Drive eCommerce Growth with Data Analytics | Joanne Davis

[EP] - Jo Davis | Drive eCommerce Growth with Data Analytics

Matt Edmundson: [00:00:00] Wow. Hello and welcome to the e-Commerce podcast with me, your host, Matt Edmondson. Now this podcast, this amazing e-Commerce podcast, is all about helping you deliver e-commerce. Wow. And help us do just that. Today we are chatting with the delightful, uh, giant Davis from Suite Analytics. Uh, we're gonna be talking about all kinds of things.

Digital Arts, Data, New Customer Acquisition, Growth Modelling. I mean we're going to get into it, we're going to get into it all. So grab your note books because there's going to be some really, really good stuff uh, here for you. Now of course, if you are subscribed to the newsletter, all of the notes are here.

From today's show will be making its way to your inbox at some point if you've not got it already. Uh, and of course, if you're not subscribed to the newsletter, subscribe to the newsletter. It's the easiest thing in the world to do. Just go to eCommerce [00:01:00] podcast.net and fill in the little name and email thing and we'll send you the newsletter once a week.

Uh, and that's pretty much all we send you to be fair. No spam, no junk. 'cause I don't want it. You don't want it. Nobody wants it. So that's what we do now, a very warm welcome to you. If this is your first time with us. Uh, it's great, uh, that, you know, we're constantly getting new time, uh, new time customers, new time podcast listeners, uh, it's great, I'm just thinking new customer acquisition is what's going on, uh, but it's great to have you here.

Very warm welcome to you. Make sure you like and subscribe, uh, to the show, wherever you get your podcasts from because it'd be great to have you with us on a regular basis. Now, let's talk about Joe Davis. Uh, now, what I'm going to do is just turn the music down in my ears a little bit. There we go. Now, Joe is an experienced retail strategist with over 20 years of expertise in product and marketing.

She is the Chief Operating Officer at Sweet Analytics. They have a strong interest in merging data science and [00:02:00] creativity to fuel brand growth. Now, Jo has worked with top fashion and homeware companies, uh, like Topshop, gotta love Topshop, especially if you're from a certain age and era, right? Uh, Dwell and Malte York, uh, she's also passionate about team empowerment, coaching, and adapting to evolving business culture changes.

Joe recognises the need for motivation shifts and adaptable work environments in the post pandemic world. Yes, we're still talking about it. Now their vast knowledge and skills make them a value, make them valuable in driving successful brands in the ever changing retail industry. Uh, Joe, great to have you on the show.

You are in fact, our second guest from Sweet Analytics, which is great. Great to have you. How are you doing today?

Jo Davis: I'm great, Matt. Thank you. How are you? I'm great.

Matt Edmundson: Well, you know, I often, when people say this to me, I, I don't think I've ever said this on the podcast. Uh, I often retort with a phrase like, well, you've got to be doing all right when you're [00:03:00] this good looking.

Um.

Jo Davis: Okay. I'll take that.

Matt Edmundson: Yeah. Yeah. And it's funny. A lot of people have started robbing my little phrase now. And my wife heard me say it the other week and, um, she said, surely I have to say you've got to be doing all right when you've got such a hot wife. And I said, well, fair play. So either way, I'm doing all right.

Great, great, great. We've

Jo Davis: already compared the weather between London and Liverpool, so you're dry,

Matt Edmundson: we're

Jo Davis: not, I think

Matt Edmundson: we've covered that. Yeah, absolutely. And so, you know, in every area of life, I'm winning right now. I'm clearly hot. Which is a beautiful thing, but I just moved to Liverpool, everything will be fine, you know, it's just the way it works.

Great, great hot. Yeah,

Matt Edmundson: it's a beautiful bottle, yeah, yeah. Now it's, uh, we met up a few weeks ago at a Sweet Analytics gig. We've known each other for a fair while now. Um, Oliver was on the show, um, who's the CEO of Sweet Analytics, so, uh, and [00:04:00] it's fair to say that we, if you're a regular to the show, dear listener, you'll have heard me talk about Sweet Analytics and a little deal that we've got going on with your good selves, Joe.

So, For people that are interested to go sign up because it's a platform that we use and yeah, it's a great little thing going on there. So do check it out. The links and stuff will be in the show notes and stuff like that. So, um, and finally we've got you one.

Jo Davis: Yes, at last. Um, we've been talking about it for a while, haven't we, but diaries merging is always, always the issue.

But yeah, great, great to be on and great to have a chat with you around data in the, in the world right now.

Matt Edmundson: Yeah, yeah. How did you end up at Sweet Analytics?

Jo Davis: Well good, really good question. Um, so I actually worked with Oliver, who you just mentioned previously at Multi Ork. He was the, he was the CEO there and I was, um, head of buying the merchandising and, uh, and unfortunately we closed that business and got the end of 2017, um, and I decided to start my own brand, uh, designing and making pet accessories.

Um, but, you know, as we know, when you start [00:05:00] a new company, you kind of need an extra bit of income. So I was working with Oliver pre launch on Sweet right from the beginning.

Yeah.

Jo Davis: Absolutely fell in love with the platform and the concept and so, so relevant to my, my career. So I could really understand the solutions that we were finding to previous frustrations and how, how real it was.

I mean, it's a bit corny to say that it's been designed for retailers by retailers, but it really, really has. And I was in the middle of retailing at the time. Um, so a year later I, I, I closed down my, my brands and went full time, joined Oliver COO, and here I am five years later, um, so that's the background.

