FSF Case Study: Analytics
Applying data analytic methods to fully capture customer sentiment for brands
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Table of Contents
When a collaboration is established between two brands of different industries, it can be a challenge to measure the impact for both of them respectively. However, advancements in data analytics for retail have been able to meet this challenge if brands understand who their customers are and what they want to achieve.
For my collaboration, I chose Vans Footwear, a subsidiary of VF Corp, and White Claw, a hard seltzer beverage. Even though these two brands come from two different industries, they do share many things in common. For example, their customer bases are comprised of young people who are focused on trends. They both also market themselves using similar cultural iconography.
Vans has a history of being part of the surfer culture as demonstrated with them being featured in the 1982 film “Fast Times at Ridgemont High” and White Claw uses the white claw wave as their logo. A more obvious connection between the two brands can be summed up by a quote from the Ridgemont film where the protagonist states “All I need are some tasty waves, a cool buzz, and I’m fine.” Above all, what these two brands want to achieve is to become a lifestyle brand which can be accomplished via this collaboration. White Claw can be associated with skate and swim ready-to-wear to fully encapsulate its status as the number 1 spiked seltzer drink and Vans can capitalize on a new, growing, trendy market for their core demographic. If both brands seize on the growing demand for spiked seltzer by working together, they could both cement themselves to each other’s customers as the must have product for their lifestyle.
Executive Summary
Status of the Brands
Launch Plan
Vans Target Benefits
Customer
Lifetime Value
Loyalty Program
Customer
Acquisition
Email marketing
Brand
Awareness
Social Media
White Claw Target Benefits
Customer
Lifetime Value
Loyalty Program
Customer
Acquisition
Email marketing
Brand
Awareness
Social Media
Separating Impact of Collab Launch
Regression Model
A/B Testing
Identifies which independent variables had the most impact on the dependent variables (given that a dataset has been standardized and each variable is in the same scale)
Compares a controlled dataset to a dataset with applied changes
1
2
3
A Set
B Set
Results
New Cust. Emails
Collab Email Promo
Current Sales
5
2
0.55
0.56
1
2
3
0.025
0.045
What brands already know:
Customer data, customer accounts and purchase history, sales figures for season, product performance at every point of distribution
What brands need to collect:
Sales data from retailers (in store or online), data from resale platforms, social media data
Collecting Third Party Information
Technology Investments
HubSpot Marketing Hub
Marketing analytics products such as email marketing, ROI on social media ads, UTM codes for QR codes to precisely track web traffic from origin post
https://www.hubspot.com/products/get-started
Flywheel by Ascential
Optimizes effectiveness on the Amazon digital platform
Influential
Uses IBM Watson machine learning AI to recommend influencers for social media campaign to monitor engagement rates, positive sentiment analysis, search interest, increase in foot traffic for each campaign
Mailchimp
Online email marketing CRM tool that gives important customer data to brands such as clicks on social media platforms, performance reports on a marketing campaign, targeted segmentation, etc.
QR Code Generator Pro
Creates custom QR code for marketing campaigns
Internal Data Teams
Both brands should invest internally in data analytics teams to properly manage company data who are skilled in data manipulation, data gathering, and visualization
https://www.microsoft.com/en-us/sql-server/default.aspx
Overview
Look at competitor/industry growth and set targets to beat that (annual projected growth of footwear retail is 2.3%; collab must add comparable growth according to collab’s units of production which would be 0.25% - 0.75%, increasing to 1% with apparel category additions)
Performance Benchmarking
Target Benefits
CA: Increase in sales growth (income statement) and loyalty program, average sign up per month (did it increase or decrease after collaboration)
BA: Growth in social media accounts, usage of brand specific hashtags
CLV: See how many products in which categories have increased after the collaboration launch
Next Steps
After 1st year of collaboration for footwear, expand into apparel (including but not limited to: men’s and women’s t-shirts, board shorts, and windbreakers) to scale collaboration for both brands to attain comparable growth rates as the US footwear industry
2.3% growth each year
Reasoning for Methodologies
Today more than ever customers are interacting with brands in many different ways
Brands must have:
As a result:
Appendix
References
Graphics Citation