Maven Rewards Analysis

"The dataset simulates the behavior of Cafe Rewards members over a 30-day period, including their transactions and responses to promotional offers.

The Data is contained in three files: one with details on each offer, another with demographic information on each customer, and a third with the activity for each customer during the period. The activities are divided into offer received, offer viewed, offer accepted, and transaction. For a transaction to be attributed to an offer, it must occur at the same time as when the offer was "completed" by the customer."

Main steps in creating a dashboard:

• data cleaning: JSON Parse and extract transformations were the two most complicated steps and I used AI for help

• creating a data model: I created a starschema

• planning the layout: I made a simple background in figma and I made the other containers in Power BI. I used coffe color theme

• creation of visuals and storytelling: I used Cards, Bar charts, Pie Charts and Scatter Plot with conditional formatting and I wrote storytelling in the titles

Key Findings:

• Customer Insight:

• Total Customers increased from 2015 to 2018 and reached its maximum in 2017

• The number of men exceeded that of women by approx. by 16%

• The proportion of women and men is roughly the same for each age

• Most customers are between 50 and 60 years old

• Event and Transaction:

• There are 4 types of events, most of which are transactions with nearly 50%

• Emails make up 30.3% with social least at 18.8%

• There are 2 offer types, discount is higher then bogo

• Over $70,000 and over age of 50 accept multiple offers

Recommendations:

1 Investigate the Decline in Customers After 2017: After the decline in 2017, focus on improving customer retention through better service, loyalty programs, and targeted marketing. Additionally, explore new markets or segments and update product offerings to align with current trends and needs.

2 Enhance Offer Effectiveness: Focus on optimizing discount offers, which have a higher completion rate than BOGO deals, by providing more personalized and appealing discounts that align with customer preferences and purchasing behavior.

3 Leverage Email and Mobile Channels: Since emails and mobile notifications are the most effective channels, invest in enhancing these mediums with more targeted and personalized messaging to drive higher engagement and offer completions.

4 Target High-Value Segments: Develop exclusive offers and loyalty programs for customers over the age of 50 with incomes above $70,000, as this group shows a higher propensity to complete multiple offers, potentially increasing transaction volumes.

5 Reduce Offer Complexity: Simplify the offers with a difficulty level of 10 to encourage more customers to complete them, as overly complex or demanding offers may deter participation and lower the overall offer completion rate.