I think, you know, am I geeky about data? Probably not, but I understand it quite well, hopefully. Um, but for me, it's the, it's the impact it can have on your business, your brand. I also, I'm the joint CEO of a Remedies brand as well. So we're really heavily data focused and it makes, you know, it can be, you A huge difference [00:06:00] between that feeling and that assumption based decisions to actually backing with some data.

And even if you do want to make decisions through a feeling or the more creative side of the reason, at least you've got the data then to back up, okay, was that the right decision? Should I go and do that again? Or should I enhance that decision? Um, so I'm, you know, I'm very, very product focused, very marketing focused, really love the creative, but without the data sitting in the middle.

I feel like often we're just shooting in the wind, so it's, yeah, so that's what got me into SWEET and I'm really passionate and I luckily have quite a natural feel for formulas and calculations and charts, so, um, yeah, it kind of just brought all my experience together in one place, really, which has been great.

Matt Edmundson: That's quite, that's quite handy. I mean, did you, when you started with Sweet, right? Um, and obviously you've known Oliver for a while, but did you have a strong grasp of data beforehand, or have you learned how to have a strong grasp of data since joining Sweet? Probably a [00:07:00] bit of both,

Jo Davis: really. Um, I mean, it's been a very steep learning curve the last five years, without a doubt, and that's, A, because I moved into a data platform, which is not something I'd done previously, but also the way the environment has changed so quickly.

So if I go back to the beginning of my career, our data was a line print that was printed out on your desk on Monday morning, and that's what you used for a week. And if you wanted a bit more insight, just going back to my Top Shop days, You wanted a bit more insight around product sales. That's kind of data I was working with then, because there was no such thing as online marketing at that point.

You'd call people, you'd ask them questions, you know, how, you know, how did that, how does it work having that window display? And did, do you think more new people come into your store? Do you think it was the same people coming back? You know, all very, very much the same concept as today. Um, But there's a good grasp of what I wanted to know, but the information just wasn't available.

Of course, now we've moved into this, you know, huge e, eCom environment, obviously, but [00:08:00] at the beginning of my career, people found the website annoying, you know, when we had to actually give stock to the website as a channel. Yeah, yeah. As a buying team and we'd be like, oh God, I don't want to give them up, we want to put that into our flagship store, Oxford Street.

But I still remember the 2005 actually, um, that the, the eCom shops and the websites get more money than the Oxford Stirkers store top shop. And all of a sudden everyone went, oh, oh, okay. Oh my God, I can sell so much more product through this channel. But it's, and then, and obviously there's so many brands now that just trade online.

COVID obviously had a big impact on that as well. Not going to talk about that too much today, but, um. We have all this data now available to us, all those questions that I always had. So I always had a really good grasp of what I wanted to know, you know, how many new customers are we getting? How are we getting them?

How are they repeating? How much have I spent in marketing to acquire those customers? I now have all that information and I firmly believe, and that's just some of the [00:09:00] basics there, that every brand should have that information. You know, you wouldn't trade your brand without a cash flow or P& L, and you shouldn't trade your brand without knowing those key metrics around the base of your business either, in my opinion.

Matt Edmundson: No, I totally agree and I, you know, I think there's that old saying, isn't there, what gets measured gets managed, and um,

Jo Davis: I guess

Matt Edmundson: I'm listening to you talk though. I wrote that today

Jo Davis: actually, that's so weird, I wrote that sentence today, what gets measured gets managed and gets done, how weird, yeah, yeah, yeah.

Matt Edmundson: It's an interesting one, isn't it, because I'm sort of thinking of you back in Topshop days, you know, on the phone, did this display work, yes or no, and you've got a sort of a data printout on your table. And I sort of fast forward to today and I'm thinking about the data that we have access to. It's fascinating.

I think for a lot of people, it can feel massively overwhelming how much data there is at the moment. And so a lot of people [00:10:00] got access to analytics or, you know, something, and they just look at it and go, I don't know where to start. I don't actually know what the data is telling me now because there's so much of it.

I'm imagining when it was a paper printout, they just gave you the headlines, like these are the bits you need to know because we can't be bothered to print all that other stuff out.

Where do you even begin? So I'm kind of curious, you know, you've, you've gone through this sort of five year journey with Sweet, um, where you, you know, your learning curve was almost vertical, uh, being in that environment. What are some of the key things you've learned? How do we extract the important bits from the data?

What have you learned over the last five years? Just sort of your top tips for dealing with the mountainous amount of data that's now available.

Jo Davis: I mean, when I second what you've just said, to start with, it can feel incredibly overwhelming and, you know, let's be honest, everyone's got their day job to be doing, um, and sometimes the data can feel like an extra task, something that, you know, and there's this [00:11:00] pressure of people hearing, I'm generally talking about SMEs here, people keep telling me I need to know my data, but what You know, what does that even mean?

Where do I begin understanding that? And there's lots of different data available. You've got your, your product sales, understand your best selling product. Um, understanding if you, this particular product selling to a new customer or a beat customer, what's my, what's the relationship between products within my customers.

I mean, you can go down such a rabbit hole and then we come on to the whole attribution side of things. And you've got lots and lots of analytics, analytics in terms of traffic data. Hmm. Trying to tell you where you're getting your traffic from. How is it converting? Um, but the, the fundamentals, I think with data where I would start and, and there, there's a huge amount we're not going to cover.

