Data Analytics Checklist for Startups

Startups are equally exhilarating and exhausting. The possibilities are endless, which is liberating but also overwhelming at the same time. So many things must be put into place, but too often data analytics isn’t one of them. 

If you think data analytics is something you can put on the backburner until the business is well established, getting to that point could be a lot more difficult. What you learn from data analytics may just be what gets you to the next stage. It’s powerful, precise information that gives you clear answers to important questions about your marketing, users, product, productivity, customer service and the list goes on.  

Data analytics sounds much more intimidating than it actually is now that platforms like Mixpanel exist. Our engineers have done the hard work of creating the code and tools needed to capture data so you get a lot more insight than using Google Analytics alone and don’t have to start at ground zero. That’s essential if no one at the startup is super technical, or the super technical people are busy making sure the product/website/app is firing on all cylinders.

The decisions you make early on in terms of your tech stack will affect data analytics now and well into the future. The checklist below walks you through all the basic steps of laying the groundwork for data analytics that can scale up as your startup becomes an established business. 

Setting goals is always a good first step as they will guide the entire data analytics process. Examples of data analytics goals include:

Try to focus on initial goals related to setting up your data analytics system in this stage. Once everything is up and running you can identify new goals that are associated with using the data, such as finding trends in user behavior that can inform product development.

Once you’ve established your goals and what you can gleam from data analytics, it’s time to make sure the decision makers are on board. In a startup you won’t get very far without having executive buy-in. Fortunately, data analytics is a fairly easy sell these days given how much information can be gathered. 

Before approaching the execs, create an outline that includes the identified goals for the data analytics system, how these goals help achieve business requirements, an estimate of the workload, an estimate of the budget and who will be working on the project.

Chances are you already have Google Analytics (GA) set up or can do so within a few hours. It’s a good starting point that will give you basic insights to build on. 

If you have a CRM that’s another resource at your disposal that you can begin using for data analytics. A CRM is limited in scope, but the sales team can use the built-in reporting to track things like customer interaction, sales rep activity and some types of conversions. 

Use these tools if you have them while you work towards finding a more comprehensive solution that can import the data. 

How much data mining can you handle on your own? The answer comes down to two factors: expertise and time. 

Right now, you don’t need an over the top BI platform that’s time consuming to implement and manage. In the initial stages of a startup you have to be doing more than analyzing for hours on end. You probably also don’t have the expertise or a data analyst on staff to effectively use that type of tool. But you do need simplistic solutions for gathering, tracking and reading data. 

In the startup phase, your best use of resources (time, manpower and money) is a SaaS out-of-the-box analytics platform. 

It will cost you more than building something in-house, but the ease of implementation and assurance that the data is accurate is worth it. Keep in mind, good data analytics tools provide a significant return. They aren’t simply an expense, they’re an investment that can help you increase profitability. 

For use and scalability, it’s best to use an analytics platform or system that can easily export data to Excel. It isn’t the simplest process or most visual approach, but Excel is still a valuable tool for analyzing large amounts of data.

Another thing to consider for the near future is how data can help various team members do their jobs to the best of their ability. Not everyone is a data expert, so solutions should provide easy-to-read reports that can be used by basically anyone.

Collecting the right data is critical. What’s right comes down to the type of business you run and what stage you’re in. In the beginning, event and product metrics are by far the most important for rapid iteration, depending on how your startup generates revenue. For instance, eCommerce startups need to know everything they can related to the purchase of a product. SaaS startups will need to closely analyze events to determine exactly how customers are engaging with their software. 

In the next stage of development, growth metrics will take priority for virtually all types of startups. But here again, exactly which growth metrics should be tracked will vary.

Be careful to avoid what’s known as vanity metrics. These are metrics that don’t actually tell you why something is or isn’t working. For example, the number of social media followers is a well-known vanity metric. 

Not sure what your key performance indicators (KPIs) are just yet? Autoreport features deliver up an array of data that can help you determine what’s most important. When in doubt, you can also use Dave McClure’s Startup Metrics for Pirates AARRR strategy for direction. It breaks analytics into five categories: acquisition, activation, retention, referral and revenue.

While you need to focus on lean analytics that measures just the core KPIs at the moment, it’s important to factor in future needs when you’re building your data analytics framework. It’s better to have robust solutions you can grow into than the bare minimum. The latter will require you to change your strategy in the future, which is disruptive and can cause gaps in data gathering.

The key to scalability is creating an initial framework that is compatible with more complex data analytics tools. Our developers understand that data analytics is an evolving process that becomes more in-depth over time. That’s why we’ve designed our platform to be a stand-alone solution that’s simple enough for anyone to use and can integrate with more complex tools like data warehouses and enterprise BI resources.

Compatibility also plays a role in importing data for analysis. Ideally, you’ll want to pull data from as many sources as possible into a single analytics system for review. This gives you the most complete picture and the ability to expand your operation without having to implement additional solutions to track the new data.    

When you create a compatible data analytics framework it allows you to balance the short-term and the long-term. 

As your startup grows, your data analytics system should too. After getting the framework in place and beginning to gather data, here’s a few additional steps your startup should take: