I'm working on a proof of concept (PoC) for an AGI-driven restaurant experience. The goal is to improve customer satisfaction through personalized interactions and streamline restaurant operations. Here’s the step-by-step PoC I've developed:
Step 1: Customer Interaction via Phone Call
When a customer calls a restaurant, the AGI assistant answers to understand their needs and preferences. The AGI checks the restaurant's availability and provides options accordingly. It can also make a booking, create a timeline for follow-up, and even call back if the customer changes their mind or needs a reminder.
Enhancement Idea: The AGI could also suggest alternative times or other restaurants in case the first choice is unavailable, offering a smooth customer experience.
Step 2: Customer Arrival and Table Allocation
When customers arrive, they're greeted either by a human assisted by AGI or through an app notification. The AGI assigns tables based on the customer's preferences and any pre-set arrangements. The system can also present a customized menu on the customer's phone.
Enhancement Idea: Add a virtual concierge service that guides customers to their table, provides information about the restaurant, and offers personalized recommendations.
Step 3: Ordering, Payment, and History
The AGI system, through the customer's phone, can handle order taking, payments, and provide information on special discounts. It can also access the customer's past food preferences to make suggestions.
Enhancement Idea: Integrate a loyalty program where customers can earn rewards based on their orders and visits. The AGI could remind them of their points balance and upcoming promotions.
Step 4: AGI with Sensory Capabilities
The AGI system has sight in the form of cameras and a "mouth" through screens or television displays. This provides visual interaction without intrusive audio, which could disturb customers. The screens could be used to share important information or showcase the restaurant's unique character.
Enhancement Idea: Implement an interactive digital display at each table where customers can explore the menu, order drinks, or even interact with the AGI to learn more about the restaurant or upcoming events.
Overall, the goal is to create a seamless and engaging experience for customers while enhancing the restaurant's efficiency. This PoC could set the foundation for a new era of smart, AI-driven dining experiences.
Next Steps:
P2 - Develop a working prototype of the AGI system, focusing on phone-based interactions and table allocation.
P1 - Collaborate with a restaurant to test the concept in a real-world environment.
P1 - Gather feedback from customers and staff to refine the experience.
P3 - Explore partnerships with AI technology providers and restaurant software developers to scale the concept.
With this proof of concept, I aim to build a unique AGI-powered solution that could transform the hospitality industry.
Proof of concept for -
1. Non-Intrusive Communication:
Most people preferred a non-intrusive voice interaction over a direct video or screen-based approach. This aligns with the idea of creating a seamless and comfortable dining experience.
Update to Prototype: Focus on using voice-based interaction that feels natural and unobtrusive. This could be achieved through conversational AI that responds to cues and maintains a friendly tone.
2. Using AGI to Encourage Positive Behavior:
The idea of incorporating real-time video feeds to categorize activities in the restaurant is intriguing. The aim is to gently "nudge" customers toward positive behaviors without being overly intrusive.
For example, if the AGI system notices that a large percentage of customers are engrossed in their phones, the system could subtly encourage them to engage with their surroundings. This could be done by referencing the restaurant's unique features, art, or social aspects.
Implementation Strategy: The AGI system could use screens or voice prompts to suggest engaging activities or highlight special events in the restaurant. It should maintain a light-hearted and playful personality to ensure the nudges are perceived positively.
Feedback-Based Adjustments: Gather additional feedback to gauge customer reactions to these suggestions. This will help refine the system to avoid any unintended discomfort or resistance.
3. Anthropomorphizing the Restaurant:
Giving the restaurant a personality can add charm and create a unique customer experience. The AGI system could exhibit human-like emotions based on customer interactions, fostering a sense of connection.
An example might be the restaurant's "sadness" if too many customers are disengaged from their surroundings, encouraging them to reconnect with each other or the restaurant's ambiance.
Customer Reactions: Collect feedback to understand how customers respond to this concept. The aim is to create a fun and interactive experience without being intrusive.
Demographic Information
Before we dive into questions about your restaurant preferences, we'd like to learn a bit about you to understand the demographics of our respondents.
1. Age Group
Select the age group that applies to you. This helps us understand if certain age groups have different preferences or comfort levels with technology.
