Voice of Lumesse

Happy customers - it's not about technology

A number of conversations with analysts and customers recently have set me thinking about how customers and suppliers often have very different perspectives on the same thing.

Specifically I've been thinking about SaaS (Software-as-as-Service) and whether the 'apps' model of mobile computing breaks or enhances the ideals of SaaS. A few days ago we launched a new version of Lumesse Mobile, a native Apple iOS app that has to be downloaded to your iPhone or iPad. This app sits on your local device and talks to our true SaaS product, Lumesse TalentLink. We probably could have built a pure, browser-based app that would require no local app to be installed but, as anybody who has tried to use a mobile browser, the experience can be… sub-optimal. Delivering a native app like Lumesse Mobile allows us to create a good user experience even with a slow connection. Of course, the Apple app model does allow for a near-automatic upgrade of locally-installed apps, so it does come close to one of the ideals of true SaaS – transparent upgrades to a common code base for all users.

Goal cascading or aligning

Goal cascading has been around for a while now; In fact it has been around for far too long. And finally we are starting to do goal alignment instead. I will explain why I think that is a step in the right direction and why goal cascading might be pretty in theory and in reports but not so useful in real life.

How an App update can mean lots of different things

Today we released a new mobile app to the Apple App store. Lumesse Mobile 2.0 is now available for Apple iOS in the app store. People might ask why is this a big deal - it's just another release of a product which was already launched in 2011? For me as a product marketer this release proves something more than just a new release: it emphasis a number of critical business values which are part of the foundation of Lumesse. As part of the team responsible for the rebrand of StepStone Solutions into Lumesse, I was part of the conversations about what we stand for and what value we bring to our customers. In preparation for this release there were two important pillars which stood out.

The challenge in making predictive analytics work

A year before the first banking crisis I sat next to a super smart guy at dinner who explained the logic behind the hedge fund he had started. He confidently and a little smugly talked through the power of the models and algorithms he had built to predict market movements and to give his fund and his clients an edge. I'm no expert in modelling financial markets but did ask a simple question: how would his models react to a combination of circumstances that had historically never existed before? The answer was unsurprising - the models couldn't predict what would happen as the data wasn't there. In fact the guy suggested I was pretty insane to even suggest this, after all we had years and years of complex data available to show what happens in the event of almost any set of circumstances, so why should anything unforeseen happen?