A store discovery queue is a feature used by ecommerce platforms like Shopify and BigCommerce to help customers easily find new stores and products to browse and purchase from. The discovery queue showcases curated collections of shops based on the customer’s interests and past browsing behavior. The main goals of store discovery queues are to:
Drive Product Discovery
The primary purpose of store discovery queues is to help consumers discover new products and brands that they may be interested in purchasing. Without an effective discovery experience, customers may never find items beyond their initial search. Discovery queues aim to surface personalized shop, product and collection recommendations. This introduces consumers to more of the diverse inventory available across merchant stores on the platform.
Increase Sales for Merchants
Discovery queues also seek to drive more sales to the platform’s merchants. By featuring merchants Shopify or BigCommerce thinks a customer will like, the queues give merchants exposure to new potential customers. This can increase store traffic and conversions for merchants who may otherwise be buried in the platform. Discovery queues are another sales channel that merchants can benefit from by selling on these ecommerce platforms.
Enhance Customer Loyalty
An engaging discovery experience improves customer satisfaction and loyalty to the ecommerce platform. When customers find new products they love through the discovery queue, they have a better perception of the platform’s value. This makes them more likely to return and browse for additional purchases in the future. The discovery queue essentially acts as a personalized shopping assistant.
Types of Store Discovery Queues
There are a few main types of discovery queues that ecommerce platforms leverage to inspire consumers to explore new finds:
Personalized Recommendations
Many platforms showcase discovery queues like “Recommended for You” on their homepages or shop pages. These highlight curated selections of shops, products or collections specifically matched to each customer’s unique preferences and order history. The recommendations get smarter over time as the platform gathers more data on the customer.
Category-Specific Discovery
Discovery queues also often appear when customers are browsing specific product categories or collections. For example, the “More to Explore” sections on a category page provide shop ideas related to that category. This helps customers delve deeper into a category they already have interest in.
Situational Discovery
Some discovery queues are tailored to certain situations, like gift-giving occasions. For example, Shopify creates Gift Finder discovery queues with curated gift ideas for birthdays, weddings, holidays and more. These situational queues anticipate the customer’s context-specific needs.
Popular Shops Discovery
Discovery queues also highlight trending or popular shops on the platform. Featuring best-selling merchants helps drive customer attention to “must-see” stores. Customers can discover why these shops attract so many sales.
Local Store Discovery
Platforms like Shopify also allow customers to easily discover interesting local small businesses near them. Local discovery queues match shops based on the customer’s location data. Supporting local stores is a key benefit of large ecommerce marketplaces.
How Store Discovery Queues Work
So how exactly do platforms create personalized discovery queues optimized for each customer? There are a few key elements powering the technology:
User Data Collection
Ecommerce platforms capture extensive data on customer interests, preferences and behaviors. This includes information like:
- Past shopping and order history
- Product/categories viewed and searched for
- Preferred brands purchased
- Content topics engaged with
- Demographics like location, age, gender
- External data like social media linked
Advanced Discovery Algorithms
Platforms feed this data into proprietary machine learning algorithms that match products or stores likely to align with each customer’s tastes. The algorithms use techniques like:
- Collaborative filtering to connect users with similar preferences
- Content-based filtering to match product attributes to users
- Item-to-item correlations identifying products commonly purchased together
Continuous Optimization
The platforms constantly monitor discovery queue performance and customer engagement data. This allows them to refine the algorithms over time to improve relevance, click-throughs and sales.
Human Curation
While AI powers personalization, human curators often help finalize the discovery queues. Curators ensure selections make sense and meet quality standards. The balance of data and human curation lets platforms scale while keeping queues engaging.
Benefits of Store Discovery Queues
When executed effectively, store discovery queues bring many benefits to both shoppers and merchants:
More Product Finds for Shoppers
Discovery queues help shoppers encounter interesting new products or stores they would likely never find on their own in the platform. This enables more serendipitous finds and inspiring browsing sessions.
