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Maven Cafe conducted a 30-day promotional test, targeting existing rewards members with diverse promotional offers to enhance our understanding of customer preferences and refine our marketing strategies, ultimately improving customer engagement and loyalty.
The primary objective of this project is to:
The findings and recommendations will be reported to the Chief Marketing Officer (CMO).
Cafe Rewards Offers from Maven Analytics.
Big Query, Excel.
In this section, I explore customer demographics, analyzing the distribution of customers by gender, age, income, and membership start year.
To accurately tailor our marketing strategies and better understand our customer base, I segmented customers based on following age groups:
Young Adults (18-24 years):Characteristics: College students or early-career individuals, prefer affordable and trendy specialty drinks, active on social media.Focus: Specialty drink promotions, loyalty programs, social media campaigns.
Adults (25-34 years):Characteristics: Young professionals, possibly starting families, with disposable income, interested in high-quality, sustainable options.Focus: Premium offerings, subscription services, environmentally friendly choices.
Mid-Age Adults (35-54 years):Characteristics: Established professionals, value convenience, quality, and a comfortable atmosphere.Focus: Quality and consistency in products, comfortable environment for family and business.
Seniors (55+ years):Characteristics: Pre-retirement or retired, value leisure, health-conscious, prefer classic and decaf options, and a quieter atmosphere.Focus: Discounts for seniors, availability of health-conscious and decaf options, a calm and quiet environment.
In this section, I outlined the workflow of the campaign and calculate the conversion rates at each stage within the campaign funnel.
Based on the user funnel outlined above, I will now analyze the performance at each stage, starting with the initial phase: the offer received stage.
Problem: When calculating the time interval between receiving and viewing offers, a user may receive the same offer multiple times.
Solution: Assume the offer viewed corresponds to the most recently received one prior to the viewing.
Problem: When calculating the time interval between viewing and completing offers, a user may complete the same offer multiple times.
Solution: Assume the offer completed corresponds to the most recently viewed one prior to the completing.
1. Optimize Offer Types
Continuously refine BOGO and discount offers using customer feedback and performance data. Prioritize discount offers due to their higher sales impact.
2. Expand Multi-Channel Distribution
Utilize a variety of platforms including social media and mobile to reach a broader audience and track channel effectiveness.
3. Enhance Timing and Content Customization
Optimize offer timing with immediate notifications and tailor content to the preferences of your main customer segments to boost engagement.
4. Targeted Marketing
Tailor marketing messages to resonate with the preferences of older, middle-high income adults and strengthen loyalty programs for members engaged since 2016.