Isabel Alexandre

Meet CODE_n finalist Intelie

Intelie uses advanced trend analysis and information correlation technologies to help businesses achieve their strategic goals by avoiding possible mistakes. The Brazilian startup created Intelie Live, a platform that can process large amounts of data in real time and correlate them to create business scenarios.

Ricardo Clemente is the founder of Intelie

Ricardo Clemente is the founder of Intelie

In today’s interview Ricardo Clemente, founder of Intelie, talks about their big data solutions, the startup landscape in Brazil and, of course, their impressions about participating at CODE_n and CeBIT this year!

What differentiates Intelie from its competitors?

We have two main differentiators. The first one is related to the technology perspective. Our platform is able to analyze huge amounts of data in real time with low consumption of memory and infrastructure. Therefore, the company is able to perform correlated analysis and get information in real time by using our distributed correlation engine (PIPES). The second differentiator is on the business side. We have developed several segmented solutions per target industry. Thus, we build indicators, alerts, and visualizations for a certain industry, such as e-commerce, retail, oil and gas, insurance, telcos, finance companies, etc.

How can big data change the way companies work today?

Our solution improves the way that people work. The current data analysis culture is based on historical data only, using BI and Analytics tools. We would like to speed up this way of thinking. With our solution, managers make the most of their huge amounts of data and become proactive. They can immediately identify failures or business opportunities and act on them. Managers cannot rely only on daily or even weekly reports. For example, if I am working in e-commerce, I should be immediately informed if my conversion rate is too low or if many payments were blocked during fraud analysis. These blocks may be incorrect and I might be losing money. We have examples for all industries I just mentioned. Our job is to educate the market and show the importance of faster thinking and operational intelligence.


Isabel Alexandre

Meet CODE_n finalist Science Rockstars

For the Dutch startup Science Rockstars, good science is the key to great business. That’s why they created PersuasionAPI, an SaaS-based API for e-commerce marketing teams. The software acts on powerful insights into the actual behavior of individuals, helping companies increase customer loyalty and conversion.

Maurits Kaptein

Maurits Kaptein is Chief Science Officer of Science Rockstars

The startup is a pioneer in persuasion profiling. And what is this? According to an article about the technique and its creators in Wired magazine, persuasion profiling doesn’t just find content your customers might enjoy, it figures out how they think. The chief science officer at Science Rockstars, Maurits Kaptein, was one of the doctoral students in communications who conducted the research about persuasion profiling at Stanford University. In today’s interview he talks about these studies, the foundation of the startup and their first product, the PersuasionAPI.

Could you briefly explain what persuasion profiling is and the main conclusions of your research on the technique?

A persuasion profile is a collection of estimates on the effect of persuasive strategies and the associated uncertainty. At least, that’s the scientific definition. Basically a persuasion profile captures the sales arguments that a customer is susceptible to. The profile is updated continuously as the customer interacts with a website. By using persuasion profiling you can make sure you select the right sales pitch for the right customer.

What led to the setting up of Science Rockstars?

Science Rockstars initially grew out of my PhD work (at Eindhoven University and Stanford University). We were gradually shifting towards a first viable product in 2012, only in our spare time, and we launched the company in August 2012 when we went live with our first Beta customer We started pulling together to form this great team to grow beyond research into an actual product. And finally, it’s ready!


Isabel Alexandre

Meet CODE_n finalist SOMA Analytics

Stress is a global health issue and SOMA Analytics aims to use big data to tackle it. A German startup based in London’s TechCity, SOMA Analytics builds evidence-based mobile health programs to increase the emotional resilience of employees, thus reducing the risk of stress, anxiety, and depression.

The startup is part of Healthbox, an accelerator program that’s supported by leading universities, researchers and clinicians in the fields of sleep medicine and occupational psychology. In today’s interview with Johann Huber, CEO and co-founder of SOMA Analytics, we talk about the way forward for the company and the amazing benefits big data can bring to the health sector.

Johann Huber

Johann Huber, CEO and co-founder of SOMA Analytics

SOMA Analytics used to be a smartphone app to help people measure stress and depression. Now the startup changed its business model to target the B2B market. Why did you make this move?

Through several tests with consumers and companies we learned that we can provide much more value for the individual if we offer our solution in a B2B context. From research we also know that in most cases work is the main cause of stress. Our solution now helps employees and employers.

Could you briefly explain how the SOMA Analytics 21-day program works?

You install our app on your phone and it starts by itself – no other devices such as wristbands or endless questionnaires are required. The app measures parameters such as sleep quality and emotions experienced during phone calls with scientifically validated algorithms. We combine these measurements with further parameters, and, based on these readings, each individual receives personalized tips and feedback on how to reduce the impact of stress. For example, by the end of the program the customer knows exactly how long he should sleep to be optimally refreshed, or if he should go to bed early or late. In addition, he also sees which of the contacts he calls is likely to put him in a relaxed or stressed mood. The user can then apply the feedback he gets and immediately sees the impact on his measurements.


Isabel Alexandre

Meet CODE_n finalist Kreditech

Sebastian Diemer is the CEO of Kreditech

Sebastian Diemer is the CEO of Kreditech

Kreditech uses Big Data and complex machine-learning algorithms to enable faster and better credit decisions. The technology developed by the German startup identifies and scores individuals online and decides over instantly paid out loans. All based on the processing of 10,000 data points in real-time.

In today’s interview, we talk to Sebastian Diemer, founder and CEO of Kreditech. He tells us how the startup uses technology to provide banking products to customers in emerging markets, allowing unbanked people with no credit bureau history to get credit. He also tells us how they are getting prepared to present their Big Data solutions at CODE_n and CeBIT.

