Online Bargaining
Design Case Study: Online Bargaining Experience
Objective
The goal of this design was to enhance the online shopping experience by allowing users to negotiate the price directly within the app. This feature aims to replicate the in-person bargaining experience, adding an interactive and fun element to online shopping while potentially improving conversion rates by offering users a sense of personalized value.
User Research Insights
Through user interviews and behavior analysis, the following insights were gathered:
Preference for Personalization: Users appreciate features that cater to their individual needs, such as price negotiation.
Ease of Use: Conversational interfaces make users feel more at ease, as they resemble interactions with a salesperson in a physical store.
Time Sensitivity: Users desire quick responses during bargaining, with clearly defined limits to avoid drawn-out negotiations.
Design Process and Flow
1. Product Detail Screen
Purpose: To serve as the starting point for the user's journey. The screen displays product details and the option to negotiate the price.
Key Features:
Product image and description for informed decision-making.
"Bargain" button that directs users to the negotiation process.
Highlighted price (MRP) to show transparency before bargaining begins.
2. Bargain Bot Chat Interface
Purpose: A chat-based UI guides users through the negotiation process, making it conversational and intuitive.
Key Features:
Welcome Message: The bot greets the user by name, adding a personal touch.
Binary Decision: A "Yes" or "No" option lets users decide if they want to start bargaining.
Bargaining Rules: The bot sets clear expectations, such as a maximum of three attempts, ensuring a structured process.
Price Input: Users type their desired price, which adds to the feeling of autonomy.
3. Counteroffer Mechanism
Purpose: To simulate a realistic bargaining scenario, where the bot provides counteroffers based on the user’s input.
Key Features:
Final Price Suggestion: After considering the user’s offer, the bot suggests the best price available.
Actionable Options: Users can accept the final price (“Shop for ₹275”) or attempt another negotiation (“New Price”) within the allowed attempts.
Challenges and Solutions
Complexity of Bargaining Logic:
Challenge: Users expect logical, fair counteroffers.
Solution: Incorporate algorithms based on predefined rules to make counteroffers that feel realistic and reasonable.
Managing User Expectations:
Challenge: Users may expect steep discounts.
Solution: The bot transparently communicates the best possible price after considering their input.
Preventing Abuse of System:
Challenge: Users might repeatedly try to game the system.
Solution: Limit bargaining attempts to three, with a clear explanation at the start of the process.
Conclusion
This chat-based bargaining design reimagines price negotiation for e-commerce, providing a dynamic, user-friendly, and engaging shopping experience. By blending conversational AI with intuitive design, the feature bridges the gap between traditional bargaining and digital shopping.