I’m joined today on the Procuretech Podcast by Sammeli Sammalkorpi, CEO of Sievo, one of the stalwarts of the spend analytics space. The well-known Finnish company were actually founded almost 19 years ago! 
During this time, what started out as a simple spend analysis tool has grown to be a comprehensive best-of-breed solution for spend analytics. We discuss how this niche has matured over the years and what direction it’s headed in the future.

Spend Analytics: Maintaining relevance and continuously improving in a crowded market
Sammeli starts off by explaining how being from Lapland, his parents’ wish was always that he would become a reindeer farmer! 

Instead, he landed into software development rather than staying in his hometown. This came about through starting out as a junior analyst in a procurement consulting firm and realising quickly that there weren’t any tools out there to efficiently analyse and report on what a client’s business is spending its money on.

In a crowded marketplace, Sammeli puts Sievo’s success down to staying on top of the latest technological trends as well as listening to and striving to satisfy their global, enterprise level client base.

Machine learning and AI is developing and evolving quickly. New technologies and more and more data is available. They have seen many well-funded competitors enter the market, and some of them fail. Even though Sievo have never received any VC money, they have gradually grown to 220 employees in their two bases in Helsinki and Chicago. 
How has Sievo grown despite the onslaught of competitors?
There are 3 factors Sammeli gives as the main drivers behind their continued success:

Procurement leaders who change companies and then lobby to introduce Sievo’s software in their new company because they have seen the value first hand.
The data they have been able to gather and, with their clients’ permission, use it to provide benefits and insights to their whole client base to improve analytics and predictive trends. Big data enables the classification process to be more efficient and accurate, due to the learnings and previous spend which has been analysed and classified through the system. The most obvious example of this is the ability to recognise duplicate vendors e.g. IBM, I.B.M. and International Business Machines as being the same organisation
Scale: being able to invest more into software development and marketing & sales as they have become a bigger company than most of their competitors.

Leveraging their data assets to add to the CX
Drilling down on this piece, Sammeli mentions three areas where they have been ableto improve customer experience through aggregating data:

Incorporating client modification or customisation requests into the general product offering to bolster the capabilities of the tool.
The aggregate intelligence gained from each spend classification / normalisation of supplier data to be able to have an ever-growing database of parent and child companies...