Don’t compare yourself to unrealistic levels, unrealistic competitors. Just use what you have.

What you have. The next reason that I think that personalization struggles to take off is developer resource. It doesn’t matter where you work. It doesn’t matter how many developers you have.

You never have enough developers, engineers, programmers. And even if you do, There’s so much to do that even if you get more developers, there’s a never ending list of priorities for them to tackle. Product releases, tech debt, integrating new tools, re platforming, DevOps, cloud services, microservice management, personalizations, normally way, way down their list. And that’s just developers.

To get personalization done, you always need data scientists to build recommendation algorithms. Copywriters to build content for different audiences, uX to sort out the page layout. Designers to build endless creatives. And they all have the same amount of backlog and competing priorities that developers do.

So how can we ever get personalization done? What we did, what you can do, is work with what we have. I showed you this slide earlier and told you about experimentation working for trade and optimization. Our trade team, they push the right products, the ones with the most demand, the ones that are selling the most.

Optimization, that does mainly on site marketing and messaging. But what’s this got to do with developer resources? We decoupled trade and optimizations work from developers. From the other teams.

I mean, there’s always some situations where developer or designer or other resources needed, but for 95 percent of the time we can do without it. We use what we call action templates, editable templates that the team can use to insert banners, images, content, messaging, just leavers to change user behavior. We edit recommendation modules so that different logic can be used for different times and audiences. But I think that’s less interesting than how we got there every week, the trade and optimization teams were looking at weekly performance across e commerce, and they’re making quick tactical fixes to fix the business challenges.

Perhaps changing some text to read or something to show a sale was on. And that was great, but it was a bit of a missed opportunity. We found we could move experimentation and personalization into this business as usual activity. So first we took the company objectives.

And broke those down into smaller OKRs. Relevant sub metrics that related to each department. OKRs that each department could realistically work towards in their day to day job. Then, when reporting was done on these weekly performance things, we could highlight where these ties in closely to what each team was working on.

So those relevant issues could be looked into, to highlight where there’s opportunities, where there’s been surprising activity, either good or bad. And importantly, Then we could work out the cause of that opportunity. The absolute key to this was research. What was the customer challenge here.

What were they not able to do? What was the cause of this opportunity that the weekly performance review highlighted? And it might come from previous experiment results, or web analytics, or content square, or customer insights, or UX research, or more realistically, a combination of all of them highlighting where and how serious each issue was. Now we are Netflix.

But using the data we did have was key. When we gathered as much data as possible, we’d ask questions like this. Who, is happening to? Why is it happening to them?

How do we know that? What other data sources corroborate this understanding? How has this changed our mind? And this loops over.

We might find that some things we thought were opportunities weren’t really opportunities at all. There were things that we thought were a problem, or were business asks that didn’t really relate to any real customer problem. This only really stuck though, we made it part of our routine, part of our BAU. We meet on Monday mornings to review the weekly performance and highlight the key challenges.

Then separately, the team research what could be leading to the customer behavior behind the change. Those issues. On Tuesday afternoon, we review those insights that he pulled out from the data sources and together collectively define an opportunity statement or what’s the customer opportunity to solve for we spend some time reviewing how we could solve these opportunities that meet Wednesday afternoon to decide which solutions and there might be many, we think are worth trying. As the team are uncoupled from dev resource from the dev team, they can put these together quite quickly.

It’s largely the targeting and metric selection which takes the time. But there’s one more thing that I think is stopping personalization from being everywhere. And that reason is buzzwords. I mentioned earlier how it feels like personalization is always coming next year.

And I think one of the reasons for that is personalization always seems to be getting more complicated. Predictive analytics, AI, machine learning, hyper personalization, digital body language, cookie list personalization, dynamic content, omni channel experiences, micro events. Every year there are more and more of these personalization buzzwords. These more powerful, fancy phrases that are spoken about, and they just seem so complicated.

That puts people off. AI, micro moments, interaction management, these are all super powerful. They’re great. But when you’re first starting out, they can be just too far away.

They can seem too unachievable. It’s like moving the goalposts further and further away. It just seems unreachable. But it doesn’t need to be.

Personalization doesn’t need to be. That complicated because what is personalization? I thought I’d check VWO’s definition for this. And it says that website personalization is the process of tailoring the website experience for a visitor based on past data or preset rules.

The aim is to delight the user and make them feel special in each website interaction. It’s really just getting the right message to the right customer at the right time. It might be as simple as. Users have bought pink before, like this, or for us, more relevantly, it’s things like behavior, a user’s favorite category switching to maternity wear indicates that they are likely to be buying maternity wear far more.