Under 18
18-24
25-34
35-44
45-54
55-64
65 or older
2. Gender
Please select your gender. This information helps us understand if preferences differ based on gender.
Male
Female
Non-binary
Prefer not to say
Other: ___________________________
3. Relationship Status
Are you single or married? This helps us understand if relationship status affects restaurant preferences or experiences.
Single
Married
Prefer not to say
4. Income Level
Select your income level. This helps us understand if income influences preferences for restaurant experiences.
Less than ₹5,00,000
₹5,00,000 - ₹10,00,000
₹10,00,000 - ₹20,00,000
₹20,00,000 - ₹50,00,000
More than ₹50,00,000
Prefer not to say
5. Geographic Location
Please write the city where you live. This information helps us understand if location affects restaurant preferences.
City: ___________________________
Now that we have some background, let's talk about your preferences and experiences in a restaurant setting.
1. Preferred Booking Method
How do you prefer to book a table at a restaurant? Knowing this helps us understand which methods are most convenient for you.
Phone call
Text message
Mobile app
Website
Email
2. Interaction with AGI Assistant
This question asks how comfortable you are with an artificial intelligence (AGI) system handling your table booking. It helps us determine if people are open to AI-assisted services.
Very comfortable
Somewhat comfortable
Neutral
Somewhat uncomfortable
Very uncomfortable
3. Preferred Interaction During Your Visit
What kind of interaction do you prefer when you visit a restaurant? This question helps us understand the level of interaction you'd like with an AI system during your dining experience.
Friendly greetings and personalized recommendations
Non-intrusive suggestions and reminders
Minimal interaction, just service-related communications
No interaction with technology during dining
4. Anthropomorphic Restaurant Experience
Would you enjoy a restaurant that has a "soul" or unique personality? This question gauges your interest in a restaurant environment that feels alive and engaging.
Love the idea
Like the idea
Neutral
Dislike the idea
Hate the idea
5. Use of AGI to Guide Behavior
Would you be okay with an AGI system providing subtle suggestions to encourage positive behavior? This could include recommendations to interact with others or explore the restaurant's unique features.
Yes, it sounds helpful
It depends on how it's implemented
No, I prefer to make my own choices
6. Unique Features
From the following list of features, which do you find most interesting? This helps us understand which aspects of an AGI-powered restaurant you'd like to experience.
AGI handling booking calls
Restaurant having a personality or being "alive"
AGI interacting with the crowd as a whole
AGI controlling music, lights, or other ambiance elements
AGI recommending activities or experiences within the restaurant
Other: ___________________________
7. Customer's Level of Technology Use
How often do you use technology while visiting a restaurant? This helps us understand your comfort level with technology during a dining experience.
Frequently, for ordering, payments, social media, etc.
Occasionally, to check messages or take photos
Rarely, I prefer to enjoy the dining experience without technology
Never, I avoid using technology at restaurants
8. Open-Ended Question
What unique features or experiences would you like to see in an AGI-powered restaurant? This is your chance to share creative ideas or suggest ways to improve the dining experience.
Date: 7th May
So I have floated the survey, So first lets review the response -
Working on -
a. After analyzing the data, summarize the key insights for each question.
b. Look for patterns or correlations, such as whether age, income, or location affects preferences.
c. Determine how these insights can inform the development of your AGI-driven restaurant concept.
d. Identify next steps based on the feedback, such as features to prioritize or new ideas to explore.
a.1. Key insights - 57 response
Location summary - Survey responses were dominated by metro cities, with Delhi leading (13), followed by Gurgaon and Bangalore (8 each), and Mumbai (4). Tier-2 cities collectively outperformed metros, with Agartala contributing the most responses (14). Tier-3 cities had lower representation, with Shillong, Pune, Hyderabad, and Bilaspur each providing one response. Agartala's high response rate stands out, while the metro areas indicate significant interest in the concept.
Age group survey - The survey responses were predominantly from the 25-34 age group, which made up the majority with 38 responses. The 35-44 age group was the second-largest, with 13 responses. Smaller numbers of respondents were from the under-18 category (3), the 18-24 group (2), and the 45-54 group (1). This suggests that the survey engaged a primarily young adult audience, with a significant focus on those in their late 20s to early 30s.
