Self-service has an inherent flaw: Effort. What would you do with all of the time and energy you’d save if you didn’t have to stop working to diagnose and resolve issues? The next evolution of self-service shifts that effort from customers and technical support staff to proactive technologies.
Today, self-service requires a lot of effort
Picture yourself as a system administrator managing an enterprise-wide application. A business leader contacts you to report concerns that have been impacting the productivity of his staff for a week. This sets off a time-consuming process of investigation and diagnostics. You could search a vendor’s support web site to find articles related to your condition or post to a community forum, but if all else fails, you’re calling the vendor for help. Depending on the complexity of the application and problem, this can take hours, days, or weeks of work. Work that takes you away from more important activities. Meanwhile, the business leader who’s having the issue is losing money, and becoming frustrated with you and the vendor. Value erosion is growing by the minute as productivity is lost.
…But there’s a brighter future
In the next generation of self-service, you’re still that system administrator. You wake up in the morning and an app on your phone tells you that the enterprise system is experiencing performance degradation. The app sends you a knowledge base article with details on the problem and lets you know that a self-healing fix is expected to resolve the issue within the hour. A Service Request has already been opened for follow-up and the system will report back at the end of the day if the fix has been successful. Since the performance degradation has been identified before it becomes a truly impacting problem, the problem will resolve itself before the business leaders lose any productivity. The system itself is doing the heavy lifting, so instead of performing manual diagnostics or researching fixes, you can finish your first cup of coffee.
Internet of Things technologies, data analytics/machine learning, and robust knowledge capture best practices are making a new proactive support engagement model a reality, transforming the technology support industry. In this new proactive model, the “voice of the product” will be the communication mechanism to vendors and manufacturers. Rich data streams will provide real-time information about assets. Analytics and machine learning technologies will identify immediate problems, trends, and patterns to knowledge in the form of recommendations, solutions, benchmarks, and self-healing fixes.
When resolutions are not already known, proactive service requests will be initiated, kicking off an investigation by the vendor’s support staff, and notifying the customer of its progress. All of this value can be delivered back to the asset or customer proactively through any number of communication channels. A loop that automatically identifies a problem, matches it to a solution or service request, and provides that solution with little to no human interaction is now complete.
This proactive support experience becomes a competitive advantage for the customer through dramatic productivity and product efficiency gains. Machine to machine communication and auto correction are becoming a reality and will be the backbone of tomorrow’s support organization.
On March 31, I’ll a part of an expert panel featuring Jordi Torras, Founder and CEO of Inbenta, and Melissa Burch, Knowledge Strategist with Irrevo, for a webinar titled Preparing for the Next Evolution of Self-Service. Join us for a deeper dive into how self-service will evolve, and hear actionable strategies that will help you future-proof your self-service experience.