Average Handling Time: A controversial metric

What is AHT:

Average handling time (AHT) is a contact center metric refering to the average duration of one transaction. It includes the duration of the interaction itself along with any prelimary tasks, post – interaction times as well as delays during the interaction.

The importance of AHT and methods to improve it:

AHT is an important metric that is tightly related to staffing needs. Assuming that the amount of incoming transactions is independent of the AHT, lowering AHT directly reduces the needs in personnel. This specific argument has been extremely popular in the past as a method to decrease call center operating costs. Smaller AHT can also help reduce the waiting time of incoming calls, which is another popular metric in various contact centers.

Many contact centers are thus aiming for low AHT and some (like the outsourcing company I was working for a few years ago) use this goal as the cornerstone of their operations. There are several methods that are typically used to reduce this particular metric, some of which are really beneficial while others can cause more problems than they solve and increase total costs despite decreasing costs of human resources:

• Reduce talking time. The only really successful way to do this is by coaching and training customer service representatives to increase their knowledge as well as their reflexes. It is a slow but highly recommended method for decreasing AHT. Other clumsy methods that are widely used in many horrible contact centers are prompting the customer to try/do something and then ask him to call again or find ways to get rid of the customer in case the call seems to take too long (I remember i was given a list of methods to achieve that during my training as a throwaway agent!).
• Reduce hold time. This can be achieved again via training agents to multitasking. Another method of critical importance in achieving smaller hold times is deploying a prompt IT infrastructure. Well designed CTI systems help agents find quickly the information they need. A good CRM is also critical in this regard.
• Reduce after-call time. This is again achieved by both multitasking training (part of post-call work can be completed while talking to a customer) as well as good IT infrastructure (eliminate the need for duplicate entries etc).
• Use automated systems like IVR to perform mundane tasks such as user authentication and gathering basic information about the customer's query so as to route the call to the best available agent to handle it. This in a very effective method of reducing AHT that is widely used.
  
  

AHT criticism:

However, AHT has lost a lot of its former importance lately for various reasons. First of all, the amount of incoming transactions is not completely independent of the AHT. A badly handled call that leaves several unresolved issues can have very low handling time but it may result in several additional calls in the future. Taking into account the fact that pre-interaction and post-interaction overheads are pretty much the same for most calls (an agent requires more or less a specific amount of time to fill in data in the CRM, regardless of the result of the call) this can severely increase the effort needed to actually solve a customer's issue, even by reducing AHT. Furthermore, pushing for lower AHT typically results in agents behaving in a certain manner, deliberately trying to get rid of a customer if the call seems to be taking too long. In general, AHT conflicts with FCR which can be seen by itself a severe drawback. This conflict between these two metrics has been a point of controversy in many companies. Many experts today believe that FCR should be the top goal of every contact center and other metrics that may conflict with it, such as AHT, should be looked at only in case they do not affect FCR.

Avaya Web.alive

Last week, Avaya announced web.alive, an “immersive collaboration platform that uses personalized avatars and rich, spatial audio and visuals to expand on current modes of conferencing and collaboration.” It is offered both as a cloud-based service as well as a standalone installation for in – house deployment. The cloud service offering is a very convenient method to use this product for inter - business purposes. 

It brings on the table attractive characteristics, including a new 3D audio engine, built-in collaboration tools, integration with other Avaya products and an analytics suite among other things. All these features greatly enhance the online collaboration experience. The classic teleconference notion is getting a step ahead, becoming more virtualized. Meetings feel like playing a massive multiplayer online game as you may see on the following videos.

Introduction

Basic Navigation

The user gets the feeling of playing Second Life or World of Warcraft type of game, but in a manner that actually facilitates and enhances business activities. A free demo is available to try this.

I must admit I am extremely impressed by this product and especially its potential. It introduces a radical change to the way business communications work and could be the foundation of a fully virtualized working environment in the future.

First Call Resolution

Definition of First Call Resolution:

First call resolution (FCR) is a CRM and contact center concept that refers to serving a customer in a satisfactory manner the first time he contacts a company. It is often used as a metric for assessing contact center performance and many companies consider it to be of paramount importance. Most of the time, FCR conflicts with average handling time (since more actions are required to resolve issues within the limits of one interaction).

Why is FCR important?

FCR is important for several distinct reasons. First and foremost, it highly increases customer satisfaction. Just try to remember how much frustrated you get every time you call a company to express a request or a problem or inquire some information. Doing it right the first time indicates that the company actually cares about its customers and their concerns, offering quality personalized services. It also shows (and requires!) that the company is well organized and knows exactly what is going on with every single customer they got.

