E102: Tal Rotman

Richard Hill:
Hi, and welcome to another episode of eComOne today's guest Tai Rotman, VP of Global Partnerships at the Namogoo. How you doing Tai?

Tal Rotman:
Hey Richard, how you doing? Thanks for the time.

Richard Hill:
No problem at all. Looking forward to this one. It's I think almost a dirty word that we're going to start with in eCommerce. That sounds wrong, doesn't it? Discounts. An episode where we're going to talk a lot about discounts. I think this is something that there's not many eCommerce stores. Well, there isn't any, I don't think there won't offer some sort of discount. Obviously there's a whole gamut of things to talk about; when, why, what and there's so many different elements to it. So I think it would be good for you to kick off with sort of discount, offering these discounts I think quite often could be quite harmful, would be my sort of take on it potentially, you know, when to offer them. But what would you say? When should a brand look at offering discounts? Should they offer discounts?

Tal Rotman:
Look, I think the obvious answer, so Richard, it is a fascinating topic, there's a lot of, both academic research and a lot of emotional responses to that particular question, especially, in 2022, after the last couple of years. I think the obvious answer is, it depends, right? I mean, we all know that it's never a wrong decision to offer a discount. But I think that what we're seeing a lot is that a lot of retailers are really feeling it, right? They're feeling the experience of offering discounts because they think that's the right thing to do. There's maybe a little bit of a follow me or copycat behavior. Right? And so people start to get sucked into this vicious cycle. Yeah. Right?

Tal Rotman:
And I think, what I'm finding fascinating, just looking back almost exactly two years ago, as we were talking about that we went into this brands that started off two years ago as pretty high end are now actually considered discount brands and customers are expecting to see that discount. And I think the they're just so used to getting to that home page, that landing page and going, "Splat, 20% off."

Richard Hill:
Yeah.

Tal Rotman:
Right? So, I think that's probably the emotional response that a lot of people have. Right? It's, "Oh, these discounts are killing our, our margins or. We don't know what to do." There's a lot of emotion, but actually discounts would be a very effective incentive. We all know that.

Richard Hill:
Yeah. I think that's the, yeah, sort of a love-hate thing, isn't it? Ultimately, because it's expected. I think it's as simple as that, but I think there's some very crude... That's doing it, and then doing it well and doing it really, really well, which is what we're going to ultimately talk about. But if we're literally, every single visitor is getting treated the same, and every single visitor is getting 10% or whatever that magic number may be, I think what we're alluding to is that's not the smart way to do it, you know?

Tal Rotman:
Absolutely not. Absolutely not. Yeah.

Richard Hill:
So what would you say around, what would be the maybe 60 second version of using different intent, different segment to offer different things at different times?

Tal Rotman:
Look, I think that the first thing is that we need to understand that a lot of retailers, there's an addiction at this point. Right? It really is. It's a vicious cycle and it's a form of addiction it's and it feeds itself, right? The retailers are following everybody else, and you ask them, "Why did you put 20%? And they say, well, I don't know. That's what everybody else is doing," right? And then it just gets worse and worse, and the customers will now expect it, and so, it kind of just feeds itself and there's this kind of addictive behavior. And what you really need to do is you need to rehabilitate it. I think that's what a lot of companies are looking at now; how to mitigate this impact by rehabilitating, from this kind of form of discount addiction.

Tal Rotman:
And so the way to do it is in a gradual stepwise, right? You need to have the visitors understand that they're not always going to get a discount. That's the first thing, right? And the right time to do that is when you know that they're going to purchase, right? When they walk into a brick and mortar shop, and they know exactly which pair of jeans they're going to buy-

Tal Rotman:
Yeah, exactly. Exactly. You don't want your clerk to say, "Oh, here you go. Here's a coupon," right? As they're their credit card details in, exactly.

