# The math behind CLTV for a SaaS Business

Anyone working in the marketing and finance business function is introduced to CLTV(Customer Lifetime Value) early in their academic and professional life. Few, however, get to calculate this very important metric from scratch. In all fairness, it is not mandatory to know the math behind calculating the value as long as one understands the concept behind it.

I am glad you have decided to learn the math behind Customer Lifetime Value. Take a comfortable seat, a pen, a notebook, and your favorite drink. We will be spending a few hours with CLTV, the most important metrics in SaaS business, specifically…

A couple of years ago, I was speaking with a CTO of a pre-product company that was looking to hire an analytics lead. His team of engineers is working on the product, which is to be launched in 6 months. He has no data available for the analyst to analyze yet he wants an analyst in his team. The analyst’s role at the time was to work with the engineers to ensure incoming data will be collected, integrated, and ready in the data warehouse as soon as the data starts flowing in. This is all the analyst will do before…

# Database for the Digital World— 1.3 (Review & Exercise)

One way you can work on your SQL skills without local data access is through the SQL Exercise website. Once you register, you will be provided with the name of the tables and columns and the query you need to write. After you write your SQL you can check for accuracy by clicking on the “Run” button.

Here is a couple of snapshots of how SQL exercise work.

# Database for Digital Marketers — 1.2 (Case Statement, Group By & Having)

The definition of the word “case” in Dictionary is “a set of circumstances or conditions” or “an instance of a particular situation” or “occurrence of a particular kind or category”.
I think the case statement in SQL closely resembles the last definition “occurrence of a particular kind or category”.

Let’s take the simple case of Customer Spend. You are starting a marketing campaign post-Christmas and would like to give a discount to your customers based on the spending this year.

You want to give 20% discount if the customer has spent \$40+, 15% to those who spent between \$20 and…

# Database for Digital Marketers — 1.1 (filters, aggregates & joins)

Filters and Aggregates

To represent the data sets (discussed in the previous article) in the database, we will need to create 3 tables:
Customers — Holds customer Information
Products — Holds Product Information
Orders — Hold information about orders of customers that bought the Company Products.
Once the tables are created, create a database diagram and drag all the three tables to get an overview of all tables (above).

Customers

Products

# Database for Digital Marketers — 1.0 (The basics)

So, you are a marketing genius! You have just rolled out an innovative digital marketing campaign and want to find out its impact on revenue. You can go two ways about it.

1. Send a series of questions to your Data Analyst, wait for her response and repeat.
2. Look at the data up and close, understand and discover what your customer wants.

If you like the latter approach, this series of posts will help you get your feet wet. I hope you enjoy and learn something new. Junior level SQL developer supporting Marketing Teams will also find this series…

# Preparing for a Data Science/Machine Learning program

If you are reading this article, there is a good chance you are considering taking a Machine Learning(ML) or Data Science(DS) program soon and do not know where to start. Though it has a steep learning curve, I would highly recommend and encourage you to take this step. Machine Learning is fascinating and offers tremendous predictive power. If ML researcher continues with the innovations that are happening today, ML is going to be an integral part of every business domain soon.

Many a time, I hear, “Where do I begin?”. Watching videos or reading articles is not enough to acquire…

# Machine Learning Project Life Cycle

Today, the term Machine Learning comes up in every other discussion. In fact, in the bay area, it is a staple. We hear about unicorn start-ups as well as established organizations solving significant challenges using Machine Learning. Then, there are many more companies who are in the process of figuring out what and how long it takes to implement Machine Learning models in their organization. This article is an effort to share my insight into the process of this new edge phenomenon, a major paradigm shift from the traditional rule-based system.

Before I go into the details, let me start…