Serve every team with rich data in real time
In this webinar Eric talks with David Annez, Head of Engineering at Loveholidays, about how they optimize for high-performance eCommerce analytics using BigQuery and RudderStack.
For modern, data-driven eCommerce companies, traditional SaaS analytics tools are no longer sufficient. Aside from not serving real-time use-cases well, they limit analyst's ability to analyze customer journeys on a granular level and syndicate their insights to other teams, like marketing and data science. In this webinar, David tells us about how his team leverages RudderStack and BigQuery to enable real-time analytics and use their warehouse to serve every team in the company with rich data.
Here's what Eric and David cover:
The evolution of the eCommerce stack: how have you modernized your data stack over the last 5 years?
Warehouse-first analytics VS traditional SaaS analytics for eCommerce
eCommerce performance: how to achieve site speed and real-time analytics
Serving marketing and data science from the warehouse
What's next: real-time recommendations with RudderStack and Redis
Eric leads growth at RudderStack and has a long history of helping companies architect customer data stacks to use their data to grow.
David is a leader and generalist engineer focused on business outcomes over technology. He leads cross-functional agile teams of engineers, designers and product owners.
Eric Dodds (00:02)
Thank you for joining this webinar with RudderStack and LoveHolidays. So, here's a quick overview of what we are going to cover today. So, the topic is real-time e-commerce analytics with BigQuery and RudderStack. We'll actually talk about more complex parts of the stack, as well, but we'll get into that in a bit. So, we'll do introductions. You'll get to meet David from LoveHolidays. We'll talk about the evolution of the e-commerce stack, so how were things done previously, and then how are they done today by forward-thinking companies like LoveHolidays. David will talk us through what warehouse-first analytics is and how that compares to SaaS analytic solutions for e-commerce.
Eric Dodds (00:50)
We'll talk about site speed, which is a big deal. It's been a big deal in e-commerce for a while. It's becoming critical for e-commerce and then also, really, any consumer-facing experience. And then David's going to walk us through how he feeds both marketing and data science from the warehouse model that we're seeing more and more in modern companies. And then we'll talk through some of their plans around doing some real-time recommendations. So, let's dive in. David, welcome. Thank you for joining the webinar. It's great to have you here.
David Annez (01:25)
Thanks for having me, Eric. And yeah, good to be here. I guess just an intro for me, I'm currently head of engineering at LoveHolidays, or in the US should say LoveVacations. We currently sell holidays and hotels in the UK and in Ireland, and we generally have around 20 million unique users a month, so pretty big scale. And data is a key part of basically everything that we do, and especially when we're trying to monitor customers. And I'm responsible for how we apply it, not how we analyze it. Luckily, that's our data science team. And then, also, how we feed that in from the website and how we filter all of those events into some good views for the business.
Eric Dodds (02:17)
Very cool. And just a quick question. What were you doing before you joined LoveHolidays to lead engineering?
David Annez (02:23)
Yeah. So, I was head of engineering for Uswitch, which is a price comparison website. And I actually did a lot of the same there. In fact, integrated with similar approaches at Uswitch. And I spent seven years there [inaudible 00:02:41] core infrastructure and the website together with about eight engineering teams.
Eric Dodds (02:47)
David Annez (03:55)
Yeah. So, I guess it depends on where you're at that business level. Right? You start up, scale up, and scale-out. But I think a lot of the time, you end up building some basic systems, they do what you need them to do, And once you start growing, you want to differentiate yourself and you want to start building out something that is potentially more custom and dealing with your demographics in a better way. And additionally want to move quicker, right? Maybe you have your own engineering team at that point and you're building several other engineering teams. Once you have that, there's probably a desire to start building out your own platform. And very much catering on the e-commerce side, you're building out... That's your differentiator, right? I think, especially in the LoveHolidays world, we've decided to build our own e-commerce platform because that's how we become the best holiday finder for customers. And I think the same can be applied for most e-commerce sites as you start scaling up and then scaling out.
