With thousands of brands evaluating their options for implementing chatbots and intelligent virtual assistants it is clear that “Conversational Commerce” is no longer about platitudes or vague concepts; it’s about money. As one of the speakers at Opus Research’s recent Conversational Commerce Conference observed, we’re way past proof of concept and into the critical paths of Marketing, Sales and Customer Support, observing “it’s gone from ego to production in record time.”

Conversational Commerce, as a whole, is not easy. In fact, it’s extremely difficult. With elements of natural language processing, intent recognition, knowledge management, and machine learning, building automated, conversational experiences for customers, prospects and employees is a tall order for any-sized business. Combined with a mandate for “digital transformation” and “mass, personalized customer journeys,” it’s no wonder brands and enterprises are starved for data and guidance on how to build Intelligent Assistance strategies.

Even more, companies are less interested in theoretical pontifications about “what is a conversation?” or “how to create a ‘customer journey’” — they just want results. They want data. They want to know that it makes business sense. They want to know that it can save and/or make money and build operational efficiencies at the same time. Sure, improving the customer experience can have tremendous business upside, but if firms are going to spend the time, resources, and opportunity costs in a proper chatbot or intelligent assistant, it better show progress.

In our experience at Opus, and working with firms who have deployed intelligent assistants, virtual agents and flavors of Conversational Commerce, we’ve found consistent attributes for success. First, identify a proper use case and map towards organizational goals. Understand and evaluate the vendor landscape to determine what might work best for specific needs. Also, set appropriate expectations to make sure technologies and architectures deliver on the promises offered. Finally, be sure to develop pet metrics to determine success.

On that last point, Opus Research is planning to publish an upcoming report, “Conversational Benchmarks and KPI’s: New Methods and Metrics,” taking a look at performance metrics and emerging key performance indicators (KPIs) for bringing “Conversational AI” into customer experiences.

In the spirit of looking forward, here are a few trends I expect to take shape in 2019:

• Employee Productivity & Job Assistants – For a while now, Opus has been tracking how NLP and conversational technologies are fundamentally changing the workplace. By leveraging AI, many businesses are building operational efficiencies and gleaning business insights with intelligent assistants to redefine CRM and sales technology strategies (Tact.ai, Vymo), digitize human workflows (Thoughtonomy), employee collaboration (Voicera), improve scheduling, and enable HR and talent acquisition (IBM). In effort to operationalize worker productivity, conversational technologies will make people better at their jobs.

• Fighting Fraud with Intelligent Authentication – New authentication technologies in personalized digital self-service can help deliver security, fraud prevention, and have a demonstrable impact on an enterprise’s bottom line. As longstanding observers of voice biometrics and intelligent authentication, Opus sees zero-effort authentication as an enabler for automation, reduced customer effort (no more PINs and passwords), reduced call handling time, and improved confidence in security. Practical-thinking enterprises will embed intelligent authentication to help prevent fraudsters from gaining access to valuable data and increase customer loyalty with pleasant, seamless customer interactions.

• Humans in the Loop – While bots will increasingly improve business operations, we don’t see humans being replaced en masse anytime soon. Certainly when it comes to customer service and customer care, agents are clearly important in training and powering virtual agents to be consistent and successful. As Dan Miller mentioned in his post about “Five Guiding Principles for Conversational Commerce” practitioners need to “support the goals of customer care employees and create new roles for employees in the creation and refinement of IA offerings.” We’ll see improvements in “hybrid automation,” the combination of automated digital assistance and employee-guided assistance, to increase the speed and accuracy of data gathering and populating tasks, freeing up humans to handle more complex situations.

• What’s Next for Conversational Analytics – Conversational data is a gold mine of information as it gathers metadata from voice and chat interactions. By getting a sense of how users interact with chatbots, intelligent assistants, and “VoiceFirst” devices, brands learn about consumers in their own words. This information, gathered in real-time, is critical for tweaking and optimizing automated conversational experiences, but does require strict opt-in transparency for data-sharing and privacy considerations.

Categories: Conversational Intelligence, Intelligent Assistants, Intelligent Authentication, Articles, Mobile + Location