You know, I can go, go on further today, which we're not gonna do. So, but number one, as a brand, you need a goal. Okay? You need, for me, you need to know what am I trying to achieve? Mm-Hmm. , is it 10% growth? Is it [00:12:00] 50% growth? Happy to stay level? What does that look like? And then you're using your data to understand and build building blocks to get to that growth is the number one starting point.

So, so we have something that's being called a growth model, um, and all, I mean, Oliver, Oliver maybe spoke about this on the, on the podcast. I listened. How about, um,

Matt Edmundson: I won't tell him. It's okay. He's probably not listening to this one. Yeah,

Jo Davis: that's a really good test. Oliver, if you're listening to our podcast, you can call me up on that.

Let's see, let's see if he actually listens.

Matt Edmundson: I really want to know. If he does, you need to email me, yeah.

Jo Davis: I will. Hi Oli. Um, Oli's really, you know, he's quite well known for the story of the White Company, um, and growing up from 6 million to 50 million in quite a short period of time. And he did that through understanding his growth model metrics.

So quite simply, you've got a database of customers, So understanding your database of customers and what value you're getting from that database on a regular value, [00:13:00] on a regular period of time is the first thing to understand. So I mean, I quite often will hear things like, I've got 50, 000 customers.

Great, you've had 50, 000 people that have shopped with you, um, over time, the last four or five years, you don't necessarily have 50, 000 customers, you know, just to be really real, because generally 60 70 percent of those shopped once and didn't come back I'd say. It varies by sector, but, you know, as a broad benchmark.

So, putting that information into a growth model and understanding, okay, you've got When I acquire a customer, how quickly do they come back? How many come back? How many come back more than once and come back often? And how many, once they've lapsed, then return? You tend to see those retention metrics really start to drop off.

Um, so, once you understand, and you, and also, you know, you understand, um, Is that a good retention rate for my sector? If you're in fashion, for example, and you acquire, I don't know, make up some numbers here, 10, 000 customers in a [00:14:00] year, you would expect 20 percent to shop again the year after.

Right.

Jo Davis: If you're only getting 5%, number one, what can I do to optimise that number and make it better?

Or you may just accept that's the metric for your brand for whatever reason. So once you understand that value and that it's unlikely just your current database of customers are going to give you enough value to grow your brands where you want to get to, you're plugging the gap of the acquisition. So understanding, okay, I know I'm going to get this much sales from the customers I already have.

So I need to plug it with this value. Number to get to my goal, so I need to go and find X number of new customers next year or this year or whatever year you're working on. And that, that, that data can be quite fundamental for, well it is absolutely fundamental to growth. Um, you know, when you're wanting to grow a brand, there's lots of activity, brands doing lots of PR and lots of email activity and it's all great.

But what is it you're trying to achieve and actually is it, [00:15:00] what should you be spending to get there would be the next question.

Matt Edmundson: Yeah, well, that's the

Jo Davis: starting point on data.

Matt Edmundson: Yeah, and the growth model, what Oliver calls a growth model, um, and my experience with this, because actually, post my conversation with Oliver, we then became a, well, one of our brands became a client of Sweet Analytics and we went onto the platform.

And we've been working with you guys to try and understand the copious amounts of data. Um, and we did your growth model, which in essence takes the data that you have on our existing customers. You figure that stuff out, don't you? You go, right, well, we understand that this many customers are buying from you on a regular basis.

And these, this is their average order value. And you pull all that wizardry together and you go, right, well, this is Matt, so this is where we think sales will be next year based on your existing customers, you know, all based on historical data. It's not crystal ball, obviously.

Um,

Matt Edmundson: but if you want to reach your goal, then you're going to need to go and get [00:16:00] X amount of new customers.

And we were, We were working with an agency at the time because I remember talking to them about it because they were like, well, we're going to go and their target was, was quite high. Um, if I'm honest with you, it was almost, it was almost 50 percent of the previous year's turnover. They were like, well, we'll add that in the next eight months using our strategy.

Um, and it wasn't until we came to you guys. And cause it, it, They were struggling a little bit, to be fair, and I said to you guys, let's look at the growth model, you know, let's understand this. And it wasn't really until we understood, actually, we really have a high retention rate in our data. So if we really want to grow, Well, we've kind of maxed out the customers that we have a little bit, there's a bit of play in there.

Obviously, we're not perfect, but fundamentally, you're going to have to go and get new customers. And so it changed the whole marketing strategy for that business because then we were like, well, listen, guys, you've got to go and get 25, 000 new [00:17:00] customers over the next 12 months or it's 24, 000 actually, if I'm going to be exact.

And it was such a useful number to know that if we want to reach our target, we've got to get 24, 000 new customers, which was, you know, 2000 customers a month. So then you can decide what actually quite quickly are we on track or we're not on track because you're, you know, you're, you're, your retention rates are pretty, they're not guaranteed, but they're pretty stable as long as you don't become a lunar ticking your business.

Um, it was the new customer acquisition ones that really intrigued me and it was a really good, powerful tool to have access to for that conversation with our marketing agency to understand the statement. And this is something that I learned from Oliver actually. Um, the statement growing your business by 10 percent doesn't mean anything, but 000 new customers, it's more definitive.

Do you see what I mean? And it was, it was super, super helpful.