Eric Dodds (05:04)
Sure. Yeah. I think an interesting way to summarize that would be platforms is great. I mean, Shopify is, really, an incredible tool in many ways, but by nature, it has to do many things for many types of users. And as you grow and focus, you reach the limitations of that.
David Annez (05:24)
Exactly. And I think it's the classic build versus buy and figuring out at what point in your time, I guess in the growth of your business, that you decide to do that. But because accessibility of building your own platform and owning that, I think it means that a lot more people are building versus buying because of the versatility they can do with it.
Eric Dodds (05:48)
Sure. All right. So, third-party vendors in black-box machine learning and AI, I put that in quotes because that's a subject unto itself, but I think we're seeing a shift towards... from some of those tools, again, as we think about companies that are scaling to massive volumes with more focus, more to an own data science function. And one example I'll give here is recommendations. I mean, there are tons and tons of third-party SaaS tools and plugins and everything for e-commerce, and a common one you see is recommendations. Right? Like make the best recommendations, and it's an outsourced algorithm of sorts. But more and more we're seeing people move towards their own data science, and I think that's something that's happening inside of LoveHolidays. Can you tell us just a little bit about that?
David Annez (06:38)
Yeah. I think it's a really interesting one. Right? I think that preset machine learning models and black box AI vendors, et cetera, could only get you so far, and I think that once you start understanding the domain and the needs of your customer and the data that you have, it starts becoming quite apparent that having an in-house data science team to build out those models is going to get you there a lot faster over time. I think like [inaudible 00:07:10] and that's where LoveHolidays is that now. Right? It takes a lot of time initially to gear up and get that all setup and make sure that you have the right people to build out those models, but also to think about what we should be looking at in the future.
David Annez (07:27)
But once you have that, you can move a lot more quickly because everything's in-house, the models and the underlying technology's understood, and we can make tweaks and we can continuously improve it to actually cater much better for our customers. I think the black box recommendation engines that you just feed it data, but in fact, if you think about a holiday and all of the different combinations and the... Actually, we know that, for example, people that are in the south of England have a larger propensity to book Greek islands versus in the north of England, and once you start thinking about the complexities of demographics and holidays, it made a lot more sense for us not to potentially buy that system, but actually build it in-house so that we can own it and evolve it over time.
Eric Dodds (08:16)
Sure. Very cool. I'll quickly go through number three, siloed data, before, to unify data in the warehouse. This is pretty common. I mean, there are still a huge number of companies who are in the process of making the shift or haven't made it yet, but I think most of our audience will really have a good handle on why that's important. The example here is many times, especially in an e-commerce context, you're using Google Analytics very heavily. It was the standard tool for optimization and e-commerce. And then some sort of combination of internal platforms, pulling data from the database and somehow combining all of that information to produce some sort of business intelligence, which is a technical mess. Versus leveraging a modern data warehouse like BigQuery and just dumping all of your data in there, which works way better.
Eric Dodds (09:12)
So, let's skip to the last one because I want to be conscious of time. It's common knowledge in e-commerce, speed equals better performance, better conversion rates, et cetera, and it's been that way for a while. But speed is really becoming mandatory. Recently, Google released news that they're going to have even more strict requirements around site speed. So, David, talk to us about your experience dealing with that. Sounds like you've had a decade of dealing with the front-end user experience in an e-commerce context, so tell us about site speed and how things have changed.
David Annez (09:49)
Eric Dodds (10:03)
We blame ourselves.
David Annez (10:04)
Yeah. That's a whole other webinar. I think that performance is something that [inaudible 00:10:11] LoveHolidays, we proved that he had a significant impact on conversion. But it's not just about the conversion side of things now. It's actually about SEO visibility. Over my 10 years working in the front-end I've focused drastically on performance, not just because it gives you a better experience, but you tend to build better systems if you focus on [inaudible 00:10:37] performance systems. And nowadays, everyone is trying to chase this performance goal, and now that Google has set it, it's obvious that we need to ensure that we're at least adhering to some sort of principles around it. Driving a better experience is one thing, but also in ensuring that you're building something that is maintainable for the future.