Improved Personal Relevance
The tailored recommendations better match each shopper’s tastes and needs. The personalized queues feel more meaningful than generic suggestions.
Enhanced User Experience
Discovery features create a richer, more immersive browsing experience. Shoppers enjoy browsing for its own sake, driving loyalty.
Higher Platform Sales
More consumers exploring and buying from personalized queues increases overall sales and conversions for the ecommerce platforms.
Additional Merchant Exposure
Being featured in discovery queues gives merchants valuable new traffic sources. Queue placement is essentially free advertising and marketing.
Expanded Customer Reach
By getting their shops in front of new audience segments, merchants can cost-effectively acquire new customers at scale.
Higher Merchant Sales
The increase in traffic and conversions from discovery queues directly translates to higher sales and revenue for merchants.
Challenges of Store Discovery Queues
However, crafting effective discovery experiences comes with some key challenges as well:
Difficulty Modeling Users
Collecting the right types of data and accurately modeling the interests of millions of unique shoppers represents an enormous AI challenge. Bad user models lead to irrelevant queues.
Matching Inventory Scale
Platforms must identify the ideal products or stores to feature from their massive, ever-changing product catalogs. Showcasing the most relevant selection is difficult.
Avoiding Filter Bubbles
Overly narrow personalization could pigeonhole users and reduce serendipitous finds of new types of items. Queues must balance customization with diversity.
Measuring Incremental Value
Isolating the incremental sales driven specifically by discovery queues versus what customers would have purchased anyway is difficult. This complicates measuring queue ROI.
Adoption Hurdles
Shoppers accustomed to search-based browsing must be convinced to explore outside their usual purchases. Queues compete with existing user journeys.
Data Privacy Concerns
Consumers are increasingly wary of platforms collecting expansive data for personalization. Transparency and controls are essential.
Best Practices for Store Discovery Queues
Platforms can maximize their likelihood of overcoming these hurdles and creating discovery queues that deliver value by following these best practices:
Invest in Powerful Recommendation Algorithms
Building cutting-edge machine learning models for recommendation relevance is key. The algorithms should combine multiple data signals and techniques.
Focus on Selecting Quality Merchants
Merchants featured should have strong brands, compelling products, and good customer service to drive conversions once discovered.
Curate a Diverse Mix
Balance personalized picks with variety to spark new interests. Feature shops of different sizes and categories.
Add Contextual Details
Provide snippets about each merchant and why it’s featured to build trust and relevance.
Test and Iterate Extensively
Continuously A/B test queue placements, picks and algorithms to learn what resonates.
Monitor Conversions Closely
Beyond clicks, track how queues influence add-to-carts, purchases and sales to accurately measure performance.
Promote Discovery Experiences
Educate users on the value of discovery queues through on-site messaging and recommendations.
Respect User Privacy
Clearly communicate how data is used and give shoppers transparency and control options.
The Future of Store Discovery
Looking ahead, store discovery experiences will only grow in importance as platforms continue innovating to help users cut through the clutter. We expect companies to focus even more on areas like:
Expanded Personalization
More sophisticated user modeling and customization based on detailed contextual signals like weather, current trends and life events.
Shopper Journey Integration
Discovery queues seamlessly woven into more touchpoints, like account welcome flows and post-purchase re-engagement.
Hyper-Relevant Content
Personalized discovery content like reviews, videos and guides matching user interests surrounding stores.
Cross-Platform Discovery
Aggregating user data from across platforms to inform recommendations and reconnect users to lost finds.
Visual Discovery
Leveraging augmented reality to enable more interactive product exploration and discovery pathways.
Conclusion
Overall, store discovery queues remain an impactful ecommerce capability for surprising and delighting customers to increase sales. As adoption spreads across more platforms, consumers will come to expect discovery-focused experiences . Brands that master product discovery differentiation will gain significant competitive advantage in retaining and engaging users in the crowded online retail space.