Jo Davis: And then [00:18:00] following up from that, because this is about projection work, using that data and that understanding that 24, 000 customers is now going into your pot for next year, you're going to get value from them next year because you understand your retention metrics from your data, and then you're understanding the gap again.

So it's not even the 24, 000 to do this year's target, it's what's the 000 going to be in the next three years, and by understanding your metrics. And as Zachary said, we're looking at what's happening historically, and now, we don't have a crystal ball, and I can guarantee that's going to happen in the next three years.

But if you're, if you're, if you're trending in that way, it's unlikely to drop off a cliff in this way of a pandemic. But, um, well, where the pandemic went, everyone's metrics up. Yeah, that's true. Most people's anyway. So it becomes quite powerful and quite, um, and I love that you see from me in the work that I do with brands because it just makes you very focused [00:19:00] on the type of activity you need to be doing.

And when you understand that, then it starts, you start to have, want to dig into your data more, and you've got more questions about that. So, okay. So, yeah. You know, sticking to a 24, 000 number, maybe the year before you acquired 15, 000 customers. I don't know, I can't remember, but I know we looked at it together, but if that 15, 000, let's learn about that 15, 000.

Okay, we've been tracking with the traffic, which led to those conversions and those new customers, what channels were working, what type of campaigns, that gives you some insight there, but also what products were they buying? Is there a particular type of product that we should be trying to acquire with big people on?

I mean, that's And that's Quite often the case in brands, there's particular intro products that people are, you know, that are much better in the campaign Yeah, they're quite getting attention and getting people into the brand than others There's also, you know, and you keep going further, so I can understand the pricing around this.

Am I getting people in on my, perhaps, my, the cheaper end of my architectural [00:20:00] products, middle, top? It might, you know, I'm not going to assume what it should be. You've got the data there to answer the questions, and then further into the pricing, how dis, how discounts work for acquisition. And also, You know, it's quite often an assumption, you know, we do, most brands do welcome codes, well, 10 percent off, 15 percent off the first order, not everyone does that, but those that do quite, they always question, yeah, but people just get their 10%, 15 percent and never come back.

But do they? You know, there's that assumption base. Let's have a look at it. And I still, to this day, I've not seen a lower, like, a lower LTV for people that acquired at the discount to those that weren't.

Matt Edmundson: Right.

Jo Davis: It's completely back to the assumption. I probably would have seen the same one once upon a time.

And, uh, but again, if you're showing that the AOV is quite often higher with people getting a discount because they're spending more, because they're getting more value, because they've

got a discount.

Jo Davis: They're coming back because they spent more in [00:21:00] the first order, they've had a great experience, they love the products.

Um, and maybe it's even, you know, you could understand, okay, people that did, were acquired using a discount code, do I need them to give me another discount code to come back or do I not? But you're testing it and you're using the data to learn what the impact is over time. So you keep building on your strategy through knowledge from the data and it's so quick, you know, that information, the thing, I'm going to talk about Suite specifically, obviously, You have the information in seconds.

Matt Edmundson: Yeah,

Jo Davis: in less than a minute I can tell you your lifetime value is better from people that require the discount versus those that want, that weren't. Great Okay, that's give me some great insight. Then you get all the creative and the fun stuff I'm sorry for people that love data, but I'm going to think about okay, how do I communicate that discount?

How do I test and use it in my offline campaigns versus online campaigns? I know actually it's a really great way to acquire customers and they do come back [00:22:00]

Matt Edmundson: Yeah, just

Jo Davis: an example, obviously, because your data might tell you the

complete

Jo Davis: opposite, but it's just an example of making decisions through, through backed up data or results.

You know, when you, when you talk about it in this way, it doesn't start to feel so overwhelming and it doesn't start to feel so complicated. And really it's a lot of the questions that we've always had in retail and always asked, but just, but just didn't have the answers and now we do.

Matt Edmundson: Yeah. Yeah. And actually you're simplifying it, aren't you?

You're focusing on a few key things that you actually want to know about. I mean, I say to people all the time, the numbers that you really want to know, your number of customers. Uh, which includes your new customers, your returning customers, and that, obviously, that breakdown, um, average order value, and average, what you guys call average order count, um, how many times they buy from you in 12 months, those three things that impact CLV, and, uh, and the question always then, as, as marketers, as eCommerce guys, is can I either increase the number of customers, uh, Either by getting new [00:23:00] ones or getting other ones back, can I increase my average order value?

Can I increase my average order count? If I can focus on these three things, it simplifies things a lot for me. Um, and it's, I mean, it's a bit old school. It's a bit Jay Abram. Um, if I'm honest with you Jay Abram, uh, from years ago, uh, the three key things, key, three key levers to grow a business or something in effect was those three things.

Um, and I, I find that super important. I find it super helpful just to go, all the other stuff is really helpful for someone who's a data scientist and can, or AI, and you know, maybe they can pull out some stuff out of that. But for me, there's going to be a few things that I want to focus in on and look at.

Uh, and they're going to have the big impact for me. Um, so what were you, you know, you sort of, you're, you've been there five years now, sort of looking back over it, you've obviously consorted with a whole bunch of companies, right? Um, not just us, but you've, I'm sure you've had phone calls with many a bright, brighter people than we are.

What are some of the common mistakes people are making [00:24:00] with their data?