David Annez (10:58)
And the Google requirements is just that last key point where I think a lot of companies that have probably been doing it, maybe ad hoc, have now said, "Well, actually, we now need to do some serious work on this because, in fact, it's going to affect our SCO," which then, of course, in turn, affects your overall revenues as you're relying on that traffic source.
Eric Dodds (11:19)
Yep. Kind of making performance the first principle as opposed to a project.
David Annez (11:25)
Eric Dodds (11:27)
Well, let's talk about warehouse-first analytics versus SaaS analytics, so going back to the point earlier around heavily using Google Analytics and all the other e-commerce SaaS analytics tools versus the warehouse. So, David, I don't want to go through bullet by bullet, I'd just love to hear you talk to these points around what are the challenges and even benefits of SaaS analytics, and then why did you make the strategic decision to move to warehouse-first analytics where you're streaming events into the warehouse and then doing what you need to do there in a variety of contexts, but a primary one being the analytics use case?
David Annez (12:13)
Yeah, absolutely. So, I mean, I think just to quickly talk about the benefits, when you've got SaaS analytics, you drop in a script and, hey presto, you've probably got some sort of view on your customers and maybe conversion and even performance metrics through some dashboard that Google Analytics or some other provider has given you. Which is great, right? I think that's a really great start to start to understand the data that you have and the website usage.
David Annez (12:39)
But the problem becomes that once you start wanting to do more with that data, once you start wanting to use that data with other parts of data and actually build out potentially more complex dashboards or, in fact, enhance the data that you have on the site, things become a bit more brittle. And because SaaS analytics tries to generalize for all, you end up with a mishmash of datasets, you probably end up with some of your custom queries, and you probably end up trying to build views on top of things that were never meant to be built upon. And I think that that gets quite hard over time because, as you want to absorb more data and as you become maybe more data assisted in your day to day, you probably want to have that beautified view of everything and you want to have that deeper dive into that data. So, that's like where the limitations lie.
David Annez (13:31)
Eric Dodds (14:19)
David Annez (14:51)
Yeah, absolutely. And I think that it's like one of the things that you end up trying to do at a larger scale when you're a bigger company and when you want to have that visibility, is unify your data and unify and visualize it in the same way so that you communicate in the same way to everybody in the business. And unfortunately, if you have 25 dashboards trying to tell you one individual thing, maybe one's for performance and one's for web analytics and the other one's for conversions, you end up not being able to create that cohesive view for the business and for yourself.
Eric Dodds (15:25)
Sure, sure. Very interesting. And then let's talk quickly about cost. So, one thing that is really common is you start out using a SaaS provider and it is very cost-effective initially, but the unit economics, especially in e-commerce, can have a very, very low tolerance for cost at scale. Right? Where, at a lower scale, the pricing models make sense, but then as you scale up, you're looking at, basically, what is the unit economics around each transaction? So, is that something you've experienced with SaaS tools that you've used in any of your previous roles for LoveHolidays?
David Annez (16:12)
Yeah. So, I think that when we start talking, I think specifically, there are some products out there that are great but, unfortunately, charge you for that, what they call, an active user or a user ID. And when you start growing and when you start adding customers to, I guess, your visits, then it becomes pretty hard to afford that sort of cost. And I think some systems go to some ridiculous numbers, which makes it really, really tough to justify. I think that the flip of that, as well, is that the SaaS analytics, where maybe it's free for a good period of time, or it's free for forever for all numbers, then actually don't give you any sort of features that you can use to then further analyze it. Right? So, it's like you have those two models where one of them is that if you're at scale, you pay hundreds of thousands, or you pick a free system that then actually to do anything with, you probably need to pay hundreds of thousands to get the data out of it. I think Google Analytics is a good example of that.
Eric Dodds (17:23)
Sure. Yeah. With 360.
David Annez (17:25)
Eric Dodds (17:26)
Yep. Well, speaking of 360, so this was the stack that you had before, so do you just want to give us a quick run through and then we can look at the stack t