Introducing R in the Enterprise

I started learning R last year and loved its simplicity and power. After using it primarily for personal projects, I came across a business problem in which R can be considered a good fit.

BUSINESS PROBLEM

The business need was to build a web-based tool for marketing budget optimization  – Marketing RoI (Return on Investments) i.e. how should a company that has multiple advertisement channels allocate its marketing budget across multiple channels to maximize profit or customer loyalty or customer life time value (LTV).

1) Input: The input to the analysis is the company’s historical marketing budget allocation, profit, customer loyalty and LTV. 

2) Analysis: This analysis is done in 2 steps.
– Step 1) Our experts create a formula that relates the inputs given with RoI and LTV etc. It involves econometric techniques etc.
– Step 2) Optimization of the formula when the user conducts what-if analysis by varying total budget and/or spend across individual channels to see its effect on RoI and LTV. The desktop optimization model written in Excel using a commercial Excel plugin.

3) Output: Optimized spend across advertising channels and ability to evaluate multiple scenarios to determine optimum marketing mix

A) Web application: The web forms needed to allow users to input data and run scenarios were simple. We develop web applications using Ruby on Rails on LAMP internally. Ruby on Rails gives us an agile environment to develop software by taking care of routine web application tasks like database connectivity. 

B) Optimization: Since, the Excel model uses a commercial plugin for step 2, the stakeholders started with the hypothesis of using the same commercial plugin’s server version for optimization in the web application too.

For this we had to prove a couple of things:
1) Optimization of formula from step 1
2) Integration with web application

Option 1: Commercial optimization engine
We did a quick spike to test optimization with the commercial optimization plugin’s server version and also its integration with Ruby on Rails web application and it was successful. We had to use JRuby to integrate Ruby with plugin’s server edition as it provides only Java and .NET API.
Option 2: R (Open source)
In parallel, we checked if R can be used. R is a leading open source statistical environment.
– To solve the optimization problem in R we found a lot of R optimization packages and started testing packages like BB as the formula (from step 1) was non-linear, and had constraints and conditions. We tested BB’s SPG function and also tried other generic algorithms. We got good optimization results from R (similar or better compared to commercial optimization engine).
– Now we had to check how to integrate R with our web application written in Ruby. We found a number of options like integrating R with Apache (rApache) or integrating R directly with Ruby (rsruby). We decided to use rsruby.

We ran a number of proof of concepts with R and shared results with stakeholders. The results were positive in terms of performance as well as the optimized results… So we got better results and that too for free!