Driven by data – supported by experts.
XPLN [ɪkˈspleɪn] enables you to make quick and confident decisions regarding your e-commerce offerings and brand positioning. We mine a trove of data and use profound analyses and interpretations to make them not just useable, but also profitable. Countless projects have shown us that this requires two things: AI-based software PLUS the human intelligence and insights you need to correctly interpret the results.
SaaS+ is more than “Software as a Service”
We call this combination of SaaS and applied expertise SaaS+. Our individual support in insightful data analysis and derivation of profitable actions for your business represent a decisive competitive advantage over the simple SaaS offers of our competitors. This is the heart of XPLN – The Importance of Insight.
Premium data for premium brands
We are committed to collecting data with the utmost dedication and in premium quality: GDPR compliant, climate-neutral, and housed on servers in Germany. We work hand in hand with you to develop customized and visionary enterprise solutions that allow you to reliably move your company forward – and free up resources for strategic projects. We provide a measurable guarantee of our services with SLAs.
By the way: XPLN is part of Parsionate
We are part of the Europe-based Parsionate group with 200 employees, which advises corporations on strategic data management projects and supports their change processes. With parsionate, we always have an eye on the market and know what is important for renowned brands and large European enterprise customers in terms of data and analytics. And our German roots help explain our high quality and security standards. Clearly, we come by our consultant genes honestly. They support our SaaS+ approach and let us work in close partnership with our clients to develop the best possible customized solutions.
We will show you data-based possibilities for increasing your performance in e-commerce: in a free and non-binding demo. We will also be happy to use your own data for the demonstration.