There has been a trending preference for self-service when it comes to service and support. It’s no wonder why offering great self-service has become the single biggest priority for support leaders around the globe. Case deflection rate (CDR), is a critical metric for self-service success and organizations everywhere are wondering how a higher CDR can be achieved. Regardless of the technologies that power your self-service sites and communities today, the hope for improved case deflection and self-service success can become reality by following our 5 best practices.
#1 Identify your self-service content across all enterprise sources and channels and unify search across all of it.
Rarely will the information people need to self-serve reside in a single repository. Realistically, it is stored and shared across various apps, platforms and databases, both on-premise and in the cloud. By determining where your information resides and allowing your customers to access it with ease, in a unified and intuitive way, they will be empowered to self-serve and will have greater success. Valuable case-deflecting information can be derived from:
- Knowledge Articles
- Product Documentation
- Updates/New Versions
- Patches/Bug Fixes
#2 Make self-service search prominent and mobile-ready.
Not surprisingly, search is one of the most popular ways customers look for answers. With that said, it’s important to have a prominent search box displayed on every page of your community or self-service portal site. As such, your customers will know that you are equipped to connect them with the answers they need, independently and conveniently.
It is important that your search functionality is optimized for every device your customers might use. An intuitive, people-friendly search experience – on desktops, tablets and mobile devices – will encourage customers to engage and refer your product or service to their network.
#3 Offer proactive insights.
Several customer attributes can be used to recommend contextually relevant helpful content to each customer before (or while) they ask a question, that might help solve their issue. These attributes include:
- Product(s) owned
- Support plan entitlement(s)
- Role (for employee self-service)
- In-bound Search Query
Offering these proactive insights and recommendations throughout the customer journey on your self-service site is ideal. However, it is absolutely essential to provide such insights at specific stages of their journey, like when they start typing in the search box and, most importantly, as they start to create a new support case on the site.
#4 Identify and resolve self-service content gaps with usage analytics.
Unifying search across all self-service content makes it easy to monitor search activity. Self-service search analytics empower support managers with a magnitude of intelligence about how customers look for help, how successful they are, where they struggle, and why.
With the right solution in place, managers can:
- Identify self-service content gaps across products, geo-regions, and audiences
- Analyze customer journeys by geography, product, entitlement – and ultimate self-service outcome (case deflection/creation, conversion…)
- Spot emerging support issues (based on the terms customers use to look), to quickly author responsive content and avoid a flurry of calls to the content center
- A/B test self-service content for its ability to help customers resolve any issue or challenge
#5 Use machine learning to continually improve search results and predict relevant content.
It’s possible for your site to actually learn from its users, automatically tune itself and continually improve self-service success over time by tracking what is making users successful. Going through this process manually requires a lot of work, and it is nearly impossible to stay on top of the always-evolving trends of your users in a scalable way.
The best solutions integrate machine learning capabilities to analyze how each user looks for help, what they viewed, and whether or not they were able to successfully self-serve. It is then able to:
- Auto-tune search results, to deliver precisely what helped others with the same challenge
- Surface proactive insights that have actually prevented case creation for prior users with the same issue
- Recommend helpful as-you-type query suggestions that have proven most effective for previous visitors
Delivering Intelligent Machine Learning was the mission that drove the development of Coveo Machine-Learning. Coveo Machine-Learning is a machine-learning service that continually analyzes how visitors are searching for information, and learns precisely what content drives self-service for every user and every issue. Coveo Machine-Learning tracks what users search for, what facets and filters they click, what results they click on, and whether or not they created a new support case.
By following these 5 best practices, your organization can deliver the self-service your customers demand. Fortunately, bringing these capabilities to your existing self-service platforms and apps is easier than you might think, with Coveo.
To learn more about making self-service success and case deflection a reality, download our ebook, Case Deflection & Self-Service Success, Proven Approaches to Reducing Customer Service Case Volume & Improving the Customer Experience