Landmark Group's Shailesh Jain on AI, Personalization & Smarter Business Decisions | Live from Paris

Hello everyone.

This is omnitalk Retail.

I'm Ben Miller and welcome back.

We are coming live from the Fusion podcast studio which is on booth E043 at NRF's Retail Big Show Europe here in Paris.

Joining me now is Silesh Jain.

Silesh is Chief customer and Analytics Officer at Landmark Group.

Welcome, Shilesh.

Thank you, Ben.

Thank you for having me here.

Oh, absolute pleasure.

Why don't you start by introducing yourself and Landmark Group to everybody.

So we are one of the largest retailers in the Middle east and we work across multiple formats of retail from fashion to the footwear to baby furniture, grocery as well as hospitality.

And we are not just restricted to the countries in Middle East.

In fact, we're based in Asia Pacific and India as well.

So if you look at our portfolio across all of these regions, we, we are more than 3,000 stores and the online omnipresence and we have more than 40 million customers on our loyalty program enrolled and actively shopping with us every year.

Amazing.

And we're going to have a really great conversation about how you add all that together, what you can do with the data and what AI is happening.

But I'd start off with some people might have visited the region.

We've got listeners in the region.

What are the sort of faces, the banners of stores that people might recognize from Landmark Group?

Some of the brands that you might have seen, especially if you've traveled to Dubai, you would see Home center, you will see Centrepoint, you will see Max.

These are some of our leading brands.

There is Lifestyle, Baby Shop.

These are everyday names for people in their whatever they're buying for their home, for their children, for themselves.

And we have Shumat, which is one of the leading brands for footwear as well.

And just our latest addition is, is Walmart, which is your convenience, everyday need, which is more on the value side, anything that you need to do with your daily life.

And then we also have Viva as one of our flagship grocery store as well, across Middle East.

Okay, fantastic.

Right.

We are now past the halfway point of NRF Europe this year.

We are a day and a half in and there has been a lot of conversation about AI, how AI is transforming retail.

So all we're going to do for the next 10 minutes is we're going to get into detail.

Okay.

And we're going to split the hype from the reality.

Okay.

Is that all right?

Yes.

Okay, let's go for it.

Let's do it.

So number one, where do you think that AI is creating the most value in Retail.

And frankly, where do you think we're wasting time?

So first of all, let's just get this straight.

AI will amplify anything that you have.

It's not gonna make you smart, but if you're already smart, it's gonna make you smarter.

If you have a history of making bad decisions, it's going to multiply those bad decisions by millions of those decisions in machine learning time.

Okay, so it's not an AI problem, it's not even a technology problem.

It is the business decision and the operating model problem.

So what are you solving for?

You're solving for business decision, you're solving for optimizing your decision, or you're solving for making those decisions faster by having the intelligence and time.

And so the hype is not about how many tokens are you using in your AI on a daily basis, how your technology teams are working on variety of AI use cases.

The hype needs to be hyped down to talk about what sort of business decisions you will be able to optimize in the next quarter or the next one year.

Okay, so let me just, let's just build on that.

Give us some examples.

What are the kind of business decisions, the logic that you think AI should be making and which are the bits where actually you think people should be making, otherwise you run the risk of amplifying bad decisions.

So anything I think, look, AI is also evolving fairly, fairly rapidly.

And that's why you need to keep an eye on exactly what's happening with the advancement in the AI as well.

Because whatever decision that you make today in terms of what you want to hand over to AI and how much you want to hand over to AI is also a moving target.

What you thought one year ago that no, AI can't do this today?

There are a lot of those areas.

AI is actually seeping into those areas too.

So coming back to, if I take the stock take of the situation as of today, anything to do with the plumbing, you can sort of easily without regrets, you would be able to hand over to AI anything to do with any critical decision making.

Let's just say when it comes to, about your customer, when it comes to, about your key and entrepreneurial decision, when it comes to, in terms of when you're communicating with your, either employees or the customer, some of those areas you still need to have human in the loop significantly.

Okay, really interesting, really interesting.

Another thing we keep talking about is scale.

So we have conversations where people are talking about trials that they're doing, but then actually what we're really interested in is how AI can really scale.

Now you have got multiple banners, multiple countries, multiple languages.

How are you trying to get crack this challenge of scaling your AI?

So that's a very good question, Ben, because look, first of all, given that we have multiple banner, multiple geographies, it gives us an advantage that anything that we develop using AI, we can utilize it across multiple brands, multiple geographies and the same customer, which is having multiple missions, the same customer, she's a fashionista on a Monday, she's a mother shopping for her kids on a Wednesday, and she's a home decorator on a Sunday.

