Ecommerce Data Demystified: 5 Insights for Success | #218 Feifan Wang

[00:00] hello and welcome to another episode of the eCommerce coffee break podcast today we want to focus back on the topic of data no data for a lot of merchants is part of their daily business and it’s all over the place but we want to find out five data challenges that plaguing e-commerce businesses the most and how you can overcome them with me on the

[00:18] show I have hey one he is the CEO of source medium and he’s a seasoned entrepreneur with a rich background and sales software engineering and product analyzers he was born in China immigrated to the asset 14 and his early experience of cultural adoption Blade the groundwork for his entrepreneurial Spirit he worked for Google followed by a vpay or VP of product and analytics

[00:39] role at residence home then he navigated the company’s rapid scale up on complex data challenges gaining unique insights into the struggles that Brands face with data management and these experience finally inspired him the creation of source medium a leader in e-commerce data software so let’s dive into the world of data and welcome Fey to the show hey how are you today thank you I’m

[01:01] good I’m good how are you class I’m very well data part of every business very very complex specifically if you grow your business might be easier if you’re a smaller Merchant a smaller Enterprise but once Things become bigger and bigger things become much more complicated you help with that give me a bit of an idea why data can be such a

[01:24] burden it can be so complex totally totally and and also thank you for that great introduction earlier um you know I think data challenges I think the most important thing to understand is that it evolves based on what stage of business that you’re in you know if you’re in the stage where it’s really still kind of looking for

[01:44] product Market fit making sure that the product is actually liked and enjoyed by a large enough audience and things like that the challenge there really is around just having something right GA is usually sufficient you just need directional insights to know if you’re doing something good or not right but once you get to the stage where a lot of these

[02:07] things are figured out at least you know 80 percent of them are figured out and you’re now really kind of thinking about the medium to long term in terms of scaling right whether that’s scaling or top line or optimizing for profitability then the data that you’re working with becomes a lot more important you you know and I think analogy that I like to

[02:29] give here is right if you’re driving at 20 miles an hour your number’s off 20 either direction isn’t going to be that big of a deal if even noticeable at all right but if you’re driving down the highway that becomes almost life and death sometimes right so that’s kind of uh you know how I would think about it

[02:50] now with the growing business as I said you get great data from all kinds of sources well some data is hosted in your own system some data is hosted on external systems and it becomes really really difficult to find out um how to compare and how to read out the data now at source medium um you do

[03:09] it slightly different tell me a little bit on what your approach is yeah so we we’re we’re ultimately a data company that is about the data and not as much about the user experience right so I think most solutions that you see out there will have you know a lot of focus in terms of their marketing on the

[03:31] visualizations that they have or integration coverage or you know things that are perhaps more ux oriented in terms of use ease of use but what we really focus on is to to to be the provider of the highest quality data in the Commerce industry right so in terms of how we think about data quality there’s a few things one is around

[03:56] accuracy right is there out of the box accuracy in the sense that do your numbers match with the whip with the platforms actually report on and but nine out of ten times once you have some complexity in the business the numbers on the platforms are actually also incorrect for various various reasons so does the data provider have

[04:19] those nuances built into the product right so that’s the accuracy piece and then the next piece is around breadth of the data right so that’s just essentially the integration coverage that it has right does it kind of sufficiently cover all of the different various platforms that you’re going to be using as you scale the business and then lastly the depth of that data right

[04:43] how rich is the data that’s actually coming out right because the richness of the richness of that data is very important in terms of how actionable that actually becomes as well so that’s kind of like the main thing that we focus on and then kind of go and then the then the operators that we cater to are folks that are a little bit more

[05:08] sophisticated in terms of what they’re looking for in the quality of the data and the type of decisions that they make that they that they think has to be uh supported by analytical insights and then we have a couple different types of consumption methods you know we have brands that are consuming our data directly in our hosted visualizations or

[05:32] we have more sophisticated Enterprise customers that are consuming our data directly within their data warehouse let’s dive a little bit deeper into quality of the data because I think that’s a bit of a gray area for a lot of merchants out there because they assume that whatever data comes in should be right but probably it’s not so what are

[05:53] the quality of data actually stands for yeah so so it again that really is a little bit different depending on the phase of the business that you’re in and what metrics your what kpis you’re orienting around you know so besides kind of the high level understanding of accuracy breadth and depth right there is also for example just to out of the

[06:22] box being able to have reliable net sales based kpis right let’s say if you base your your CAC on that sales or your LTV on that sales that has a lot of implications there for example you have to have the correct gross calculation and uh correct discounts and refunds calculation and that also differs across borders right so the Shopify data that comes out of

