Teneo.ai and Kenway Consulting Partner to Deliver Industry-Leading IVR Solutions with Unmatched NLU Accuracy

STOCKHOLM (Monday, December 2, 2024) Teneo.ai, a pioneer in next-generation Voice AI, proudly announces a strategic partnership with Kenway Consulting, a leader in call center solutions and contact center consulting. This collaboration combines Teneo’s cutting-edge voice automation and its NLU Accuracy Booster™ with Kenway’s extensive expertise in IVR modernization to revolutionize contact center automation.

The partnership brings unparalleled accuracy and efficiency to contact centers, enabling businesses to achieve up to 100% accuracy in customer interactions while reducing implementation timelines by 50%. With seamless scalability, integration and advanced capabilities, the solution addresses the complexities of IVR migrations, ensuring businesses can deliver exceptional customer experiences.

Upgrading Contact Center Automation:

This strategic collaboration offers the following advantages for enterprises:

  1. Unmatched Accuracy and Clarity
    • Teneo's NLU Accuracy Booster™ enhances natural language understanding (NLU) accuracy by up to 30%, achieving better-than-human precision when integrated with platforms like AWS Lex and Google Dialogflow.
  1. Faster Go-Live and Streamlined Deployments
    • Kenway’s agile methodologies, combined with Teneo’s plug-and-play capabilities, reduce deployment timelines and minimize post-launch adjustments, lowering the total cost of ownership for enterprises.
  1. Effortless Scalability and System Integration
    • Teneo enables contact centers to scale over 90 markets and millions of interactions in just one day, offering unmatched flexibility to meet global demands.
    • Simplifies API integration, data mapping, and routing rule configuration for seamless IVR modernization with reduced infrastructure complexity and costs.

“Our partnership with Kenway marks an important milestone in redefining IVR solutions for businesses worldwide,” said Per Ottosson, CEO at Teneo.ai. “By combining Kenway’s expertise with Teneo’s advanced AI technologies, we are setting a new standard for accuracy, efficiency, and customer satisfaction in contact centers.”

“Kenway has always focused on empowering enterprises with innovative and scalable solutions,” said Kyle Finke, Contact Center Solutions Practice Co-Lead, Kenway Consulting. “Our partnership with Teneo.ai aligns with our mission to deliver excellence and sets a new benchmark in IVR modernization.”

Advanced Features of Teneo's NLU Accuracy Booster™

To learn more about the partnership and explore how Teneo.ai and Kenway can transform your contact center, visit teneo.ai/solutions/partners or contact kenwayconsulting.com/contact-us.

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About Teneo.ai

Teneo.ai is at the forefront of AI-driven automation for voice and text-based customer service. Our Teneo platform leverages cutting-edge Conversational AI, Generative AI, and Large Language Models to enhance the efficiency and effectiveness of customer interactions. We simplify Voice AI integration, ensuring a seamless experience that reduces losses in automated conversations and maximizes the value of existing technology investments.

Our innovative solutions help businesses expand their customer base, boost revenue, and reduce churn, enabling the realization of the Agentless Contact Centre concept. This approach delivers tangible ROI through lower cost as contact center agents are freed to conduct higher value tasks, improved customer satisfaction (CSAT), first contact resolution (FCR), and call containment.

Proudly serving global leaders like AT&T, HelloFresh, Swisscom, and Telefónica, Teneo.ai has revolutionized customer service automation, directly automating up to 40% of operations and achieving up to 50% cost savings. Our patented technology integrates effortlessly with any Conversational AI, and contact center platform, supporting both chat and voice applications. This integration enhances critical metrics such as growth, FCR, CSAT, and Net Promoter Score (NPS), ensuring our clients achieve superior outcomes in customer service.  Learn more at www.teneo.ai.

About Kenway

Kenway Consulting is a management and technology consulting firm whose entire reason for existence is to help companies and its employees. Founded in 2004 on the principles of being good and being truthful, Kenway’s set of Guiding Principles steers each employee’s decision-making process and centers on integrity, quality, value and respect. The company focuses on the means and not the outcomes, always in line with these Guiding Principles, and always with integrity as its cornerstone. Kenway strives to provide all clients with unmatched quality and service, and specializes in customized business solutions using its Application Development, Artificial Intelligence, Contact Center Solutions, Data and Analytics, Data Compliance and Privacy, Program and Product Management, and Salesforce practices.