And she's doing it across channels, she's doing it in person, in store, she's doing it online, or she's just looking at an ad on the social media and clicking and following the journey through that link.

So our ability to develop something in AI and reuse that and multiply it is far more in comparison to somebody who might be actually a single category kind of retailer.

Okay, so you get a multiplier effect.

Yes, we got a multiplier effect.

But again, if you look at whether it's in the area of supply chain or it's in the area of CRM or if you look at in the area of buying and merchandise planning or all of these areas, we have plenty of examples from the stock health from the customer communication perspective and the personalization from the supply chain processes, we have plenty of examples.

We have been able to scale this up a simple use case across multiple brands and geographies.

Amazing.

In which case Shailes, give me one of those examples.

Give me the example you think best.

Shows how you've been able to scale and actually benefit operationally from AI.

Not theoretical, but in practice.

So I'll give you an example of the CRM.

In the CRM, the ability to go below the eight segments which your K means cluster analysis gives you.

It was always there.

And if you go below into micro segments, let's just say you're creating about 2000 micro segments that was also existed even before the AI arrived.

What you didn't have is the ability to personalize the content for each of those 2,000 segments every day and every week.

You could not create that.

It was not humanly possible.

Now it is possible.

Now you have multiple variation and multiple variation for different missions of the customer from the fashion buying perspective, from the footwear buying perspective, from the journey of a baby, from the journey of decorating or redecorating your home.

So the content, personalization, timing, personalization channel personalization, and doing all of that, as opposed to having a whole lot of hundreds of people sending those campaigns out on a personalized basis, we also have now AI doing it.

What is really important here is, and this is the most important part, you need to have the appropriate guardrails in place.

Just having more knowledge about the customer is not that you will communicate more, you will send more campaigns.

Sometimes the best decision is not to communicate.

Okay, and I wanted to ask on that one, because this is.

It's an uncomfortable question, but at what point does personalization, so demonstrating, you know, a customer become, you know, creepy becomes surveillance becomes I know too much about your customer.

How do you.

How do you try and draw that line?

So what is the customer asking from us as a retailer?

Customer is asking from us to be relevant, to recognize them, and in short, understand them.

They should not be in a situation that I'm being watched because the moment from understanding it actually goes into the realm of being watched then.

And it doesn't really matter.

It's not a question of legality.

What is legal, what is not legal, what is covered in GDPR and what's not covered in gdpr?

You can be fully on the side of the law, but still you make the customer uncomfortable.

Because if a customer goes into, the customer starts asking the question, how on earth they know this about me?

You've already crossed the line.

Yes, of course.

And that's why it's not about what exactly you're going to be using to make recommendations more personal for the customer.

That is far more important, as opposed to demonstrating how intelligent you are.

So it's an ethical business call.

Coming back to the question earlier about what can AI decide versus what do you need to decide as people and culturally?

Look, this is exactly the very first thing that I said at the beginning.

The operating model, what decisions you are trying to influence, you start from there, as opposed to CEO asking the question as to what are the 10 AI projects that you have in your list to deliver in the next quarter?

Because if you're starting from what AI project that you have, you're basically developing a solution.

And after that you will try and find a problem that you can fit that solution in, as opposed to starting with the problem itself.

Okay, go on.

I'm going to ask you one last question.

This has been fascinating, Charlie.

If you were to be able to.

If you were to be able to give a retailer CEO a single bit of advice about AI and maybe something that they're not hearing all the time, what would you tell them.

What would that bit of advice be?

The first thing is what are the business decisions?

What are my operating model opportunity areas that we can use either technology or AI or anything else in terms of even redesigning the process itself sometimes gives you loads of opportunity areas to make your business decision faster, more efficient or better.

You start with that as opposed to you are a CEO, you need to have the eye on the commercial side of things as opposed to becoming, becoming a techie.

A techie cannot give you the answer in terms of how to improve your operating model.

But again, if you have your eye on the prize as to what commercial decisions you're trying to influence and then you work backwards from there, that is the right point to start from.

Really interesting look, Silas, thank you so much for joining us.

There's lots of thoughts going through my mind, but we're going to wrap it there.

Thank you to the team at Fusion and the NRF Big Show Europe for making this happen and making all of our coverage possible.

And stay tuned for more.

Thank you, Ben and thank you, omnitalk.