Could you briefly explain what Big Data scoring technology is?

Our technology takes into consideration every piece of information that can be obtained about a person. We then analyze the results with statistical methodologies collected from our self-learning technology. Several thousand individual data points are merged into one big mosaic picture within seconds. It is not about single stones being red or green but the shape of red or green areas, illustrating the importance of coherence among several different data pieces.

What sets Kreditech apart from its competitors?

In comparison to other financial service providers and established credit bureaus, Kreditech only does live scoring. That means that we rate a customer’s creditworthiness by only looking at his or her current financial status instead of historical data. We gather information at the moment the application is submitted. Unlike FICO or Schufa, we neither retain the data afterwards, nor do we sell it. Our unique sophisticated credit scoring process uses the powers of machine learning algorithms and Big Data infrastructure to allow us to analyze more than 10,000 data points in less than one minute. That is what is unique about Kreditech.


Janina Benz

Viewsy wins CODE_n14 award

Since Monday, 50 startups from 17 countries have been presenting their innovative business models related to the topic of Big Data in the CODE_n hall 16 at CeBIT. The CODE_n jury has reached a decision: The coveted CODE_n award and prize money in the amount of EUR 30,000 were awarded to London-based Viewsy.


What does Viewsy do?

The UK startup offers retailers the possibility of understanding the behavior of their customers in detail. How much time do they spend on average in the store? Which areas do they frequent the most? How frequently do they visit the store? Viewsy’s technology acquires a variety of different data sources, such as distances walked, interprets them using statistical methods, and thus offers understanding of the behavioral patterns of customers. Brand owners can benefit from this as well by obtaining exact insights into where to best position which products in the store. Conclusions regarding the identity of individuals are purposefully excluded by Viewsy. All data is acquired anonymously. If a customer does not want to be analyzed while shopping, he can simply switch off the WLAN connection of his smartphone. More…

Isabel Alexandre

Meet CODE_n finalist Streetspotr

Streetspotr is a smartphone app that builds mobile workforce. The German startup from Nuremberg created a fast, easy, and cost efficient way for businesses to collect data and insights from anywhere, at any time and on any scale using mobile crowdsourcing.

Dorothea Utzt is co-founder and CMO of Streetspotr

Dorothea Utzt is co-founder and CMO of Streetspotr

The startup has already more than 220 thousand “Streetspotrs”, people that use the app to earn money doing seriously small tasks using their smartphone. Clients like Red Bull, Microsoft and Sony also employ this mobile workforce. In today’s interview with Dorothea Utzt, co-founder and CMO of Streetspotr, we talk about the trajectory of their company and their expectations of presenting themselves at CODE_n and CeBIT.

What was the inspiration for creating Streetspotr?

Actually, there has been more than one inspiration. Firstly, we came from the App development business and have known each other for years, working together on many projects at different companies (e.g. miCal- the missing calendar). We always wanted to do something “bigger” than the next customer project or simply the next App. As techies, it is no wonder that all of us founders have owned their first iPhone in 2007 when it came out. We played around with them all the time and spend hours and hours checking our emails or Facebook. That was the time when we first thought “How nice would it be if one could do something really meaningful in waiting times with a phone that´s so smart?” When BMW approached us in 2011 asking if we could map parking garages (opening hours, floors etc.) all over the country for them, the idea of Streetspotr was finally born.

Streetspotr connects Big Data and crowdsourcing. How does the app work?

Our premise is collecting data and insights for businesses via a smartphone workforce. Due to the size and scope of our workforce (230.000+ people), we enable them to collect exactly the data they need. That´s our way of “scanning” the mass of data that can be found everywhere, even on the streets and in shops (addresses, opening hours, menus, product placements). You can say that we take a step further than other Big Data companies: Instead of analyzing a mass of data that has no structure and is hard to define, we make it possible to skip that step and simply collect the right data for our customers instantly – anytime, anywhere and on any scale.


Isabel Alexandre

Meet CODE_n finalist Sablono

Sablono uses big data to help construction companies solve project management problems and deliver buildings on time. The German startup developed a software solution called BIMtime, a collaborative scheduling tool that assists organizations with the set up of complex construction projects.

Felix Enge

Felix Enge is CEO and co-founder of Sablono

The startup was founded in 2013 as a spin-off from the Technical University of Berlin. It is now a member of the SAP Startup Focus program and is seed-funded by Hasso Plattner Ventures. Sablono is also one of the 50 CODE_n finalists and in today’s interview we talk to the CEO and co-founder, Felix Enge.

When thinking of big data, the construction industry wouldn’t normally instantly come to mind. How can this technology benefit the construction sector?

The construction sector is one of the most analog, or non-digital industries in the world and keeping track of complex building projects is a very challenging task for the people involved. Often, the full complexity of a project is not completely considered in the planning phase and the initial schedules and cost estimates don’t reflect all relevant details. New technology can have a huge impact in this respect by connecting a variety of key data sources from the beginning and by enabling the people to systematically analyze past and recent projects. If this information can be made accessible in real time, project managers can better plan, decide or reschedule – based on the very latest developments and past experience. This will help to significantly increase the certainty of construction projects and will make estimates and schedules more reliable and accurate.

How does BIMtime work?

Sablono BIMtime is the first software tool to create reliable project schedules based on a virtual 3D building model. The components of the model are associated with Sablono-certified building processes, which contain all relevant information on the manufacturing process of every single component. The result is a significantly more reliable schedule with a thousand-fold increase in the level of detail. Throughout all construction phases, the schedule remains connected to the 3D building model and is automatically updated if a change or delay occurs.