Richard Hill:
It's funny. I actually went into a store on Saturday and I used to go in there a lot where the office used to be. I always used to get a discount, just because I knew the guy, or only knew him from going in there. And I haven't been in there for probably 18 months and I bought a new shirt, bought a new jumper and it was around £200, I think. And I don't know what happened because normally I will always ask for a discount if I want something, I am that guy. I am that guy. I've got my father in my ear. I've been brought up like that. And my kids, my wife hang their head in shame when they go shopping with me, because, even now-

Tal Rotman:
That's way to do it, absolutely. I'm there too.

Richard Hill:
I can't buy anything full price. It's just who I am. And I went in there and because they've always given me a discount, I actually forgot to ask for the discount I got shop. And I was like, "Oh man, I completely forgot." Because sort of, I expected it, but he didn't add it. I got the receipt, I was like, "Oh crap. I wasn't even thinking I've got 20, 30, £40 knocked off," but I didn't go back in. And obviously didn't say anything, but-

Tal Rotman:
Sure.

Richard Hill:
That expectation for me is just there, obvious that's in a shop, but also online, yeah.

Tal Rotman:
Yeah. And it's a great analogy because you had the intent to purchase, right?

Richard Hill:
Yeah.

Tal Rotman:
You knew you were going to buy it, and you still had this kind of thing on your shoulder that said, "I'm always expecting a discount," but in that moment you had intent to purchase, you didn't get offered the discount, and you still converted, right? And that's exactly-

Richard Hill:
He didn't offer it. He probably remembered me from a couple of years ago and thought maybe he could this guy, but then he obviously-

Tal Rotman:
"Let's see what happens." Right?

Richard Hill:
See what I would say. And I didn't say anything.

Tal Rotman:
Let's see. And if you start to show indicators that says, "You know what, maybe he's going to turn, he's going to walk out," then maybe, "Okay, that's the moment to hit him with the discount." And that in a nutshell is what Namogoo intent-based promotion does. Right? That's actually what we're we're using. We're focusing on the intent of the visitor when they hit the shop, and saying, "Okay, look, this visitor needs a different incentive, as opposed to a different visitor when he comes to the shop at the same exact time. And it might be that same person returns the next day and they exhibit different behaviors, right? And there's nothing untoward about this; we're just providing the right incentive at the right time, right?

Richard Hill:
Exactly. You know, so you're a merchant, you've implemented Namogoo, what are some of the, I guess playbooks of discount? Discount is just one thing that you do, but what would be some of the playbooks that you and instances for, you know, still doing apparel £10 million or @20 million, I know the conversion very slightly off there, but yeah. 10, 20 mil turnover, a lot of orders pumping through the system, what would be some of the playbooks that Namogoo would be able to help our listeners with?

Tal Rotman:
So what we can do is we can help actually take that generic, one size fits all site wide campaign. Yeah. And say, you know what, let's let the machine learning actually decide what is the right decision for that visitor at that time. So we can run a campaign, and we can focus on particular groups, we can say, "You know what? Traffic from Instagram, let's run it on that traffic. Or something that came in from Facebook, let's run on that." Or we can do it across the board in parallel too. Right? And we can say, let, let's take those visitors. Yeah. And let the ML decide, the machine learning, decide. Yeah. What is the right incentive. So as they go through the shopping journey, maybe we're not going to do the, "Splat, 20%" on the landing page. We're going to wait a little bit further when they go in, they look at some genes and then we say, "Okay, they're exhibiting some behaviors. They're coming in from a specific channel, there's a lot of different data points that we're gathering," that are by the way, not PII, not GDPR-

Richard Hill:
That end of saying you different data sources, you know, so you're coming from paid ads,, you're coming from search coming from whether it's Google, Facebook, obviously it's going to be a definite element of intent, very different intents-

Tal Rotman:
100%.

