GenAI Solution for Effective Upselling and Cross-Selling
Challenge
The Client:
Our client operates a vendor platform that connects restaurants with corporate offices, hospitals, universities, retail locations, and distribution centers, serving more than 10 million meals per year. It also offers popup events, café services, and food delivery perks for companies.
Challenge:
Our team needed to implement a feature within the client’s existing food delivery app to increase average order value and enhance the customer experience by intelligently suggesting relevant items to upsell and cross-sell. Traditional methods like manual recommendations or basic collaborative filtering were considered too time-consuming and costly to develop and maintain.
The Client:
Our client operates a vendor platform that connects restaurants with corporate offices, hospitals, universities, retail locations, and distribution centers, serving more than 10 million meals per year. It also offers popup events, café services, and food delivery perks for companies.
Challenge:
Our team needed to implement a feature within the client’s existing food delivery app to increase average order value and enhance the customer experience by intelligently suggesting relevant items to upsell and cross-sell. Traditional methods like manual recommendations or basic collaborative filtering were considered too time-consuming and costly to develop and maintain.
Solution
Solution:
Leveraging the power of generative AI, specifically large language models (LLMs) like GPT-4o, GPT-4o-mini, and Gemini Flash, First Line Software implemented a system to dynamically generate personalized upsell and cross-sell suggestions within the client’s mobile app. This system analyzes the user’s current order and generates recommendations and explanations for why the items might be appealing to the existing order. The system also collects user feedback on these suggestions to continuously improve its performance and personalize the experience further.
Key Benefits:
Contextual Cross-selling:
When users add an item to their order, the AI suggests a complementary item leveraging the AI’s understanding of food pairings and preferences. The AI can go beyond basic pairings to suggest diverse cross-sell options, such as adding a side dish or recommending a specific drink.
Explanatory Upselling:
When a user selects an item, the AI suggests an upgrade, while explaining the value proposition of the upgrade, highlighting the extra items included. Building trust and transparency by explaining why an item is suggested, the AI builds trust with the user and increases the likelihood of conversion. This transparency is crucial for upselling, as users need to understand the value they’re getting for the extra cost.
Location-based Suggestions:
The system uses location to suggest items based on the current weather. For example, on a cold day in Seattle, the AI may recommend warm and comforting dishes. Restaurants could upload their sales data to the platform, allowing the AI to analyze order patterns and identify popular pairings. This provides valuable insights for menu optimization and targeted promotions.
Solution:
Leveraging the power of generative AI, specifically large language models (LLMs) like GPT-4o, GPT-4o-mini, and Gemini Flash, First Line Software implemented a system to dynamically generate personalized upsell and cross-sell suggestions within the client’s mobile app. This system analyzes the user’s current order and generates recommendations and explanations for why the items might be appealing to the existing order. The system also collects user feedback on these suggestions to continuously improve its performance and personalize the experience further.
Key Benefits:
Contextual Cross-selling:
When users add an item to their order, the AI suggests a complementary item leveraging the AI’s understanding of food pairings and preferences. The AI can go beyond basic pairings to suggest diverse cross-sell options, such as adding a side dish or recommending a specific drink.
Explanatory Upselling:
When a user selects an item, the AI suggests an upgrade, while explaining the value proposition of the upgrade, highlighting the extra items included. Building trust and transparency by explaining why an item is suggested, the AI builds trust with the user and increases the likelihood of conversion. This transparency is crucial for upselling, as users need to understand the value they’re getting for the extra cost.
Location-based Suggestions:
The system uses location to suggest items based on the current weather. For example, on a cold day in Seattle, the AI may recommend warm and comforting dishes. Restaurants could upload their sales data to the platform, allowing the AI to analyze order patterns and identify popular pairings. This provides valuable insights for menu optimization and targeted promotions.
Results
Outcomes:
Increased Average Order Value:
Early results indicate a potential increase in average order value due to the AI’s ability to suggest relevant and enticing add-ons.
Enhanced Customer Experience:
The personalized recommendations and explanations provide a more engaging and satisfying user experience.
Cost-Effective Solution:
Implementing LLMs for this task is highly affordable, with ongoing expenses remaining relatively low. For one particular client, A/B testing of the system powered by LLMs was conducted for just $16, demonstrating its cost-efficiency even in real-world scenarios.
Rapid Implementation:
The GenAI solution was implemented within a few sprints, allowing for quick deployment and faster return on investment.
Continuous Improvement
The system’s ability to collect and learn from user feedback ensures ongoing optimization and personalization.
Outcomes:
Increased Average Order Value:
Early results indicate a potential increase in average order value due to the AI’s ability to suggest relevant and enticing add-ons.
Enhanced Customer Experience:
The personalized recommendations and explanations provide a more engaging and satisfying user experience.
Cost-Effective Solution:
Implementing LLMs for this task is highly affordable, with ongoing expenses remaining relatively low. For one particular client, A/B testing of the system powered by LLMs was conducted for just $16, demonstrating its cost-efficiency even in real-world scenarios.
Rapid Implementation:
The GenAI solution was implemented within a few sprints, allowing for quick deployment and faster return on investment.
Continuous Improvement
The system’s ability to collect and learn from user feedback ensures ongoing optimization and personalization.