Our objective with this virtual internship is to provide a complete analysis of the behaviour of the customers and understand the target audience that would respond better to the campaigns.Marketing Campaigns are a vital part of how companies promote their interests, whether that be raising awareness for a new product or capturing customer feedback.
About the Company
Growisto is a Digital Growth company that specializes in technology and marketing services. Based out of US, and India we consult companies in accelerating their growth, building their moat, and improving outcomes & efficiency through digital innovations.
We provide end-to-end solutions for custom software development, website and mobile app development using Headless framework through React.js, Node.js, Magento and Shopify. We also offer growth marketing solutions for website and marketplaces such as Amazon & Walmart.
We have worked with over 300+ brands including Puma, Victorinox, and Justrite. Our technology-first approach and experienced strategists across a gamut of sectors have helped us provide high-impact solutions to our clients. We are Adobe Solutions, Shopify, and Amazon Ads Partners.
Learning Outcomes
- Understand the KBRs and KPIs of a marketing business
- Cleaning data and feature selection.
- Learn EDA through SQL and Python, to find all the relevant metrics that would help us to track our KPIs.
- Dynamic Charts creation and Dashboarding through Tableau using various metrics to clearly analyse the company’s performance.
Detailed Description
Aim: To have a complete understanding about the marketing campaigns, and their conversions. Knowledge of business terminologies and remarketing concepts that are very important and provides useful insights about the business.
Business Problem:
Marketing Campaigns are a vital part of how companies promote their interests, whether that be raising awareness for a new product or capturing customer feedback. That's why it's important for the data analysts of any company to be able to gauge customers’ participation in marketing campaigns, assess the success of past campaigns, and propose data-driven solutions to increase participation in future campaigns.
Our objective is to provide a complete analysis of the behaviour of the customers and understand the target audience that would respond better to the campaigns.
Learning Outcomes:
- Understand the KBRs and KPIs of a marketing business
- Cleaning data and feature selection.
- Learn EDA through SQL and Python, to find all the relevant metrics that would help us to track our KPIs.
- Dynamic Charts creation and Dashboarding through Tableau using various metrics to provide a clear analysis of the company’s performance.
Initial Skill Requirement:
Proficiency in Python libraries: pandas, numpy, matplotlib and seaborn.
Hands on experience with SQL DQL and DDL queries and a good understanding of databases.
Along with that Tableau dynamic charts creation and Dashboarding skills.
Data Dictionary:
Variables | Description |
ID | Unique ID of each visitor |
AcceptedCmp1 | 1 if customer accepted the offer in the 1st campaign, 0 otherwise |
AcceptedCmp2 | 1 if customer accepted the offer in the 2nd campaign, 0 otherwise |
AcceptedCmp3 | 1 if customer accepted the offer in the 3rd campaign, 0 otherwise |
AcceptedCmp4 | 1 if customer accepted the offer in the 4th campaign, 0 otherwise |
AcceptedCmp5 | 1 if customer accepted the offer in the 5th campaign, 0 otherwise |
Response (target) | 1 if customer accepted the offer in the last campaign, 0 otherwise |
Complain | 1 if customer complained in the last 2 years |
DtCustomer | date of customer’s enrolment with the company |
Education | customer’s level of education |
Marital | customer’s marital status |
Kidhome | number of small children in customer’s household |
Teenhome | number of teenagers in customer’s household |
Income | customer’s yearly household income |
MntFishProducts | amount spent on fish products in the last 2 years |
MntMeatProducts | amount spent on meat products in the last 2 years |
MntFruits | amount spent on fruits products in the last 2 years |
MntSweetProducts | amount spent on sweet products in the last 2 years |
MntWines | amount spent on wine products in the last 2 years |
MntGoldProds | amount spent on gold products in the last 2 years |
NumDealsPurchases | number of purchases made with discount |
NumCatalogPurchases | number of purchases made using catalogue |
NumStorePurchases | number of purchases made directly in stores |
NumWebPurchases | number of purchases made through company’s web site |
NumWebVisitsMonth | number of visits to company’s web site in the last month |
Recency | number of days since the last purchase |
Road map:
Phase: 1
Initially we need to have a good understanding of the project requirement and domain knowledge about how marketing campaign data is processed, and the terms used for tracking its several metrices.
- Step 1: Explanation of all the metrices and dimensions recorded in this dataset.
- Step 2: What are business insights and how are they best detected.
- Step 3: Understanding the data with the help of excel and then loading it into SQL for further analysis followed by an EDA in python.
- Creation of KBRs and KPIs and maintaining proper notes for it.
- Step 4: Required Installations – SQL, Python, MS Excel, Tableau
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Phase: 2
After planning and studying our business requirement, it’s time to perform further data cleaning using SQL and EDA in Python using various Python libraries.
- Step 1: Creating a schema in MySQL Workbench for the project and importing the dataset as a table.
- Step 2: Perform initial analysis through SQL queries, to get an idea about the dataset and the missing values in it along with other insights.
- Step 3: Perform tests and data transformation for the features we selected initially during our KBR and KPIs selection.
- Step 4: Export the data from SQL and then load it in Python as a data frame for further EDA
- Step 5: Using various functions of pandas library in Python for further statistical as well as visualize insights for the features we have selected.
Phase 3:
In this phase our focus would be to visualize the data through Tableau as, dashboards are the end product that needs to be delivered to the clients as the most part of our analytics services. All the insights related to this marketing campaigns business needs to be presented in the best way possible.
- Step 1: Connect the data in Tableau.
- Step 2: Create the required charts, according to the features and metrics selected during EDA.
- Step 3: Add dynamic features to the charts to make it more insightful and user friendly with the help of parameters and calculated fields.
- Step 4: Design a Dashboard that perfectly presents all the business insights with the dynamic chats created above in step 3.