A marketing campaign refers to a focused set of activities designed to promote a product, service, or brand to a specific target audience. It involves various marketing strategies, such as advertising, content creation, social media engagement, and more.
New marketing campaigns can have a significant impact on overall business growth. By effectively reaching and engaging the target audience, a well-executed campaign can generate increased brand awareness, attract new customers, and drive sales. For example, a clothing brand launches a new campaign showcasing its latest collection through a mix of social media ads, influencer partnerships, and email marketing. As a result, the brand experiences a surge in website traffic, a rise in customer inquiries, and ultimately an increase in sales.
Analyzing marketing campaign data is crucial for staying ahead of market competition. By closely examining metrics like reach, engagement, conversion rates, and customer feedback, businesses can gain valuable insights into campaign effectiveness. For instance, a software company runs a marketing campaign promoting a new feature of their product. Through data analysis, they discover that the campaign resonated well with their target audience, resulting in a spike in free trial sign-ups and positive customer reviews. Armed with this knowledge, they can optimize future campaigns to maximize their impact and stay ahead of competitors.
- What is the zip code most purchased from the ads?
- Is there a relationship between customers who received a discount and purchases?
- Customers who get an offer do they buy?
- What are the channels that customers use the most and buy through?
Here we are using a random data of marketing campaign, downloaded from https://www.kaggle.com/ . It contains data about:
- recency
- history
- used_discount
- used_bogo
- zip_code
- is_referral
- channel
- offer
- conversion
The required Python libraries are-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
** The Dataset, analysis report and notebook are uploaded above. Thank You!