Media Contacts

Michael Kenney
US Director
[email protected]

Theresa Hennessey Barcy
for Kenway Consulting
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IVR Best Practices: Making Dollars and Sense of IVRs

What is IVR/IVA?

Intelligent Virtual Agents (IVA) are a critical part of any customer service operation. They can help to automate tasks, reduce costs, and improve customer satisfaction. However, to get the most out of your IVA system, IVR reporting can track and measure key performance indicators (KPIs). Our Contact Center Solutions consultants help turn your data into information with a unique methodology and set of metrics to evaluate the health and success of your Intelligent Virtual Agent (IVA). Using the metrics included in this article combined with our Information Insight Capability, your company can turn your IVA data into useful, actionable information to improve the customer experience and reduce costs.

Every business is unique and there isn’t just one KPI that proves an IVA, IVR, or chat bot is functioning well. Given these differences, Kenway recommends evaluating an IVA on KPIs that illustrate the efficiency and effectiveness of the bot that drives customer satisfaction and result in cost savings for the organization. Increases in an IVA’s efficiency, effectiveness, and customer satisfaction demonstrate improvements in performance. This indicates customers are utilizing the bot more frequently to resolve their questions and are requesting to speak with a customer service agent less often, which reduces call center costs while also improving customer support. Companies that track these KPIs will glean deeper insights into the health of IVA processes and identify opportunities for improvement.

HubSpot surveyed more than 1,000 people in the United States and found that the most popular communication channels for customer service were email (61%), phone (60%), and live chat (57%). Social media came in fourth place, with only 29% of respondents saying they prefer to contact companies through those channels. A 2017 study conducted by Microsoft found that 70% of the customers still prefer to contact customer service by phone, while 20% prefer email and only 10% prefer chat. Since 2017, email and chat have significantly increased in popularity. However, when customers cannot resolve their issue, phone support is still the preferred method of contact for most customers.

Since the voice channel is still the preferred method to resolve the most important issues the remains a key component of a company’s customer engagement, evaluating them and improving their responsiveness is smart business. For this blog we will focus mostly on IVRs and voice calls, but the same metrics can be applied to chat bots too. For the past 15 years, Kenway Consulting has helped design, implement, test, and report on an IVRs for our customers through our Contact Center Solutions practice.

For more information on our thoughts and experiences in the Contact Center world, read our blog, The Dos and Don’ts of IVR Design and our case study, Seamless IVR Solutions Mastering System Conversion & Overcoming Challenges.

KPIs to evaluate an IVR

  1. Average Call Length: This measures the average time it takes for the IVR system to handle a call from start to finish. A low average call length can indicate that the IVR system is efficient in handling customer requests and reducing wait times, while a high average call length may indicate that the IVR system is too complex or confusing for customers. According to Zendesk, the biggest frustration with virtual agents for 54% of customers is answering too many questions.
  2. Containment Rate: This measures the percentage of calls that successfully complete their intended task using the IVR system without the need for human intervention. A high containment rate indicates that the IVR system can effectively handle customer inquiries and requests, reducing the workload on live agents, improving customer satisfaction, and reducing organizational costs. According to Emplifi, 35% of customers want a complete self-service option to resolve their issue.
  3. Agent Transfer Rate: This measures the percentage of calls that are successfully routed to the appropriate department or agent based on the customer's intent. A low agent transfer rate indicates that the IVR system is effective in understanding and interpreting customer responses and can route calls to the right agents, reducing frustration, agent Average Handle Time and wait times. A low agent transfer rate also impacts directly on Call Center costs, since customers directly reach the correct agent skill.
  4. Agent request rate: This measures how many callers request an agent during their call. Some callers will immediately request an agent and not “play” in the IVR. Callers that do play along but request an agent usually means they are confused, or the application is having trouble moving them forward. A high agent request rate indicates your solution is not effective or efficient.
  5. Call abandonment rate: This measures the percentage of calls that hang up or are disconnected from the IVR before reaching an agent or completing a self-service task. A high call abandonment rate may indicate that the IVR system is difficult to use or does not provide enough information or options to customers. Understanding both frequency and timing during the call of this KPI can help Contact Center Organizations to optimize the IVR customer experience and measure Self-Service success accurately.
  6. Customer satisfaction rating: This measures the level of satisfaction customers have with the IVR system based on their experience using it. A high customer satisfaction rating indicates that the IVR system is effective and efficient in meeting customer needs and providing a positive experience.

