How can you use data to improve your startup?

The “right” data will be wholly dependent on the use case/value proposition. But the right source of that data isn’t always the obvious and directly correlated ones. Consider “alternative data” sources from other industries, geographies, applications, processes, etc. that could provide insights into the problem/question you are looking to address. For example, instead of looking at hotel occupancy through bookings, perhaps data from companies doing the laundry of the sheets is a good enough indicator. In the end, only data you can get your hands on has any worth.

One of the biggest mistakes early stage startups make is not leveraging data to inform their decision making. It's not uncommon for startups to be so focused on moving fast that they make hasty decisions without considering various different angles. In my nearly 20-years working at startup technology companies, I have come to learn that it is absolutely worth "measuring twice, cutting once," even if it means taking a little more time to gather the data you need to do so. Nothing sucks more than building and scaling an operation only to realize you did it wrong and need to go back to the drawing board and start from scratch. I speak from personal experience.

Here is an example of minimal viable product MVP goals that can be tested in an experiment: 1. User Acquisition: The goal could be to acquire a specific number of users within a given time frame. 2. User Retention: The goal could be to test the engagement and retention of users with the product. 3. Market Fit: The goal could be to determine if the product meets the needs of the target audience. 4. Revenue Generation: The goal could be to test the potential revenue streams for the product. 5. Scalability: The goal could be to test the scalability of the product, especially if it involves technology or infrastructure. It's essential to define clear, measurable goals to evaluate the viability and potential success of the product.

Data is a crystal ball that unlocks glimpses of the future—if you know how to read it. Like a fortune teller, analyze metrics to reveal insights. Let data guide iterations and new features by revealing pain points and opportunities. Perform regular retrospectives, reviewing what worked and what didn't. Seek out customer feedback to put stories behind the stats. And don't blindly follow data off a cliff; numbers don't replace intuition. Use data to test assumptions, drive innovation and validate gut checks. With an inquisitive, open approach, data can unveil growth opportunities hiding in plain sight. You’ll be making predictions that make Nostradamus blush.

Check out how UBER tested its MVP Uber was born out of difficulty finding taxis the traditional way. And that was the problem Uber was focused on in the first stage. UberCabs was an iPhone app meant to connect the users with cab drivers and offer them a way to make payments via a credit card. The MVP version offered only these features on an initial level. The fancier features were added only after the app received significant downloads signalling its validation. At first, they handled all the orders manually. Uber offered drivers to accept and manage orders on their own. Only 3 out of 10 agreed, giving a start to the no. 1 taxi app in the world. Uber started with just 3 cars and validated its idea before its competitors could.

I will never not have a data analytics platform like Looker, Tableau, or DOMO ever again. Sure, they are expensive SaaS products and you can create your own analyses for significantly cheaper in Excel or Sheets, but when it comes to data, it's worth investing in these tools. They make data integrity and analysis so much better and easier.

As innovation facilitator, I would like to advise you to PRETOTYPE, like TESLA. Tesla used a Lotus Elise to interest potential buyers for their first car. In 2003, the founders of Tesla had an ambitious idea (an all-electric 2-door sports car). In order to convince potential buyers to order its car, Tesla created a pretotype of what the car would look like. Lotus supplied Tesla with a ‘glider’ Elise - which was filled with models of key components. This was not a prototype, because the vehicle didn’t function. Tesla was able to show buyers a proxy for the final design. Tesla asked “Will you put down a $5,000 deposit to have it built to order?” From this test Tesla secured several hundred deposits, a great result to reassure their Investors

The mantra I've come to live by in startups is "what gets measured gets better." Often times this ethos doesn't develop until later stages in a startups maturity, but the sooner you adopt it, the better. Strategy and iterative process design based on minor changes to data can yield significant wins for the business that last years. If you're not testing your assumptions, you're missing out.

Use econometrics to understand and explain causal effects and demonstrate a deep understand of the customer behaviour. Often founders confuse correlation with causality, leading to a string of misjudgements. Having a deep understanding of cause effect relationships and articulating it simply to your stakeholders is key to building long lasting organisations

One of the most valuable lessons I learned in my early career working at startups came from the first VP of Operations I ever worked for. He was a really seasoned executive and would always challenge me to use data to back up my claims. It was not uncommon for me to make declarative statements about how things were based on my opinion or gut and he said: "your opinion matters, but it isn't fact. Data is fact. Use it if you want to be taken seriously in business." Those words have stayed with me throughout my career.

Data integrity is essential to the success of a startup and configuring the plumbing needed to maintain it is very important. It's not uncommon for startups to have tons of data silos because they lack the organizational maturity and system sophistication of larger companies. This is a pretty common, glaring issue facing startup companies. It's best to have a tool that can make data management and analysis easy, and a smart analyst on the team who can constantly maintain that data and flag issues.

AIRBNB is a great example of how customer data validation creates success. Brian Chesky and Joe Gebbia moved to San Francisco where they shared a three-room-apartment. When a design conference came to town in 2007, they saw an opportunity to earn some extra money by renting out their floor space, and called it Airbed and Breakfast. After two years of experimenting and failing, they focussed on New York as a popular tourist destination and had meetings with their New York-based users: —both hosts and guests. These meetings were crucial in making solid offerings with that included professional pictures of good-looking spaces. The basis of success of AIRBNB, was laid fifteen years ago, when the founders connected to their users.