Skip to content
0
Photo by Annie Spratt on Unsplash

Where to focus a marketing campaign?

📖 Background

You are a data analyst at a crowdfunding site. For the next quarter, your company will be running a marketing campaign. The marketing manager wants to target those segments that have donated the most in the past year. She turned to you to help her with her upcoming meeting with the CEO.

Introduction

Crowdfunding is the practice of funding a project or venture by raising money from a large number of people, in modern times typically via the Internet.Here we are going to analyze the last year data to find out the groups with large fundings.Let's check data distributions

💾 The data

You have access to the following information:

Historic crowdfunding donations
  • "category" - "Sports", "Fashion", "Technology", etc.
  • "device" - the type of device used.
  • "gender" - gender of the user.
  • "age range" - one of five age brackets.
  • "amount" - how much the user donated in Euros.

Importing Libraries

import pandas as pd
import plotly.express as px
import numpy as np

from plotly.subplots import make_subplots
import plotly.graph_objects as go
df = pd.read_csv('./data/crowdfunding.csv')
df.head()

By looking above first five rows of the dataset we get the general ideas how our dataset looks. Let's check dataset in statistical prespective by running the following cell of info() and describe () functions to better understand the data

df.info()
# There is no missing vlaues in dataset, Let's double check it
print(df.isna().any())
df.describe(include="all")

There is no missing value in the dataset as we can see it from info() or isna() functions and describe() function provide us Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.It analyzes both numeric and object series. From this we have frequeny of object data appear and count of its appearnces and unique value in object data

From numeric data analysis minimum funded is €1 and maximum is €101 and other percentiles respectively and average of €39 is funded

Total donation collected in the past year (euros).

total_donation = df['amount'].sum()
print(f'total donation collected last year : € {total_donation} euros')

💪 Challenge

Create a single visualization that the marketing manager can use to explore the data. Include:

  1. What are the top three categories in terms of total donations?
  2. What device type has historically provided the most contributions?
  3. What age bracket should the campaign target?
‌
‌
‌