Wunderdata is a great startup in Berlin providing business intelligence tool to eCommerce companies.

In this interview, Richard Neb and Mike Roetgers share the idea behind their business model. They also explain their competitive advantage and provide advices for avoiding mistakes for the first time entrepreneurs.

 

Interviewer: Hi. Today we are in Berlin with Wunderdata. Richard and Mike, who are you, and what do you do?

Mike: We are three founders who founded Wunderdata a year ago. We are basically developing business intelligence software for online shops.

Interviewer: Please tell us a bit more about your background, what did you do before you started this company, and what made you switch from being an employee to starting your own company?

Mike: I was a software developer for ten years. Actually when I was 17, I started doing a bit of freelance work, and at 18 I started my first startup. Back then I didn’t know it was a startup, I thought it was just this small company, and we had venture capital, even though I didn’t call it that. For the first some years we did music downloads with MP3s, it was back 2005, long before Apple did that. But when Apple switched to MP3s in 2007 we failed. Then I worked as an employee for some years until last year when we started Wunderdata.

Interviewer: And you Richard?

Richard: I’ve worked for four different online shops so far. Mike has also a lot of experience, and also Stefan who is our third founder, in ecommerce. And what we realized or the learning out of it was that it’s very important for online shops to work data-driven, but at the same time it’s very hard to work data-driven professional online tools because all the solutions in the market are either self-built or in enterprise field, both of which are very, very expensive, and it takes of time, several months, until you can deploy it to work. And it takes a lot of knowledge to work with it. Every developer can build some SQL queries, but to build a scalable, flexible data solution is a whole other game. So what we realized there was that we could do it better, so we founded Wunderdata.

Interviewer: Tell us about how the business model of Wunderdata works. Is it subscription or SaaS model? Tell us a bit more about what the software is really doing, what the data output looks like, and how you try to store the data in this data warehouse?

Mike: What we do is we create a data warehouse for the individual customer, and we try to identify data sources for the customer, which are relevant. Of course the base level is the online shop in our case, and very important is web-tracking, like Google Analytics, or Web Track, or whatever. And then it’s often about getting all the different costs. Like if you have marketing, you want to know what you spent on Ad Words, what you spent on ZaNote, etc. and you want to know logistics costs, for example, returns, what you pay for pay for that and stuff like that. We bring all that together in one big data model and we connect everything with each other. So our big difference with a lot of other BI vendors is that we are not totally generic, but we know what data we have and what it means. It’s not like a number we know what it means and that’s where we interconnect it. Then we are able to do, for example, filter globally. I can look at a certain customer corporate and see what brands they bought, I can bring everything together, I can try to find correlation between different data, which you normally don’t look at together. So that’s the basic idea.

Interviewer: Assuming you had all the clean data and access automatically to all the data sources, are there different types of data sources like Google Analytics, etc. is quite easy to extract, but maybe there is some other data, from like logistic quest, or specific other marketing or offline expenses that I generated that I might put in manually in the other system or so, so how do you extract this data and put it into the data warehouse?

Mike: Of course we love APIs, so whenever it’s possible to get it through an API we get it through that. We can use direct databases access. If you have your open system, for example, we can connect directly to database if you allow us, and we try to find what we need. And we can also use spreadsheets, for example, so if once a month you give us payment correction cost on that spreadsheet, and we import it so we’re pretty flexible. As long as it’s structured data we can use it.

Interviewer: But API or Quora or whatever, you will need to develop ones?

Mike: Exactly. As soon as we have a new data source, we connect it and then we have it. It’s a bit more if it’s completely new data, like I can exchange Analytics with Web Track, that’s pretty easy, but if it is something new then we have to integrate into the other model and see where are our correlations and where do we need to put links, stuff like that. But normally it’s pretty fast.

Interviewer: So it doesn’t take that much time?

Mike: No, not a lot. A new API if we know the data it’s about a day or two. We have to integrate new models, maybe four or five days.

Richard: But it’s only once and for the customer it’s always only a few minutes of effort.

Interviewer: I am just thinking on the one hand you have this subscription model where you have something 200 to 1000 Euros a month, for example, and you are not charging for the individualization, and it takes three to five days, it’s additional cost you would have to incur that you need to cover a longer period that the customer’s paying you money.

Richard: But what we saw in ecommerce especially that it’s a very homogenic market. 40% of all online shops are using Magenta, for example. More than 80% are using Google Analytics. So we don’t really have a lot of variance there.

Interviewer: In terms of your distinction from other competitors, there are a lot of analytics software coming out over the last two or three years, what makes you unique?

Mike: Because we specialize in ecommerce, we have prepared a huge amount of analysis and dashboards for you, so you don’t start with a white paper with nothing on it, rather you have a functioning system which you can just use. You just get your account and log in and you can work with it, not only the BI expert who knows all the data but also the intern, for example, can access to his parts that he needs and he just sees the KPIs which are important for him. That’s one thing. Then this global filtering, no one can do that except for the very generic SaaS systems, because it is so generic that you don’t know that this order has to do with this customer. And we know it’s an order from this customer, so we can do the link. You have to do that by yourself in a lot of systems, we can do it automatically. So we connect you, and it’s working, and it is 100% there.

Interviewer: Nobody else is doing this kind of global metering?

Mike: They all do a very generic system. Of course if you have relational data, it’s pretty easy to match them. For example, Magenta is not very relational, there’s a lot of crazy tables, and if you put in a very generic system you have to do a lot of work in order to make it work. And with us it is different, because we understand Magenta, we understand the data model, we can just do it automatically.

Interviewer: As I understand, you have different target groups you want to offer your services to, like startups such as Amorelie and Lesara. Are there any other specific criteria for selecting the target group?