And segments for us have been the easiest and most effective way to move with personalization. One to one personalization, micromoments, customer feedback loops, these can all come later. Like I said earlier, don’t let great be the enemy of good. But how can you, how can we decide which segments to focus on?

Research. Without the research, we’d just be throwing up ideas in the air and seeing what sticked. That research is the lifeblood of personalization. So it’s the same things.

Web analytics, previous experiment results, UX research, customer insights, content square. The data we have is powerful. I showed you this slide earlier. But the important question for personalization here is, who is this happening to?

It may not be a situation to personalize. If it affects everybody, great! We should optimize that opportunity for everybody. But if it affects a specific audience, we should personalize for them.

It can be tricky to work out the audience, and if it’s worth investing effort to solve the opportunity. If it affects 50 people in London, it’s probably not worth putting the effort in to fix it for them when it’s a more pressing issue that fits everyone who is plus size, for example, we need to work out where to focus our efforts. So to help with that, I’ve VWO ified one of the frameworks that I use to work with to decide on what segments to work on. I’ve very creatively called it the VWO segment framework.

You’re First, can we target it? Is it accessible? It’d be great if we could tell everyone who loves Girls Aloud that Nadine Coyle just dropped an edit, but that’s not targetable. We need to make sure we can target the segment, so we have a technical plan to target Second, wide.

How big is that segment. The bigger the segment, generally the more it’s worth. Now, I spoke earlier about not targeting 50 people in London. The bigger the audience, the more likely they are to collectively spend and make more money for us.

And also, the bigger they are, the faster to reach statistical significance and confidence and get a result from the test on this segment. Thirdly, oodles or lots. It could be. That those 50 people, London are responsible for 80 percent of the purchase made.

I mean, it’s not for us. But you get the point. We use Adobe Analytics and Databricks to understand the value of the segment. And we look not at just conversion, but the value of the audience taking the next step in their journey.

So for example, if a plus size buyer is on PDP and we show the fit of clothing better, what’s the value to us of that audience size collectively adding to basket more by 1 percent say? Goal trees are extremely valuable for us to show the quantified value of revenue impact of sub metrics for an audience. They can take quite a bit of effort to set up in the first place, but it pays off for us very quickly. Another segment we use is an RFM model.

So as well as opportunity driven personalization, we use RFM segments so we can treat our different core customers, those audiences. But just to stress again, for all of this segmentation, the absolute key is research. Those previous experiment results, web analytics, UX research, customer insights, any other source you have. Use the data you have to understand who is doing what.

and why they are doing it. And on that point, we don’t see experimentation and personalization as completely discrete separate things. An experiment might highlight specific audiences that need specific Personalized experiences and personalization is always an experiment. Always measure to prove it’s the right thing to do and we aren’t just throwing ideas at the wall.

Just because we’ve done that research and hypothesized the cause behind the customer opportunity doesn’t mean we get it right every time. Every piece of personalization is still a full experiment and we analyze them in exactly the same way. Every piece of personalization is given the same amount of rigor as a typical experiment. Because personalization is still just part of the standard experimentation process.

We’re just wrapping different teams at the right stage and empower them to do great work. So, how did we get past these three blockers of personalization? Of it looking too hard, of dev resource, of buzzwords. Well, if it looks too hard, just work with what you’ve got.

We don’t have Netflix’s data, or Spotify’s data, but it’s not a blocker for us. We use what we have to understand where the problems are, and for whom, and then do simple things to fix them. The more data we get, the more granular we can be, but for now, we start very simply and build up from there. And don’t let dev resource be an issue.

Just use what you have. You may have a team to work with like we do in trading optimization. You may have completely different teams. Make friends with them.

Work together. You can help each other and be each other’s best ally. And don’t let scary buzzwords or moving goal posts scare you. Just start small and work up from there.

What it comes down to is just use what you have. Because if you do, you’ll maximize impact with what you’ve got. So thank you for your time today. Love to any questions.

Vipul Bansal: great. That’s a really insightful presentation, Jai. And I really loved, uh, how you defined the each letter in, uh, in BWO. And, uh, the BWO, wide and oodles.

I think nobody ever would have imagined that, you know, the letters could mean. More than what we set out to define them, right? So so much for actually thinking so creatively. That’s, that was really, really good.

You know, I was, I was, uh, attentively watching your presentation as well. Jan, uh, personalization is a topic that not everyone has been able to ace. Uh, I’ll be very honest with you. I speak to a lot of people, uh, and they do present their hesitation to actually run a full fledged, uh, you know, personalization campaign.