Tal Rotman:
And we have a wide variety of customers and different sub-verticals, and each sub-vertical has similar behaviors as well in conjunction with all those data points. And what, what I'm saying here is we've got more than 200 different data points and we let the machine learning decide. This is not a human making decisions; this is based on learning the behaviors of the visitors on that site during that day, during that month and so on, right? This is technology that exists by the way, this is not something that we're going to sell to Amazon. Right? Amazon has thousands of machine learning folks who can do this. This is us, and I don't want to sound like Robin Hood here, but this is democratizing that technology for companies that are making five, 10, 20 million per annum right? That's what we're doing. We're breaking that technology out of the big boys and bringing it to people who actually need this and try to find a way to rehabilitate.

Richard Hill:
I think, listeners, when you think about the different people coming to your store, you've got people that, yeah, they're there, they want it now, and they maybe want a little cheeky discount very likely. But then a lot of people are coming to your store and I don't want to buy that thing right now. So offer them a discount right now, or an aggressive discount now, is not going to work in reality because they're thinking so that obviously, it'll vary from price point industry to industry. But ultimately, if you are getting a discount right now, for something that, if it was for a wedding, for example, you're not just going to go to a website and buy a wedding suit, for example, I don't think. But over the course of the weeks and months. So the potential to obviously vary that based on product, but also ask that question in that form, when you're looking to purchase, potentially. So if they're looking to purchase in two, three weeks, then obviously the system then would then know, potentially, or whether you're using your system or another to offer a discount at the time of potential purchase. Yeah.

Tal Rotman:
The, the other interesting thing is to your point, right? Sometimes when people see that discount, they think, "Oh, wait a second. I got a discount here, where else can I get a discount?" You're stimulating them and pushing them out. Right? So you need to really hit them at the right time. And by the way, sometimes that massive discount isn't necessarily what you need, right? A 5% discount might be just as much of incentive as a 20% discount, right? As they're going through the journey. Or free shipping when they're in the cart, right. Might be the right thing. And that's what we're actually doing. We're finding the right incentive at the right time in the journey and we're letting the machine learning actually decide that across in different experience.

Richard Hill:
Rather than just blanket all this-

Tal Rotman:
Exactly. It's conformity, right? We don't want a conformity of the discounting. We want the discount to be individualized to that particular journey.

Richard Hill:
So it's fascinating, but I would love to know how you got into this game. You know, I think I'm always really, really fascinated with every guest, and all the different stories of coming, usually it's by chance or by a chance meeting with somebody or trying something. How did you get into the industry?

Tal Rotman:
So, most of my career, I was in telco, both on the vendor side and consulting and a little bit of security, but always in the telco space. And through friends I connected with our CEO Clemmy and the story was just so exciting, a really, really interesting technology. The domain itself is so vibrant and growing. And what I love about it is the amount of mom and pop shops that there are out there. Right? I mean, there are amazing large retailers and we work with some of the biggest; Marks and Spencers, for example, we just put out a great case study in the UK. So we've got those big enterprises, but I love this massive ecosystem of people who said, "You know what, I'm going to give it a go." Right. And it's really, really interesting to see that for me, that's really, really exciting and leads to a lot of innovation.

Richard Hill:
To be fair, they're the episodes I enjoy the most, no disrespect, but we get people on that go. Yeah, "Well, eight years ago we were offered half a dozen X sunglasses and I put them on eBay and I went to bed and woke up and we'd sold them all. We made £100. So I bought another 100, and then a year later we got a little unit, or we did it from the garage, and then we moved from the bedroom to the garage, to the unit, to the big unit." And then now they're doing like £50 million pounds a year, got 40 staff, 20,000 square feet.

Tal Rotman:
I absolutely love those.

Richard Hill:
Those journeys, we've had a lot of people, and they're just so relatable ultimately, because that's nearly every business on the planet-ish, not quite, but especially-

Tal Rotman:
On the flip side, there's that, on the other side, there's the innovation, right? There's so many interesting technologies, and how they work together and constantly coming up with new ways to improve the experience. And so, that's really exciting for me as well. Right? And in my role, particularly, in the world of partnerships is really about establishing those connections, building relationships and finding ways to, the overused one plus one equals three. Right? Trying to find ways to create additional value for our end customers. I love that. I find that really exciting, it gets my creative juices flowing.