Jo Davis: Um, I think number one, just not looking at it. I'm happy. I'm happy I'm doing someone told me I should know about data. So I've got a data platform, but I think if you haven't logged in for three months, yeah, yeah, I will do, I will do.

So number one, not looking at it. Being afraid of it, probably is the main reason most people don't look at it. Um, or they don't prioritise they want to understand. So I think. It's that, you know, there's that culture change and that change to mindset, or actually I do, there's certain things I need to look at every day, um, and there's certain things I need to look at weekly, some things monthly, and some things annually, and some things are just there when I want to look at them, if I've got those questions.

So it's kind of understanding those priorities. Um, And then, in terms of mistakes in actually using the data, um, [00:25:00] nothing massively springs to mind in terms of thinking about any huge mistakes, other than just not using it really. Um, and, I mean, the integrity of the data is really, really important, so ensuring that the right data is being fed into a platform.

Um, but I wouldn't say it's generally a mistake, it's normally just a challenge and we can really help brands get that right quite quickly. Um, but just not using it, I think, is, is the, and not embracing it is probably the biggest, the biggest challenge I have, um, with brands.

Matt Edmundson: Yeah. Yeah. Yeah. And I, and that's what we said at the start, isn't it?

There's just this monumental amount of data you, you can feel very overwhelmed by, especially if you never did math at school, you know, and you were, you weren't, you know, I did stats at A level, A level statistics, and I still look at that and go, what the world? My son is doing. He's just about to graduate, uh, from St.

Andrews with a Master's in Theoretical Physics. And I've, I've, I've gone, finally, someone can [00:26:00] understand all the data, right? Someone in our family can finally get it. So have a look at that and tell me what you think. Uh, no, I, I, I just, but I, I think it, it, it could almost feel like you, you need to be a rocket scientist, isn't it really with the, with the stuff out there?

Yeah.

Jo Davis: But you don't, I think it's just breaking it down and the key things that make a difference. So, one of the first things I do when I have my breakfast is check the traffic to the website yesterday because it's the one I work very closely with. Um, I just couldn't go a day without knowing. I just couldn't, I just have to know, is the traffic still there, is the conversion rate where it needs to be, AOV and how many new customers did I get yesterday, is that on track, what I expect to happen this week, based on my growth plans and knowing my data and what I've planned for the year, it takes me 30 seconds.

Yeah,

Jo Davis: if it's not right, then I'm instantly, ah, okay, what's going on, you know, and then you're digging in and finding out what's happening.

Matt Edmundson: Yeah, that's the beauty of eCommerce, isn't [00:27:00] it, that you can, you can see pretty soon if the changes you're making are having an impact, um, normally within a few, sometimes within a few minutes, sometimes a few hours, sometimes a few days, but, um, it's not months, you know, uh, really isn't months, it's, it's, it's quite a quick thing.

You know, the idea then, um, that growth modelling, understanding what you're going to get from your existing customers. And bridging the gap, I like that phrase, mind the gap, I, you know, uh, bridging the gap, fill the gap, yeah, fill the gap, uh, that's a T shirt, yeah, that's the

Jo Davis: next, the next piece of analytics.

Um, stand, but I've never done one. Um, come

Matt Edmundson: get your fill the gap t shirts. Yeah, yeah.

That's awesome.

Jo Davis: And we just talk fashion, I much prefer fashion.

Matt Edmundson: Yeah, t shirts, how do you just, t shirts with data.[00:28:00]

In terms of, uh, so you obviously, you know, you fill the gap with new customer acquisition and, um, And I get all that. Where do you see AI playing a role in data these days? Because I often hear people talking about AI and merging that with data. And I'm kind of curious to know if you guys at Sweet have been playing around with that, or whether you're kind of staying away from it, or, you know, where you see the opportunities there?

Jo Davis: No, we are embracing it, and we're going to call it S I, which is Sweet Intelligence,

It's a big subject, but in a nutshell everything we've talked about for the last half an hour is about dealing with overwhelm and what to do, what numbers should I be looking at, and there's so much data available and some of those Metrics actually are, well, they're all, they are. They're calculation based.

So, mm. If you put a plan into, this is inter suite, for example, and you've told the [00:29:00] system, I want to do 5 million pounds this year, and I want to do that by recruiting. New customers, an AOV of 300, this might not add up, I'm making these numbers up, um, I'm retaining my current database at a rate of 15%, whatever that may be, and you phase that across the year, so you know what that looks like by week, it's very easy, essentially, to be able to compare that's happening or not.

So we're looking at ways in which we can automate that communications customer to say, Hi, I'm going to be positive here. You're way over your goal.

Matt Edmundson: Yeah.

Jo Davis: Do you realize, you know, you are over performing on your goal. And the reason is because you've acquired 20 percent more customers than you plan to at this point in the year.

Or your AOV is trending up by 5%, or your customers returning in your, you know, that, that orders per customer is, is, you know, 25 percent higher than what you'd planned it to be. Therefore, we think the action is [00:30:00] this. Could be pre forecast, have you got a stock based on your trending up? So it's, it's the patterns that they're, um, and we really think that will help with the the overwhelm of going, what should I be looking at every day?

So if everything's working fine and on plan, maybe you just want to know

that.

Jo Davis: And if it's not, which most of course, it isn't, it's generally up or down, it's highlighting that really quickly and then with the actions. So it's so, I'll give you an example, um, Fashion. You know, in fashion that's mainly who I work with.

