Ada Defensible Differentiator: Industry-Leading AI

Description:

• Pioneer an AI-First strategy within your CX Organization

How it Works:

• Generative AI capabilities (LLMs) to prove quick time to value and continuous improvement 
  • Generate common FAQ answers live, grounded in your knowledge base
  • Identify opportunities to optimize the knowledge base to unleash the full potential of Generative AI
• AI-Driven annotation that ensure accuracy, relevance and safety of responses
  • Relevant: Ada effectively understands the customer’s inquiry, and provides directly related information or assistance
  • Accurate: Ada provides correct, up-to-date information with respect to the company’s knowledge and policies
  • Safe: Ada interacts with the customer in a respectful manner and avoids engaging in topics that cause danger or harm
• Industry leading investment in AI since inception (patents, talent, etc.)
• Global, local and industry specific NLU models for top performing recognition rates
• Machine learning surfaces actionable insights to improve customer experience

Customer Value:

Increase Revenue: 

• Uncover new revenue opportunities with AI-driven customer behavior insights
• AI-First strategy results in freed up agent time to pursue advocacy
• AI-First strategy results in a better CX which drives customer loyalty and increases retention

Reduce Cost: 

• Generative AI reduces time to value and the total cost of ownership 
• AI driven insights and suggestions reduce time spent on manual maintenance and improvement
• Automated resolution of customer issues which drives lower cost per conversation

Reduce Risk:

• Minimize response time to critical and unforeseen events
• Reduce the likelihood of a potentially harmful AI generated response

Customer Examples and Defensibility:

• Wealthsimple | Problem: Wealthsimple was looking for a way to scale their customer support during peak seasons (taxes) without compromising on the CX experience, accuracy and the brand standards. | Solution: Unleashed the power of their KB by upgrading to a ully generative experience. | Result: The upgraded generative experience increased AR% from 15% to 62%.
• Leading UK Satellite Pay Television Company | Problem: With their previous chatbot supplier, each customer intent needed to be trained 8K times. | Solution: Ada smart training capabilities allowed the brand to train a customer intent with only 10 phrases. With freed up resources, they are expanding beyond an FAQ bot and enhancing their data collection and personalization. | Result: Ada is helping the brand automate top FAQ drivers, decrease agent handle time, and improve support team processes after they struggled with managing their prior enterprise bots.
• Moka | Problem: High number of general queries monopolized support bandwidth. | Solution: Positioned the chatbot at the top of the support funnel, offering both self-serve and routing options for visitors. They used APIs to automate ticket submission directly from the bot. | Result: Automated repetitive support requests to free agent hours, resulting in a 75% customer engagement rate, 95% recognition rate, and 41% of interactions resolved within the bot. | Quote: “Ada has freed some precious hours of our agents' time, who can now focus on more complex cases and deliver the best support experience possible to our users.” - Head of Customer Success
• BFA | Problem: When ecommerce brands IPSY and BoxyCharm merged, they were using different AI support solutions to manage product, subscription, and delivery inquiries. | Solution: Consolidated AI solutions under Ada to create a seamless customer experience for their subscribers and established an efficient handoff to a live support agent. | Result: 93% reduction in average first response time, 64% reduction in total resolution time, CSAT up from 58% to 76% , and $2.7M USD in estimated annual savings. | Quote: “Our agents are intelligently powered by the information they’re getting from Ada. Their job is easier and more impactful now.” - Vice President of Customer Care

Trap Setting Questions:

• Trap = Measuring AND Defining the Quality of Resolution
  • Set Topic: Have you ever been stuck in an endless loop in a chatbot? Describe what you did as a result?
  • Open Trap: For our competitors that would be considered a win, would you agree?
  • Close Trap: How valuable would it be to measure if an inquiry was resolved through automation?

• Trap = Measuring the Quality of Resolution
  • Set Topic: Describe how you would ensure the quality of your automated conversations…
  • Open Trap: How do you ensure that you have full line of sight into the quality of all your automated conversations, regardless of receiving customer feedback?
  • Close Trap: If resolution could be determined without human effort, how would that impact your program?

• Trap = Defining the Quality of Resolution
  • Set Topic: How would you define a resolution?
  • Open Trap: In scenarios where a human is not involved, how do you ensure the automation provided accurate and up-to-date information that was relevant to the inquiry and delivered in a respectful manner?
  • Close Trap: How would that level of insight help to bring clarity to agentless customer experiences?

• Trap = Taking the Right Approach (Generative or Declarative)
  • Set Topic: Describe some use cases that either require more control or action from your agents …
  • Open Trap: When a generative approach is not an option, how do you ensure both a low effort build and high quality customer experience?
  • Close Trap: Would it be helpful to still leverage proven alternatives to the generative approach?

For more information check out this sales play: https://ada.highspot.com/items/63eacef76541a717e2aadb0b?lfrm=shp.0