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What Contact Centers Can Learn From Uber and Lyft to Perform and Scale

The One Thing

I was not exactly an early adopter of Uber and Lyft services, but have since been using them regularly. I have to say that my customer service experience with them has been better by a mile (or two) than with the businesses in other industries. Industry analysts have been tracking the CX performance of the latter—just look at the Forrester CX Index data of the past three years!

GPS guides the driver (agent) to the right destination (answer)

Going back to Uber and Lyft, I started thinking about why they are able to deliver a far more effective, efficient, and consistent experience. The one transformational enabler that their drivers use is GPS guidance in getting to the customer’s destination. Here is what the consistent use of guidance enables Uber and Lyft drivers to do that contact center agents often struggle with.

1. Performance consistency

Compared to a typical contact center, the variance of performance among Uber and Lyft drivers in terms of getting from Place A to Place B is minuscule. In my specific case, their ability to get me to my destination has been 100%. The one time I had to help with directions, not surprisingly, the driver was not using GPS! By contrast, answer shopping is often an undesirable, yet rewarded, best practice to get customer service in other industries. As a Forrester survey of 5000 consumers revealed, inconsistency of answers among agents is a major deterrent to good CX.

2. Multiskilling

Though there may be a few exceptions, any Uber driver can take you to any location in any city, including in outlying, unfamiliar suburbs! Driver guidance is what enables this capability. By contrast, while contact centers would like any agent to handle any call, they spend considerable effort and money in developing complex algorithms to route the “right” customer queries to the “right” agents for a desirable outcome. Again, the results have been limited at best, according to survey after survey.

3. Scaling on demand

Uber’s resourcing model allows them to scale their driver pool up and down easily without affecting performance. Guidance is what makes it possible since almost any new driver can go from “zero to driver” instantly with GPS guidance! By contrast, contact centers find this to be a challenge. Seasonal agents struggle even more than regular employees to answer customer questions, often causing irreversible damage to their brands.

How does it apply to contact centers?

What does guidance exactly mean for contact centers? It is the consistent use of knowledge management and technologies such as AI reasoning to provide step-by-step conversational guidance to agents when the customer is on the line. Done right, these technologies, combined with best practices and the right incentives, can transform your contact center. Here are some examples from our clientele.

Performance consistency

A telecom giant in Europe had high performance variations across agents in its contact center and among its store associates. Moreover, they had four disparate knowledge bases (KB) across business units. To address the problem, they first consolidated all the KBs into our knowledge base and added a layer of AI-enabled conversational guidance on top. This new solution was then implemented across 10,000 agents and 600 retail stores.

Results? It saw a 23% improvement in First-Contact Resolution (FCR), 100% improvement in agent speed to competency, and 30% improvement in Net Promoter Score (NPS)!

Multiskilling

A multinational banking giant wanted to expand market share and improve contact center agent performance, while enabling all agents to take all calls.

With consistent use of AI reasoning and knowledge management, they went from #3 to #1 in NPS for agent knowledgeability and service availability, while reducing training time from 8 weeks to 4 weeks and agent churn to 1%. Moreover, any of their agents can handle any call, achieving Uber-like “any driver, any location” skill parity!

Elasticity or Scaling on demand

Contact centers in many industries need to be elastic, i.e., they need to be able to scale up or down, depending on seasonality or ad hoc events. Holiday season in retail, open enrollment in health insurance, tax filing season in the tax payer services, and elections in the government sectors are some seasonal spikes. Event-based service spikes could be triggered by new product launches, natural disasters and the like.

Uber and Lyft are able to scale their workforce up or down with their hiring strategy and rapid time to driver competency, enabled by GPS guidance. Our clients across retail, telco, government, and consumer services are doing the same by deflecting users to AI-enabled self-service guidance, with or without a virtual assistant. Where agents need to be involved, these companies scale by speeding up training time and accelerating resolution by guiding agents with AI reasoning.

Conclusion

While the concept of guidance is so obvious to get drivers to perform effectively, efficiently, and consistently, it is not yet so in the case of contact centers. However, the golden age of AI for contact center customer service is here, and it can lyft all contact centers to uber-high performance levels, pun intended!