High Note case StudyEquation PCit f Dit Bit Lit ?WijYjt 

High Note case Study

Equation: P(Cit) = f (Dit, Bit, Lit, ?WijYjt)  this equation is used to determine the probability of the conversion for a customer its simple called network equation

WijYjt = Social Network Features

According to our case study, I declared below variables are used accordingly

Cit = Number of subscribers conversion to premium service

Dit = Age Gender

Bit = Number of songs Loved tracks

Whereas I refer to the individual person number and t refer to the time period

Defining the objective is the primary goal of predictive modeling by lining up the function of the model with business goals & business strategy.

My focus to convert Free users to paid users. At first, I need to analyze the data in stages.

In figure 1: Which age bracket we need to target, according to given data it must be between 18-25 age group.

Figure 2: Maximum count of songs hear in a given period of time is 10 songs.

In Figure 3: Percentage of users enrolled for at least one loved track is nearly 38%. The next 2 to 5 loved tracks are nearly 20% (both free and premium subscribers).

In Figure 4 we need to analyze the relationship between two variables X and Y whereas X refers to no of songs and Y refers to Age. Here Y is the dependent variable and X is the Independent Variable. According to the given data, the Age 20 category has enrolled more than 800 songs in their playlist. By applying linear regression test the connection between two variables can be abstract visually in a scatter plot graph by using R.

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Each of the dots in this scatterplot might represent a connection between a user, age, and number of songs that they pay attention to. By using trendline, we can elaborate the connection between X and Y. With the help of Linear regression test it draws the line through the two variables, the scatterplot graph of the two variables, that reduce the error or the distance between the aggregation of all y values for any x values. So, all of these distances would be minimized by the line drawn by a regression. It is very important to collect accurate data for reducing the margin of errors. By using regression maybe males who are willing to convert premium subscribers rather than females according to fig 6. Why would I waste my ad budget by showing sale ads to the Female category? Analysis comparing Adopters and Nonadopters according to fig 5 and 7 stating that value 1 on X variable has registered higher no of songs and playlists rather than value 0.

Due to the data collected on subscriber demographics, behavior/preferences by knowing past listening behavior, time of day and location. Through continue investment in data collection through machine learning algorithms. These also help to decode customers purchasing behavior and interaction to the website/app (High note). By using segmentation to the target market in different ways to be successful.

Demographic information may authorize precise targeting, which may make for more successful campaigns. Secondly demographic profile of selected customers may not be characteristic of the total people of prospect adopters. Hence, we study local average treatment effects among those customer segments in our sample that are likely to adopt playlist.