Startup (Analytics Journey) and Applications — Startup Analytics

Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge. Startup Analytics is the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference

Nate Silver

Any startup or any corporation for that matter has only two jobs :

A startup typically runs lean, at least the successful ones while a corporation has the whole middle layer bridging the two aspects of what and how.

Given the understanding. Startup-Analytics has a role to play in both what and how part of running a startup as it evolves. The kind of analytics needed will evolve with the business.

A startup journey from a strategic perspective will have 3 stages :

Pre-Product Market Fit Analytics

• Analytics at this stage is largely a part of the product team and more about what.
• The kind of work required will mostly be around defining metrics/KPIs and measuring if the direction for the startup is correct or not.
• The most important basic set of tools involves Google Analytics, Google Adwords, CRMs, Excel and customer surveys.
• The core goal should be around defining the ICP aka Ideal Customer Profile for the business.
• How to reach these people as in figure out the distribution scheme. This could be a manual process with lots of touchpoints.
• Keep it simple and make sure all decisions are backed up by data where ever possible: design, product decisions, feature priorities etc.
• The goal should be to figure out the one market and the one core problem with the specific 2–3 feature set which solves the problem.
• Horizontal scaling of features is not the recommendation I would give. Builders especially engineers and product people tend to over optimise on the building aspect whereas getting the market right should be the key here.
• Use tools like Hotjar to optimise landing pages.
• Hypothesis, collect evidence and reject/continue with direction.

Post-Product Market Fit Analytics

• At this stage, the start has largely figured out what is working and what’s not. We are basically having repeatable sales or recurring user activity. Also, with a large incoming business, substantial data would start flowing.
• This will be about building for how part. Data engineering will be key at this point in time. The data pipelining needs to be robust. If it is properly engineered, it will not require to be re-worked until further scale.
• Relational Databases in form of SQL and PostgreSQL continue to be critical. Sometimes, I have seen unstructured data get dumped into MongoDB etc with an intention to use data later.
• Scripts/Module schedulers like Luigi/Airflow. Though basic Cron Jobs can be good enough.
• The engineering team will have their task cut out in trying to store and make sure the data is in a retrievable format. At this stage, Startup Analytics will be mostly around making sure key reports are of a fast, consumable format.
• Analytics at this stage is still restricted to the strategy part but more from operational effectiveness and streamlining of the business.
• Most likely you should have defined coding standards, automatic build setup and removed any kinks in the core architecture. This ensures the future feature additions can happen without breaking or interfering with the core architecture.

Mature Startup

• The business has grown past the startup stage with a stable or growing business. The engineering is well oiled and running largely smoothly.
• The Startup Analytics now will now be about improving the top-line. This is where advanced analytics or data science will be taking over, product features like recommendation engines, AI assistants etc can be picked up. Also, some key problems that can be picked up:
• Customer Retention/Churn.
• Up-sell Opportunities
• Lead prioritisation
• Feature Gamification etc
• The more operationally measurable, the better the problems are. Swing for the fences.
• At this stage, analytics will also have evolved to have a separate division different from product and engineering. We can term the team as data science or AI depending on the industry and marketing suaveness.
• The amount of mathematics involved will increase, the goal now is primarily differentiating and really innovating. The keyword is optimisation and exploitation.

Irrespective of the kind of business B2C or B2B, analytics will and should evolve with the business. The backbone for strong analytical practice is solid product thinking and engineering practices. Furthermore, without those in place, any firm will fail to leverage data science well.

Without a doubt, it should be clear where and how analytics is being used and can be used. In the initial stages, what should be the focus? As things get streamlined and the direction is set, analytics can be useful in the how to part as well.

Originally published at https://startupanalytics.in.