Building Soda to monitor from data to decision

I love talking about: machine learning, data quality, decision making, post deployment data science, performance estimation, and bunch of other stuff. 

Always open to have a chat 🙂

Hakim Elakhrass

I’m Sachin Kumar, a Analyst with 2+ years of experience helping businesses unlock the power of data to improve user retention, operational efficiency, and revenue performance. My expertise spans data analytics, BI reporting, product metrics, and process automation across telecom and B2C product domains.

Core Strengths:

1) Data Analytics & BI Reporting – SQL (PostgreSQL), Python (Pandas, NumPy), Power BI, Tableau, Advanced Excel.

2) Product Analytics – Funnel analysis, A/B testing, cohort analysis, retention tracking, KPI monitoring.

3) Automation & Data Engineering – ETL pipelines, Power Query, data transformation, process optimization.

4) Stakeholder Collaboration – Partnering with global teams, translating analytics into actionable product improvements.

Sachin Kumar

Customer Acquisition Cost (CAC) — Explained from a Analyst’s Lens

Its represents how much a company spends to acquire one new customer.


At a surface level, CAC looks like a marketing metric.
But from a Product Analyst’s perspective, CAC is a unit-economics and sustainability metric.

How CAC is calculated?
CAC = (Sales + Marketing Costs) / New Customers Acquired

This includes:
Paid marketing (ads, influencers, promotions)
Sales team costs
Discounts & onboarding incentives
Tools and platforms used to acquire users


CAC helps answer:
Is our growth efficient or just expensive?
Are new features reducing dependency on paid acquisition?
Which channels bring high-quality users (low churn, high LTV)?

CAC vs LTV (The real insight)
CAC alone means nothing without LTV (Customer Lifetime Value).


LTV / CAC

General benchmarks:
< 1 → Losing money
1–3 → Risky growth
3–5 → Healthy & scalable
> 5 → Possibly under-investing in growth


CAC tells us how expensive growth is.
LTV tells us whether that cost is justified.
 Product teams exist to close the gap between the two.


Day 3 of Product Analyst : https://lnkd.in/gRz2FahG


#ProductAnalytics
 #ProductManagement
 #DataDriven
 #Analytics
 #BusinessMetrics

Data Analyst (Product-Focused) | SQL • Power BI • Excel • Python • Grafana | Ex-UPSC Aspirant

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