Richard Hill:
Yeah. Same, very much so. That's one of the reasons we do the podcast, that's sort of people we meet and the things that come from that. But so obviously a lot of clients, a lot of customers, in the thousands of customers. And I think a lot of people must come to with discounts in place, and maybe you've been doing it the whole, "We've been doing it this way for so long, this is how we do it." Type scenario or mentality, how can companies get out of this pretty, almost crude cycle of just, "Right, discount, 5%, that's what we do." How what's the sort of process of going, "Right. We need to change that," and how are we going to then adapt, you know, a methodology like you are talking about, what would be the process? Because obviously we've got customers that just have people who have been buying of us for years that just know they're going to get 5% off. What would be sort of a roadmap for these eCommerce stores to sort of integrate something new?

Tal Rotman:
So, first of all, obviously, Namogoo TM, right? This is what we do. And really, you could try to figure it out yourself, right? Do a lot of maybe some kind of AV testing. But in this particular domain, if you're a five to $10 million company, you need a lot of volume of traffic in order to get some kind of statistically significant result. You could try 5%, but was it the 5%, or was it the seasonality, right? It becomes a very difficult thing to try to figure out, right? And do you really want to reduce your discount because maybe that discount, reducing it actually will impact your sales and you never want to do that.

Tal Rotman:
So this is why we suggest, look, we've got a very simple plugin, depending on which platform you're on, Genta, Shopify, whatever, install it and run a campaign. Right? Work with us, run one campaign. Maybe even do it in parallel, right? Side by side. Take 50% or some of your traffic and run a machine learning based campaign and say, "Okay, let's see what that does." And then, "Okay, let's tweak it a little bit. Let's try to do BOGO, or let's try to do free shipping, or let's try to run a one discount level, give Namogoo a discount level of five to 20% on clearance items, right? On the luxury items, no discount." Try different campaigns to see, to see where it runs, but let the machine learning determine which visitor is actually going to be the right one to give those discounts to. And visitors will start to get less discounts. They'll start to feel that they're getting them at the right time as well.

Richard Hill:
Yeah. I think that's the key, isn't it? Ultimately the machine learning will kick in and offer things at the right time as opposed to that blanket piece. So I think the message there is, you can trial, you can test for a month or so, and obviously, ultimately, the returns will be there, or they'll vary dependent on who's listed and what business you've got. But ultimately, if you could put a blanket discount permanently, there's options there to trial Namogoo, et cetera. So, do you see very specific patterns with, obviously different industries, different things are maybe expected more so; you've got fashion, which is, we've got a lot of listeners in fashion on apparel. We've got a lot of listers in jewelry and that type of thing. We've got a lot of listeners with sort of outdoor seating, barbecues, you know, maybe more physically bigger products. We've got a lot of people with, you know, white glove furniture; two, three, four, five grand expenses.

Tal Rotman:
Higher order value, yeah.

Richard Hill:
I mean, a whole mix is out there, and I know obviously we have a whole mix of listeners, but jewelry, apparel are big verticals for our listeners. I know they are. Is there any sort of sweet spot discounts that you see in any particular industries?

Tal Rotman:
It's a great question. A lot of folks will say, "If we just follow what all the other folks in there in their sub-vertical are doing, then we should be fine, right? If all the big players in jewelry are doing 20%, then we should do 20%, right?" That's where you get that copy cat behavior. And of course, then they try to do each other. Right?

Tal Rotman:
"They're doing 25%, I'll get more of that more of that share." And I think that, so, there is a lot of that. In terms of the sweet spot of the verticals or the sub-verticals look, that's the exciting part of this technology, right? We have many customers in these sub-verticals, and it's leveraging that network effect, right? We're not taking any data from each of the customers, but we're seeing what are the behaviors for this kind of sub-vertical different geographies and so on. And of course this is all automated, right? It's happening behind the scenes. And that's actually, what's exciting. So you can let the machine learning use those learnings and those findings from those sub verticals, and find what is the sweet spot at that time, in that month, with seasonality, all in an automated way. There isn't a sweet spot for the technology for Namogoo, right? We have customers who are doing very high order value, customers who are doing high volume, right? Really across the board. And the reason is because we've got this network effect, where we've got a lot of customers across the board, in all those different geographies and sub verticals and the technology knows what to do for those particular verticals.

