At the beginning of 2020, I started my business as a freelance software engineer. Since then, I have been collaborating with many startups, trying to help them develop their products and grow as companies. Before launching my own company, I had planned to study other successful businesses.
Out of all the startups I have been working with, one particularly impressed me. It is an Italian startup trying to redefine the rules in the sports industry by developing highly-tailored software products based on data analysis, integration, and exploration. In less than a year, its financial value increased from 3 to 21 million euros.
During this seven-fold growth process, I had the opportunity to learn insightful lessons for all the data-driven startups around the world. Although this happened in Europe, what I experienced is easily generalizable and applicable anywhere.
Your data-driven startup may explode in the next few months. Let’s have a look at what you should expect and prepare for.
Data Is Here to Help, Not Replace
In such a rising environment, you should expect new people and companies to get interested in your products. First and foremost, what matters is conveying why data can help end-users achieve their goals and how. In particular, in a decision-oriented industry like sports, data should be a tool to help humans make decisions.
This is one of the best ways to overcome the stigma and fear about the idea that data-driven algorithms will completely replace humans. For example, when dealing with first-class sports staff, they seek ways to improve their strategies and routines. They want tools to help them make decisions, not tools that make decisions for them.
This is why developing the best data analysts, and exploration systems may not be enough. Offering too complex or advanced tools compared to what is currently on the market or what people are used to may discourage users from using them. Keep in mind that data-driven software products are still relatively new in many industries. Developing only advanced tools is unlikely to allow people to overcome the switching cost barrier.
Getting Everyone To Understand Data
Even though my experience in data science has drastically improved since I started working for such a data-centric startup, I cannot easily understand algorithms, concepts, or analyses coming from more experienced data scientists.
During such a growth process, you should expect other people to enter your team. This is why procedures should be in place to explain the pivotal elements previously exploited or conceived by other data scientists until up to that point. Based on my experience, it is simply essential to have recurring internal webinars and share knowledge. In these meetings, experts should explain why a particular analysis was done, how data can be explored, what the most critical data-driven features, are and what they are currently working on. Also, by recording them, you will already have content to train new employees joining your startup.
Plus, keep in mind that data is what your startup is about. Consequently, every employee, regardless of their occupation, should be able to explain how data is currently analyzed, explored, and integrated. For example, this helps developers understand how to design new features. Similarly, salespeople should know how to explain the distinctive processes the startup can operate on data, communicating and showing products to new potential customers smoothly.
Do Not Let Customers Get Lost in Data
At the same time, as your startup grows, you may want to focus solely on acquiring new clients and customers. This might turn into a huge mistake, especially if your startup is offering advanced and never-seen-before data exploration tools. Do not abandon your customers, clients, and partners. On the contrary, try to help them as much as possible, taking your time to introduce them to new features and be sure they have been genuinely understanding the potentialities of the tools you sold them.
For example, a good practice may be to schedule a 15 or 30-minute call after a customer buys one of your products to answer their questions, if they have used the tool, or explain how to use it. Likewise, it would help if you kept this practice in place to ask them for feedback from time to time. Then, you should always be ready to guide them in exploring the data in the most effective way to reach their objectives.
This becomes easier if you use monitoring and behavior analytics tools, such as HotJar or Google Analytics. In fact, by integrating the data collected by these tools with the data you already have, you should be able to establish which are the most common errors made by users. At the same time, you could detect inefficiencies in their procedures, helping them resolve or avoid them accordingly.
Using Data to Get Bought
Regardless of your thoughts and goals for your startup, it is never too late to consider selling. This is especially true when its financial value becomes huge. Specifically, I learned from this experience that whatever your financial results, you should not expect buyers to come out of anywhere.
On the contrary, your goal is to let everyone know that your startup is thriving. After all, if your business is booming, other companies or investors may want to get on board. So, why not apply your data-related expertise to produce financial and marketing reports to show why you should be the next company to buy or invest money in? After all, this is what you are good at, so instead of pouring money into hardly effective marketing practices, you should focus on devising data-centric reports.
So, start collecting and exploring your startup’s internal data to understand your growth’s key factors. This may not ensure you achieve your exit goal, but at the same time, it will strengthen your skills accordingly.
Conclusion
Working for a data-driven startup whose financial value rose from 3 to 21 million euros in less than one year allowed me to experience what happens after such incredible growth. Out of all the lessons I learned, four aspects are the most important ones, which we have just seen in this article. Understanding how to use data to address them is the key to dealing with an unexpected or unplanned boom in your data-oriented business. On the contrary, ignoring it might be the consequence of your failure.
Thanks for reading! I hope that you found this article helpful.
