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"Predicts user behavior to enhance digital experiences."

Utilizes sophisticated AI to predict online user actions in real-time, optimizing digital interactions for personalization. The solution enhances engagement, improves conversion rates, and builds customer loyalty by tailoring the experience to individual visitor behaviors across web and app platforms.
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quin's Website Claims...
Our AI found these claims on quin's website.
Predictive audience technology
QUIN AI claims that their platform can accurately predict customer behavior in real-time, resulting in proactive business transformations.
Hyper-personalized experiences
QUIN AI claims that they create unforgettable, personalized user interactions that drive engagement and boost conversions seamlessly.
Enterprise innovation integration
QUIN AI claims that their Enterprise Innovation Hub enables scalable growth and rapid innovation through seamless tech integration.
BlurbSTAR Case Study
GAP & Quin AI
Quin AI boosted GAP's basket sizes and conversions significantly.
89%
boost in conversions
3.16X
increase in average basket size
1.
Situation
GAP's Digital Experience Challenges
→
GAP needed to enhance digital customer experience.
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Faced declining basket sizes and conversion rates.
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Sought innovative techniques to engage online shoppers.
GAP, a renowned global retailer, faced the challenge of enhancing their digital customer experience to align with the demands of tech-savvy shoppers. Despite a strong brand presence and robust sales during daytime hours, GAP struggled with declining basket sizes and nighttime conversion slumps. Understanding the tech-oriented needs of their audience was crucial to maintaining their competitive edge. They needed an in-depth analysis and innovative solutions to boost engagement and conversions, especially during their off-peak hours.
2.
Task
Leverage Quin AI for Improvement
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Partner with Quin AI to improve engagement.
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Identify shopper behaviors and peak hours.
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Deliver personalized shopping experiences.
GAP embarked on a mission to revamp its online presence by partnering with Quin AI, with the aim to improve customer engagement during low activity periods and to maximize basket sizes. Quin AI's challenge was to provide real-time insights about shopper behaviors and deliver data-driven, personalized shopping experiences that would turn browsers into buyers. The task included pinpointing non-peak hours where conversions dipped drastically and offering a tailored shopping journey that aligned incentives with customer preferences to boost purchases.
3.
Action
Deploy AI-Driven Personalized Tactics
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Utilize real-time data analytics for insights.
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Develop targeted nudges for purchase encouragement.
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Implement personalized incentives tailored to customers.
Quin AI leveraged its advanced predictive technology and analytics to enhance GAP's understanding of shopper behaviors. By analyzing real-time website data, it identified key non-performing hours (22:00-08:00) and shopper tendencies. The AI technology deployed targeted offers and personalized nudges at critical moments to prevent checkout abandonment and encourage additional purchases. It moved beyond generic discounts, providing crafted incentives in response to individual shopper preferences, thus increasing engagement without eroding profit margins.
4.
Result
Significant Gains and Continued Growth
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Achieved a 33% increase in nighttime conversions.
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Realized a 3.16X rise in average basket size.
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Recorded an 89% boost in overall conversions.
The collaboration with Quin AI resulted in impressive outcomes for GAP. There was a 33% rise in nighttime conversion rates, transforming those hours into lucrative revenue slots. Additionally, GAP saw a remarkable 3.16X increase in average basket size, with 30% of shoppers adding a second product to their purchase. Conversions overall experienced an 89% boost. This success story showcased Quin AI's precision-targeted approach, driving better consumer engagement and sustainable profitability for GAP's digital channels.
Keywords
QUIN AI
DIGITAL TRANSFORMATION
PERSONALIZED SHOPPING EXPERIENCE
RETAIL ANALYTICS
PURCHASE BEHAVIOR
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1.
What types of audience data do you provide (behavioral, transactional, location-based, intent, psychographic, etc.)?
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2.
How do you collect and enrich audience data, and is it sourced from first-party, second-party, or third-party data?
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3.
Can your platform integrate with our existing MarTech stack (CDP, DMP, CRM, ad platforms, etc.)?
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4.
Do you offer real-time audience insights, and how frequently is your data refreshed?
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5.
How does your platform enhance first-party data and help with identity resolution?
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6.
Do you provide predictive analytics, AI modeling, or lookalike audience generation?
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7.
How does your data support media activation (programmatic, CTV, social, retail media, etc.)?
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8.
How do you measure audience quality and performance (uplift studies, match rates, incrementality testing, etc.)?
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9.
Can your platform provide closed-loop measurement to track how enriched audiences impact conversions and sales?
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10.
What is your pricing model (subscription, data usage-based, CPM-based, or custom enterprise pricing)?
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