Richard Hill:
So it's vertical specific, but it's all back to the machine learning at the end of the day.

Tal Rotman:
All back to, yes, exactly.

Richard Hill:
It's a bit of a, see if we can catch you out there.

Tal Rotman:
I'm used to it.

Richard Hill:
So back to the machine learning, ultimately, there is no one size fits all, no matter what industry you're in.

Tal Rotman:
Yeah. You hit the nail in the head, that's the point, right? There really isn't a one size fits all right> I mean, there might be certain types of products that like, for example, this might not be the best solution for a B2B, right? If you're selling very low volume, but very high order value, you know, where you're selling one product, 10,000 orders, this isn't what's going to work, but we always knew that in the first place. You weren't going to put a discount code on a B2B site that if you're going to go and order 10,000 nails-

Richard Hill:
Everyone

Tal Rotman:
You're not going to get a discount code. Right? Yeah. So that's really, it's common sense. Where you would expect to see a discount, this is relevant, it works.

Richard Hill:
Yeah. I think you've also mentioned, which is, part of the machine learning, but that seasonality piece. Obviously every industry pretty much is probably a bit of a caveat there, the odd one, but we deal with, there's not a month goes by where we haven't got clients that are at peak potentially. Obviously we've got mother's day coming up in the, I don't know if it's the same date in the US or the UK or not, but that's coming up, is it next... Well, probably need to get this right-

Richard Hill:
It's this weekend, for us. And then we've just had Valentine's well, a month ago, obviously Christmas, but then there's obviously, weather dependent, the weather's turning here's. Now, spring is eeking its way out, so it's the best time of year. I love it. I've been searching for barbecues. It's time. It's finally time after-

Tal Rotman:
Exactly.

Richard Hill:
Switching that thing on for the last 12 years. I've chucked it in a skip actually about three weeks ago. So there's no excuse; I have to buy one, it's as simple as that. But it does like-

Tal Rotman:
Can't wait for a Clack Friday now; you have to get it.

Richard Hill:
I know it's happening. I've already eyed up a pizza and barbecue, fridge, beer pump, it's all happening.

Tal Rotman:
When do I get the invite?

Richard Hill:
Yeah. I'll let you know. I'll let you know. So, that seasonality element. And what do you say about that specifically?

Tal Rotman:
And geography too, right? The seasonality plays with geography, especially for companies that are trying to be international. They might be headquartered in the UK, but they have customers in Spain and France and seasonality is different there, right? So, 100%

Richard Hill:
Yeah. It's definitely there, isn't it? Okay. So obviously you've worked with a lot of brands, we've said this already. Is there any particular brands that just are getting it so wrong with a discount? I mean, it's a bit of a tough question cause I don't really want you to call out-

Tal Rotman:
Throw anybody under the bus, eh? Yeah.

Richard Hill:
Maybe just doing it wrong. And they're probably doing the things that we're talking about, just that whole consistent, same old discount at the wrong time, no thought to any sort of AI/thinking, well, layering any tech in there, or so there's any brands particularly that maybe some big brands that are seriously leaving.

Tal Rotman:
You really are setting me up. Huh?

Richard Hill:
Yeah. I'm waiting. Just put this bit out as the sound clip for the episode.

Tal Rotman:
That's right. That's right. "Tal Rotman says that Is doing it wrong." Look, I'm not falling into that trap. I don't think that there are any brands that are doing it wrong. They're all just trying-

Richard Hill:
Ah, too diplomatic.

