CEO Adomas Malaiska tells us about the NextQuestion’s vision for Data Pitch and beyond.


Q: Describe your Data Pitch challenge idea


NextQuestion is a smart retail inventory management system, aimed at offline and online retailers who need to better manage their stockholding and improve operational efficiency. Our solution is powered by machine learning, allowing for the capture of a wide range of internal and external factors influencing consumer purchasing behaviour and supply constraints. Our algorithm, underpinned by a neural network, is able to digest a large range of different factors, as well as learning over time, resulting in self-optimisation. We are therefore able to offer forecasting accuracy, which is significantly superior to traditional solutions.


Q: What does the idea set out to achieve?

Can you imagine living without weather forecasts today? Now try to imagine what other forecasts will be taken for granted tomorrow! This is the niche NextQuestion is targeting and we’re aiming to be on the frontier of this wave.

The first application of our solution is in retail, aiming to reduce sales lost due to items being out of stock and stock wastage due to insufficient inventory planning, all while reducing retailers’ environmental footprint.


Q: What makes your idea different or unique?


Our idea is unique in that our forecasting accuracy is powered by machine learning, allowing the capture of a wider range of influencing factors and guaranteeing superior level of accuracy overall. Our business grade cloud solution also has natural language based platform access.


Q: Where did the idea come from?


The idea was born out of the combination of our accumulated experience. Adomas Malaiska, CEO of NextQuestion, has been working in strategy consulting advising large retailers on their strategy, while Gedas Stanzys, CRO of NextQuestion, has been working on timeseries forecasting solutions at hedge funds and focuses on machine learning applications. We started considering how to take the most successful approaches to the issues from the finance sector and apply them in today’s highly competitive retail environment.


Q: What excites you about the challenge you applied for?


Retail is an exciting industry to be involved in. As it is such a crucial aspect of the day-to-day of consumers and retailers, the industry’s offline business model and overall operational efficiency haven’t changed much. The potential scope for impact is immense, and when combined with the total number of consumers it could lead to wide-reaching impact.


Q: How did your team meet?


We met by chance at a data science event in early 2017. We kept the conversation going and started bouncing ideas together, leading to where we are now.


Q: What’s the best thing about working with data?


The shared passion for working with data is one of the team’s core values. We enjoy the process of working with data – every bit of work involves a mix of research and learning, goal setting and calibrating the applications of algorithms to real life problems, eventually resulting in finding the solution. That “aha” moment of finding the right solution is so rewarding.


Q: Anything else you want to tell us about your startup and why you do what you do?


We personally believe in the power of data and its ability to increasingly shape our personal and business lives. But it is therefore important to build the right algorithms now for business problems of the future. Otherwise the abundance of data will lead to deadlocks in decision-making.