The Top 3 Ways AI Is Reducing Onboarding Time for Sales Reps

Employing new sales reps usually involves an onboarding process. But the typical onboarding process is getting longer and less efficient as time goes on. While there are many ways to improve onboarding for sales reps, organizations can get the most “bang for their buck” by incorporating knowledge automation into their onboarding program through the use of AI.

Proof That Sales Onboarding is Stuck

An eye-opening report from Qvidian in 2015 surveyed hundreds of executives and sales leaders from various industries, markets, and company sizes to assess their objectives and challenges. What they found was that sales organizations were struggling with getting the right information into their new sales reps’ hands, and doing so in a way that led to success. Noted in the report:

  • 36% of organizations mentioned “ramping up new sales reps takes too long” as one of the top reasons why their teams were failing to make quotas.
  • Indeed, onboarding of sales reps took an average of 7 to 9 months (measured as the time until a sales rep becomes fully productive). (Note this is an average: 1 in 5 sales reps take over a year to reach this level of performance!)
  • Sales teams were not meeting quotas largely because they were failing to personalize the buyer journey for their customers and effectively communicate value.
  • 55% of the organizations surveyed indicated that part of the reason they failed to communicate value was that reps were struggling to identify the tailored selling content among all the materials they had.

This inefficiency carries a steep price tag, to say the least. Not only does slow and inefficient onboarding mean lost productivity, but it also leads to higher turnover, added expenses for recruiting and training, lower employee morale, and a net negative impact on clients. By one estimate, it costs approximately $115,000 to replace a sales rep…and about 28% of sales reps, or more than a quarter, turn over in a given year.

Indeed, Zappos CEO Tony Hsieh is well known for completely restructuring his company upon learning that bad hires were costing him well over $100 million a year.

Why Sales Onboarding is Failing

That sales onboarding is failing across industries is not a secret. Why it is failing, though, is something that makes sales managers scratch their heads.

That’s because the issues do not stem from the onboarding program itself. We have identified four main reasons why traditional onboarding is failing in today’s sales environment, and they all have to do with the flow of information:

Reason #1: Distribution. Sales reps are scattered across the country, and even across the globe. They are often not located in the same places as the subject matter experts and marketing teams that they need to engage with for information.

Reason #2: Product Evolution. Products are evolving faster and faster. While the innovation is great, what was true of a product line a few months ago might not be true today. Sales reps cannot rely anymore on a single “information dump” at the start of their tenure.

Reason #3: Customer Expectations. Customers and prospects expect faster responses in the digital age. The amount of onboarding needed to keep information fresh in a sales rep’s brain is mind-boggling.

Reason #4: Increased Competitive Pressures. Yes, products and expectations change. But so does the competition. Knowing the competitive landscape and the target market takes much research and study. When sales reps are given this task, they are expected to do more, in less time. When it is given over to marketing, the information too often remains “siloed.”

The Top 3 Ways AI Can Reduce Onboarding Time

Sales is a uniquely human endeavor. How can AI help sales reps become more productive more quickly? We see three main ways that AI can reduce onboarding time:

1. By eliminating the need for “perfect” product information.

If product information is always changing, perhaps the best approach is to stop trying to frontload that information into the onboarding period. By using a knowledge automation tool like Nimeyo, organizations can ensure that sales reps have the right information at the right time, without the need for extensive search or periodic training.

2. By finding tailored content for sales reps in a timely fashion.

Remember, sales teams are, by and large, failing to communicate value because reps struggle with identifying tailored selling content within their own organizations. Wading through large amounts of content is something AI is now really good at doing. Sales reps can use knowledge automation tools to identify specific pieces of content that will help enable sales teams, even down to the level of the individual customer. This cuts down on the time that sales reps need to become familiar with sales materials and helps facilitate their researching the market.