[06:49] Europe for example has certain implications that’s different from the states um and then you know if and then you kind of as the brands grow and they get to a point where they want to for example optimize around profits then that can open a whole can of worm right because the expenses data and the cost data uh are very fragmented and

[07:14] there’s a lot of different opinions as far as how certain things should be calculated right where are you pulling those numbers so you know accuracy the way that I think about it is you can never have 100 accuracy it’s it’s almost physically impossible because of just the the levels of layers and complexities that exist but also you also get to a point

[07:40] where you enter into the world of opinions right so that’s where a data culture becomes very important having a standardized taxonomy within the organization having a source of truth that everybody in the company is orienting towards is extremely important um you know so for example right you could calculate your row ass based on your gross sales or your net sales

[08:04] well which one do you do right the marketer may prefer to look at it based on growth and the finance person may prefer to look at it based on that and the CEO may prefer to look at both but really they’re tracking one of those right so you do also end up getting to that where it’s just about the human

[08:23] beings and the governance that’s kind of coming with that as well a very good point that you mentioned there have been sitting in a lot of shareholder meetings looking at the same data and everyone was basically reading something different out of it now obviously you use the data to make your decisions based on that and I think

[08:39] visualization is a point there and that’s something you do in your help was so how do you visualize all this data for the different players in the game great question yeah so our visualization design Philosophy from the get-go has always been orienting towards the more sophisticated operators right so our you know when you kind of look at our hosted

[09:05] visualization templates it’s it can be overwhelming for some right so we’ve seen other Solutions where for example it’s really about the simplification of that right go into scoreboard it’s very simple graphs and lines and stuff like that but you know our product where you open it look almost looks like a Bloomberg terminal to some so it takes a

[09:28] lot of uh intentional learning and uh onboarding with us to to really kind of understand how to look at it right but of course that granularity and richness of data can only be leveraged if the human being on the other side is already sort of committed to working that way right so ultimately you know we’re very sort of

[09:53] clear about not everybody is the right fit for our solution and then I think for folks that are consuming our data directly to build their own visualizations uh you know we’re we’re more than happy to provide a ton of advice there in terms of how can they connect the dots between our data and the charge that they wish to build and

[10:16] of course anything that we’ve already created ourselves will be able to help them stand that up as well you know within their own bi of choice a lot of companies or probably every company has a different data stack where they get or data sources where they get the data from how do you integrate with all these different data sources and

[10:39] give me a bit of an example on what sort of the starting point and best case examples great question yeah so we are a vertically oriented data infrastructure solution so then the question might be what is a horizontal data infrastructure solution right so I think the way you can think about is like the super metrics funnel five Tran of the world

[11:05] right they’re there to serve basically a lot of different Industries so they have a particular boundary where they won’t go further right so with the solutions that I mentioned they’re only kind of Mo is to move raw data from A to B right but that doesn’t really that’s kind of where your problem begins right because there’s so many other things that kind

[11:30] of comes from that and but for us not only do we take care of data uh movement and extraction from your data silos we also have our Advanced Data transformation uh Technologies and in terms of how we actually active that the the reason that this works for Commerce is because there has been sort of an aggregation of customer base and user

[11:58] base into a you know a finite set of platforms right so if you think about why this is this will be way harder for SAS is every SAS company have their own schema have their own database model right that have their own intricate sort of ways of operating but you know most Merchants are on you know especially uh

[12:21] brands that have started in the last few years is you know finite number of platforms right you have your your e-commerce transaction and customer data in in platforms like Shopify Magento of the world right you have your customer communication data and support data and platforms like gorgeous zendesk clavio attentive right you have GA you have your marketing data from you know meta

[12:47] and Google and Tick Tock Etc right so that actually makes it possible for us to really Orient towards a set of platforms that that covers the 80 20 as far as what the brands are using and then essentially creating a standardized taxonomy you know one of the things that’s really powerful with our offering is that it’s omnichannel by

[13:11] Design so what that means is if you just understand for example how net revenue is calculated for Shopify you’ll be able to understand that for any Amazon business for anybody on stripe on charge beat but really on any other e-commerce platform that we would integrate with down the road right so that’s really kind of what people love about our

[13:32] offering is that you just need to really learn the taxonomy one time right and and again that becomes impossible with SAS because every SAS company’s offering is just could be so so wildly different yeah I can totally follow that um thought of train train of thought um obviously was a technical stag Shopify clay view gorgeous whatever you

[13:59] made it a lot of merchants a lot of listeners will find themselves in that kind of ecosphere and then it’s relatively easy to do the next step now what’s the kind of um homework or when do as a merchant do I need to think about getting up to the next level and maybe getting contact with source medium.com to get my data

