How Scaleups Can
Leverage Analytics
Getting a company off the ground can be a roller coaster ride! In the first couple of years, you have convinced investors, built an amazing product and established a large customer base that absolutely loves your offerings. Now you are wondering, what will trigger the next big inflexion point to achieve your growth ambitions? The answer is hidden within that data gold mine you are sitting on!
Most scale-ups 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 is 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 Data Now, Not Later
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% of 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 expand your effort. 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 is difficult to have a holistic view over how and where your business is growing. Worse still, you do not 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 do not standardise your definitions and data sources, then you are 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 is 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 Will 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 is much better than starting completely fresh X months down the line.
If data is such an obvious solution, why do 60% of business delay investment in this area…
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.
Candice Ren, Founder
Key Barriers To Starting With Data
Data-driven decision-making is not just a buzzword; it’s a proven strategy that can propel startups to success. So why isn’t every startup simply starting with data from day one?
The Cost Barrier
For most scaleups, finances are precious and the upfront costs of setting up data infrastructure simply are not palatable. Building a robust data infrastructure requires significant initial investment in technology, tools, and expertise. Let’s delve deeper into each of these elements:
Technology Costs:
Cloud Data Warehouses – Pricing is usually based on data storage (per TB per month) and data processing (per query).
Traditional Data Warehouses – Typically involves significant upfront costs for hardware and software licences, with ongoing maintenance and support costs.
EL Tools – Pricing is often based around the volume of data that you are extracting and the frequency. Where you are creating a custom script, there will also be associated costs and resources needed for maintenance.
Visualisation Tools – Costs are usually subscription-based per user per month, with additional costs for premium features or higher user tiers.
AI/ML Components – Costs can include model training hours, inference costs, and storage of training data.
When you are first building out your data stack, and you have low data volumes, you might be surprised at how low the tooling can actually be! The cost that is almost unavoidable however is in getting the right people to set all of this infrastructure up.
People Costs
The cost that is almost unavoidable, however, is hiring the right people to set up all this infrastructure correctly, especially for scaleups aiming to grow fast and stay competitive.
Data remains a highly specialised field, requiring expertise in data architecture, data engineering, and analytics. The demand for skilled professionals has long outstripped the supply, making experienced people both scarce and expensive. Even for established businesses, the cost of hiring data people is high, and with that high cost also comes a high risk of hiring the wrong person, especially where that skill is not present in the business or they might not fully understand what is needed, and so how can you mitigate that risk?
The Skill Barrier
With data talent being so costly, how do scaleups try and adapt to data today and where do we see them most often going wrong?
Use Excel/Sheets: Traditional reporting still has a place in most businesses. Whilst obviously excel has some limitations in terms of manual input leading to errors, and a latency wherever people are having to update spreadsheets – excel is still a great tool for reporting that nearly everyone knows how to use.
Use Off-The-Shelf: There are many ETL tools that allow you to get started with data quickly and without heavy technical knowledge. These platforms are aimed at business users and can help you centralise and visualise your key metrics from popular data sources. Whilst often these tools will not scale – they can be a great way of getting value from your data in your early stages so long as you think of them as a short-term solution.
Don’t Hire A Unicorn: A mistake that we often see growing businesses make is that they try to hire one amazing person to handle their entire pipeline. The problem with this is that building a data stack requires a team with diverse skills and knowledge. Data engineers are needed to design and maintain the infrastructure, data analysts to interpret and visualise the data, and data scientists to develop advanced models and algorithms. A multidisciplinary team brings a well-rounded perspective and expertise, ensuring a robust and effective data infrastructure. What we often see happening is that businesses will try and hire a unicorn, someone with a bit of all-round experience. They get the stack up and running, but often when data volumes start to scale, what they have built has not been optimised for this and so the cost increases significantly and the stack often has to be completely redesigned.
The Complexity Barrier
Each different element of your data stack will have different considerations in order to ensure it is set up for optimal performance, and this leads to a high level of complexity in set up. There are often business requirements that, on the surface, seem simple, but in practice may have huge ramifications later on. A great example of this would be ‘Real-time’ analytics. Nearly every stakeholder would tell you they want or need this, but might be unaware of the significant complexity and cost they would be adding to your analytics infrastructure.
It is only really when someone has extensively used a particular tool that they will know all of the considerations in optimising its performance. Most often this means that data professionals will recommend a stack that they are most comfortable with – but in some cases this may not align closely to the needs of the business, or means they are the only one who understands the logic within.
