Providing high-quality and timely support is key to establishing and maintaining a strong relationship with your customers, whether they are consumers or your business partners. Making that support readily available, relevant and accessible not only builds trust and brand loyalty, but also increases the likelihood that your customers will buy more of your products and services as well as recommend your company’s offerings to their peers.
Organizations also recognize that most customers and partners would prefer to resolve their first-line support issues themselves, provided they can do so quickly and easily.
According to research and advisory firm, the Technology Services Industry Association (TSIA), the second most-popular preferred channel for product support after Google is self-service. In its 2018 TSIA Support Services Benchmark survey of its member organizations, the firm found that 80% of respondents are using self-service up from 61% of those surveyed in 2017.
Freeing up support staff from continually dealing with the same sort of low-level queries over and over again means that your company can empower and focus those employees on identifying, fielding and resolving more out-of-the-ordinary and complex support cases.
Released from overly repetitive and monotonous daily tasks, support staff are more likely to stay engaged, and in turn, will remain with their current employer who can leverage their growing high-level support expertise.
In the TSIA 2018 survey of its members, in organizations with strong self-service, employee satisfaction is at 82% versus their peers with weak self-service where employee satisfaction only sits at 67%. With regard to employee attrition, in the strong self-service organizations, attrition is at only 12% compared to weak self-service organizations where attrition is at 21%.
Making the Case for Investing in AI-Powered Search and Relevance
Given the positive impact on both customers and support staff, it’s clear why organizations would look to continually improve the level of the support they provide.
However, as companies start to reimagine their support experience, they often tend to overlook or discount the vital role played by search and relevance technology. In some cases, organizations lack a clear business case to justify how investing in a sophisticated search and relevance platform, which leverages artificial intelligence and machine learning, can benefit all three of the communities they’re looking to better serve — customers, partners and employees.
Search may often be an afterthought but it is the main way in which we navigate our digital and physical worlds. The first thing most people do when they encounter any kind of problem is to turn to their search engine of choice and type in their query on their computer, smartphone or other device. Their assumption is that they will then receive immediate, relevant and actionable results. This is our standard operating procedure in how we solve problems which is why it is so critical for organizations to put search and relevance at the core of their support strategy.
In Search of Instant Resolution
What your customers are expecting when they search for support help on your organization’s digital properties -- be it your own site, customer community or customer portal -- is that a single search will result in the instant assistance and resolution that they require.
However, your company may struggle to deliver that kind of immediate self-service gratification if you’re not providing a unified search experience which brings together all relevant information.
Just consider all of the different content types and locations for where useful support information may reside both within and outside of your company. These can include your knowledge base, specific product documentation and update release notes, how-to videos, and community or social threads located inside your own organization or on external third-party websites.
Putting the “Power” in AI-Powered Search
AI-poweredsearch can aggregate and index all of those varied content sources and present the search results as thumbnails of content with highlighted search keywords. In that way, your customers can see at a glance which particular search result might be the best, most relevant fit for them, given their context in that moment. Some people may prefer a text answer from your organization or from your user community or from some other trusted authority, while others will welcome a step-by-step explanatory video response.
You’ll also want to offer customers the ability to easily filter the search results to meet their own particular needs. This is especially helpful when the user is starting off with a broad query where they may want to hone in on a specific brand or a product or feature.
The goal is to get the customers the type of help they want as quickly as possible. If they are initially unsuccessful in finding what they need, machine learning capabilities leverage usage analytics and identify trends to highlight previous popular queries and resolutions as the user is typing the details of their specific support issue into your case management software.
Learn from Customers’ Queries
By analyzing your customers’ support searches on an ongoing basis, your organization can continually hone the type of self-service support experience you provide, particularly in determining where current gaps exist in the support content being offered.
Through the use of AI and machine learning, you can start to determine patterns in the kinds of questions customers typically ask. You can then potentially be able to anticipate or predict those queries and the best answers to resolve the issues.
Metrics of search behavior can help you dictate future content strategies by examining:
- Queries with no results. These queries provide instant feedback into what materials your content team needs to produce, for instance, more knowledge base articles on specific topics.
- Pages that lead to case creation. These web pages may be outdated, unclear or missing key information resulting in extra cases which could be easily resolved by improving the content.
You can tune the support responses to users’ queries to become more relevant, intuitive and personalized based on your organization’s prior engagements with an individual customer in terms of purchased product and the person’s role in the company.
Armed with that knowledge, your company can then be more proactive in recommending specific tools that may help a customer voicing a particular query such as access to additional training videos.
Lessen Customer Friction and Effort
As companies measure customer engagement, they’re tending to place more emphasis on the amount of effort customers have to exert to do business with their organization. For instance, how easy or hard is it for a customer to find the correct solution to a support issue?
This metric is proving to be both a strong indicator of customer loyalty -- less effort results in a customer buying more products -- as well as predicting whether a customer may defect -- a lot of effort leads to a customer severing ties with a vendor.
By providing a more relevant digital experience, companies can ensure low scores on customer effort, which will also have a positive impact on other metrics such as overall customer satisfaction (CSAT) as well as the likelihood that customers will recommend your company to their peers -- the Net Promoter Score (NPS).
At the same time, organizations are also looking to continually raise their case deflection rate through the use of successful self-service support resolution. As mentioned earlier, most customers and partners would prefer not to engage with a human support rep but instead act as their own support resolution technician.
By analyzing search terms in real-time, companies may also be able to get out ahead of any emerging search issues related to a new product or an update and quickly create the self-service content users will require.
Deploy AI-Powered Search and Relevance to Drive Continual Support Improvement
The aim of deploying AI-powered search and relevance across your customer support operation is to set in place an automated, virtuous support lifecycle that gets better with every use.
An organization first analyzes its customers’ search behavior, then optimizes the content being displayed to customers as search results, and ultimately learns more about what constitutes a successful support outcome for those customers.
Each iteration of the cycle results in improvements in the efficiency and effectiveness of your customer support experience resulting in happier customers, partners and support employees.
Part 1 of a 2 part sponsored article series from Coveo, a leader in digital experience, unified search and analytics.