Gender - The gender distribution among survey respondents was fairly balanced, with 31 males and 26 females out of 57 total responses.
Relationship status - Out of 57 survey respondents, 27 were married, while 28 were single (including 2 who indicated "complicated"). This indicates a relatively even split between married and single respondents, with a slightly larger single demographic.
Income - Among 57 survey respondents, the majority (17) reported an income of 20-50 lac. The 10-20 lac range had 13 respondents, while the 0-5 lac and 5-10 lac ranges had 8 and 6 respondents, respectively. Only 1 respondent reported an income above 50 lac, with 12 choosing not to disclose. This indicates a varied income distribution, with most respondents in the middle-income bracket.
Current behaviour - Prefered booking: Among survey respondents, the preferred methods for booking a table at a restaurant were mobile apps and direct visits, each with 17 and 16 responses, respectively. Phone calls were equally popular with 16 respondents, while text messages and websites were less favored, with 4 responses each. This suggests a mix of traditional and digital preferences for booking, with a notable tilt toward mobile app-based reservations.
Acceptance sentiment - Interaction with AGI Assistant: With a PMI score of approximately 83.33 out of 100, there is a strong positive sentiment toward an AGI system handling table bookings in a restaurant. This high score suggests that most respondents are comfortable or neutral about AI-assisted services, indicating a positive reception to AGI integration. Minimal negative responses indicate that resistance to AGI in this context is likely low, suggesting a high likelihood of acceptance for the concept.
Feature: Customer Interaction via Phone Call
When a customer calls a restaurant, the AGI assistant answers to understand their needs and preferences. The AGI checks the restaurant's availability and provides options accordingly. It can also make a booking, create a timeline for follow-up, and even call back if the customer changes their mind or needs a reminder.
Enhancement Idea: The AGI could also suggest alternative times or other restaurants in case the first choice is unavailable, offering a smooth customer experience.
Crazy anecdote - A girl called khan chacha resturant cause she had just finished here another mistake i.e. watching horror movie when alone in the house. The best step for her, right now is to go to sleep. However, she is hungry. She calls khan chacha to order and has to wait 30 mins alone. Khan chacha talks to her the whole time.
Preferred Interaction During Your Visit: The survey results for preferred interaction during a restaurant visit indicate that the majority of respondents (27) prefer friendly greetings and personalized recommendations. Other preferences included minimal interaction, just service-related communications (16), and non-intrusive suggestions and reminders (5). A smaller group of respondents (3) preferred a combination of friendly greetings and non-intrusive suggestions, while single responses were given for friendly greetings with no technology interaction, minimal interaction with non-intrusive suggestions, and non-intrusive suggestions with no technology interaction. Overall, the most popular choice is friendly greetings and personalized recommendations, while a significant number prefer minimal interaction.
Overall planned bucket feature preference score is encouraging at 91%.
Derived unit: Feature Preference
67%
96%
100%
91%
67
45
64
67
61
Feedback & Feature preference mapping
Frequency
Customer Interaction via Phone Call
Customer Arrival and Table Allocation
Ordering, Payment, and History
AGI with Sensory Capabilities
Friendly greetings and personalized recommendations
33
1
1
1
1
Non-intrusive suggestions and reminders
12
1
1
1
1
Minimal interaction, just service-related communications
19
0
1
1
1
No interaction with technology during dining
3
0
0
1
-1
Note: The responses to the multiple-choice use case are organized by rows, and the features are grouped in columns. The values 1, 0, and -1 represent the balance of each feature with the feedback. For example, AGI with sensory capabilities could have a negative impact on interaction during dining (represented by -1), while a value of 1 indicates that AGI technology enhances friendly greetings and personalized recommendations.
Anthropomorphic Restaurant Experience: The PMI index for the anthropomorphic restaurant experience is approximately 86 out of 100. This high score suggests that most respondents are either positive or neutral towards a restaurant that feels "alive" or exhibits a unique personality. This indicates a strong potential for customer acceptance of an anthropomorphic concept, with very few respondents expressing a negative view.