So I have a few questions for you, right? Uh, I’ll start with the most pressing question. When, um, whenever we think about personalization, this question comes up and it is now more relevant than ever. Um, the concern about privacy, right?

Personalization is something where it is, I think, important. Where you have to collect all sorts of data, uh, pertaining to a specific user. But in today’s day and age, when there are so many prevailing data laws across the world, you’re not actually allowed to track a lot of things, right? And browsers are also coming up with new governance and new, new changes in, in, in there.

Software that restricts how, you know, third party cookies are, uh, you know, placed inside the browser, stored inside the browser. What is your take on this? How does privacy concern influence a personalization campaign? Shall we just stop creating personalization campaign or how should we actually adjust our personalization strategy?

Jay Lansdown: That’s a good question. I think that we need to have levels and extended. We have them. So I mentioned in my presentation that we don’t need to have all the information.

There’s a whole loads of non cooking information. You can register to give a very personalized experience. And that’s. Good in itself.

That’s still a valid personalization experience that is more than most people are getting. That’s not the only level. The more engaged a user is, the more likely they are to log in. And at that point, you aren’t using their third party data, not using cookies.

You can tie it into your own data ecosystem, your own code base. And this is good for everything. It’s not just personalization. The more engaged they are in your ecosystem, the more likely they are to buy.

If they have your app, for example, we know that increases a tendency to purchase again. So it’s the same question as it is for any level of maturity with our users. Let’s get them to log in. Let’s get them to see the benefit of being engaged.

And maybe a benefit is we can give you more relevant content. We can give you stuff that you like better. Personalization. One of the slides said earlier that 76 percent people expect personalization.

and they get frustrated not getting it, tell them, if you log in, if you sign in, we can give you these, whatever is for you, very clear benefits that personalization provides.

Vipul Bansal: But I think that that, uh, incentive of sorts, I think, uh, is something that needs to be communicated quite clearly to the end consumer that yes, the companies are collecting data, the online businesses, they are collecting data, but it’s for the purpose of making their own purchase journey easier. And more relevant to them itself, right? Of course, a lot of consumers, including me, you know, we, I shop, uh, from e commerce websites a lot, right? Uh, sometimes bill bills are quite high, but that’s a story for a different day.

But, uh, You know, I, I do want, I do want the website that I’m visiting that they know something about me, right? Um, but there’s also a concern at the back of my head that, yeah, maybe, uh, they are tracking this each and every step that I take on the internet, not just on the website, but on the internet, right? Be it, let’s say I’m on social network, or maybe I’m on scrolling on Twitter, or maybe I’m scrolling on Instagram. Right.

And I, uh, I’m sort of concerned that, um, the brand might be different. All the different brands in the world might be tracking me right now and might be recording. Hey, this person just looked at, you know, a picture of a sofa. This person just interacted with a tweet that mentioned, let’s say cosmetics, right?

So these are the privacy concerns that are prevailing and keep users from, uh, you know, openly sharing their data. But I think the, you mentioned a great point that needs, there needs to be an incentive and there needs to be. Uh, a proper communication that,

Jay Lansdown: honesty is vital, Just tell them what you’re doing and be honest about it. People are happy to give to get if they know what the parameters are and I think honesty always helps in life doesn’t it?

Vipul Bansal: absolutely. Absolutely. Honesty always helps. Honesty is the best policy. Uh, we were told in, back in school.

Uh, my next question to you is about, you know, personalization for different forms of traffic. Do you think there needs to be a different personalization strategy or a personalization campaign altogether for people, for all the traffic coming to your desktop website versus your mobile website? Do you think both of these should be deemed as a separate entity?

Jay Lansdown: That’s a really hard question to answer. I think it depends on what the audience is and what their needs are. It’s so hard to give a definitive answer because if that audience is drastically different and their behavior indicates that backed up by other data sources, then yeah, probably. But if they behave the same way, if they have the same requirements, same needs, then give them what that audience.

It could be that they’re different, or it could be that there’s the same audiences, different strata, different layers that need the same thing on different devices. So yeah, just research, see what your customers want.

Vipul Bansal: Yeah, that’s, uh, that’s always a very hard question to answer because it requires a lot of segmentation and a lot of understanding of how your audience is behaving on both of these different platforms. Um, but I’ll move to the next question, which is, you know, which is more circle, which is more centered around the AI because AI is. You know, a trending topic these days and, uh, suddenly just so many businesses have sprung up, uh, that use and heavily use AI to, uh, to enable different business functions in your view of being in this space for all this while and being working on organization campaigns for all this while.