3. By gaining insight into success and failures in a timely manner.

Many organizations struggle when it comes to gaining visibility into the sales cycles. And when they do not have visibility, it can lead to bad decision-making—or no decision-making at all. This is especially true when employees who are not a good fit are allowed to linger in an endless onboarding process.On the other hand, proper sales analytics can identify patterns, allowing organizations to duplicate successes and minimize failures. This can feed back directly into coaching efforts.

And, with less time spent on product information updates and search, more time can be spent on that kind of coaching during those first formative months.

So will AI fix everything that is wrong with sales onboarding today? Of course not. Crucial elements like mentorship and processes that follow best practices will still be needed. But knowledge automation systems can fix the element that is causing the onboarding process to balloon in modern organizations: The sheer amount of knowledge needed to be successful. Cut this bloat from the onboarding process, and your sales reps will become productive much more quickly.

Why Traditional Knowledge Management Doesn’t Work in the Digital Age

Knowledge management—the efficient handling of information and resources within a commercial organization—is not a new concept in business. In fact, it has been around since the ’90s. But even as businesses are still trying to get a handle on knowledge management practices, the concepts and processes developed back then are aging to the point of becoming obsolete.

In other words, knowledge management as we know it is not destined to survive the digital age. The issue is that digital technology has, far from making management easier, contributed to the explosion of available information.

To survive the digital age, organizations will need to start thinking, instead, in terms of knowledge automation.

Proof that Knowledge Management Falters in the Digital Age

Knowledge management grew out of a realization that large organizations needed to organize their information in a more holistic manner. This meant capturing and retrieving needed information from a variety of sources: databases, documents, policies and procedures, and even the expertise and experience implicit in the practices of individual employees.

This sounds like just the sort of thing that should be easy with cloud technology and better integration tools. But consider:

Information is everywhere in organizations. But employees are spending an inordinate amount of time trying to find just the information they need, when they need it. As the information in an organization grows, this situation gets worse, and management of that information itself because an ever greater task.

In other words, knowledge management has led to better collection of, and access to, information…but it has failed to make the retrieval of pertinent knowledge faster or easier.

What Knowledge Management Promised

Still, the original needs that knowledge management was supposed to address will not go away. In an ideal world, proper knowledge management would enable things like:

  • Rapid data-driven decision-making
  • Fast dissemination of relevant information across siloes
  • Minimization of duplicate efforts
  • Broadcasting best practices and solutions in a timely manner
  • Better utilization of existing knowledge assets, both formal and informal
  • Better leveraging of SMEs’ knowledge
  • Better use of SMEs’ time
  • Standard and repeatable processes, procedures, techniques, and templates
  • More accurate and timely information for sales teams
  • More rapid response by customer service and support teams

Notice that these benefits are about speed and relevance as much as anything. But these are exactly the areas where traditional knowledge management has been slow to develop.

Even more disheartening is the cost burden that knowledge management has brought, without realizing return on those investments. Improper planning, design, support, and evaluation can easily lead to a lack of widespread contribution, which further erodes usefulness, relevance, and quality. And, even when moderately successful, using older knowledge management system is costly to maintain and difficult to scale because of their dependence on “top down” knowledge management rather than “self-building” knowledge.

From Knowledge Management to Automated Knowledge Curation

“Knowledge automation” is becoming a popular way to describe how machine learning and artificial intelligence (AI) can be used to automate more of the knowledge management process. (“Automated knowledge curation” is another, although less popular. It means much the same thing.)

Much of the knowledge within organizations is generated by the activities of your people. They email questions and answers back and forth. They use informal communication tools like Slack. They produce wikis and sales sheets and blog content. All of this knowledge is there in the organization—it just needs to be curated and made automatically available with the touch of a button.

This is the idea behind “self-building” knowledge: knowledge that is already present in an organization and that is continuously curated instead of “managed.” Knowledge automation creates access to self-building knowledge, rather than relying on “top-down” management.

After all, information itself is cheap; as the list above shows, it exists in many ways, and in many different forms. That information only becomes knowledge when the right slice of information can be applied in the right situation.