[14:20] consolidated absolutely yeah so I think you know we have this concept internally of the data maturity curve right so uh you know for example in my last company I got to sort of participate in the scaling of the brand as we went from seven to nine figures and and now Beyond right as kind of the the the the biggest online

[14:43] mattress retailer in North America now where I started right under 10 million dollars in sales I remember but each stage of that Journey required a certain amount of self-awareness in terms of what it is that you actually need right so how do you kind of think about that right it’s in terms of the maturity curve it’s it’s kind of made up of how

[15:09] large your business is but also how complex your business is right so uh you know you could be for example a a decent sized business but you’re only advertising on Google meta and selling through Shopify there are businesses out there so it’s not you could one could argue it’s not as complex although that is very rare but you could be for example a 10

[15:37] million dollar brand that’s selling on retail Amazon Shopify right you may have more than one brand right or you may be selling in both the US and the UK right so all of these things are going to very rapidly increase your complexity level regardless of your actual Revenue size right so that’s kind of the most important but for every business they

[16:08] will get to a point where their spreadsheet setup is not really doing it anymore right so whether that is a trust of rely a lack of reliability or it’s just not loading anymore right um you know or maybe the out of the box Shopify app that you installed is it doesn’t have the richness of the data that you require to make the type of

[16:33] decisions that you need to be able to make right um you know or you realize that the numbers that you’re seeing is let’s say 20 off then what’s actual right so but now you actually care whereas before maybe you were fine with that right because it was smaller um so so once you kind of get to that

[16:54] threshold then it’s time to really think about this concept of a data strategy right in terms of what are you going to be adopting that is going to not only support you today but also the next two three four acts of growth that you’re going to experience right because the the last thing a lot of people want to

[17:16] do is probably to get to you know another threshold in six months and you have to do a whole other round of vendor evaluation and and things like that right and that can really really slow down a business um if you do that yeah makes total sense um obviously some businesses are growing slower and more predictable and some

[17:38] business and hopefully you’re among that as a merchant have this kind of ice hockey stick growth which for a lot of them comes with a ton of problems and challenges and then it’s better to be to have the right setup already done now you spoke a little bit earlier you mentioned is the the onboarding process where you help with data quality and all

[17:58] of that how does that work yeah great question so for us it’s pretty straightforward you know you sort of just oauth into uh your various platforms and then we ingest the data you know that’s pretty much the same with every data provider out there but you know with our onboarding you know because of our focus on data quality we

[18:19] actually do a preliminary data quality check right making sure that our numbers are matching right for example what’s coming out of the Shopify sales report right yeah and if there are differences we actually spend the time to to to to to understand ahead of our actual onboarding call right that’s kind of one layer of just kind of like numbers

[18:43] matching right so that’s very easy to understand but then there’s a lot of other data hygiene things that we help identify uh proactively for example you have a lot of non-nons or direct nuns in your UTM values right so or let’s say you’re not tagging your utms correctly where you’re not for example using something like UTM campaign that can

[19:07] give you some richer information there right where you’re using inconsistent naming conventions or discount codes are being used uh you know in inconsistent ways right or let’s say for a zero dollar Waters the Cs department is is doing different types of things that actually makes cleaning that data more tricky so all of those things we would be identifying proactively but also sort

[19:33] of what are some of the things that we know that is a generally accepted best practice that the brand is not doing so an example that I can give there for example is not uh doing post-purchase surveys right getting information on how did you hear about us right so we integrate with our partners in the ecosystem there as well uh you know but

[19:56] I think ultimately the reason a lot of people haven’t done some of those things even though they may already know the importance of it is they haven’t had an actionable way to really look at it right so the aha moment that we typically get is okay well I can actually use this Insight now I can actually make types of decisions based

[20:18] on utms that I didn’t even think was possible right and then that opens up a lot of other thoughts there right I think just being able to look at your last click attribution relative to the zero party attribution AKA how the customer heard about you becomes very insightful right because you typically see for example a lot of Google taking a lot of last click

[20:46] credits right but then you might say you might actually see that well they actually heard about you on YouTube or they actually heard about you on classes podcast or what have you so all of a sudden it’s like you’re going into high def right whereas previously you know you’re just kind of like suffering under the obscurity of Google

[21:07] CPC Source medium right and and having a lot of opinion based debates around are we overspending on Google or how do we think about this right you really want to just be moving your discussions from the land of opinions to a land of right a a consistently uh um analyzed a consistent analysis based on you know a trusted source of data