Perhaps the biggest element which adds complexity is scalability. Data is such a fast-moving world with so many different tools that it can be difficult to understand the implications as to whether that stack will still be useful in a few years time. Embedding the best practices around documentation, quality and governance to ensure this healthy growth requires a senior data person who has done it before. Here we come back again to our cost barrier in attracting this talent for start-ups.
Alternatives
Instead of hiring expensive people to build out your analytics, hoping you can find a unicorn or making do with point solutions for a few years – we truly believe that working with an agency like 173tech offers a cost-effective alternative. Our experienced team have launched data stacks for 40+ businesses with scalability and longevity in mind.
Expert help is only a call away. We are always happy to give advice, offer an impartial opinion and put you on the right track. Book a call with a member of our friendly team today.
Aligning Data Strategy With Company Growth
Aligning data infrastructure with business growth is critical to ensuring that the right tools, processes, and governance are in place to manage and leverage data effectively for informed decision-making.
If Your Company Is Generating $500K – $1M Annually
At this early stage, businesses prioritise quick, cost-efficient solutions. Off-the-shelf tools offer a convenient starting point for data management, providing pre-built functionalities with minimal setup. These tools enable businesses to establish a data strategy without heavy investment. However, their limitations in scope and flexibility can hinder deeper insights, leading to missed opportunities for optimisation. We estimate that an effective data strategy should contribute to at least a 20% annual performance boost through efficiency gains, revenue improvements, and cost optimisation. Relying solely on basic tools can impose an opportunity cost by keeping your company blind to areas of improvement – but we understand that at this point, often your focus is on market-fit and nothing else.
If Your Company Is Generating $2M Annually
Hitting the $2M revenue mark is a pivotal moment for your data strategy. The limitations of off-the-shelf tools become increasingly evident as costs rise and fragmented systems create inefficiencies in data integration, reporting, and decision-making. At this stage, relying on multiple tools for different use cases leads to operational complexity and lost opportunities. This is the point where implementing a data warehouse can be a game-changer. A centralised data infrastructure enables bespoke analytics, automated reporting, and deeper operational insights, helping businesses optimise strategies and drive additional revenue growth. Companies that fail to take this step risk a growing opportunity cost, as their ability to extract meaningful insights remains constrained by rigid, pre-built tool limitations.
If Your Company Is Generating $5M Annually
By the time a company reaches $5M in annual revenue, its data strategy must evolve significantly. Custom and centralised reporting are no longer optional—they are essential. A dedicated data stack provides complete control and flexibility, allowing businesses to move beyond off-the-shelf tools that no longer meet their needs. Companies that invested in a data warehouse early find themselves at a strategic advantage here. Instead of scrambling to build a centralised reporting system, they are already leveraging custom analytics to optimise operations, increase efficiency, and unlock new revenue streams. Those that delayed this investment will now face a more significant and urgent transition, potentially falling behind competitors that made data a priority earlier on.
Over our five years in business, we noticed two interesting trends…
The first is that a lot of the startups that we have helped have second-time founders. These are people who have typically built up and sold their business and now, armed with that experience, understand the power of data and how it can be leveraged to gain a competitive edge.
The second is that while you might be familiar with the likes of Numan, Treatwell, MUBI, and Plend today, they were not big names when we first worked with them! By utilising data from the beginning, they were able to make informed decisions, optimise their operations, and scale effectively.
Adrian Macias, VP Engineering
Analytics goes well beyond reporting. As your customer base scales, it becomes the glue that ties together your business.
Ensuring ROI From Your Investment
If now is the time to implement your data stack, make sure you have the right strategy in place.
Data Needs A Purpose
Your data strategy should be guided by your business growth objectives. Everyone on your team must be clear on what they aim to achieve and how success will be measured. Begin with an overarching view of your metrics, then apply the same approach to each business function and product squad. Consider these questions:
• What are your short-term and long-term growth goals?
• Can you identify a ‘North Star’ metric that reflects the health of your growth?
• What factors contribute to your North Star metric? Define each one precisely, as this will set measurable KPIs.
How do you put this into practice? Document your KPIs in a data dictionary, a glossary of business terms, their definitions, how they are calculated, and where the data is stored. This creates a common data vocabulary and facilitates discussions across the organisation. The document need not be exhaustive; it will naturally evolve as your business and analytics capabilities grow. Once you have these definitions in place, you can build an analytics toolbox to help your team achieve their goals more swiftly.
