Wayfair Marketing Analysis Virtual Experience Program - Completed on 8th January 2023
Like any home, what’s inside ours is unique. Wayfair is home to great people, ideas and ambitions. Their marketing teams are highly quantitative and analytical, optimizing all their channels to provide the organization with cutting-edge strategies to attract, acquire, and retain customers.
During this program, I got the opportunity to step into the shoes of a Wayfair team member and complete tasks that replicate the daily work of a marketing analyst at Wayfair. I received critical thinking, data analysis, root cause analysis, prioritization, synthesized communication, and experiment design skills.
Analyzing Declining Return on Advertising Spend (ROAS): Used my critical thinking and data analysis skills to isolate problem metrics
BACKGROUND:
Wayfair is an e-commerce platform selling a variety of home goods. The company advertises through digital channels, such as Facebook display ads and Google search results. Wayfair monitors the success of advertising using ROAS, which divides advertising-driven revenue by ad spending to measure spending efficiency.
The Bedding and Bath division lead recently noticed a decline in ROAS through Facebook advertising, most prominently in June 2022, and identified this as a high-priority issue to solve. As a marketing analyst intern, I was assigned to find the root cause of the decline. The division lead wanted a few slides explaining my initial analysis and a brief summary of the next steps.
I decided to start by examining the numbers. In this scenario, I asked the division data manager for a report that includes the ROAS component metrics over the last six months so I could identify any underlying trends.
Practical skills I gained from working on this task: Root Cause Analysis, Data Analysis, Synthesized Communication, Critical Thinking.
Addressing Low Conversion Rate (CVR): Find specific reasons CVR is declining and propose solutions
BACKGROUND:
The team now understood a decline in CVR was the source of the ROAS issue and was aligned with my recommendation to examine CVR by product category. The division data manager has now provided me with a summary of typical CVR drivers organized by category and performance over the last six months.
Drivers of CVR include out-of-stock rate, the lead time for products to ship, and average pricing. Customers are less likely to finalize a purchase when products are out of stock, take longer to receive, or are more expensive.
Based on my findings, the team wanted me to summarize what near-term actions we could take and how they should prioritize the next steps.
Practical skills I gained from working on this task: Prioritization, Data Analysis, Experiment Design, Synthesized Communication.