IVR Reporting

Expert tips for IVR Reporting

Data collection and management within your application are critical to your ability to report on the above metrics. Insights into the strengths and weaknesses of the IVA and offer opportunities to improve the customer experience and find cost savings. Specifically, companies should evaluate each prompt in their IVR and track its responsiveness to include, at each stage, the customer’s average working time and how many callers abandon, opt-out, or transfer to an agent.

Companies also should monitor the IVR’s ability to route calls in the minimum number of steps; because the sooner a caller hears the correct prompt, the sooner the customer will resolve their inquiry. Expediting the customer’s engagement with the IVR also will improve the IVR’s first call resolution which typically increases customer satisfaction.

Virtual Assistants are not often the most beloved customer service tool, but they can be immensely helpful in navigating customers to someone who can help them. Furthermore, for some businesses, a high performing IVA can be a competitive advantage. If you have an IVA with more data than information, are struggling to evaluate said IVA, or simply have an opinion on our recommended metrics, we would like to hear from you at [email protected].

 

The Dos and Don’ts of IVR Design

Agent. Agent! AGENT! Having worked the majority of my career as an IVR consultant doing IVR design and enhancing Interactive Voice Recognition systems (IVR's), I’ve seen a lot of frustration from the users. When I tell my friends and family what I do, I get reactions such as "I hate those things," or "Why won’t companies let me speak to an agent?" When I speak with other IVR designers, I hear comments such as "customers don't even try" or "how could they have picked the wrong option?"

So, who is right? Is the company to blame for forcing this upon customers or is the customer to blame for not using it right? As with most things, the truth lies somewhere in the middle. Here are some dos and don’ts of IVRs for both IVR designers and users.

IVR Design

Below are some IVR design best practices

  1. Use the collected data from customers’ account intelligently. This data can be extremely powerful to personalize the IVR customer experience and reduce customer frustration. It is extremely frustrating for a caller to answer questions they feel the company should already know (e.g., what products and services they have or are they an existing customer).
  2. Even further, when considering what to automate, analyze what it will take to automate the function. Do you have the data available to support the function? Will you see a return on your investment?
  3. Implement a feedback loop. Good IVR design requires a commitment beyond the initial design. A company needs to be dedicated to collecting data, listening to calls, and analyzing the results. Too often, companies believe they know exactly how their customers are going to behave and align their IVR design to those assumptions. Inevitably, some of these assumptions will be incorrect and changes will need to be made. To avoid this scenario, verify your assumptions by working with your customer base to perform usability testing. Further, it is crucial that companies be prepared to make changes after implementation and design an application that is flexible and easy to change. Doing this due diligence up front will result in cost reduction and reduce customer frustration.
  4. Keep the menus simple and make sure the IVR design always gives the caller the sense they are moving forward. Don’t use jargon. Speak about your products and services in a way that your callers will understand. This may sound obvious, but I have seen many designs which do not follow this guideline. Callers will request an agent as soon as they feel they are going down the incorrect path. If the caller has the perception that the questions are leading them down the correct path, they will be more likely to continue in the IVR.
  5. Do not hide the option to reach an agent. It is impossible to automate every task that a customer can perform; therefore, reaching an agent should always be an option. A well thought out IVR architecture and IVR design do not need to hide the agent option because many customers are willing to use the IVR and will not fall back on the agent option. It’s like knowing you have a safety net. You’re more willing to take a chance when you know it’s there.
  6. Collect, manage, govern, and report on your data. IVRs can produce massive amounts of data and that data can inform not only how well the application is performing but also help you identify future enhancements.
  7. Customers say a lot of things, make sure you can understand them and handle their need correctly. Natural Language Processing (NLP) and Natural Language Understanding (NLU) can be used to understand a customer’s complex question and to provide more accurate and relevant responses. For example, if a customer asks "I spoke to an agent yesterday and they said that I shouldn’t have been charged for that service and I should get a refund. I am calling to find out how I can get my refund.", an NLP-enabled IVR system could understand that the customer is asking about a refund and could provide the customer with the information they need to process a refund. NLP gives you the flexibility to support additional customer needs beyond the typical press 1 or press 2 menus.
  8. AI and predictive intelligence can also be used to enhance the customer experience by providing personalized recommendations and offers. For example, if a customer has previously purchased a product from a company, an AI-enabled IVR system could recommend other products that the customer might be interested in. An application using predictive intelligence can be used to anticipate customer needs and to provide proactive support. For example, if a customer's account is about to expire, a predictive intelligence system could send the customer an email reminder to renew their account.