They, um, the refunds coming absolute killer online. Yeah. I mean, it's a, it's a really, really difficult area, generally around 35, 40% if that moves even a couple of percentage points. And it's a, and it's a high turnover brand. That's huge. Yeah. So if we're highlighting that very quickly to say. Your, your refund rates going up, that's why you're not reaching your target.

And then we take them straight to the dashboard to say, where is that increasing? So, is it new customers repeating [00:31:00] more, existing customers repeating more? What's the average basket size for the people that are refunding? Is it a particular product? To be told, to get an email in the morning telling you that information.

Yeah. I mean, it's a huge time saving and you go straight to, say it's a particular product, for example, okay, go straight to the QC team, for example, why are they coming back? What's the feedback? What do we need to do a new image on the website? Do we need to explain something better? Is the sizing off? You know, it gives you, again, you're doing something based on knowing something and in really, really quick time.

So I think from an analytics point of view, that's what we're focused on. We're, we're, we're starting to build around the intelligence side. Broader, in my opinion, where I feel the AI is really coming into play is the personalization of experiences and messaging. So we talk about personalization an awful lot in retail and making messaging and [00:32:00] landing pages and and views of websites and Very specific to that person.

Obviously there's lots of data needs to be built up to be able to put those albums in place. Yeah. But for me, I think it's a huge opportunity for, for retailers. Mm-Hmm. Great. And, and it's there to save time and be efficient. Um. But I think for me, that's something really, really, it's that personalisation.

Matt Edmundson: Yeah, no, I get that. I, I suppose this is one of the areas where I'm excited about AI is both in its ability, you know, with machine learning to understand the data and to, To in effect be that chief data officer, you know, and, and you get the email every morning, Matt, this, these are the important things you need to know about your data.

And it's constantly telling you like, oh, your sales are up 5%, but that's because, and it's drilled down to a campaign on, I don't know, TikTok, that's just, it's gone, you know, it's [00:33:00] gone much better than expected. And, well, let's, you might wanna double down on that campaign 'cause it's performing so much better.

We could, we could, we could kill it here.

Yeah. And

Matt Edmundson: actually having somebody, or somebody as some, some thing and an AI system constantly refining and looking at the data, you know, and, and, and giving you that information, I think is very exciting. Because that again prevents the data overwhelm, doesn't it?

Um, I suppose the, the complexity is how do I know that the AI has given me the right data, right? And, and this is, uh, the, the, some of the big questions. Um, so I can see it working in, in, in actual, and to help me understand the data. And like you say, I think the other big thing for eCommerce is actually, especially if you've got a lot of SKUs, um, is seeing how AI can personalize your website around visitor experience in quite an extraordinary way.

Yeah.

Matt Edmundson: Um, yeah, I, I, I've seen some platforms out there and, um, uh, what's the guy's [00:34:00] name? Gomley, Alan Gomley. Um, you know Alan from Shop Box? Uh, Alan, uh, he's he great guy. Uh, I love his mustache. His, his, his, I mean, I would just get his platform just on the base. Is that fat enough excuse? But he's a really interesting guy with what he's doing with AI in that space.

And I think, yeah, I'm intrigued by both and I, and I, I'm kind of hoping and praying that, um, you guys are sweet, just. Bring out this amazing, you know, uh, AI analytics platform, which in effect is my chief data officer. Um, yeah, it would be amazing. Um, no pressure, Joan, no pressure.

Jo Davis: No pressure, no, not at all.

I'll build it tomorrow. I don't build anything. All credit goes to Miguel and the development team. Yeah. But the trust is an interesting point. It's either trusting, and that comes back to the integrity of the data and ensuring that you're, that you're feeding the right data [00:35:00] through and the data is understood.

There's so many caveats to data that have to be unpicked sometimes to make sure that that integrity is correct. So that's something we work quite closely with our clients on. But trusting is a good point. I mean, you probably want to be told something, go and check it yourself a few times before you're, before you're happy you're getting the right information.

Matt Edmundson: Well, especially with AI, because AI likes to make stuff up. Yeah. Just, there it is. That's what it's known for, isn't it? But I think that trust, you're right, trust is an interesting one, isn't it? Because I suppose it's, you know, Not only trusting that it's given me the right answer, I think I'd be okay with that.

I suppose my question would be, is it giving me the answer that I should actually have? Like, is it telling me Maybe one of the keys for my business is average order value from, uh, women between 40 and 50. I don't know, there'd be some really obscure stats somewhere that would be a really interesting thing for me personally as a business to look at versus maybe not for somebody else.[00:36:00]

And can you get AI to pull out beyond the just the usual stuff? Can it dig in and find some really interesting stuff for you? I guess that's more my, um, uh, My thing, uh, could I trust it to do that? And I think it's getting clever enough now, um, to start doing things like that. It's

Jo Davis: getting, that's comparing demographic data to sales data, isn't it?

Which is tricky. It's um, because they. I never thought Geek analytics would do that, provide demographic data, but I actually wouldn't trust that, it's probably better to trust the other thing. And it does actually tell me, it doesn't know 45 percent

of

Jo Davis: what the data is, so you're only getting half of it anyway.

