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Use casesData for subscriptions podcast - Episode #7
Overcoming the Growth Inhibitors
Guest
Jonas Wallenius
Strategic Product Manager at Digital Route
Guest
Jonas Wallenius
Strategic Product Manager at Digital Route
Episode description
How can subscription-based business leverage service usage data? There’s a right way, and lots of wrong ways.
In this episode, host Behdad Banian is joined by Jonas Wallenius to share the some of the painful real-life examples of how businesses struggle to leverage usage data. They also share some best practices to help avoid finding yourself in the Library of Pain.
Highlights
So when I say pain, I’m usually referring to some frustration you’re facing as a business. It could be an emotional frustration. It can even be a personal frustration. It can be a financial frustration for the company or even an operational frustration. The common denominator here would be that they typically keep you from growing at the pace you want to grow.
Well, the first pain I want to talk about is a situation where you’re not even collecting usage data. It may not seem apparent initially, but this can really inhibit your growth. For instance, we had a port operator that was charging companies for the time their containers were spent in storage. The problem was their whole operation was on paper and time stamps.
At the end of it all, we would have some poor guy collecting all of this into one large Excel file. And when you’re doing all of this manually, you have longer billing cycles, meaning you have less cash flow to invest in growth. So a usage data problem becomes a growth problem here.
The second one I want to talk about is when you have a supply chain with many vendors providing services and products to your customers. Besides the delivery of services and products, you’ll also need smooth operations when you settle up with your partners. Once again, there’s probably an Excel Guy.
There are so many ways in which this can go wrong and then you have to manually reconcile the numbers to settle disputes with your partners. This can happen in everything from amusement parks to media ad companies. I have seen Excel Guys working at $5 billion, IPO-ed companies.
The final pain I want to talk about is when you start scaling rapidly. Rapid scaling means more data coming into your billing and backend systems. They simply were not designed for this and you’ll start seeing outages in your billing process. You might end up doing reruns of your billing process. All this delays the cycle causing cashflow problems, as we talked about before.
Well, first off, is the unawareness that there are solutions and systems purpose-built to solve this very problem. They just don’t know.
Another aspect is that the problem appears in the billing system, it’s where you see the pain. The billing system crashes and burns because it can’t handle the data volume. So everyone sorta runs to fix that first.
First ensure that you have the data you need, which means both the users’ data and the contract data. Then make sure that your systems can cope with the volume on those two because ultimately you’re going to need to put those two together at increasing scale over time.
The second is to clean the collected data and bind it to customers and products right on the way in. Here, there are two paradigms where you can either store everything and a database or something first, and then you get to work on it. Or you clean it on the way in, so you can use it immediately. I’m obviously advocating for the latter because the data will be available faster for the business this way.
So, DigitalRoute obviously is pretty good at doing this. We’ve done this for more than 20 years. But we’re not the only option in the market. I mean you can do it using Excel, you can build your own data management solution, using databases and ETL tools, more integration tools, and it works in the beginning.
But there’s a point where it will start breaking down, most likely when you’re starting to scale. And then what happens is that instead of focusing on scaling and growing the fastest you can, you’re forced to divert energy from the scaling of the growth into fixing data problems.
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