Tal Rotman:
They're all just trying. Yeah, absolutely. But I think that some of them got stuck. Right? Some of them got stuck in a pretty bad loop. And I mentioned earlier, I'll step in it a little bit. I mentioned earlier, brands that were high end and then got sucked down. Right? And we're seeing, for example, The Gap is struggling big time, especially in the UK. We know that they're closing a lot of their shops. Right? Think about Banana Republic. I moved to the US 10 years ago, Banana Republic was top brand, very high end, part of The Gap group. And over the last pandemic period, we see that they are really experiencing this kind of like discounting behavior; people are expecting very high discounts. And I've seen 40%, and that sort of thing, on a brand that's just touching the bottom of kind of, almost semi luxury sort of, top end, professional clothing or apparel.

Tal Rotman:
So I'm not saying that they're doing it wrong, but I'm saying that was the behavior that we saw that kind of led us to this situation. A lot of different brands that, struggled with it and I don't think they had anything else that they could do. Right? They just were trying to keep it going. And in particular with the struggles that they've had in brick and mortar, they're doing whatever they can to try to help buffet up the bottom line overall, right?

Richard Hill:
So if you're a big brand, and you're doing big permanent discounts, Tai's going to save you a fortune and mate, you look like a golden boy, basically.

Tal Rotman:
I'm here for you.

Richard Hill:
Okay. So, okay. Discounts, I think we've done quite a bit on discounts. And forever they remain, so we can get a bit of discount, but as a merchant, obviously, ultimately we want to be giving them at the right price and not just hemorrhaging money, hemorrhaging our margin. Ultimately, obviously everyone mostly are working on quite tight margins. Difference between a 5% or 10% can be profit or no profit. In reality, you scale that back, throw in returns, etcetera, et cetera. it's a huge, huge difference. And obviously, that's what we're trying to do. Everything we talk about on the podcast, you know, if you're an eCommerce store, 1000% you should be running Google Shopping, but it doesn't mean you just take a feed, push it in and you have do the same as everybody else, which is what we're saying with the 5%, 10% flat rate. What you've got to layer in is the automations, you've got a lot of in seasonality, you got to pull in the weather. Maybe this is Google ads, but this is what we're talking about here. Very much, we're layering in some technology piece and the understanding that business, the vertical that business is in, using data from a lot of other merchants potentially. So, yeah, brilliant.

Richard Hill:
So, okay. I'm convinced, but now with maybe, as a potential customer or existing, customer's already bought something, they're now in the system, they've registered for X, Y, Z, or they've been offered X, Y, Z at that point, or at the right point when the intent is matches better, a bespoke discount, we've got them in the system, what other things can we be doing to via maybe email, SMS, to entice them to maybe come back, obviously if they've already bought or to obviously to get them back, not just with the discount, but with other things. Remember talk about that for a bit.

Tal Rotman:
Absolutely. So, we talk about intent a lot, right? The machine learning is actually devising an intent scoring, right? It's telling you, what is the intent of that visitor to purchase, to abandon, right? And I keep on saying, "Machine learning" there, but what you're trying to do is you're trying to ensure that the customer has a good experience. We can do the same thing, both in the journey, and you can do that offline as well, right? Instead of hitting them up afterwards saying, "Don't forget us. We love you. Sign up." And all that good stuff if you know that visitor had high intent to purchase, but didn't, and maybe a certain something might actually get them over the edge, you can continue to follow them in a respectful way, right? In other channels. Try to create a continuity to that experience instead of just hitting them up with straight, kind of simple generic and conforming experiences.

Tal Rotman:
And so yes, we can integrate that intense scoring and even some of the underlying data points for that scoring into other channels, be it an email, or an SMS, whatever your marketing automation is, but you could also do the same thing via a remarketing campaign, right? You could push those visitors and say, "You know what, those visitors, we think if we give them a certain kind of incentive when they're on Facebook and they get hit up with an ad unit," maybe you want to focus on that, right? Focus on these high intent visitors. Right? And so,, we can take that scoring and that capability in a really, really easy way. It doesn't need to be complicated. You don't need a team of folks to actually get this implemented, and you can keep that experience kind of, as we like to say, the digital journey continuity platform, right? And so, it's actually a continuous journey and try to create some loyalty to that brand as well. Some consistency to the experience.