And it, again, comes back to the integrity of the data, it's doing it based on the IP address. of the person of the of the laptop or the device that's making that order you know really noticing that so it's um it's yeah it sometimes it is questionable and I think [00:37:00] understanding that's quite important but you're generally looking for the trends I think when it comes to the data you're looking you know I've done something it's had an impact it's trending yeah you know when data you know we always know it's not 100 percent of science it's there's an art involved as well

Matt Edmundson: yeah yeah Fantastic.

So where do you think it will all go to in the next three to four years? If you're, I guess, think about this. If I can qualify my question now, I think it through, I'm, I'm launching a brand new eCommerce website, right? In a couple of weeks time, uh, hopefully it should have been launched three months ago.

So you just never know, but let's assume it's actually, let's assume it's going to be launched on time. Um, so, And I, I go and I sign up to the suite, you know, deal that we've got going on, I get a suite installed on my site and I'm looking at the data. Where, where should I be thinking, sort of three to five years down the line, I'm thinking, I'm a new startup [00:38:00] business.

One, does data matter right from day one? And two, where is it going to be in three to five years time that I should be thinking about now?

Jo Davis: It does matter from day one. Um, and I think I have, I have worked with brands and, you know, installed a suite with them before they've even started trading. Um, and especially around tracking days, because there's certain data you can't backtrack on.

So if you want to relate your, sorry about my computer, it's gone very funny.

Matt Edmundson: Yeah,

you're coming through fine.

Jo Davis: I know, I can't see all of a sudden.

Um, sorry, I keep talking but I've lost you. Um, if you want to understand your customers in relation to, Marketing, so, you know, understanding their traffic through UTMs and what channels they're coming through, is it online, is it offline, etc. You can't backtrack on that data. [00:39:00] So if you have that in place from day one, you're building up those insights quite quickly.

You may not be looking at them in a lot of detail in the first six months, but if you don't collect that data, you can't, you can't look back. And it's not very expensive. So we're talking quite a small investment to start building up that, that, that data for yourself, which I would highly recommend.

Yeah.

Jo Davis: In three to five years time, I think AI will be a bigger part of, of what we're, much, much, in fact, much bigger than what we're seeing at the moment. I think if we think AI is big at the moment, the race in which it's growing, It's just huge and it's going to get bigger and bigger and it kind of makes sense in my head.

And obviously when we talk about AI, we're talking about non generative AI and generative AI. Now the generative AI, I'm not so sure about because I think, um, there's lots of scepticism about that. A lot of countries are starting to put laws in [00:40:00] place, um, you know, for obvious reasons. But the non generative AI is actually It makes a lot of sense.

It's taking the data, it's analysing it, it's telling you something, and then it's doing something with that information. It's what we've been doing for many, many years in spreadsheets and, and tools alike, BI tools, Tableau, etc, but incredibly slowly and very, very manually and very time consuming and very expensive.

But it's just, it is just calculations at the end of the day. And if we can do that quickly. And it, and it tells you something and does something that's just going to get bigger and bigger. Um, and we know that one size doesn't fit all. So we're talking about websites, for example, we know not everyone wants to see the same product.

Not everybody has the same, you know, price sensitivity. Um, and if I was followed around the web, you create a very clear persona about what I look at and what I do. And it'd be very, very different to yours, for [00:41:00] example.

Yeah.

Jo Davis: If you and I went to. Wait's website would see exactly the same thing. Yeah. That, that's just not clever.

And if we can, and using AI, because you've gotta do it instantly, it's a huge opportunity. It's gonna get bigger.

Matt Edmundson: Yeah. Yeah. No, interesting. I agree. I I think it's, it's gonna be an interesting sort of playing field over the next few years.

Mm-Hmm. ,

Matt Edmundson: um, to see where it goes. I'm intrigued. It's interesting you say about if, even if you're just starting out in ai, start, uh, in ai, if you're just starting out in e-com, uh.

Track your data from day one, because even if you're not looking at it, you've still got that data. Um, and the best time to start collecting data is was yesterday. And the second best day is today, right? It's one of those things where, um, you just got to get on and do it. And the sooner you do it, there's how much.

I've asked this question a few times on EP to various different people and I'm kind of curious to know what your answer is. How much data is actually useful? How much do you need to gather before it starts to really get [00:42:00] useful for you, before you can start to see trends? How

Jo Davis: much, how do we want to quantify, like time or?

In nutshell.

Matt Edmundson: Yeah. In rows of, I mean, rows of data,

Jo Davis: I think, uh, well, I think it's relative to the brand, but six, six months to a year for your first six months a year. Then you've got the fall, you've got the full 12 months and full seasons, and, you know, every sale period, every Black Friday, every Christmas period to, to probably having a bigger impact with the data.

Mm-Hmm. , um. It's interesting, if you're looking at data in terms of marketing, you probably need a longer time. If you're looking at product data in terms of product, you need to understand that very quickly.

You're

Jo Davis: not going to wait a year to understand what product you're selling and how that relates to customers, and if someone buys this product and they come back and then buy that product.

That's

Jo Davis: pretty much instant, you'll be looking at that from day one. But marketing trends takes a little bit longer to understand.

Matt Edmundson: Very [00:43:00] good. Yeah, very good. This is why it's important to start gathering now, isn't it?

Jo Davis: The tracker data, your transactional data, so you can, you know, you can get that any time.

We can, we can ingest data for the last 10 years, but tracking data, which is the website data, and you're creating that strong single customer view. So you've got a customer, Matt, I want to know, how did I get you? What channels did you come from? How much did I spend to acquire you? What did you buy? How many times have you bought?