Below are factors customers should consider when using an IVR

  1. I will not try to convince you that all IVRs work well. However, I will ask you to assume that there are some good ones out there. I talk to several people who refuse to try navigating through an IVR even if they are using it for the first time. Instead, their immediate reaction is to say, “agent.”  Those who make an effort will find that a good IVR design can be helpful. It’s better than spending time waiting for an agent, getting bounced around to another agent, or being disconnected with the agent after waiting.
  2. Remember, a company's goal is to get callers that need to talk to a person to the appropriate agent, not prevent them from getting to an agent at all. Companies spend countless hours and money training their agents to be experts in certain areas (orders, billing questions, technical support, etc.). Give the IVR a chance and take the time to answer the questions. This improves the chance of getting to the right agent the first time and avoids longer wait times as a result of being transferred.
  3. In many instances, agents have the same access to your bill or offers that the IVR has. If the IVR says there are not any offers available, it’s likely that the agent won’t have anything else.
  4. Do not be afraid to give feedback if you have a bad experience in an IVR. Companies who design IVRs often deal with a large customer base that can lead to a complex design. There is a good chance they have not thought of everything and are not aware of some of the challenges their customers are facing. Companies do care about customer satisfaction and their goal is to make the IVR experience as easy as possible for you. Your feedback will be welcomed unaware of some of the challenges their customers face.

Both parties involved (companies who implement them and customers who use them) must remember that the intent is to improve customer service and reduce costs. Those outcomes are goals both parties would find beneficial. If the IVR design doesn’t improve customer experience or reduce costs, question the implementation and the change management associated with it.

As IVR consultants, we take pride in asking the right questions and engineering an IVR implementation approach targeting those two objectives and ensuring an improved customer experience and cost reduction. Connect with us to learn more about our Contact Center Solutions practice.

Know Thy Data

An Incredible RBI Season

Dante Bichette had 133 RBI’s in 1999.

Bichette is a former major league baseball player.  He spent time with five different franchises, but his longest tenure was with the Colorado Rockies, where he played outfield during the 1999 season.

RBI stands for “run batted in,” and is a baseball statistic intended to measure the number of runs an offensive player is responsible for producing.  For instance, if there’s a player on third base, and the batter hits a single, allowing the player on third to score, then the batter gets one RBI.

In 1999, Bichette had 133.  This is a large number.  It was the 8th highest number of RBI’s in all of major league baseball that year.  It’s more than Giancarlo Stanton had in 2017, when he led the majors with 132.  In fact, 133 would have been enough to lead the league in each of the last 4 seasons, and 6 of the last 8.  Bichette’s 1999 season is the 170th greatest individual RBI season since 1900, which might not seem like much, but consider that there have been over 15,000 individual seasons in that period of time.

The RBI tells us that Dante Bichette produced a lot of runs in the 1999 season, and, after all, the batter’s job is to produce runs.  Given that information, it might be reasonable to think that Dante Bichette was a good, perhaps even great, baseball player in 1999.

Taking a Second Look at the Numbers

With all due respect to Mr. Bichette and the Colorado Rockies organization, Bichette was neither great nor even good in 1999.  Bichette was a bad major league baseball player in 1999 and produced negative value for his team.  The Rockies would have won more games in the 1999 season if they had replaced Bichette with an outfielder from their AAA Minor League affiliate (at the time, the Colorado Springs Sky Sox), or just about any other professional baseball player.

How is that possible?  After establishing that Bichette’s RBI total, a number indicative of his offensive production, was elite in 1999, how was he an objectively bad professional outfielder?

