Two AI Use Cases for Customer Support and Services

In our last post, we highlighted the fact that many companies assume that the more “human” parts of business —sales and customer service—have little to gain from Artificial Intelligence. Of course, this assumption is incorrect, and liable to mislead companies who could otherwise stand to benefit.

Consider:

  • According to Forrester, 72% of businesses say that improving the customer experience is their top priority.
  • Most contact with customer service now takes place via the web using a chatbot, via email, or via social media. The set of skills and tools needed here are different than, say, handling a case via phone.
  • Customers have ever-growing expectations with regards to response time. A decade ago, customers were willing to wait 24 hours for an answer to a question or a solution to their problems. Now they want an answer right away…if not instantly.
  • As business grow and expand their global reach, more and more customer support cases begin to look similar. Solving each case independently is burdensome, if not impossible.
  • The average customer triage and resolution cycle takes five or more steps having to do solely with information search among the organizations various data sources.

In other words, the need for a human being with “people skills” is diminishing just as the strengths of artificial intelligence agents—such as the ability to query multiple data sources quickly and efficiently—are coming in high demand. Indeed, one prediction holds that, by the year 2020, more than 85% of all customer interactions will be handled without the need for a human agent.

But what does customer support via artificial intelligence agent look like? Again, we can illustrate this best with two use cases around our own knowledge automation solution, Nimeyo.

Use Case 1: Resolving Customer Issues When Knowledge is Siloed

The Context:

Today, customer service reps are expected to resolve customer issues faster and faster, even as they take on huge case volume to justify their job roles. In order to do a good job of meeting customer expectations and succeed in their roles, a single pane of information and knowledge access is essential.

The Challenge:

Again, the typical customer resolution in an organization of any appreciable sizes takes researching five or more data-sources. These include:

  • Querying customer-facing case management systems (such as Salesforce Service Cloud or Zendesk) to identify duplicates and bring up relevant contextual information
  • Comparing across internal incident management systems (such as Jira) to find similar cases already being addressed, or that have recently been addressed successfully.
  • Searching KnowledgeBase articles and wikis for quick resolution of common problems, or concise answers to frequently asked questions
  • Combing through off-band but relevant conversations in emails or Slack channels

Already, this process is pretty daunting. When you consider that two or more of these steps could be taken for cases that are very similar, and for which solutions already exist, it becomes painfully obvious how much time is wasted and productivity sacrificed. Currently, organizations are struggling to find ways to integrate these various sources into a single pane.

How AI Helps:

This scenario is easily fixed with a solution like Nimeyo knowledge automation. Using Nimeyo, customer service reps can address cases more readily, thanks to instant access to knowledge of similar cases across content silos of customer issues and internal product ticket systems.

Nimeyo can also integrate with management systems like Salesforce, as well as incident management systems like Jira and chat channels like Slack. It can then access these systems instantly and use the information in them to help zero-in on the resolution for a given case, relieving the customer service rep from having to do these searches manually.

More importantly, Nimeyo helps customer service reps deflect more cases by giving them increased visibility of similar cases across customer issues and internal product ticket systems.

All of this results in more rapid response which, ideally, leads to improving their first contact and/or first time resolution times.

Use Case 2: Self-help Bots For Customers and Service Teams

The Context:

As we all know, a lot hinges on having a positive customer service experience: It can mean the difference between a loyal customer, and a disgruntled one. Speed and accuracy matter crucially, and customer demand instant responses. If they don’t find an answer immediately, they are disappointed and are quick to share their bad experience on social media or other public channels.

But increasing complexity of products and services, along with the high turnover rate of most call centers, means that it is almost impossible for service reps to keep up with the content needed to resolve issues in a timely fashion.

These dynamics are fundamentally changing how both customers and service reps seek out information. For example, the majority of Millennials actively avoid situations for which human interaction is necessary to solve an issue, much preferring self-service options instead. One study of the generational divide in customer service found that a whopping 72% of Millennials believe a phone call is not the best way to resolve their customer service issues.

So how are consumer resolving their issues, if not calling customer support? Right now, they are using a mix of chat bots on websites, social media sites for the relevant brands, chat channels, and Google searches. In other words, they are already going with digital self-help solutions.

The Challenge:

Companies face two choices: Either improve the self-help bots they make available, or better empower their service teams to compete with these bots.

Most of the current self-help systems are web centric, so customers are relegated to searching for a solution themselves—and are often confronted with more pages than they are willing to review. Even if they do find the  answer they seek, it may not be the most accurate or latest answer.

That said, many Customers are still “put at ease” knowing that there is a customer service rep in the interaction; but this “human touch” engagement is costly, often only available during business hours, and is (for the most part) unscalable.

