data science, the buzzword of this decade, came into the spotlight probably with the article “Data Scientist: The Sexiest Job of the 21st Century” by Dj Patil, originally published in HBR and then fueled by various companies utilizing the power of big data and analytics. The tech startup ecosystem of the west and other corners of the world adopted data analytics culture early.
Today, almost all big startups (of valuation around $100 million) have their in-house data science or analytics or business intelligence team. Whatever we call it, the core functionality of the team is to bring actionable insights from the data their users generate and help use that insight in building their products.
In Bangladesh, to my knowledge, Pathao was the first successful startup to build an in-house data science team (I am proud to have been a part of it). Other startups followed eventually and built data science teams to empower their day-to-day business operation.
If we consider the life-cycle of a startup, the operation starts with a seed or pre-seed round investment. The goal in the early stage is to raise the next rounds of investment while growing exponentially. However, it often happens so that people get confused about team building and structures despite having sufficient capital. It is needless to say that if a startup raises a round of investment, they must allocate the capital strategically. Optimal resource allocation is the ultimate challenge of building companies.
This is where the main thesis of this article comes in. I am writing today to suggest that building a data science or analytics team early in the life of startups can help a company to make better decisions at almost every stage of its operation. Data can optimize the operation and help make better decisions. My reasons to advocate the idea are clear:
1. Empower your day-to-day operation: From the day your operations start, users start generating and providing myriad types of data and footprints in your platform. This includes demographic, behavioral, and transactional data points. Consistently monitoring these data by the growth operators can provide an edge to your startup. For example, if the data says there is a demand-supply mismatch at certain hours of the day, you always should take some actions. The interesting part is that without looking into data understanding these types of mismatches is often hard.
Let's try another example. Suppose you see a drop in user engagement when there is a big cricket match happening, what actions would you take? Maybe you could send a notification saying something relevant to that match or you could partner with some company to offer a pizza with a discount from your app. Actions like these are likely to improve your customer engagement and retention eventually.
There is a myth that data science is only “science” but it is not. It is a practical thing and should be an integral part of your day-to-day operations. Data can help you with moving with trends and stay ahead in the market. This is why big startups like Uber, Airbnb, or Netflix have integrated analytics or business intelligence teams in every part of their operations.
2. Reduce your cost: Money has always been the center of growth for startups. With every ‘capital raise,’ there is always a target to reach with that money. Any business that has a transaction involved, generates enormous amounts of data from the signup to revenue funnel. These data points always imply a pattern of customer behavior and preferences.
Let's take an example of something more fundamental, suppose a company raises 1M USD for the next 18 months. They have a target of acquiring 250k customers within this period. Now, they are trying 4 different growth channels for the first 6 months. Let's analyze the data to see which is the optimal channel:
Clearly, channel C is better because it provides better user quality, which is evident from their ARPU to CAC ratio (USD 2.2 vs $1.1-USD 1.
