Ada Value Driver: Drive Cost Effective Resolutions

VALUE DRIVER: DRIVE COST EFFECTIVE RESOLUTIONS
Description: Use people, processes, and technology for better, cost-effective outcomes

Before Scenarios:

• High builder effort (implementation & maintenance) required to address common FAQs
• Lack of visibility into how to improve the quality of the conversations you are automating
• Not enough agents in seat to service day-to-day support volume
• Reactive agent hiring or reliance on BPOs leads to lengthy and expensive ramp times
• Customers are not aware of / do not trust / avoid self-service offerings
• Reliance on technical teams limits CX’s ability to efficiently own the customer experience
• Agents require too many touchpoints to resolve a customer’s issue

After Scenarios: 

• Resolution of inquiries with less reliance on builder implementation and maintenance
• CX teams are empowered to build, measure, and improve processes rapidly and autonomously
• Effortlessly scale service levels up and down without investment in headcount (good-bye to BPOs)
• Utilize AI-driven automation to redefine the role of human-labor
• Customers are more inclined to leverage low-cost automation channels 
• CX has full ownership over customer experience outcomes
• Agents are assigned to higher value activities that drive advocacy

Negative Consequences: 

• Expensive to scale service levels up or down
• Higher cost per resolution leading to an increased cost to serve 
• Higher customer acquisition costs
• High human capital costs on in-house and/or BPO resources
• Wasted time and budget on underutilized self-service offerings
• Expensive automation development costs
• Limited control/influence leads to missed outcomes, burnout and turnover

Positive Business Outcomes:

• Reduced dependency on ineffective or costly BPOs 
• Controllable costs as support levels scale up and down 
• Improved gross margin and agent efficiency
• Reallocation of human capital to high-value projects
• Savings in support can be re-invested into executing projects to continue to improve AI’s impact 
• Lower total cost of ownership (people costs / operating costs / technology costs)

Customer Examples Proof Points:

• Mailchimp | Problem: Mailchimp was an early adopter of AR% measurement and they were looking to identify quick improvements to increase their AR% from 31.9%. | Solution: Ada identified three specific opportunities to improve AR% through AI Insights capabilities. The opportunities focused on broadening integrations, reducing handoffs, and deepening the context of specific answers. | Result: In one example, simple updates to an Account and Billing automation presented an opportunity for a 6% basis point improvement in AR% and over $3500 in monthly savings.
• Shapermint | Problem: Grew from 0 to 4 million customers in 2 years and needed to quickly scale their 1:1 customer experience. | Solution: Designed and deployed an AI-powered chatbot in less than one month to automate basic customer support inquiries and elevate agents to drive upsell/cross sell revenue. | Result: Because average handle time (AHT) was reduced to 20 seconds, live agents could focus on revenue, generating a 50% increase in sales from the previous year. This earned them enough savings to hire twenty additional agents. 
• BFA | Problem: Agents were missing context to resolve customer issues appropriately and efficiently, leading to longer than average wait times. | Solution: Integrated Ada into their existing tech stack to provide agents with the information they needed to offer a more personalized and consistent experience. | Result: This led to a 93% reduction in average handle time (AHT), 64% improvement in average resolution time, and a $2.7M reduction in annual spend.
• AirAsia | Problem: Fully reliant on live agent support, working within local hours and languages - leading to an average customer wait time of 45 minutes. | Solution: Built and launched a 24/7 multilingual chatbot ready to assist customers instantly across the brand’s website and mobile application. | Result: Within four weeks, Air Asia was able to solve 75% of support inquiries without a live agent and reduce average customer wait time by 98%.

Discovery Questions:

• Before Scenarios/Negative Consequences:
  • Walk me through your experience building or maintaining automation programs …
  • Tell me about some of your top support case drivers …
  • Describe how you balance the cost to serve, with exceptional customer experience …
  • When demand peaks, walk me through what happens within your service model …
  • Describe the effectiveness of your current self-service assets …
  • Describe IT’s involvement when building or maintaining your current program.
  • Describe an agent’s process and systems workflow when delivering a support resolution.
  • Explain how productivity and efficiency are measured within your group …
• After Scenarios/Positive Business Outcomes:
  • Describe a world where you were able to prove quick time to value … 
  • Tell me about your plans to measure and improve your automation efforts?
  • Describe a scenario where you were less dependent on BPOs … 
  • Where do you see your team in terms of pioneering AI technology?
  • Describe a world that would enable greater adoption of self-serve offerings …
  • With CX empowered to control people, process, and technology what impact would that have?
  • Describe how you would like your agents spending their time …

Sales Play in Highspot here: https://ada.highspot.com/items/63da1b1041d1b12ca6e27467?lfrm=rhp.0