Automation Saves Time
Automating processes today will save you time in the long run. The pursuit of efficiency is essential for growing your analytics, with automation at its core. Our guiding principle is: if you find yourself repeating a task, automate it, it is likely you will need to do it again. Automation can save your team thousands of manual hours, which can be redirected to fuel growth. This is akin to increasing your headcount by 10% (a conservative estimate) without having to recruit additional staff!
Data automation occurs in several layers. First, establish a robust infrastructure to ensure an automated, efficient flow of data from various sources into your centralised data warehouse (e.g. Snowflake or BigQuery). The raw data is then modelled according to your data dictionary definitions and automatically transformed into meaningful aggregations within your warehouse. For maximum analytics efficiency, we recommend that all data processing logic is captured within the modelling layer (a single point of work, for example, dbt) Why is this beneficial? Modelled data can serve all analytics purposes including automated dashboards, in-depth analysis, the development of data science algorithms, or even sending predictive data signals to various applications (such as churn propensity alerts to your app). Beyond saving time, automation ensures data accuracy and consistency in insights, minimising human error.
Empower Everyone
When all teams and applications query the same set of data models, you achieve a single source of truth. With all data sources centralised, you also gain a complete 360-degree view of the customer, capturing every touchpoint of the user journey. Data democratisation empowers everyone to make informed decisions, arguably the most important aspect of building a data-informed culture. So, what can you do to speed up data adoption?
Make data insights as accessible as possible: Most of your team will consume data via automated dashboards, so provide training and ongoing support to ensure they make the most of your reporting tools. Develop layered reporting that starts with a high-level overview and allows for drill-down into specific areas. Ensure that key metric definitions are easy to find to prevent any confusion.
Designate data champions within each business function: Identify the most data-savvy individual on each team and form a power-user group. Hold regular sessions to update and train them on the latest analytics developments (for example, addressing their most pressing questions). These champions will act as the go-to resource for day-to-day data queries and help spread the power of data across the organisation.
Continue to highlight the value and benefits of data: As with any product or service, make the value proposition clear and consistently add and communicate new features. Consider a monthly newsletter or commercial report showcasing the latest analytics milestones, successful use cases, and practical how-to tips.
Finally, make data engaging: Data is much more than charts, trends and predictions, it tells a story about your customers and their behaviours. Take the time to look beyond the quantitative ‘what happened’ and explore the qualitative ‘why it happened’.
Mai Nguyen – Data Analyst
Implementing analytics is normally a change from the norm and needs to be considered as a change management process.
Which Projects To Start With?
For most companies a ‘data-driven culture’ is a change from the norm, even now. And where there’s change, there needs to be management. This means that before you do anything, you need internal support from the c-suite, a project leader and a clear outcome.
Customer Journey: Map out your customer journey to better understand your data touchpoints, KPIs and decisions across different departments. You will need a clear understanding of what is important to your business, and where the gaps are today before anything else.
KPIs: Decide on which data points you need to track, how they are calculated and what the source of this data is. Don’t just track everything and anything! Think about key decisions and actions your team can actually take and work from there.
Data Stack: With a sound understanding of your business needs, consider the technical side. You should think about expected data volumes, your existing tech ecosystem, any specific security or data privacy concerns as well as considering the cost when starting to plan out what you need to build.
Core Management Reports: Your first use case should be a master dashboard for your C-Suite. We like to think of this as a five minute health check on how the business is doing. By focusing your initial outputs in this way, you can demonstrate value without needing to integrate every single data source across your customer journey.
Marketing Channels: For growing businesses, marketing is a big area of spend and so centralising and overlapping data from different channels helps to create a holistic picture on performance.
Online Advertisement Optimisation: If every business in a market relies on the same algorithm for their ads, they will all be optimised to the same customer profiles and so instantly lose competitive advantage outside of increasing their budget. Leverage your first-party data to optimise campaigns towards high LTV customers which will lower CAC and improve ROI.
Advanced Attribution: With marketing and advertising already incorporated, you can think about how sales teams win new business and apply advanced attribution to help identify winning trends and focus efforts.
From here the possibilities are endless, but we have seen that for most growth-focused companies, focusing on these areas represents a significant return from their analytics spend.
Of course if you are a scaleup and you want to lower the time to value – why not get in touch with 173tech.
Our core team previously led analytics at dating giant Bumble and we have since been using data to accelerate growth for the likes of MeUndies, Plend Loans, Wizards of the coast, MUBI and many more.
