Boosting Sales and AOV with Personalized Quizzes: A Case Study on Ellie & Mac
- Wearables - Smart Clothing
- Apparel
- Retail
- Sales & Marketing
- Traffic Monitoring
Ellie & Mac, a digital sewing pattern company, was looking for ways to improve their onsite customer experience. The company's mission is to help customers customize and personalize their clothing, and they wanted to extend this personalization to their customers' online shopping experience. The challenge was to find a way to engage customers, understand their preferences, and provide them with personalized product recommendations. The idea of using a quiz to achieve this was inspired by the owner's positive experience as a customer on another platform.
Ellie & Mac is a digital sewing pattern company that aims to provide modern, trendy sewing patterns that are inclusive. They offer sewing patterns and embroidery designs that allow people to love the way they feel and look in the clothing they make. The company is committed to helping customers customize and personalize their clothing. The owner of Ellie & Mac, Lindsey Essary, is always looking for innovative ways to improve the customer experience and was inspired to implement a quiz on her Shopify store after having a great experience as a customer on another platform.
Ellie & Mac decided to implement a product finder quiz on their Shopify store. The quiz asked shoppers about their favorite fabrics, styles, and sewing abilities. After completing the quiz, customers were given personalized sewing pattern recommendations based on their responses. To continue the conversation with potential and existing customers, an email opt-in was included in the quiz. Additionally, the company introduced an exit-intent quiz pop-up that was triggered when shoppers were about to leave the website. This pop-up embedded the main quiz experience and helped to retain customers. Furthermore, a conversational pop-up was created to capture emails, learn about key customer preferences, provide incentives for opt-ins, and recommend products based on the initial answers.