Richard Hill:
Yeah. Love it. So we're layering in a multitude of new touch points, whether that's SMS, I think it surprises me how many brands, merchants are just not using SMS, really, really does. How many times have you had an SMS, or a brand that you like, I would imagine most people not, and you are probably not doing it either, the listeners. So I think, how many times, if you get a text message, do you open it every time? Is the reality pretty much. Email, it's not, as we know.

Tal Rotman:
The ROI on SMS is... No emails ROI is incredible, right? But the ROI on SMS is even more so.

Richard Hill:
Yeah. You just need to get more mobile numbers, is the trick. But obviously, we've got SMS, we've got email, layering remarketing. I think all sounds lovely and nice, but ultimately, maybe step us through an actual project or two where you've layered all the way through, from the discount piece to then layering in the different touch points, all the way through that cycle. And then, maybe that's improved, well, it should have improved results. Give us a couple of examples.

Tal Rotman:
Okay. Richard, it's a great question. So we've got a great customer out of Australia company called Getwag, love them, as a owner of a pandemic puppy. I'm a-

Richard Hill:
You and half my team, I think. Yeah.

Tal Rotman:
There you go. So, Getwag were one of our earliest customers and actually, what they do is they sell high quality product, that's healthy, that's additive free for the dogs. And they've got a club and they're what they're trying to do is, improve sign up for their loyalty program. They use in segmentation and they're pretty advanced, they use SMS and email. What we actually delivered for them was our core solution, intent-based promotions and the ability to leverage the machine learning was really what caught her eye with Rama. Now, actually what she did was she just ran it across the site. Right. She just deployed and it's a simple Shopify plugin, she could install it herself, obviously we've got folks to help if necessary, but oftentimes it's not and just ran a simple campaign.

Tal Rotman:
Now we've got a really nice studio for running those campaigns. You could say, "Okay, what is it that you want to focus on in this campaign?" Do you want to increase conversion rate? Do you want to improve your average order value? What are the different KPIs that you're focusing on? You know, focus on that. What is the segment that you want to focus on for this campaign? In her case, the first campaign was everybody, "I want this to run for all my customers." Yeah, later on, she was like, "Okay, let's take a look at just the customers that are coming in from our Instagram feed and give them a dedicated campaign." Right? Yeah. And then you can set up the graphics, the image, right? For that coupon. Make sure that it's on brand that it's matching the look and feel of your site. We've got our homegrown popups and that sort of thing, but if you want to put in your own branding and image, of course you can do that.

Tal Rotman:
And then, what she did was she set up a range, between five and 20%, I want to give the visitors between five and 20%. I don't want it to go up to 40. I want it to go somewhere between five to 20%, or no discount. Or no discount. Right? And so that's what she did. She ran that campaign for two, three weeks. And the first couple of days, the machine learning is getting to know the site, learning the behaviors and patterns of visitors on the site. And after a number of days, we started to see things really bump. We saw the amount of conversions definitely go up, but that is obviously very interesting. We certainly want to see that, but that's actually kind of a secondary effect. Because if you think about it, even if the imagery is really not effective and that sort of thing, but if you can actually reduce the amount of discount on average that you're giving, you've improved your margin. Right? There's a significant return on investment just by reducing the discount and that's what she saw. Yeah. So, Rama actually saw a 10% increase in AOV in that first campaign.

Richard Hill:
Wow.

Tal Rotman:
And a 30% increase in revenue.

Richard Hill:
Wow.