It goes without saying that the threshold for playing major league baseball is incomprehensibly high, and calling Bichette a “bad” baseball player is untrue.  To judge a player’s value in practical terms, baseball statisticians invented the concept of the “replacement-level player.”  Such a player is one that’s readily available to any club that wants him, and, in real terms, can be described as a player at the high level of a franchise’s minor league organization.  This player can be called up to the major league club to “replace” another player with little or no cost to the organization.  If a player on the team is performing below this level, he shouldn’t be on the team, because he can be readily replaced at a lower cost.

Bichette was performing so far below the replacement level in 1999 that he cost the Colorado Rockies the equivalent of more than 2 wins over the course of the season.

There are many reasons for this, but let’s talk about the RBI first.  The RBI is a statistic with good intentions, but those intentions cloud the truth that the metric is not particularly effective at measuring value.  Teams win games by scoring runs, so it would make sense to identify not only the players that score those runs, but also the players whose performance at the plate allow those players to score runs.  The problem is that in order to get an RBI, unless you hit a home run, it requires context that is outside of the batter’s control: someone has to be on base.

Bichette batted with a lot of players on base during the 1999 season.  The on-base ability of his teammates allowed him to balloon his RBI totals, in spite of being roughly league average at the plate.  Bichette’s on-base percentage was below league average.  His home run total and slugging percentage were high, but he played his home games in Denver, where the thin air notoriously inflates power numbers.

However, teams also win games by preventing runs, something that Bichette did extraordinarily poorly.  By defensive metrics, Bichette was the worst defender in all of major league baseball in 1999.

The end result is a complete picture of Bichette’s 1999 season, which stands in stark contrast to only looking at the RBI.  The RBI is a statistic that requires accuracy, but it’s a perfect example of why data requires quality and governance, in addition to accuracy.

Do You Know all the Numbers? 

Baseball is a game, but it’s also a business.  They didn’t know it at the time, but Colorado paid Bichette a veteran’s salary for production that they perhaps could have gotten from someone making the league minimum.  Think about your business.  Think about all the roles, projects, programs, investments.  Do you think that all of those entities are delivering positive value?  Do you really know how much value every decision is delivering?

This was a challenge faced by one of our clients.  The client had a project that was great in theory.  It was a good idea, and good ideas deliver value, right?  As demonstrated by the RBI above, knowing a number isn’t enough.  Trusting a number requires an understanding of what it means and from where it originates.  Otherwise, it’s just a number, and it can make you look foolish.  In our client’s case, the number that they were using seemed foolproof.

The client had a Short Messaging Service (SMS) program, where a text message would go out to their customers, with the hope that the message would either prevent the customer from making a call that would cost the client money (e.g. the message would contain an appointment reminder) or generate a call that produces revenue (e.g. the customer calls to make a payment).  This sounds like a no-brainer.  The concept is solid enough that one could be forgiven for assuming it can’t miss, much like the RBI.

Kenway doesn’t believe in making such assumptions, so we performed a deep analysis of the data, merging SMS databases with Interactive Voice Recognition (IVR) call system databases, looking for call prevention and/or generation.  Kenway evaluated a cost calculator that combined labor and materials costs associated with the desired benefits of the SMS program, along with efficacy of the initiative altogether.  In doing so, we could understand the data and assign precise cost-based values to every message sent from client to customer.  Compare this to baseball, where you can combine context, home-field advantage, performance in other facets of the game, etc., to create a single metric that measures a complete view of a player’s overall value to his team, rather than using one number (e.g. RBI) that sounds good in theory.

Kenway’s analysis confirmed some of the organization’s return-on-investment assumptions, but was also able to shed light on initiatives which may not have been providing the benefit needed to justify their continuation.  Then, not only was Kenway able to perform this analysis, but we also implemented a new process that allowed the client to continue to perform audits on their own for as long as the project would continue.  The end result is an organization that runs more efficiently and has more accurate and detailed insight into the projects that it is funding.

As for the Colorado Rockies, they lost 90 games and finished 12.5 games out of the playoffs.  Short of replacing Dante Bichette with the ghost of Babe Ruth, no recommendation was going to save them.

If you have numbers, but you’re not sure about their meaning or effectiveness, we would like to hear from you at [email protected].