Use of AGI to Guide Behavior: The PMI index for the use of AGI to guide behavior is approximately 69 out of 100, indicating a generally positive sentiment towards the idea of AGI providing subtle suggestions to encourage positive behavior. The majority of respondents (27) found the idea helpful, while 25 were neutral, depending on how it's implemented. Only 5 respondents were against the concept, preferring to make their own choices. This indicates that while there's a strong inclination towards AGI-driven behavior guidance, the implementation approach will play a crucial role in determining its acceptance.
Feature Preference
67%
39%
21%
100%
92
62
36
19
92
Feedback & Feature preference mapping
Frequency
Customer Interaction via Phone Call
Customer Arrival and Table Allocation
Ordering, Payment, and History
AGI with Sensory Capabilities
AGI handling booking calls
19
1
1
1
1
Restaurant having a personality or being "alive"
26
1
0
0
1
AGI interacting with the crowd as a whole
9
0
0
0
1
AGI controlling music, lights, or other ambiance elements
21
0
0
0
1
AGI recommending activities or experiences within the restaurant
17
1
1
0
1
a.2. Key insights - 122 response
Age group survey - The survey responses were predominantly from the 25-34 age group, which made up the majority with 84 responses. The 35-44 age group was the second-largest, with 24 responses. Smaller numbers of respondents were from the 18-24 category (7), the Under-18 group (5), and the 45-54 group (2). This suggests that the survey engaged a primarily young adult audience, with a significant focus on those in their late 20s to early 30s.
Gender - The gender distribution among survey respondents was somewhat imbalanced, with 76 males (62.3%) and 46 females (37.7%) out of 122 total responses.
Relationship Status - The relationship status distribution among survey respondents was as follows: 72 respondents (59.0%) were single (including 4 with complicated status), and 50 respondents (41.0%) were married, out of a total of 122 responses.
Income level - Among the 122 survey respondents, the majority (33) reported an income of 20-50 lac. The 10-20 lac range had 32 respondents, while the 0-5 lac and 5-10 lac ranges had 16 and 14 respondents, respectively. Only 3 respondents reported an income above 50 lac, with 24 choosing not to disclose. This indicates a varied income distribution, with most respondents in the middle-income bracket.
Location - Survey responses were dominated by Tier 1 cities, constituting approximately 70.5% of the total responses, with a notable presence of Gurgaon, Delhi, Bangalore, and Mumbai. In contrast, Tier 2 cities, accounting for about 24.6% of responses, featured cities like Agartala, Nagpur, and Vizag. Tier 3 cities, making up around 4.9% of responses, included locations such as Shamli, Khowai, and Dausa. Analyzing these city distributions can provide valuable insights into location-based preferences.
Current behaviour - Prefered booking - The data reveals that the highest portion of respondents, at 32.8%, favor using a mobile app to secure their reservations. Following closely behind, 27% of respondents opt for the traditional method of booking through a phone call. Direct visits to the restaurant for booking are also popular, with 25.4% of individuals preferring this approach. On the other hand, a smaller percentage, 7.4% each, choose to book via text message or through the restaurant's website.
Acceptance sentiment - Interaction with AGI Assistant:With a PMI score of approximately 82.3 out of 100, there is a strong positive sentiment toward an AGI system handling table bookings in a restaurant. This high score suggests that most respondents are comfortable or neutral about AI-assisted services, indicating a positive reception to AGI integration. Minimal negative responses indicate that resistance to AGI in this context is likely low, suggesting a high likelihood of acceptance for the concept.
Feature: Customer Interaction via Phone Call - The bot can assist in resolving the decision of where to go.
When a customer calls a restaurant, the AGI assistant answers to understand their needs and preferences. The AGI checks the restaurant's availability and provides options accordingly. It can also make a booking, create a timeline for follow-up, and even call back if the customer changes their mind or needs a reminder.
Enhancement Idea: The AGI could also suggest alternative times or other restaurants in case the first choice is unavailable, offering a smooth customer experience.
Crazy anecdote - A girl called khan chacha resturant cause she had just finished here another mistake i.e. watching horror movie when alone in the house. The best step for her, right now is to go to sleep. However, she is hungry. She calls khan chacha to order and has to wait 30 mins alone. Khan chacha talks to her the whole time.
