We live in a do-it-yourself world, and it seems we all really like it that way. Whether as customers, as partners, or as employees, we prefer to find what we need when we need it, then get on with our lives. There are plenty of stats supporting the preference for self-service, and when 34% of millennials say they’d rather get their teeth cleaned than call a customer service line, you know the popularity of self-service will continue to rise. So, it’s no surprise that companies today are committing to building great self-service portals and online communities and are achieving self-service success with intelligent insights.
The ability to self-serve makes customers happier, and it has the additional bonus of reducing the volume of cases and calls to the contact center. This win-win proposition has made self-service success a top priority for support leaders across the globe.
No matter how well intended, a support site can’t enable self-service unless it delivers answers that are actually relevant to each customer’s need.
In order to effortlessly deliver relevant results, you must enable your customers and partners to easily search across your entire ecosystem of record. In so doing, you are empowering them with all of the content that can help them self-serve, whatever it is and wherever it resides, YouTube videos, documents in a SharePoint site, KM articles in a legacy database, you name it. Your customers and partners need access to it all, directly from your self-service site.
But how do you make sure every visitor is always getting precisely what they need, every time?
Use Machine-Learning to drive relevance.
You need to leverage the intelligence of your community members by using machine-learning technology to analyze precisely what each visitor is trying to resolve on your site, how they look for answers, and what content ultimately helps them succeed. With this data, a well designed machine-learning solution can continually and automatically fine-tune the relevance of your community search experiences.
Bringing machine-learning intelligence to self-service sites was the mission that drove the development of Coveo Coveo Machine-Learning. Coveo 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.
With this data, Coveo Machine-Learning learns from the activity on your self-service site and can predict which content will be most helpful to future users based upon their specific query.
It’s like having a data scientist in a box, and it makes support portals better in three powerful ways:
#1 Continually Tunes Results for Self-Service Success
Coveo Machine-Learning not only monitors what each visitor clicks and views on your community, but also whether they were actually successful. Coveo Machine-Learning capabilities impressive insights gives you greater visibility and a deeper understanding of user engagement. With it, your organization can see if your customer started creating a new case, but then abandoned the process, or if they left the community directly after reading a particular article or document. These are all critical measurements and are signals that your customers successfully self-served. Furthermore, Coveo Machine-Learning learns precisely what content drives self-service for every issue and query, and automatically fine-tunes the relevance engine so future visitors will find more successful content even faster.
#2 Makes Self-Service Rapidly Adaptable to New Issues & Trends
Coveo Machine-Learning continuously analyzes community activity to predict what content and information will help future visitors self-serve. This means it will automatically identify any changes in user behavior and respond accordingly. For example, say an electronics company sends out an update to one of its Smart TV models, and the update has a bug that leaves its TVs incapable of streaming Netflix content. On the company’s community there is a knowledgebase article that explains how customers can fix the problem, and the customers who visit the community and find this article are able to fix the problem on their own. Those who can’t end up creating a ticket or calling the company’s contact center. Coveo Machine-Learning will identify that this knowledgebase article is driving self-service, and automatically boost it higher in the search results for future users with the same issue.
#3 Makes Community Managers’ Jobs Easier
Coveo Machine-Learning automatically tunes relevance for support portals, meaning administrators no longer have to manually tune search. With no need to manually review search analytics data, boost queries, or manage synonyms, community managers can focus on more strategic, less tedious tasks.
Relevance is the jet-fuel of self-service. If your organization is committed to helping your customers, partners, and employees self-serve, consider bringing unified, intelligent search powered by machine-learning to your support site. Leverage the intelligence of your crowd to deliver a self-service experience that truly empowers your users.