There is no doubt that data forms a really solid basis for making business critical decisions not only in large organizations but also in startups. That especially holds true when ressources are tight, and the ambitions are grand; you need to really ensure that what you’re spending your time and money on truly works towards keeping the momentum high.
In reality it might not always be so easy to work with data in the most impactful way. Because it’s not only about looking into the numbers and keeping track of incremental improvements. It’s more about knowing where you want to go with your startup, figuring out which metrics make sense in order to report on your progress and then setting yourself up with data sources that enables you to keep track and optimize the operation, so you end up meeting or exceeding whatever goal you might have.
Doing this the right way takes dedication and a fundamental feel for and understanding of the underlying business dynamics, your products or services and – not least – your customers and their needs and expectations. In other words, in order to be able to use data in the most efficient strategic way, there is a lot of prep work you need to do beforehand, which doesn’t necessarily have a lot to do with data in itself.
And this is precisely where I often see warning signs when I look at especially early stage startups and the way they try to work with data in order to grow and scale their business. Many of these don’t have as much of a strategic view of how to enable their business to run on data as they have a more tactical view on using data.
So what is a tactical view on looking at data?
An example could be that you’re trying to grow engagement of your app. You want to get users to spend more time in the app and engage more by liking or sharing things. You could pretty easily define a couple of more or less standard metrics, and you could also quickly find a ton of tutorials online that will help you optimize for those more or less generic metrics (let’s just choose Daily Active Users or DAU as an example to make it concrete).
It would indeed be possible to apply well-recognized best practice ‘hacks’ towards optimizing for those metrics, and there would probably also be some improvements to show for it. But the trouble is that not only is this a very mechanical, one-size-fits-all approach towards working with data. It is also short term and has no real bearing on either the quality of the product or service, you’re offering, let alone the needs and aspirations of your customers.
Thus, in essence, by applying this generic tactical approach towards working with data in your startup operations, you end up optimizing for…what exactly?
This is precisely the reason why it’s so important that any effort working with data to improve the prospects of your startup and meet the goals, you have set up, needs to be strategic in nature. So what does that mean?
First of all it means having a general direction of travel, you want to take your startup on.
Second, it’s about validating with customers and market research that the direction is the right one and – if executed in the best way – will actually bring the wanted results to your startups prospects for success.
And third it’s about figuring out what that direction in tandem with the validation from the market and customers means in terms of defining custom metrics that both prove to be valid indicators of success and which are also possible for you keep track on.
Once you have those metrics in place to represent desired strategic outcomes for your startup, you can start doing the setup of your data and analytics to support keeping track of it all. The first time you go through that exercise it will probably feel like a lot of work, but just like plumbing for your home, if will not be something you need to do more than once. Once you have it settled, the systems are in place, and data is flowing like you want to, you’re set.
And then – and only then – are data set to work efficiently for you and your startup.
(Photo by Stephen Dawson on Unsplash)