[21:37] awesome example that you gave there was the last click attribution um all the providers out there always want to have the last click attribution because they want to claim the business or they click for themselves and it screws up the data and it shows you also that having a good structured system and an external partner who knows on how to

[21:55] look in the data will help your business massively now who’s your perfect customer yeah we typically work with brands that do sales between 20 million to up to a billion um you know but with that being said we also work with brands of all sizes because we have a very selective few agency partners that are able to

[22:20] essentially use our data as their infrastructure and service that to their own clientele so you know but we actually also work with SAS companies who take in our data to power their own reporting uh or power their own feature sets and of course then we indirectly serve those customers as well um but you know I I would say in terms of

[22:47] brands that are coming to us directly and we’re working with directly there really has to be a commitment to um onboarding with us and and kind of like stepping into our methods uh you know and that that that also requires work on the Brand’s end in terms of let’s say cleaning up things like utms right cleaning up internal

[23:12] um practices to make sure that the data coming in is going to be standardized and clean right actually learning about some of these neural Pathways you know going from let’s say having an analytical question to getting to a trustworthy actionable piece of insight so um you know but but then I would say in that case it’s typically brands that

[23:37] have some level of complexity baked in apparel is a great example of that right they have a ton of exchange data reshipments influencer samples right tons and tons of orders that you really don’t want to be calculating in your metrics right or let’s say they have Amazon and Shopify right or let’s say they’re leveraging a retail partner like

[24:03] leap right that’s kind of sitting on top of their Amazon store or tap car and you need to be able to to look at the performance of those channels separately right so all of those things will be sort of just like out of the box for us you know but again right that’s not for every Merchant right the

[24:23] ultimately the long tail majority of merchants are are are are probably just on Shopify probably just using Google ads and meta and probably just having GA um you know so in that case we could obviously absolutely still work for those Brands but it’s really for brands that already have some sense of the complexity that’s either incoming or

[24:43] that’s already in their business I like to focus that in your onboarding you basically completely dissect what they have and not only provide a technical solution but also the expertise on how to make it better and I’m sure you must have seen a huge mess with one or other clients onboarding you I can’t just imagine that some people

[25:04] just do not have control over their data um give me the overview about what’s the pricing structure just a rough idea yeah so it sort of depends on the use case you know so we have more of a wholesale model for agencies and SAS companies that’s more so as a potential replacement for products like five Tran that they

[25:28] may be using uh you know but direct to Brand we look at they’re trying 12 months sales and we have different sort of levels there depending on how much sales that you’re you’re actually doing that’s actually flowing through our system so the the the the for example the decision to say whether or not we want to integrate Amazon becomes

[25:48] relatively easy because you know how much sales is coming through that and how much increased cost and that would sort of uh be bundled into that as a result okay before we come to the end of our coffee break today is a one final thought that you want to leave our listeners with um well thank you for for allowing me the time and air space to uh talk about

[26:11] Source medium but uh yeah no I think the final thought really is to reiterate right this this relationship between understanding where you are in terms of your own maturity as a brand and having uh establishing opinion between the complexity of your business and the needs that you actually have when it when it comes to data and then being

[26:37] very intentional about selecting the right solution there uh and and of course I think there’s always the build versus buy discussion as well so it’s really important to also understand if one were to build internally right what are the implications and um and scaling challenges that they will inevitably sort of come into play as the businesses grow so uh you know there’s there’s

[27:03] hours and hours of conversations we can have about that but uh you know I would say uh happy to kind of chat with anybody that’s that’s in in The Listener base of the podcast here uh feel free to just email me directly at faisourcemedium.com that’s efficacyfrank E.I source medium.com and we’d be happy to to to dig deeper into some of those

[27:25] topics excellent I will put the links in the show notes as always then you just one click away hey thanks so much for giving us a in a very in-depth insight into Data why it’s so important and what you can do to make it better and I hope a lot of listeners will get in touch with you thanks so much for your time today

[27:41] plus take care bye hey Claus here before you go I would like to invite you to become part of the e-commerce Merchant Pro Community to get actionable advice from other Shopify Merchants who already have achieved what you are aiming for our community is a safe place to actively grow your online retail business with the support of the

[28:02] most amazing and helpful group of e-commerce entrepreneurs behind you running a Shopify business is tough don’t do it alone join us now you will find the link in the show notes also if you think your online store has conversion or marketing issues and you would like to have a fresh set of eyes on your business then drop me an email

[28:19] at Klaus klauslaughter.com and let me know a little bit about your business it might be beneficial for you to have me look over your store offers emails and ads and get an unbiased outside perspective and guidance to help you make most of your online business thank you as always for tuning in today I appreciate you until next time and I

[28:39] talk to you soon