We first ran into the predictive marketing startup Datatrics from the Netherlands at the PAPI’s Connect event in Valencia earlier this year, where they competed in the first ever AI Startup Battle. The Dutch startup offers marketing teams an easy and actionable way to leverage Machine Learning with its innovative data management platform, which we believe sets a great example for other startups in showing how BigML can add to their competitive edge and supercharge their growth. So we interviewed Bas Nieland, CEO and co-founder of Datatrics to find out more.
BigML: Congrats on your high score at the first ever AI Startup Battle. Can you tell us what was the motivation behind starting Datatrics?
Bas Nieland: Nowadays digital marketers are awashed with data due to the fragmentation of consumer attention on many more channels. Naturally, they are all looking for better ways to leverage all the data their companies collect, yet there is a big gap between what data can offer marketing teams and what marketers actually use. The main culprit is the fact that there is a perceived necessity of a team of data scientists and collaborating developers to make sense of all that data. Since the average small and medium sized marketing teams do not have access to such resources, new tools are needed to translate data into meaningful actions to optimize the digital customer journey.
‘An example of a 360 degree customer profile in Datatrics’
BigML: What is the lowdown on Datatrics? How does it help bridge that gap?
Bas Nieland: Datatrics was founded in 2014 and it currently has 10 employees in the Netherlands. We define ourselves as a data management platform (DMP) that helps marketing teams gain actionable insights. It is an easy and accessible platform that gives concrete insights and actions every marketer can understand. It allows marketing teams to build 360-degree customer profiles, based on internal data sources such as their CRM tools, social media accounts, websites and external data sources such as the weather, social trends and traffic information. By following the recommended Next Best Actions by Datatrics, marketing teams know exactly who to contact, at what time, with what content, and through which channel.
BigML: Can you tell a bit about how Machine Learning comes into play?
Bas Nieland: All of this is driven by smart algorithms applied to those data sources, which is powered by BigML’s Machine Learning platform, among other components that make up our platform. We especially love how BigML helps us to deploy many predictive models in a fast and scalable way by abstracting away the infrastructure level concerns needed to crunch the data. This way our product team can concentrate on the actual analytics tasks and development of the platform for our clients. BigML is also very user-friendly and has a well-documented API, which is very important if you want to go beyond simply gaining insights by deploying scalable predictive applications to your end users.
‘An example of a Next Best Action in Datatrics’
BigML: What are some of the predictive use cases you have and which other ones are you looking to add?
Bas Nieland: I already mentioned the Next Best Action models, which is a big benefit to our audience. We also are in the process of testing BigML’s ‘Associations’ functionality to see how it can benefit us. We believe it can make our product recommendations even more relevant.
BigML: Can you share specifics on customer traction and measurable business outcomes Datatrics have been delivering?
Bas Nieland: We are seeing great uptake especially in retail and travel industries. Over the past year, we have noted a clear demand in the travel industry for DMPs such as Datatrics. As it is a highly competitive market, it is important for companies such as travel agencies and hotel chains to use customer insights from their data in order to communicate in a more personal and relevant way. Some of our customers have increased their revenue by as much as 30%!
BigML: That sounds great. What would you recommend other startups and self-starting developers that want to implement similar smart applications? Any key lessons learnt that you would like to share?
Bas Nieland: They should think hard before going the route of building their Machine Learning infrastructure from scratch. Provided that you have pertinent data, platforms like BigML can help you in building real world applications very fast while letting you get there at a fraction of the cost of hiring a new analyst. Of course our platform consists of many more components and there is not one solution that fits all, but a good Machine Learning platform such as BigML provides can get you a long way.
BigML: Thanks Bas. It is very impressive to see how you have been able to ramp up your Machine Learning efforts in such a limited time period despite constrained resources. We hope stories like yours inspire many more startups to realize that they too can turn their data and know-how into sustainable competitive advantages.