The One Transformational Thing That Contact Centers Can Learn from Uber and Lyft



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

Going back to Uber and Lyft, I started thinking about why they can 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 with which contact center agents often struggle:

  • Performance consistency: The variance of performance among Uber and Lyft drivers in getting from Place A to Place B is minuscule compared to a typical contact center. In my specific case, their ability to get me to my destination has been 100 percent. 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 required, best practice to get customer service in other industries. As a Forrester survey of 5,000 consumers revealed, inconsistency of answers among agents is a major deterrent to good CX.
  • Multi-skilling: Though there might be a few exceptions, any Uber or Lyft driver can take you to any location in any city or even 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.
  • Scaling on demand: Uber's and Lyft'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 driverinstantly 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 this 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 store associates. Moreover, it had four disparate knowledge bases across business units. To address the problem, it first consolidated all the knowledge into our knowledge base and added a layer of artificial intelligence-enabled conversational guidance on top. This new solution was then implemented across 10,000 agents and 550 retail stores. The company saw a 23 percent improvement in first-contact resolution (FCR), 100 percent improvement in agent speed to competency, and 30 percent 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, it went from #3 to #1 in NPS for agent knowledgeability and service availability, while reducing training time from eight weeks to four weeks and agent churn to 1 percent. Moreover, any of its agents can handle any call, achieving Uber-like any driver, any location skill parity.
  • Elasticity: 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 examples of seasonal spikes. Event-based service spikes could be triggered by new product launches, natural disasters, and the like.

Demand for drivers could be based on ups and downs as well. 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 (though self-driving Uber and Lyft cars have yet to arrive!). Where agents need to be involved, these companies scale by speeding up training time and accelerating resolution by guiding agents with AI reasoning.

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!


Anand Subramaniam is senior vice president of global marketing at eGain.


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