Optimization services are a form of data science, the goal of which is to figure how to employ data to make a system or company as efficient as possible. Several different types of optimization services exist. For instance, a data science service could go through a manufacturing process and ensure that the process runs as fast as possible and uses as few parts as possible, thereby minimizing costs with the end product while still being fully functional. These services could also figure out which terms can be embedded into a website that will cause it to be indexed by automatic search engine processes, and therefore placed higher on a search engine results page. For any optimization problem we need a tailored soluton but the approach is quite general. Following we will demonstrate the steps that are required to form any optimization solution using one of the most common examples of these types of services – increasing ad effectiveness.
What are the Steps of Data Optimization?
STEP 1: Figure Out What Needs to be Optimized:
The next step is to identify the range of parameters that contribute to the value of the optimized variable. In our specific example, optimized revenues for a campaign are generated by creating the most attractive, enticing ads possible for a certain demographic. Thus, the parameters can range from the placement of the ads, its content, the format in which the ad is displayed, what colors the ad is printed in and the location of the ad to the wording and images on the ad. The coloring could interact with the medium, or the font and the images could influence one another. Certain parameters could interact in a way that would make one ad optimized for a particular demographic. For instance, an online for life insurance ad that is placed on a children’s website in dark colors will not be nearly as effective as an ad for candy in bright colors. The aim of the second step is to map the entire range of parameters that are involved and to estimate their contribution to the value of the optimizaed variable. Sometimes these relations reassemble a complex network of dependencies between the parameters and the optimized variable.
STEP 3: Estimate the Relation Between the Optimized Variable and its Parameters (Prediction Models):
STEP 4: Find the Values of the Parameters which can Bring the Optimized Variable to its Optimum State
1. Ad Network Optimization
2. Media buying Optimization
3. Transportation Network Optimization
4. Minimum Description Length (MDL)