The second reason FCR is considered very important is cost savings. High FCR rates directly result in less incoming calls. Various surveys have studied the effect of this in contact center operational costs and they report cost savings that can reach up to 30% for high FCR rates. A simplified way to calculate how much savings can be achieved by improving is described in this article.

There are other benefits also in measuring FCR. It is an indication of agent performance that can be used to reward skilled employees. It helps improve overall business processes by analyzing what went wrong in the cases where one interaction was not enough. And, finally, it helps appreciate the reasons why customers call and what conditions caused their problems in the first place.

Measuring FCR.

Due to the importance of FCR, most contact centers today actively try to measure it. The calculation is included in the metrics presented by many contact center statistics packages available today. However, there are significant problems in measuring FCR accurately.

The first and most important problem is the perspective under which to measure it. Does it matter if the customer is happy with the resolution? This is a subtle difference between methods of measuring FCR and sometimes even subjective. A popular method used to calculate FCR taking into account customer satisfaction is actually asking the customer. This can be done either by an agent or even by a simple IVR question. This approach is not enough to yield accurate results though.

Another problem that cannot be solved by asking the customer is the asynchronous nature of some customer requests. For example, a customer may call about a problem he has and request some credit on his account as compensation. The representative verifies the issue and assures the customer that he will get the credit. However, whether the credit will be actually finalized might not be obvious until the customer receives a future bill (which might occur several months later). At the time the transaction takes place, neither the customer nor the agent really know if the issue was solved in a single interaction. Thus, FCR calculation is usually a long procedure of collecting and evaluating data, so as to be able to take into account as many exceptions as possible.

Improving FCR.

To improve FCR rates, a company must first trace the root cause of failures to deliver fast and efficient customer service. Typical methods used are:

• Improve agent skills by training.
• Improve agent access to CRM information, using better integration among company systems. Ensure that these systems are accurate, current and always working.
• Allow the agents enough time to solve the customer’s issues and avoid rewarding them for achieving a short average handling time.
• Ensure that back end systems operate correctly, without any bottlenecks or points of failure

Contact Center Statistics Overview

The interactions that take place through the contact center via various channels offer a vast amount of information that companies seek to consolidate, and gain useful insight about their customers as well as about themselves. For this reason, an integral part of contact center software suites are packages designed to collect statistics. 

Data collection and consolidation:

Collection of data can take place on various parts of a contact center. Some usual points this is done are: the PBX, which is a central hub for all calls and can yield information related to call routing, the CTI that might control the PBX, the IVR that shows, among other things, information about the self-service behavior of the customers and the call logger where speech analytics can apply to extract semantic information from actual discussions. In an outbound contact center, the predictive dialer can be a source for information as well. In the case of a contact center based on SIP, the SIP servers can also provide important information. Workforce management software is another source of information, mostly related to human resources. In short, depending on the specific implementation of each contact center, statistics can be gathered from almost every node in the ecosystem, about practically everything that takes place within the center.

For each of the aforementioned points in a contact center, the vendors that produce them tend to include in their software offers extensive analytics tools. These tools not only gather the data about the interactions being performed through the contact center, but also they consolidate them, applying various formulas to extract a variety of metrics (a future post will examine in more detail some of the most widely-used metrics) that can be used to oversee performance. 

Using statistics:

These consolidated statistics can provide a wealth of information that can help substantially improve the contact center. Metrics related with customer service representatives can be used to improve the human resources of the company in various ways. Statistics that show customer preferences over a variety of offered contact channels (phone, e-mail, web chat) can be combined with performance metrics on each of these channels to indicate ways to improve customer service and satisfaction. Other types of statistics may help improve the business processes of the company, and its efficiency in general, showing where bottlenecks typically occur. Outbound campaign statistics can improve, among other things, the marketing tactics of a company.

All the benefits mentioned, and a lot more can be easily gained by including sufficient data gathering and reporting tools in the contact center. However the most important thing is to actually use these statistics after viewing them and also use them wisely. Many companies seem to fail in this area, underestimating the importance of such metrics. On the other hand, some companies go to the other extreme, becoming too attached to specific metrics and trying to improve them at all costs. In the process, they tend to miss the bigger picture and do costly mistakes. Statistics is a great tool but it has to be used with caution.

Speech Technology: Voice verification

Introduction:

This is the third and final part of a series exploring the most frequently used speech technologies in contact centers. The first two parts discussed Speech Recognition and Speech Synthesis (or Text to Speech). We will now turn to voice verification, an exciting and relatively new technology that can greatly enhance security.

