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FindMine

"Findmine crafts personalized product recommendations using AI."

Case Study
Frequently Asked Questions
Findmine uses artificial intelligence to create personalized product recommendations, enhancing customers' shopping experience. It's an AI-driven ecommerce solution, providing style guides, and automatic content for dynamic landing pages. It reaches consumers across platforms like eCommerce, email marketing, and advertising.
FindMine Stated Claims
These are public claims Blurbs AI believes to have been made by FindMine.
Seamless Content Creation
Findmine claims that they can automatically create visual content for every product, build dynamic landing pages, and enhance the checkout experience.
Personalized Email Campaigns
Findmine claims that they can send personalized & inspirational style guides via post-purchase follow-ups, win-back campaigns, and more with no manual setup.
In-Store Inspiration
Findmine claims that they can inspire shoppers with in-store styling content that customers can view on kiosks, tablets, or even their own phones.
CONTENT ENGINE
STYLING SOLUTIONS
OUTFITTING AUTOMATION
COMPLETE THE LOOK
ECOMMERCE INTEGRATION
BlurbSTAR Case Study
FindMine & adidas
Automate content to boost customer engagement and revenue.
700%
Higher conversion on curated landing pages
5%
Increase in revenue per visit
1.
Situation
Facing Fierce Market Competition
→
Retailers face tough competition and fluctuating consumer lo...
→
Investments in data platforms weren't maximizing customer li...
→
Existing personalization strategies were limited by static c...
FindMine, a provider of content creation tools, identified that many retailers face fierce competition and fickle consumers. Despite investing in personalization technologies like customer data platforms and recommendation engines, retailers struggled to maximize customer lifetime value, increase average order value, and retain customers. Personalization efforts were hampered by a lack of dynamic, scalable content, which hindered the effectiveness of their personalization strategies. The challenge was to generate sufficient content to fulfill the promised results of their existing investments.
2.
Task
Need for Dynamic Content
→
Generate dynamic, real-time content using machine learning.
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Adapt content based on multiple changing variables.
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Improve personalization and customer engagement.
FindMine's task was to help retailers enhance their personalization capabilities by exponentially increasing the amount and variety of inspirational shoppable content. The goal was to leverage machine learning to automate the generation of on-brand, real-time composable content that could adapt to changing variables such as customer data, behavior, and inventory levels. By doing so, retailers could offer more personalized and engaging shopping experiences, which would ideally convert more visitors into loyal customers, thereby boosting average order value, conversion rates, and overall customer satisfaction.
3.
Action
Leveraged Machine Learning Tools
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Implemented ML algorithms for dynamic content.
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Automated content creation to maintain brand consistency.
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Enabled rapid content segmentation and personalization.
FindMine implemented machine learning algorithms to create real-time, composable content for its retail clients. This automated system allowed creative directors and marketers to focus on strategic tasks while ensuring the content remained on-brand and relevant. Real-time composable content was dynamically generated based on customer behavior, inventory availability, and other data points. This eliminated the need for extensive human involvement in content production. The system also enabled rapid segmentation and personalization, such as creating a segmented lookbook for leather-loving consumers in Chicago looking for extra-small sizes.
4.
Result
Significant Business Impact
→
700% higher conversion rates on curated pages.
→
5% revenue increase per site visit.
→
150% boost in Customer Lifetime Value.
The introduction of FindMine's AI-driven shoppable content solution led to measurable improvements for retailers. Hyper-specific curated landing pages saw a 700 percent increase in conversion rates and a 5 percent hike in revenue per visit. Additionally, when 90 percent of customer interactions with products involved inspirational shoppable content, retailers experienced a 150-percent boost in Customer Lifetime Value. Automated content management reduced the reliance on super sales, preserving brand value and eliminating the need for discount-driven tactics. Ultimately, this approach resulted in higher engagement, extended customer retention, and increased ROI for marketing technologies.
Keywords
CUSTOMER LIFETIME VALUE
MACHINE LEARNING
SHOPPABLE CONTENT
PERSONALIZATION
RETAIL ANALYTICS
AUTOMATED CONTENT CREATION
DYNAMIC LANDING PAGES
IN-STORE ENGAGEMENT
CONVERSION RATES
AVERAGE ORDER VALUE
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20 Frequently Asked Questions
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