Did you use a discount? Did you pay shipping? Where do you live? In terms of understanding, you know, not one by one, but regional data and start to understand demographic data. If you, if you want the full picture against, against that activity, then yes, it's, it's do it as soon as possible.

Matt Edmundson: Yeah. How easy is it going to be to keep tracking this data in light of the cuckoo less landscape that's in front of us?

Jo Davis: I've put a bit too technical for me, but from the [00:44:00] conversations that I have, because it's first party cookie data, it's absolutely looking fine on the horizon, yeah, but

Matt Edmundson: the third

Jo Davis: party obviously is what's phasing out.

Matt Edmundson: It's complicated, yeah, yeah, yeah, well this is why UTM data is getting more and more important again, isn't it, so things go in cycles, yeah, yeah.

Yeah.

Matt Edmundson: All comes and goes in cycles. Funny, funny, funny.

Yeah. Um,

Matt Edmundson: Jo, listen, one, I love the conversation, two, uh, it's that time of the show where I'm going to ask the question for Matt. So if you're listening to the show, uh, for the first time, you may not know, a recent thing that we have started to do on the eCommerce podcast is I ask the guests.

for a question that I have to answer on my social media channels. Um, so I'm going to ask Joe for that question. I'm going to then take that question, play it on my social media channel and give the answer. So if you don't follow me on Instagram, go find me on Instagram, just search Matt Edmundson. I'll be there.

Uh, and you'll hear me answer this question. Joe, Over to you.

Jo Davis: Well, I believe there's a big European football [00:45:00] tournament coming up this year, and I'm saying it in that way because I don't actually know what it's called,

um,

Jo Davis: and I knew it said Eurovision, which I know that's wrong. Yeah, definitely not

Matt Edmundson: Eurovision, but okay, close.

Jo Davis: Euro something, isn't it? Yeah, the Euros. Just the Euros. Okay. Yeah. I don't know. I don't know. I'm not going to watch it. It's not funny. Anyway. Um. I'm not a football person, am I? We've picked that up, Jo.

Matt Edmundson: You're definitely better at data.

Jo Davis: I supported Switchtown because of my dad and we've just gone into the premiership.

I'm a little bit more

Matt Edmundson: reliable. Oh,

Jo Davis: fantastic. I don't know. Um, but anyway, who's going to win? Who's going to win that European? Who's

Matt Edmundson: going to win the Euros? Okay, I'll give my answer out on Instagram, hopefully before they've ended as well.

Jo Davis: That's

Matt Edmundson: a great question. Doesn't, um, Isn't Ed Ipswich Town?

Jo Davis: Yeah, no, yes, yes, sorry, I was going to say, no, it's not, it's not, it's absolutely, yeah, his stuff works the same as me, absolutely.

Yeah,

Matt Edmundson: yeah, yeah, I saw that in the paper, he was a big Ipswich Town fan, I'm very excited because of Yeah, his

Jo Davis: face was everywhere, wasn't it, yeah, [00:46:00] yeah, the promotion. Um, it's very exciting. Very exciting for Suffolk. Yeah,

Matt Edmundson: very good. Very good for Ipswich. Yeah, absolutely. Absolutely. Jo, listen, it's been lovely chatting to you.

If people want to reach out to you, if they want to connect with you, what's the best way to do just that?

Jo Davis: Um, I think we're going to circulate details of this, but sweetanalytics. com is our website. I'm on LinkedIn as Jo Davis under Sweet Analytics. Um, so yeah, we're happy to have a chat with anybody at any time.

Matt Edmundson: Maybe not any time, but certainly, you know, most reasonable times, 9 to 5, Monday through Friday, no, no, she can't,

we will of course put Jo's information, uh, what, the links to LinkedIn and the, and Sweet Analytics in the show notes, and of course, don't forget to check out that offer we've got going on with Sweet Analytics as well, um, you're really going to want to have a look at that, uh, for your business, if you're not using analytics, it's a good place to get started.

I actually use Sweet. It's a good, it's a good platform, really good platform, it'll give you that [00:47:00] data and I'm looking forward for the AI. Was that a good plug?

Jo Davis: Brilliant. Thank you. I couldn't think of any better myself.

Matt Edmundson: Brilliant. Absolutely brilliant. Okay. Well, there you have it. What a fantastic conversation.

Huge thanks again to Jo for joining me today. Also be sure to follow the eCommerce Podcast wherever you get your podcasts from, because we've got some more great conversations lined up and I don't want you to miss any of them. And in case no one has told you yet today, let me be the first. You are awesome.

Yes, you are. Create it awesome. It's just a burden you have to bear. Joe's got to bear it. I've got to bear it. Now, the eCommerce Podcast is produced by PodJunction. You can find our entire archive of episodes on your favorite podcast app. The team that makes this show possible is the delightful Sadaf Beynon and amazing Tanya Hutzlek.

Our theme music was written by Josh Edmundson. And as I mentioned, if you would like to read the transcript or show notes, head over to the [00:48:00] website, eCommercePodcast. net, where you can also incidentally sign up to a podcast. The Newsletter. Dun, dun, dun, dun. Uh, and get all of the good stuff directly to your inbox for free.

But that's it from me. That's it from Joe. Thank you so much for joining us. Have a fantastic week wherever you are in the world. I'll see you next week. Bye for now.