Tal Rotman:
Really, really significant results. And after that, we started working on, okay, let's take those, those visitors, and you know what, if they didn't convert, let's actually integrate that into the pretty advanced email marketing suite that she had built in. And we just can push those segments into that email marketing suite. If you don't have an email marketing suite or you're still trying to figure out what to do with SMS, we've got something built in that you can work with, that we actually can offer, that's already doesn't require any integration, but if you've got something in place, we can plug into all of those different emails.

Richard Hill:
You have both options there, you use both.

Tal Rotman:
Yeah.

Richard Hill:
Yeah. Okay. That's brilliant. That's great. Isn't it? There's nothing like a sort of real life case study to just really get a feel for things out in the wild. Because obviously we can get a lot of theory, potentially on the podcast, which I'm not keen on. Obviously our guys very much, they're in the trenches, they want to be in the right direction. And I think, obviously, looking at what you guys are doing clearly doing some great stuff, you've grown massively, haven't you the last year or two. So.

Tal Rotman:
Absolutely.

Richard Hill:
Okay. I always like to, well, not always, but sometimes; crystal ball, we sat here in a years' time, we're still talking about discounts. Well, everybody will be using some system by them potentially, hopefully yours, I'm guessing.

Tal Rotman:
No doubt, no doubt.

Richard Hill:
But what else do you think, we've sort of brand much about AI and machine learning, and I think that, I mean, obviously put words in your mouth, but what would you say 12 months time, 18 months time, what's going to be the things around, discounts that we'll be focusing on?

Tal Rotman:
I think that what we're going to find is, today what Namogoo doing is taking in all these data points, right. That are factors in Namogoo's machine learning, deciding what's the right decision. I think that, as we progress, we're going to want to break down those different data points and say, "You know what, I want to actually get more granular, right? Instead of letting Namogoo machine learning, make a decision, which is interesting, we'll continue using that, let's try to refine that and say, "Okay, let's go after one, or two, or three different data points and try to work with those." And try to create some segments and that sort of thing. And those underlying data points can be really, really interesting and valuable. Be it, the network strength of a particular device, or what the weather is you talked about, or whether that particular visitor has a high CPU. All these different things are really, really interesting, and again, not personally identifiable, but really interesting about the journey. And these are all different factors that kind of feed Namogoo's decisioning on intent. But actually, if you can take those underlying data points and leverage them to try some different experiments, I think that's where things are going to go to. Right? That's, for me, what's really exciting as you can actually try to break it down even further and get more granular.

Richard Hill:
Love it. Absolutely love it. Well, thank you for being a guest. It's been a brilliant, brilliant episode. Thank you so much.

Tal Rotman:
Thank you.

Richard Hill:
I would like to end with a book recommendation. Tai, have you got a book that you'd recommend to our listeners?

Tal Rotman:
So I've got young kids, so if you have young kids, I'm a big fan of Zog. It's a lovely story, I've got a daughter and it's a lovely story about a princess and a dragon, and the princess ends up being the doctor for the dragon. Oh, sorry to, spoiler alert, but-

Richard Hill:
Damn it.

Tal Rotman:
Yeah. And it's a lovely book. And I read that a lot to my daughter.

Richard Hill:
Well, that is lovely. That is absolutely lovely. My kids are a little bit older now. I can remember sitting there reading, they're there sort of 17, 18 now, 17, 16. But yes, I know we'll have a lot of parents listening in, a lot of mums and dads listening in. So there we go. Zog, love it. That is the first time I think we've had a book like that. So, that's actually made my day. That's a bloody good choice. Good on you. So for the guys that want to find out more about you guys and yourself personally, what's the best way to do that?

Tal Rotman:
So my, my name is Tai Rotman, tal.rotman.com, or you can just reach us at partners@namogoo.com. And if you want to just hit up Namogoo, we're at info@namogoo.com.

Richard Hill:
Don't forget that extra O on the end.

Tal Rotman:
That's right. Namogoo.

Richard Hill:
Namogoo. Right. It's been an absolute pleasure, Tai. I'll speak to you again.

Tal Rotman:
Thank you, Richard.

Richard Hill:
Bye-bye.