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.

Initiator: Maven Analytics

Period: August/September 2024

Maven Rewards Challenge results

One of the five finalists!

See the Maven Rewards Challenge’s Winner Selection Voting Round (start 42:35)

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.