Preferred Interaction During Your Visit: The table summarizes customer preferences regarding various features in a dining scenario:
1. Friendly greetings and personalized recommendations: Highly preferred (frequency 68)
2. Non-intrusive suggestions and reminders: Well-received (frequency 50)
4. No interaction with technology during dining: Least preferred (frequency 11)
This feedback mapping illustrates how these features align with different interaction types during dining, from phone calls to AGI with sensory capabilities.
Feature Preference
79%
93%
100%
85%
150
118
139
150
128
Feedback & Feature preference mapping
Frequency
Customer Interaction via Phone Call
Customer Arrival and Table Allocation
Ordering, Payment, and History
AGI with Sensory Capabilities
Friendly greetings and personalized recommendations
68
1
1
1
1
Non-intrusive suggestions and reminders
50
1
1
1
1
Minimal interaction, just service-related communications
21
0
1
1
1
No interaction with technology during dining
11
0
0
1
-1
Note: The responses to the multiple-choice use case are organized by rows, and the features are grouped in columns. The values 1, 0, and -1 represent the balance of each feature with the feedback. For example, AGI with sensory capabilities could have a negative impact on interaction during dining (represented by -1), while a value of 1 indicates that AGI technology enhances friendly greetings and personalized recommendations.
Anthropomorphic Restaurant Experience: The PMI index for the anthropomorphic restaurant experience is approximately 84 out of 100. This high score suggests that most respondents are either positive or neutral towards a restaurant that feels "alive" or exhibits a unique personality. This indicates a strong potential for customer acceptance of an anthropomorphic concept, with very few respondents expressing a negative view.
Use of AGI to Guide Behavior: :The total responses amount to 122, yielding a PMI Index of 66.39%, indicating an overall positive inclination towards utilizing AGI for offering subtle suggestions to encourage positive behaviors in dining settings. 62 respondents consider it dependent on implementation, 10 prefer autonomy, while 50 find it beneficial, endorsing subtle behavior suggestions for positive interactions or exploring restaurant features.
Unique Features: Customers seem to value features that enhance their dining experience and personalize their interactions with the restaurant. *Features related to ambiance and customer engagement are also highly sought after. *There is a small but significant minority of customers who are not interested in any of the offered features.
Feature Preference
67%
67%
23%
98%
196
132
132
46
192
Feedback & Feature preference mapping
Frequency
Customer Interaction via Phone Call
Customer Arrival and Table Allocation
Ordering, Payment, and History
AGI with Sensory Capabilities
AGI handling booking calls
48
1
1
1
1
Restaurant having a personality or being "alive"
43
1
1
0
1
AGI interacting with the crowd as a whole
13
0
0
0
1
AGI controlling music, lights, or other ambiance elements
47
0
0
0
1
AGI recommending activities or experiences within the restaurant
43
1
1
0
1
Other (Extreme negative)
2
-1
-1
-1
-1
Customer's Level of Technology Use: A significant majority of customers (62%) are comfortable using technology during their restaurant visits. *A small minority (4%) actively avoid using technology at restaurants. *The remaining customers fall into two groups: occasional users (22%) and rare users (12%).
Restaurants can cater to the needs of "Frequently" users by providing seamless technology-driven experiences for ordering, payments, and entertainment. *For "Never" users, restaurants can offer alternative non-technology-based options for tasks like ordering and payments. *"Occasionally" and "Rarely" users can be provided with optional technology-based features that they can utilize as per their comfort level.
Open-ended suggestion analysis -
Thematic Analysis: Grouping responses into themes or categories based on commonalities. This can help in understanding the key topics that emerge from the data.
Sentiment Analysis: Identifying the sentiment or emotional tone of the responses (e.g., positive, negative, neutral). This can help in gauging overall satisfaction or dissatisfaction.
Word Clouds: Creating visual representations where words are sized based on their frequency in the responses. This can give a quick overview of common themes.
Text Mining: Using algorithms to extract insights from the text data, such as identifying keywords, patterns, or trends.
Qualitative Coding: Applying codes to responses based on their content. This method requires human interpretation but can provide rich insights.
Machine Learning: Utilizing machine learning algorithms to analyze and categorize responses automatically based on patterns in the data.