How AI Helps:

With a Nimeyo AI solution, customer service organizations can create a foundation of knowledge and insights from approved content sources like FAQ databases, product documentation or issue tracking systems. Subsequently, bots or auto responses act as the first line of defense to respond to common question with known answers or fixes.

When a customer sends an email to a support email address, the email autoresponder can look at the knowledge available to instantly respond with links to most relevant answer. If the customer is happy with the answer, then the customer service team can mark the issue as resolved. If the customer indicates that more assistance is needed, a rep can reach out for additional information.

What about availability and scale? Typically, a customer service chat is available only during business hours (unless you have a globally distributed service teams.) However, if a customer initiates a chat with a rep during off hours, an auto responder bot can respond to customer query with knowledge from approved sources. Queries such as the status of a case, answers to FAQs, or product specific questions can be responded to in seconds without any human intervention. Again, the chatbot can be the first line of defense before a rep needs to be engaged.

Again, these are just two simple ways that AI agents are changing the face of customer service. Counter to many of the assumptions surrounding AI, human beings will always have a role to play in customer support, since there will always be difficult cases requires a person’s  ability to understand the nuances of the case and find creative solutions. Increased productivity comes when human beings can be freed from routine and easily-solved cases, and allowed to focus on more complex cases and tasks. Artificial Intelligence can potentially leave service reps free to tap into the critical thinking and problem-solving skills, not to mention emotional intelligence, when they are needed most.

If it still sounds like a pipe-dream to empower human interactions through AI technology, we recommend you try Nimeyo yourself to see how this can be done in your organization. Sign up for a free demo, and we would be delighted to give you a tour and show how the Nimeyo AI can be best used by your customer service teams.

Can Enterprise Search effectively serve employees’ needs?

My toddler has an impressive collection of toys. He tries to keep his favorite ones somewhere “safe” but then he cannot find them when he wants to actually play with them. While trying to find it, we both know that the one that we are looking for is somewhere in the house and yet it remains alluringly out-of-reach since the exact places we determine to look for never have it. It’s frustrating for him to have this happen on regular basis.

Unfortunately, millions of enterprise employees feel similar frustration when they can’t find the information that they know is around them in various forms. They could be in mailing lists or in SharePoint or Box repositories, or in internal chat rooms, or on some internal wiki pages.

Realizing the potential value of unearthing information that employees need to do their jobs, companies – particularly large ones – employ enterprise search. Unfortunately, most of those engines remain poorly deployed and minimally used by the end users.

imageSo the question is why do enterprise search engines do such a poor job at engaging users whereas search outside of the corporate firewalls are part of our daily lives?
Although there are number of technical reasons, we believe the key problem is a “lack of user-orientation”. In other words, these solutions are neither attuned to the actual needs of the end users nor they understand the data itself in a meaningful way to be able to serve it in a meaningful way.

Let’s take few examples –

  1. Content and Users: Search engines’ key strength is in indexing wide ranging data types – web pages, documents, CRM systems etc. So when user searches with few keywords, search engines define success by uncovering wide-ranging data in a sequential manner based on some ranking criteria.However, not all data types are created equal. For instance, email communications are a lot more meta-data rich and time-relevant compared to documents. If used intelligently, such data specific analysis can be immensely useful in understanding how row communication relates to the end user needs.
  1. User interface:   Needless to say, we all are used to typing a simple search query (or question where there is a unique answer – e.g. “Father’s day 2015”) and expecting search engine to return “satisfactory” results. However, in a corporate context, this model is highly limiting as any one article or document is unlikely to provide a comprehensive answer in most real world scenarios.For example, when a sales rep gets into a competitive situation, a query like “MyCompany vs MyCompetitor” should surface variety of information including product differentiation, pricing, and nuggets from other similar situations. All these pieces are equally important to put together a competitive tactic to win the opportunity. A linear listing of results based on uniform ranking criteria does not do justice to needs of that sales rep.A UI that allows users to navigate through these various pieces in a consistent manner and “assist” in creating a cohesive picture would be much more effective in creating engaging user experience.And finally,
  1. User preferences and behaviors: In most of our enterprise search experience, presentation of results is “black magic”. Let’s say you searched for some information today, worked through the hits and were fortunate to find what you were looking for at the 20th You had to put the effort but you found what you were looking for!Unfortunately, say a month from now, if you are looking for the similar information through a similar query, you would still find the information deep down in the ranking.If solutions allowed ways to capture user preferences – expressed implicitly and explicitly – it would be able to return results that are a lot more aligned with what the end user needs are.

We at Nimeyo believe that to achieve the true potential of enterprise search, we need to stop viewing it through the prism of consumer search technologies.

To fulfill the promise, enterprise search products must understand not just who the user is and what role she performs in the organization but also identify optimal mechanism through which to deliver knowledge to the employee.