What is voice verification and how it works:

Voice verification is a biometrics technology which focuses on matching a person’s voice with a pre-recorded sample, to verify that the speaker is who they claim to be. Each person’s voice is completely unique, much like a fingerprint.

The speaker initially recites some text or phrase or some discrete words, numbers and so on. The uttered speech is digitized and stored. Biometrics engine splits each spoken word to small segments called formants (much like speech synthesis engine works with phonemes as it was described in the text-to-speech article). These formants are then analyzed into tones that can be then captured in a digitized format and stored in a database. These are the physical characteristics of the voice. In addition to these, additional characteristics are recorded and stored, the so-called behavioral characteristics. An example of behavioral component is pronunciation. The speaker is typically prompted to utter the text/words several times to gather more information about his voice and allow for greater variation.

When the speaker utters the same text in the future, the same procedure takes place and the extracted tones are compared to the stored ones.

Voice verification accuracy and other issues:

The accuracy of this verification can be affected by numerous factors. A person’s voice can change over time based on health issues (having a cold significantly alters voice) or even psychological issues. Background noise is another problem which can distort the uttered speech and microphones tend to enhance this problem. Voice distortion over the telephone can also affect the accuracy of the verification process.

To ensure the highest possible accuracy, the conditions of sample gathering should apply as much as possible to future verification attempts. For example, if a verification procedure is going to be used over phone in the future, the sample gathering should also be performed using a phone. Also, both the sampling and the verification procedures should be performed under low noise conditions.

In any case, the aforementioned limitations sometimes make the verification procedure harder to complete successfully. Therefore, most implementations opt to use the voice verification combined with the classic PIN approach. In this approach, the speaker is prompted during the sampling procedure to utter a series of digits which comprise his PIN. When the speaker tries to authenticate himself in the future, he speaks his PIN again and two procedures take place in parallel. The voice verification engine tries to match the speech characteristics to the stored ones in the database. A speech recognition engine tries to understand the digits been uttered and produce the PIN in a text format. Both engines should confirm that the speakers are who they say. This approach results in substantially higher recognition accuracy overall, and renders systems that use it resistant to fraud attempts.

Speech Technology: Text to Speech

Introduction to Speech Synthesis or Text-to-Speech.

This is the second part of a 3-post series discussing the basic speech technologies that can be used in contact centers. Automatic Speech Recognition (ASR) was presented in the first part, a technology allowing a computer to interpret human speech and convert it to text. The opposite procedure, producing human speech artificially from a piece of text, is called Speech Synthesis or Text-to-Speech (TTS).

How Speech Synthesis works.

The procedure of synthesizing speech from a piece of text generally works in three steps. In the first step, text is converted to a normalized form that consists of only words (eliminating abbreviations, numbers etc.). The speech engine assigns to each word phonetic transcriptions (using a phonetic alphabet). In the final step, the speech synthesizer uses the phonetic transcript to produce the actual sound.

Speech synthesis is primarily done via concatenating segments of recorded speech by actual humans. The produced waveform consists mostly of actual human speech and is only adjusted by processing at the points of segment concatenation. This type of synthesis is the most natural – sounding, producing speech that closely resembles humans.  Larger databases of prerecorded segments highly increase the quality of the output, but also increase processing power and memory requirements. Another frequently used speech technique, with substantially less computing power requirements, is additive synthesis. This method does not use actual human speech samples, but it is based instead on mathematical models. The speech produced this way sounds robotic. However, it is easier to produce and more uniform than concatenated speech, which might produce glitches at the points of concatenation. Some engines use a combination of both methods.

Quality of synthesized speech.

The quality of synthesized speech has been greatly improved over the past few years. Large amounts of money have been invested to fine tune the synthesizers for languages such as English, which apply to very broad markets. Engines produced by market leaders such as Nuance, Loquendo and Acapela can be often indistinguishable from actual human speech, especially when applied over the phone (these companies, and many others, offer free demos for their engines - check them out at their websites). TTS engines are available for various languages; however their quality is usually somewhat lacking compared to English (though still very good).

TTS and ASR usage in IVR.

Synthesized speech is very convenient to use in IVR systems, when a large amount of required prompts has to be changed frequently, providing large cost savings as well as deployment efficiency (synthesized speech can be produced instantly without any human intervention). Its quality is still not the same as prerecorded prompts, but it gets a lot closer for some languages. Thus, many companies opt to use TTS in their voice applications, especially for English language applications. TTS can be combined with ASR for a complete cycle, as the schematic shows.