For example, suppose a prospect has a question about a particular feature on one of your newer products. Your sales team should be able to answer that question without wading through a pile of sales sheets and development wikis—or worse, waiting for an answer from a SME halfway around the world.

Another example: A customer has an issue and reaches out to your organization via social media. Your customer service team now has to query several separate systems in order to handle this: a case management system, an internal incident management system, a knowledge base, and several off-band communication channels. This would usually take a full day; imagine, though, if the relevant knowledge in these systems could be made available instantly, so that a reply could be made within the hour. (In fact. call center costs and volumes can decrease by as much as 30% when better search and automation tools are implemented.)

These are just a couple of simple examples—you can follow the links for more detailed use cases. Still, they are good examples of why traditional knowledge management is not surviving the digital age. They also show why we developed our app, Nimeyo, as a way to automate the gathering of information across channels. It is a knowledge automation solution that brings both speed and relevance to those who need the right information at just the right time.

Like Beanie Babies and dial-up internet, some things should be left in the ’90s. It’s time to update the way we access information in this digital age.

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.

Two Real-Life AI Use Cases for Sales Productivity

That Artificial Intelligence (AI) is having a huge impact on business is nothing new. Market projections of revenue gained from AI deployment will be in the neighborhood of $36.8 billion by the year 2025, and are already at the $643.7 million mark today.

But for all the buzz surrounding AI and its cousins, Big Data and digital transformation, it has been hard to imagine what concrete business applications the technology would have outside of business intelligence and marketing and CRM. Indeed, one assumption we keep running into is that the more “human” side of business—sales and customer service—has little to gain from AI.

That assumption is being proved wrong. For one thing, sales roles are getting closer scrutiny, with over 3 million sales jobs expected to disappear over the next 5 years. This means that fewer and fewer reps will be expected to drive more and more growth, just to survive. It also means a keener focus on activities that lead directly to more closes. In short, higher sales productivity.

 

And yet, one of the biggest obstacles to greater sales productivity is knowledge hunting, or more precisely, the time and effort sales reps have to put in uncovering just the right bits of information they need from the content silos at a given time.

This is something AI has become incredibly good at: Extracting just the needed information from a large base of unstructured data and conversations. We can illustrate this best by looking at a couple of the use cases for our own AI knowledge automation solution, Nimeyo. Nimeyo provides some good examples of how sales teams are now using AI as their sales enablement tool of choice.

AI Use Case #1: Smart Knowledge Bases and Email Autoresponders

The Context:

Sales teams depend critically on being able to answer a prospect’s questions quickly and accurately. For a large sales organization, this requires a huge chunk of a sales rep’s time. Complicating this picture is the fact that many organizations are geographically scattered, and large numbers of reps are expected to learn about complex products and respond to customers’ queries about them in a short amount of time.

Reps often rely on sales enablement and product/technical marketing to provide them with technical information about product features, capabilities, solutions, and roadmaps. Many have set up email distribution lists to align sales and marketing teams in an effort to control the flow of customer-related information.

The Challenge:

What these distribution lists look like in real life can be less than stunning. Reps ask questions and then wait for responses from a subject matter expert (SME)—often someone located halfway around the world. This means an average response time measured in days, with many inquiries requiring multiple follow-ups. Even after such an extended process, roughly 50% of information requests are left unanswered.

Life is not easy for the SMEs either, who often find that more than a third of the questions they receive are repetitive, having been answered in previous conversations. While SMEs are using wikis and other tools to disseminate knowledge, these rarely are used regularly by reps. Nor do SMEs have a way to “check the work” being done by sales reps by providing feedback on things such as product decks. The result is a lot of redundant effort, and less time for sales teams to actually work on closing deals.

How AI Helps:

Nimeyo’s AI solution allows these kinds of sales organizations to automatically build and dynamically sustain a knowledge base of information from email threads in a distribution list. An email autoresponder can be set up that automatically reviews each new email sent on various distribution lists, automatically identifies the information request, and responds with the most appropriate knowledge. The responder can also carbon-copy the relevant SME so he or she can further contribute or revise the answer, if needed.

