Personalised User Experience: Conversational AI that increases your brand reputation and saves you money
How conversational AI can help
ENTRY LEVEL: Out of Hours Conversational AI Agent Problem
1.
Problem: Generic, one-size-fits-all interactions often fail to adequately meet the diverse and evolving needs of different users, resulting in disengagement and frustration.
2.
Solution: Conversational AI personalises interactions by understanding user preferences, history, and context, delivering tailored recommendations.
Netflix deployed AI to enhance personalising viewing recommendations
- Impact of $1 Billion in additional revenue.
- Companies typically see from 20% - 82% gain in customer satisfaction from personalised engagement.
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2024
2024 Conversational AI created
20 %
Customer Satisfaction Gain 2024
82 %
Highest Increases in Customer Satisfaction
90 %
Businesses using Conversational AI by 2026
Business Benefits
10 ways Conversational AI can benefit your business with greater personalisation
Understanding User Context
Challenge: Generic interactions fail to address user-specific needs.
Solution: Conversational AI uses natural language processing (NLP) and machine learning (ML) to analyze user input, behavior, and history, providing contextually relevant responses.
Custom Recommendations
Challenge: Users are overwhelmed by too many irrelevant options.
Solution: AI analyses user preferences, past interactions, and data (like purchase history or browsing patterns) to offer personalised recommendations for products, services, or content.
Proactive Engagement
Challenge: Businesses often react to user queries instead of anticipating needs.
Solution: AI proactively engages users with personalised notifications, reminders, or offers, such as abandoned cart reminders in e-commerce or tailored workout plans in fitness apps.
User Profiling
Challenge: Providing personalised experiences requires understanding individual preferences and habits.
Solution: Conversational AI builds detailed user profiles by collecting and analysing data over time, enabling hyper-personalised interactions.
Multilingual and Cultural Adaptation
Challenge: Language and cultural barriers can hinder user experiences.
Solution: AI supports multilingual communication and adapts interactions to cultural nuances, ensuring users feel understood and valued.
Proactive Assistance
Challenge: Users may not always articulate their needs clearly.
Solution: AI predicts what users might need based on patterns, offering suggestions before they even ask (e.g., predicting food preferences in a delivery app).
Adaptive Learning
Challenge: User preferences and behaviours change over time.
Solution: AI continuously learns and evolves based on new data, ensuring it stays aligned with user preferences.
Emotional Intelligence
Challenge: Flat, emotionless interactions can feel impersonal.
Solution: Advanced AI systems detect sentiment and tone, adjusting responses to empathise with users, whether they're frustrated, excited, or confused.
Cross-Channel Consistency
Challenge: Users interact across multiple platforms, and inconsistent experiences can be frustrating.
Solution: Conversational AI maintains consistency across channels (e.g., mobile apps, websites, and social media), ensuring personalised interactions follow the user seamlessly.
Dynamic Responses
Challenge: Static, one-size-fits-all replies can frustrate users.
Solution: Conversational AI adapts responses based on the user's tone, intent, and previous interactions, making conversations feel more human-like and relevant.