What does your sweet spot look like in terms of customers? Does it look like having multiple locations or a requirement of size?
We primarily work with, mid-cap businesses. That is, 50 million to 1-2 billion in revenue. We found that it’s that size business where we can typically bring a large step change in operations. They’re probably pretty fast-growing and may have outgrown their current systems. We can come in and just really take charge as far as their forecasting and inventory management. You can buy any one service on its own, then all of their data is acquired in the system and training those models begins.
What human capital investment does a company need to make to get this up and running?
It is enterprise software. So it’s not quite a point and shoot and it depends on a great deal of things. One of them is the services required. Forecasting, for example, is far easier to implement than inventory management. It’s just that it’s a whole lot simpler. On the client-side, where the data is coming from, can speed up or slow down the process as well. If they have one of the well-known systems that we’ve integrated, they’re far easy to work with. So on average, one in three months. And in the grand scheme of enterprise software, that’s really quick.
Is there an element of Machine Learning pertinent to per client that improves their optimisation and an aggregated level that you can pull in? Insights from cohorts across the industry?
When a customer comes on board, we build up an entire version of their relevant models using as much data that we can add in – such that there’s never any customer data shared between models. However, where we can further customise them is where we can start to add in external data streams that might be relevant. If you’re an eCommerce business in Germany, for example, Oktoberfest is probably a big event for you. Having adopted a relevant data stream that for events such as Oktoberfest, we can adjust for seasonal factors. The new Machine Learning way – we always knew that was a spike there, but we didn’t quite know why.
And as far as the customers you’ve got, how quickly do they generally see a return on investment?
It depends on the size of the business. For example, if you’re a large business that is turning over five or 10 billion dollars in revenue – you’re probably losing 10 to 20 million per month of opportunities. At the smaller end or a business that hasn’t seen much growth since they started, it may take more time. The sooner they have access to all these data points, however, and start recording it over a longer period of time, the bigger that benefit is going to be. We can be up and running for a few thousand dollars a month. It’s almost a no brainer, isn’t it?