If a response is not available (because the product being discussed is brand new, for example), the AI system waits for responses to the email thread from the SMEs, and that knowledge is then added to the knowledge base for future use.

With this AI technology, reps are able to get their questions answered in under 30 seconds—from anyplace, and on any device, using just their favorite email client. They no longer have to monitor emails on distribution lists but can still leverage the collective knowledge generated by the people on that list over time.

For their part, the SMEs no longer have to deal with repetitive questions, and when they do respond to questions, they can be assured that their contribution will be leveraged in all future inquiries. Large organizations that rely on email as a primary tool for collaboration can leverage a tremendous amount of tribal knowledge that field teams generate for efficiency and effectiveness.

AI Use Case #2: Extracting Information from Unstructured Collaboration Tools (Slack)

The Context:

Today’s modern sales organizations are using popular collaboration tools like Slack, HipChat or Microsoft Teams to communicate and collaborate. In some of the growing organizations, channels in Slack or HipChat are extensively used to discuss products, pricing, competitive situations, solutions, and related topics. These channels are treasure troves of tribal information relating to product, customer, and business.

The Challenge:

Slack, to take one example, started out as a medium for informal communication. It quickly became an alternative to email in many organizations, with broad adoption. However, tools like Slack don’t impose any logical structure on the content thus generated, focusing solely on making conversations flow naturally. As a result, search and retrieval are slow and painful.

While there are benefits to this kind of communication, more and more sales organizations are now realizing the costs of using such synchronous, always-on, noisy mediums. Sales reps in particular are spending more and more of their time trying to extract the useful information they need from a sea of noise.

How AI Helps:

Nimeyo’s NLP-driven bot can provide answers to sales-related questions right within Slack.

Sales reps can direct-message the Nimeyo bot in Slack and get relevant information, irrespective of where that information may reside.

For example, the Nimeyo bot can fetch a competitive battlecard from Google Drive or Box, or fetch the status of a particular deal from Salesforce CRM or status of a customer issues from Zendesk or answer an RFP question from RFP database. The bot can also present snippets of conversations that might have occurred within Slack itself and that are relevant to the request.

The Nimeyo bot also becomes a virtual user in the channel and listens to the chatter. When a real question is asked by a rep for which it feels it has an answer or relevant information, it will jump in with that information—like a real human being. Again, the information source can be any document, CRM, ticketing system, email, or the Slack channels themselves.

 

In short, AI can be used to sort through unstructured data, extract relevant knowledge, and present that knowledge on demand as if it were a regular user—but without the delay.

 

These are just two simple ways that AI agents are automating knowledge-hunting activities. Sales organizations that do this remove a major time-waster, thus freeing their reps (and their SMEs) to focus more on revenue-generating activities and boosting productivity. In fact, Gartner predicts that, by 2020, 30 percent of all B2B companies will employ AI to augment at least one of their primary sales processes.

 

And if you are still in doubt… we would love the chance to prove the value of such automation. Sign up for a free trial of Nimeyo, and we would be delighted to give you a tour and show how the Nimeyo AI can be best used for sales productivity in your organization.

qPod now available on Amazon AWS marketplace!

We are delighted to announce that qPod from Nimeyo – a “self-help” knowledge system for pre- and post-sales organizations – is now available through Amazon AWS marketplace.

Why AWS?
Amazon Web Services or AWS offers reliable, scalable, and inexpensive cloud computing services.

With our qPod Saas solution exclusively hosted on AWS you can be assured of robust security and data protection guarantees provided by AWS. In addition, for those
customers who use AWS private cloud for added security and control, qPod can be deployed with a single click in their existing VPC!

We continue to strive to make qPod deployment fast, easy, and intuitive so that customers see the value of qPod within minutes of deployment.

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