Analytics can be a tremendous competitive advantage for your startup, I am sure you already know this. If you get it right, it empowers business users to make the right decisions, it helps fuel growth via product and marketing ROI optimisation, it enables quick feedback cycles on product releases and the list goes on. The key question is: how do you get it right? If you have yet to leverage data to its full potential, or do not know where to even start, then this article is for you! It is the first of a two part series aiming at helping startups set up an analytics function quickly and efficiently.
If you are a startup, it is quite likely you already have some kind of analytics in place. You might have a combination of Google Analytics deployed via GTM, rely on some dashboards provided by a 3rd party such as Salesforce, Shopify, etc. Those are a great start, they can give you an overall idea of where the business is heading and do some simple user segmentation. However, you will certainly face limitations of such tools quite quickly. For example, what if you want to join data from multiple systems? What if you want to get a deeper understanding at user level? What if you want to avoid having your team spend hours every week stitching reports together? What if you want to go beyond those limitations and really unlock the true power of analytics?
All those questions can be answered by a more sophisticated analytics setup.
If you want the aforementioned questions answered, then you need to start thinking about investing more into analytics. The first thing to do is often to build the infrastructure so you can extract and manipulate data efficiently. Such infrastructure can be represented in a simple manner through five core layers as shown on the chart below:
Below is a brief definition of what each layer is. The second part of the series will provide a more detailed overview of each layer and the tools you might want to consider for those.
An efficient way of getting started is to build the Extraction, Modelling and Reporting layers first. This will enable you to implement quick, reliable and efficient reporting allowing users to make data-driven decisions on the fly. Reporting can easily become a highly time-consuming task in early stage startups — and, spoiler alert!, the value of your team members is not in crunching numbers, it lies in taking decisions based on those! Once you have reporting sorted, you can move on with leveraging analytics in order to improve the various business areas of your company. For example, you might want to analyse cohorts with a high level of granularity, build LTV models and use those for your digital marketing optimisation, etc. The applications are endless, really!
When embarking on your analytics journey, check SAYN (our open source data processing framework tuned for simplicity and flexibility) which helps organising and scaling your data processes easily.
The next question you might ask is “who should I hire?”. It is extremely important to choose your first analytics hire well as this person will heavily influence the direction of analytics within your company. Here are a few things to bear in mind when doing so:
So this is it, you should now have a good idea of what to do in order to get started with more advanced analytics. Once you have established a solid base, you can expand and start doing more sophisticated things such as data science in order to build predictive models. This is where most of the value lies and is worth the investment. For example, user segmentation and LTV prediction models can help you optimise your product and marketing towards high value customers and generate hefty returns!
If you want to know more, read the second part of this series which gives specific details on each layer. Or if you need some advice, we are always happy to chat :) Best of luck in your analytics journey!
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