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Why Your Startup Needs Data Now

Why Your Startup Needs Data Now

A lot of startups we talk to are worried about making an expensive mistake when it comes to setting up their data stack, but often the more costly mistake is putting this off until later…

Why You Need Good Data

Most startups are focused on rapid growth. In some cases growth at any cost. But as they establish their place in the market, it becomes more and more important to fine-tune elements of their acquisition, retention and marketing strategies. That’s where historical data can make a big impact in helping you to understand what’s lead to your success so far, and where you can focus your attention and budget.

Why You Need It Now, Not Later

“We’re all familiar with this concept of failing fast, but this is flawed unless you are capturing the right data to see where you were going wrong and how you could improve.”

Successful Product/Service Launch. 95% of new product launches fail. Is this because they were bad ideas? Not always. In an era of user reviews, comparison tools, and personalisation the smallest element could make a crucial difference. Details matter. So how do you find that 5% winning ideas quickly? Market research, focus groups, interviews etc are all time consuming and expensive and have made some high-profile mis-steps. (Anyone remember New Coke?) The most efficient product development cycle is to build a minimum viable product (MVP) quickly, test release to your market, and gather data. If you receive early signals that it is something people want, then all go in! This approach has already been used to great success on sites like Kickstarter. Prove your concept with a real audience, gather data, optimise your offering and then expand your product launch with confidence.

Financial Health. When you have scattered data, it’s difficult to have a holistic view over how and where your business is growing. Worse still, you don’t know where you are spending and wasting money. It is not uncommon for us to hear horror stories where companies didn’t realise they were losing money for months because some of their systems took too long to reconcile, leaving them with blind spots of their daily financial health.

Adhoc Data Means Inaccuracies. If you don’t standardise your definitions and data sources, then you’re likely making mistakes when pulling together data for investor decks or company updates. It’s no good having a slide on CAC where that number means one thing one month and another the next! Not only is this a time-drain, could lead to the wrong decision being made but its something which can easily be automated and modelled to perfection.

Growing Pains. Marketing is key to any startup growth, but also often an area of waste in terms of both opportunity and budget. Marketing channels are greedy and different platforms will claim the same conversion, costing you more money but also distorting your true cost per acquisition. It’s a daunting task to centralise all marketing spend, connect with your first-party data and apply an attribution model that will give you clarity on what is really converting customers. But doing so enables you to model everything from Customer Lifetime Value, Customer Acquisition Cost, ROI Calculations and more. By de-duplicating conversions we also typically see a cost saving of at least 20% of ad spend.

You’ll Need The Data Later. The more data you have, the easier trends and analysis becomes. So think about what metrics would be useful to you in the future and start recording those numbers today. It may take weeks or even months to get an accurate picture but it’s much better than starting completely fresh X months down the line.

“It’s a story we’ve heard many times before. When a startup is looking for their next round of investment, they suddenly they need all this data they haven’t been recording.”

Why You're Putting It Off

Data is niche. Most founding teams are amazing product, marketing or technical people. Founders from a specialised data background are rare (unless it is a data product). Many founders are very technical when it comes to building an application but it’s a very distant cousin compared to data and analytics.

Getting started is expensive. Building a robust end-to-end data solution requires team efforts and a range of skills: A data leader with business acumen, a data engineer, an analyst and a system architect to design the infrastructure. To ensure your stack is fit for business purposes and future-proof, you need experts across all disciplines to avoid headaches and technical debts down the line.

Difficult to hire. It takes time and resources to hire a full data team. It is especially difficult if the team does not yet have a senior data person. It typically takes around 6 months for the data leader to come onboard and another 6 months for the leader to put together the rest of the team.

How 173tech Can Help

We have worked with a lot of start-ups who are looking to rapidly scale and need to sort their data out. We look to implement a scalable data stack, configured to your business logic in a fast and efficient way. Get in touch to find out more!

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