Building Your Analytics Growth Engine.
November 8, 2021 | By .
Key Takeaway: Data needs to be purposeful, automated, and democratised.
Getting a startup off the ground can be a roller coaster ride in the first couple of years - you convinced investors, built an amazing product and established a large customer base that absolutely loves your offerings. You’re now wondering — what will trigger the next big inflexion point to achieve your growth ambitions? The answer is hidden within that data gold mine you’re sitting on!
It’s likely that you already have a backend database and various reporting functions spread across different places. Now, it is time to step up your game. As serial data-driven entrepreneurs, we have gone through this journey many times and learnt the hard way. In this blog, we will share our top learnings and nuggets of wisdom — setting you up for scale!
Lesson #1: Data needs a Purpose, otherwise, it’s just Numbers.
Your data strategy should be driven by your business growth objectives. Everyone in the team needs to be clear on what they are trying to achieve and how to measure success. Start with a bird’s eye view of your metrics, then repeat the above for each business function and product squad. Consider the following topics:
- What are your short and longer-term growth goals?
- Can you pinpoint a North Star metric that measures the health of your growth?
- What are the contributing factors to your North Star metric? Define each precisely as this sets measurable KPIs.
How do you apply this? - Document your KPIs in a data dictionary. This provides a common data vocabulary and facilitates discussions across the board.
Start by defining the business definitions and delegating responsibility to individual teams to maintain them. This allows your analytics team to create technical definitions for automation. This will naturally evolve as your business and analytics capabilities grow.
Now that you have defined the purpose, you can build an analytic toolbox to help your team achieve their goals even faster!
Example: Decomposing Monthly Recurring Revenue (MRR), the north star metric for a subscription business.
Lesson #2: Automating today will save you time forever.
The pursuit of efficiency is a mindset and automation is core. Our guiding principle is — if you are doing something for the second time, automate it, because it’s highly likely that you will do it again. Automation saves your team thousands of manual hours, which can be deployed elsewhere to fuel growth. It’s the equivalent of adding 10% to your headcount (a conservative estimate), without having to hire anyone!
“If you are doing something for the second time, automate it.”
Data automation comes in many layers. Firstly, you need to set up a solid infrastructure to ensure an automated, efficient data flow from various sources into your own centralised data warehouse (e.g. Snowflake or BigQuery). Raw data is then modelled based on your data dictionary and transformed automatically into meaningful aggregations within your data warehouse. For maximum analytics efficiency and return, we recommend all data processing logics to be captured within the modelling layer (one place doing the work, e.g. SAYN).
Why is this useful? — Modelled data can be used for all analytics purposes including; automated dashboards, deep-dive analysis, building data science algorithms or sending predictive data signals to various other applications (e.g. churn propensity to your App).
For example, knowing your user segmentation based on past behaviours allows you to customise their product journey to drive greater engagement and retention. This is how you personalise at scale!
In addition to time-saving, automation ensures data accuracy and insights consistency (less human error). Since all teams and applications are querying the same set of data models, you have achieved your single source of truth. Once you have centralised all data sources, you also have a complete 360 customer view capturing all touchpoints of the user journey!
Lesson #3: Data Democratisation empowers everyone to make Informed Decisions.
This is 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 easy as possible to digest. Most of your team will consume data via automated dashboards. Provide training and ongoing support to make the most of your reporting tool. Create layered reporting starting from a high level (bird’s eye view), which can be drilled down into specific areas. Finally, make sure key metric definitions are easy to find to avoid friction.
Appoint data champions in each business function. Find the most data-savvy person in each team and form a power user group. Run regular sessions to inform and train them on the latest analytics releases (I.e. answers to their burning questions). They will act as the go-to person within their business function for day-to-day data questions and help spread the power of data.
Continue to highlight the value and benefits of data. Like any product or service, make the value proposition clear and continue to add and communicate new features. Consider a monthly newsletter or commercial report that showcases the latest analytics milestones, successful use cases and how-to tips.
Make data fun! Data is much more than charts and trends and predictions. Data tells a story. A story about your customers and their behaviours. Dedicate some time to look beyond the quantitative “what happened” and into the qualitative “why it happened”. For example, we previously examined user behaviours on dating Apps through the lens of evolutionary theory. The findings revealed deep behavioural insights that we never imagined. This trickles down to marketing and product teams — taking advantage of previously hidden trends!
Purpose, automation and democratisation — the three pillars to building your own analytics growth engine. We hope the ideas shared above help you accelerate your data journey as you scale!
You can also find this article as part of the #ScaleUpLessons series. ScaleUpLessons is written by serial entrepreneurs, engineers and technologists to share knowledge about scaling startups across a range of industries.