William Skelly
Data, Business Analytics, Customer Acquisition
November 11, 2024
Generation Z is an elusive consumer requiring multilayered outreach – from experiential marketing and product sampling at festivals to partnering with influencers and encouraging user-generated content. Leading brands actively pursuing Gen Z, those born 1997 – 2012, are modernizing their marketing strategies, including multiple, varied touchpoints and more personalized messages.
Optimizing first-party data and the information your customers share with you will be critical to building loyalty, gaining new markets and growing sales.
The iconic handbag brand, Coach, recently revealed how they wooed Gen Z customers to buy their purses by harnessing consumer data. As a well-established handbag manufacturer, Coach was perceived by younger generations to be a “mother’s brand” of purses. Coach initiated consumer segmentation to identify the “timeless Gen-Z” shopper who preferred a classic style. They focused on the Coach Tabby bag as an opportunity to attract young buyers with a high lifetime value.
The Tabby was considered accessible luxury in the U.S., but Coach made the bag even more appealing by expanding the design to include new sizes, entry level prices, a quilted style and other versions. Using a dashboard, Coach used hard numbers and qualitative data to create campaigns like “Courage to Be Real” with Lil Nas X, “In My Tabby” and “Wear Your Shine” to build on the accessory’s viral moment. In a February 2024 performance review, Coach says the Tabby activations outperformed expectations, “nearly doubling versus last year and over-indexing with new and younger consumers at above average selling price.” [1]
The Coach case study demonstrates that simply collecting customer data is no longer a differentiator. What's separating the best performers is how well they can put that data to use to solve business challenges.
The most advanced companies are merging typical customer data, such as transaction histories and browsing behavior with qualitative data they collect on what customers think and feel in order to know them on a deeper level and forge emotional connections. By anchoring strategies in consumer needs and desires and arming decision makers with as much data as possible, innovative organizations are turning numbers into greater insights.
The Coach example shows how data analytics help improve marketing strategies and identify customer needs and preferences. Business analytics can also help organizations to stay ahead of the competition by enhancing operations, predicting market changes and trends.
Data scientists and data consultants can leverage three types of business analytics:
1. Descriptive analytics uses data aggregation and data mining to analyze historical information to uncover trends and patterns.
2. Predictive analytics layers data mining with statistical modeling and machine learning to define the likelihood of future outcomes based on historical data. Predictive analytics that use deep learning mimics human decision-making. Examples include helping HR professionals to lower employee turnover, more accurately predicting which borrowers might default on bank loans and automated prediction of MRI scans and X-rays to help doctors with diagnosing patients.
3. Prescriptive analytics gathers data from descriptive and prescriptive sources combined with sophisticated algorithms to create and recreate possible decision patterns that could affect organizations in a variety of circumstances. The actionable insights help businesses to save time and money while achieving their goals. Prescriptive analytics can be used by sales teams for lead scoring, bank fraud detection, product development and improvement, email automation and more.
Data can streamline decision-making for the toughest business challenges, including manufacturing, operations, business administration, customer service, marketing and sales. But the plethora of data available is a double-edged sword. It’s easy to become overwhelmed and confused about which data to monitor and what metrics will make the most positive impact on your business.
Partner with your inhouse data science teams or enlist support from data consultants to process vast amounts of data, while spotlighting overlooked factors and eliminating unnecessary details.
Your data partners will help cultivate your first-party data, capture new prospective customer information for lead nurturing and create effective feedback loops. The goal is to extract the necessary data more quickly and efficiently to assist with decision-making. Working together, you will be resilient, nimble and innovative in a dynamic business landscape.
William Skelly is founder and CEO of Causeway Solutions, a leading provider of Acquisition Analytics and innovative data services. Bill serves as advisor with some of the nation’s most influential organizations—from grassroots public affairs efforts to U.S. Presidential campaign strategies. Causeway Solutions empowers clients to make smart, timely, data-driven decisions through real-time consumer insights to better reach target audiences. Learn more at Causeway Solutions.
[1]: "How Coach Used Data to Make It’s Tabby Bag a Hit," Business of Fashion