Analytics is one of the most important things for all businesses, not just startups. It is defined as the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data.
It also entails applying data patterns toward effective decision-making.
It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.
Basically, it’s just a way of using metrics to assess how your startup is doing
(this is not new information to Basketball fans).
We can’t talk about analytics without mentioning the funnel/funnels.
For most startups, whether B2B or B2C, This refers to the process where you acquire a user/customer, engage them in some way, and hopefully find a way to monetize them.
Somewhere between engagement and monetization lies retention.
So metrics are gauges to see how you are doing along every stage of this funnel.
it’s called a funnel because the number of people decreases at each stage.
In the customer acquisition stage, you can track something like the number of sign-ups across some period of time which usually depends on the type of startup you have.
In the engagement stage, you can track how many of the people who have signed up are actually using your product and when it comes to retention, you can see how many of those people are still around after a set period which once again depends on your specific startup.
Then comes the most important stage which is, of course, monetization. This one is actually quite simple as you just track the amount of money you’ve made per user be its recurring revenue, one-time sales, or a combination of both.
So if we use TikTok as an example, they could track how many people are signing up for the platform daily for the acquisition stage, how many videos
(or TikToks as the kids say these days)
are being watched or total watch time if you wanna be fancy for their engagement stats and finally ad revenue because that’s how they make money.
So now that you have established the type of metrics you wish to measure, how do you actually go about it? There are a whole bunch of analytics APIs you can plug into your code, such as Segment, Alation, MixPanel Google Analytics, RudderStack, mParticle and Swrve, and so on.
Next, you really wanna clean, test, and run quality assurance on the data that you’re getting to make sure you don’t focus on the wrong thing.
The next step is to add an analytics tool such as Amplitude for better data presentation and you’ll be able to see your progress in graphical form for better interpretation.
Metrics to Focus On
- So there are a few different metrics literally any startup should laser focus on starting with the acquisition metric that is the number of sign-ups in keeping with our TikTok example. What you want to do with this metric is really divide the sign-ups into different distinct buckets depending on how the users were acquired. So for Tik Tok, they might divide the users based on whether they were directed from back-links, ads, invite links or organic that is they just signed up without needing to be prodded. This enables you to really focus on the best avenue of traction for you. Usually it’s best to focus on maximizing the number of organic sign-ups as opposed to the others. All this is very easy to set up in any good analytics tool like Amplitude but if you can’t you can always google.
- When it comes to retention and engagement, it’s also a good idea to divide up your users. This time you should divide by time of sign-up week over week as well as by type of sign-up. For example, TikTok might look at how many of the users that signed up between date X and date Y are still actively engaging on their site. This is important because you can draw vital insights from this. For instance, you might find that users who signed up on January 6th have not been participating and you can deduce that they were probably on the site looking for a particular video probably due to some news story and once they saw that, they left and never came back. A great way to see if you have achieved product-market fit is to see if your retention and engagement are trending downwards. If they are, then you don’t have PMF. If you did, the graph would be level-ling off or actually trending upwards
- Monetization metrics are usually pretty simple. One might even argue that this is the one type of metric immune to Goodhart’s law that once a measure becomes a target, it ceases to be a good measure simply because getting paid has been the target all along. But for good measure, you should probably segment your users using the methods above.
Usually, there’s one metric that can act as a north star in that it captures the true value of your startup and incorporates information from the types of metrics above. Here’s a table of some popular and successful startups and the metrics they focused on to help you get a sense of what I’m talking about here:
|eBay||Merchandise Sold||Daily/ Weekly|
Recommendations for your Startup Stack
If you managed to make it here thank you for reading all the way down to the bottom and for that I have a little treat for you (if you can call software recommendations a treat).
One thing to note here is software tools change all the time and better alternatives will be made in the future and you should adapt accordingly. This list is in no way comprehensive but I hope you’ll find it helpful.
- Google Analytics. This one is good for finding out where the traffic on your website is coming from.
- Google Ad Words. You can use it to analyze how your Google ads are performing
- Meta analytics. Good for monitoring your Facebook and Instagram ads for growth and customer acquisition.
- Customer.io is for messaging and emailing your users
- Fullstory. This is for gaining insight into how your users are navigating the website and improving the user experience.
- Intercom is basically a CRM for early-stage startups
- Amplitude is for feature analytics
- Google big query is democratizing data access
- Mode runs on top of google big query to make it more usable
- Segment manages all the other tools
Analytics won’t solve all your problems and as any basketball fan will tell you, they don’t tell the whole story so they’re no substitute for human intuition.
I’ve already mentioned Goodhart’s law and it’s indeed something to be on the lookout for to avoid making a measure a target because employees might find a way to game this and even the startup founders themselves might deceive themselves into thinking they are doing well when they’re not because they are focusing on the wrong metric.
However, analytics are a great way of making your way of running your startup more data-driven and this usually leads to better outcomes.
Recommended Read: The Biggest Marketing Fails of All time.
Data analytics helps give businesses the facts needed to make decisions. It can spot trends, reveal hidden opportunities, or help explain problems. “For our company, data analytics is important for two reasons. First, it gives us an edge because a lot of our competitors are not using data to drive their decisions.
Here’s some stuff you might want to know
Why data analytics is important for startups?
As stated above, data analytics can help you understand and optimize your business processes, identify and correct any issues early on and improve customer retention rates. It can also help you create better marketing campaigns and track the progress of your products and services.
How business analytics helps in startups?
With the help of top business analytics, we can accurately predict future events that are related to the actions of consumers, and market trends, and also assist in creating more efficient processes that could lead to an increase in revenue. Business analytics is used to analyze data from a variety of sources.
Why is it important to use analytics?
Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.