Richard: We can connect every shop or shop-like system which is transactional based. It doesn’t matter what you sell, shoes or events or something, everything will work. As to how big the customer is, it doesn’t matter whether it’s a startup or a big online shop, we can do it. Technologically we have a high speed database, but from the sales approach we are more targeting the smaller and not the enterprise field because there are a lot of big, strong players, and the sales cycle itself is just longer.

Interviewer: How do you acquire those customers?

Richard: We have different acquisition channels. One is very classical, it’s just direct sales, calling over the phone. One is content marketing, we’re trying out building educational content, especially what are the best KPIs for ecommerce, etc.

Interviewer: Like a blog or…

Richard: Yeah, kind of whitepapers, blogs, yeah. The third channel, which is interesting, which we are working on at the moment, are multipliers. For example, IT agencies, a lot of them asking us, because they already have ecommerce customers, they’re building the shop system for them, but they often get asked whether they can also build a business intelligence solution, but it’s a whole different field, it’s not their core competency, so it’s very interesting for us to work with them.

Interviewer: Is this based on a revenue share model?

Richard: Yes.

Interviewer: Do you also have an affiliate model or something like that?

Richard: Not yet, but if you refer a customer to us we can work something out.

Interviewer: Tell us a little bit more about your future plans in terms of whether the product is really finished, you’ll never work on it for years, or is there something that you would love to work on in order to create more value for customers?

Mike: First of all, it never stops that new shops pop up or new APIs we could connect, so that’s always a thing we can invent time in. I think there is some stuff to do on the front end. We are three technical founders but we are both backhand developers. So it’s not as beautiful as it could be. There is definitely potential, also some pretty normal frontal functionality like comparing charts, stuff like that. We have some technical debts, we know about them, and we know there is stuff we need to do in order to make it really really good. We are working on that, but it’s a bit more time, maybe some expertise from the outside. And it’s about atomization of course, we want to automate more stuff. Right now technical debts are there so not everything is 100% automated, and we are currently working on that, on becoming better and faster so that more tasks and can rebuild your data warehouse faster and stuff like that. There is a lot of potential to optimize, but the product itself is usable and is used every day.

Interviewer: Imagine, I am a client of a BI analytics software and now you’re pitching me, “Martin, we would like to offer you our software, Wunderdata.” Then I will ask you how hard is it to switch to your product, do I need to change anything, or do I just need to pay the money and that’s it, all the rest will be done, and I have my past data, I have all the APIs working from day one.

Richard: The effort on your side would be limited to a few minutes. You just need to connect the data source, but the work on our side is all automated. Then you can start working and you wouldn’t need to train all your employees on how to use the tool, how to build the report, etc., especially when it comes to more complex KPIs like customer lifetime value, cohort analysis, etc., it’s already there. So you can really implement the tools within a few minutes and start working with it.

Interviewer: Let’s talk about how you want to pursue creating the competitive advantage? I totally understand that you have this more specific kind of framework for matching different data sources and creating data of that, which others don’t have, because they only fit the generic model. Is this something that is the core value of your software, or is there something else you would like to develop for creating value to shop owners?

Richard: We’ll go even further in automating the whole experience, not only in terms of information and data but also a little bit – I can’t go into this too deep – but we believe that if you want to implement data driver culture in a company it is very, very important that it will to grow more and more, then you need a tool which is super-easy to use, so everyone from the CEO to intern can use it from the first minute. And then, and only then, you will have a data driven culture and you won’t have any presentation meetings of some bullshits storytelling, you will only look at the data and then make the right decisions.

Interviewer: What is the typical lifetime of a customer. Assuming I am subscribing to your service, how many months am I typically a user of your service?

Richard: Statistically speaking for eternity, because no one’s gone so far. That’s very good. But on the other hand we are pretty good for a startup that launched several months ago.

Interviewer: If it keeps like that, that’s awesome.

Richard: Definitely, yeah.

Interviewer: We always try to share some insights and advice to first-time entrepreneurs, what will be your top one or two advice that you could give to first-time entrepreneurs so they make less errors?

Richard: One important is not to underestimate the amount of time it takes to close a deal, because on B2C you can test out very very fast a feature or your whole product, but on B2B it can take long. We built a product which is multiple times faster implemented than the current products in the market, but it doesn’t mean that the sales cycle will shorten to the same degree. This is one thing that we learned.

Interviewer: Any other lessons in terms of product development?

Mike: In general we can say everything takes a bit longer than you think. It’s not only B2B, it’s cycle, it’s everything, raising money, everything you cannot influence directly, you can’t rush it. As soon as you depend on other people, it’s always like you have to wait and they have other stuff to do and it is weeks and months sometimes, so it’s frustrating if you don’t plan for it from the beginning. You think you’re going to do this and this, and that takes a week, and then at the end it’s not like that, as soon you’re not the only one involved. So that’s something we learned the hard way to be honest in the first month, and it’s frustrating sometimes.

Interviewer: But right from the start your product was the same product marketed or was there some kind of iterative process where you learnt along the way what the customers really wanted, and how did you approach that?

Mike: We built a prototype which worked, which was not perfect but had the basic functionality we wanted to build, and then we brought in first customers to learn from them, and their feedback was very valuable because you don’t see all the stuff they do. We knew the ways to do it, for example, but then some stuff Amorelie did different, like Lesera is more focused on sourcing than Amorelie on marketing, so you can learn from the first customers very much, and you have to really integrate them, and get the feedback fast, and then move fast, and adapt it to them. That was very good.

Interviewer: Great. Mike and Richard, thank you very much for your time.

Mike: Thank you very much.

Richard: Thanks.

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