MAVEN REWARDS CHALLENGE
Setting
THE CHALLENGE
For the Maven Rewards Challenge, you’ll play the role of a Sr. Marketing Analyst at Maven Cafe. You’ve just run a test by sending different combinations of promotional offers to existing rewards members. Now that the 30-day period for the test has concluded, your task is to identify key customer segments and develop a data-driven strategy for future promotional messaging & targeting.
The results need to be summarized in a report that will be presented to the CMO.
Data that 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.
POWER BI
DASHBOARD REWARDS CHALLENGE
THE DATA
This is how I interpreted the data from the ‘events’ table:
- An offer is posted through various channels, timer starts, offer received.
- Customer A reads the offer at time X (offer viewed).
- Customer A accepts the offer at time Y (transaction), and at that same moment, the offer is completed (offer completed).
Alternative scenario:
- An offer is posted through various channels, timer starts, offer received.
- Customer B accepts the offer at time X (transaction), and at that same moment, the offer is completed (offer completed).
- Customer B only reads the offer details afterward. Moment Y (offer viewed).
The same customer can receive the same offer multiple times. This caused complications, as it wasn’t possible to determine with 100% certainty which events were related to which offer.
I considered transactions that couldn’t be linked to a specific offer (i.e., not recorded within the same timeframe as an ‘offer completed’) as transactions from previously offered deals. I excluded these transactions from my analysis.
General selection
Since the focus of the question is on the customers, I excluded offers linked to customers whose gender, age and/or income are unknown. questions.
DESIGN & DATA ANALYSIS
I set myself the task of building a one-page dashboard. This meant that I really had to focus on identifying key customer segments and developing a data-driven strategy for future promotional messaging and targeting.
Using bookmarks, I guide the user through the most important outcomes of my data analysis, which were:
Focus on higher-income customers: Develop targeted marketing campaigns and personalized offers for higher-income customers.
Optimize communication for lower-income customers: Test new approaches such as visually appealing and concise offers through popular channels for this demographic.
Increase use of social media: Leverage social media more extensively to distribute offers due to its positive impact on reach and engagement.
Improve web and email campaigns: Optimize the content and timing of web and email offers using A/B testing to increase effectiveness.
Target loyal customers: Develop loyalty programs and personalized offers for long-term customers to boost conversion rates.
Focus campaigns on female customers: Design campaigns that cater to the preferences of female customers, given their higher acceptance and spending behavior.