What do you think, uh, how AI can help in in running personalization campaigns.

Jay Lansdown: Loads of ways. I love AI. I think it’s a great way to, first, you can use it in your data stacks to understand where the demand is. A really useful way that I see coming.

is to create enough content. So create a load of visuals, creatives for different audiences. This is longer term, not any time now. But you can have different visuals, creatives, designs for all the different audiences we have.

It’s really helpful to help you with coding, to get stuff out of the way. I mentioned dev resource, that can be a challenge. It’s no big deal. replacement for developers.

You still need the hands on check over it, but just to save time as a first run, that’s been useful. And I use it particularly for driving narratives. What’s the narrative for this group of people? What’s narrative for those you need to use it as an assistant, not as the work for you, but it just saves you this first.

Vipul Bansal: Got it. I, I also, uh, I’m of the opinion that, uh, you always require a touch of, uh, the human efficiency. Like, uh, AI can enable you to do your work, uh, better, but maybe, uh, you do require an input from an actual human, uh, because the way that humans can think, at least, uh, in today’s day and age, I am not very confident that AI can actually replicate or imitate the actual thinking patterns of, um, you know, that match a human’s. Right?

Uh, the way we can create ideas, right? That’s difficult. The way we can execute on those ideas. I am not very confident, but the way we are progressing, I feel that The day is near when, uh, when, you know, um, AI can start thinking creatively.

And maybe they do take away our jobs. Do you think that that’s a possibility?

Jay Lansdown: I mean, you can never say never. But I think of AI as like a parrot. It can copy stuff. It can tell, it can give responses it knows are relevant.

It doesn’t know what it’s saying. It doesn’t really get the context. It’s just parroting information that humans have done elsewhere on the internet. Taking all this content in and spitting it back out again.

I think what it will do is, Save us effort, and it makes the first step on any career ladder harder when you do that low level stuff, but I can’t see personally it ever reaching a stage where It replaces jobs, whole scale, you still need someone in control, understanding the responses and doing something with them because it doesn’t understand what it’s saying. It’s just giving you stuff. That’s my opinion. Anyway,

Vipul Bansal: a great answer. Uh, Jay, thank you so much for sharing your, uh, insights and opinions. Uh, and being patient with, uh, with, with all the questions. Um, this, this last question of mine has sort of become a tradition over the past four years of us.

Running this event. And people are quite interested in knowing what books are you currently reading?

Jay Lansdown: what books? Well, I’m reading at the moment, um, David Mannheim’s stuff. Loving that. Um, and I’ve dug out some old ones.

I forget his name now. About conversion experimentation. And some Steve Barry stuff, which is a bit less highbrow.

Vipul Bansal: Great. And then, uh, are you also, uh, what do you say? A Netflix person? Do you, do you watch series?

Do you have any recommendations for the audience?

Jay Lansdown: I’d love to. I feel like I’ve got too much stuff going on. I’ve got two children and my time is always spent doing a thousand things at once. I’d love to have time to watch a series.

I want to hear your recommendations. When I get time, what should I be watching?

Vipul Bansal: Uh, so the thing is that I’m not very much into watching CDs. I do have an, do have an active Netflix subscription, but that’s being used by my sister. But I, uh, I like to watch a lot of documentaries, and I recently watched this documentary about MH370, the airplane that disappeared, the Malaysian Airlines airplane, and it was a very, very unfortunate incident. So I have been quite interested in that topic, and I’ve been curious as to what really happened.

Is there any news? So that was a really interesting documentary. And, uh, I also recently watched Or rather re watched, uh, this animated movie called Hotel Transylvania, Hotel Transylvania 3, right? And it’s, it’s quite funny.

I usually like to watch documentaries and comedies and I want to stay away from, you know, all the, uh, All the crime thrillers and, you know, any, any, any content which has a negative undertone. So yeah, that’s, that’s my recommendation. If you haven’t watched guys, if you haven’t watched, uh, uh, MH370, the documentary, do go and check it out. It’s, it’s a really good, uh, piece of research.

Jay Lansdown: When I get even free, I’ll definitely have up my to do list.

Vipul Bansal: Awesome. Awesome. I think, yeah, with that, let’s wrap up the session. Thank you so much Jay for taking out the time, uh, to.

prepare this presentation and sharing your insights, uh, with our audience today. I hope the audience were able to quickly jot down all the insights that you shared. Uh, but of course, folks, you can rewatch the session anytime that you want and, uh, gain all the knowledge that you always wanted. Thank you so much and have a great day ahead.

Jay Lansdown: Thank you very much.