Quality management projects in contact centers have not always been especially effective in the past. One of the main reasons for this is that it is not possible for human quality monitors to listen to every call. Speech analytics technology can get us closer to the ideal of an automated quality monitoring process.
Most speech analytics systems work by identifying key phrases which are input into the application. They can either be phrases that the agent must say or must not say, or they can be phrases that we would like to hear the customer’s say. These include “buying signals” during a sales conversation, or phrases that we hope the customer’s won’t say, which indicate that the conversation is not proceeding as planned.
Most calls have prescribed opening and closing phrases. These ensure that the company is identified to the customer clearly and consistently. At the end of the call, set phrases also help to ensure that the customer is left with a polite last impression of the company in his mind.
The Script Adherence
Script adherence is related to Average Handling Time (AHT). When agents go “off script” for large parts of the call, their AHT usually increases. Speech analytics can identify the calls where the agents do go “off script” and the lengths of these calls can be analyzed to see just how much it affects AHT.
Script adherence is also important for legal and regulatory compliance. The set phrases, such as security questions, warnings that calls will be monitored and recorded and the verbatim recital of terms and conditions can all be input into the speech analytics application. In this way, agents’ adherence can be monitored quite closely.
The “yes or no problem”
Another area which is ripe for automation is the verification of verbal consent. Not all speech recognition systems can recognize individual words such as “yes” or “no”. In addition, without some kind of context, it is difficult to be sure that they would recognize the specific “yes” or “no” you are looking for in the call. What is likely to work better is to include the agent’s question to which the “yes” or “no” is the required answer in the phrase that is the search term with a conditional “yes” or “no” added to the end. In the early stages of implementation, a certain degree of trial and error will be needed to identify and input the variants of “yes” or “no” which customers may use. UK customers, for example, might say “Yeah”, “Go on then”, “Why not?” or “Aye” instead of “Yes”.
Certain customer phrases may also be worth analyzing. Listening to successful sales calls will enable the sales manager to identify commonly used “buying signals” which show the customer’s interest in the product. These might include: “How much does it cost then?”, “When’s it going to be delivered?”, “Have you got it in green?” or “When do I have to pay by?”. Identifying the calls these appear in and comparing them with the calls’ outcome or wrap-up codes may help the sales manager quantify opportunities that are being lost and focus on where his sales agents need additional training and coaching.
In the support environment, customers use specific phrases that relate to certain issues, products or services. By entering these phrases into the speech analytics application, the system will produce an analysis of how many calls are made which relate to certain issues without depending on codes input by agents. It may even be a worthwhile exercise to analyze how closely the agents’ issue codes match the results of the speech analytics, and then look at the correlation of the 2 sets of results against First Call Resolution (FCR) figures.
Spotting unhappy customer
Customers also express their dissatisfaction. Where they use commonly occurring phrases such as “This just isn’t good enough” or “I’m not at all happy about this”, these can also be entered into the application and reported on. Once again, these can be compared with outcome codes for specific calls which may give us further insights into how we can improve FCR.
As with many other aspects of technology, speech analytics, if used properly, will enable us to gain further insights into how are agents are performing on a global scale and help us to drill down, identify and solve the issues.
Learn more about ZOOM Solution for Speech Analytics.
Liam Anderson is an experienced contact center professional who has worked in the Asia Pacific region, Eastern Europe and the Former Soviet Union. He has an MBA from the University of Leicester. With ZOOM International, he has set up and run consultancy projects in Europe and the Asia Pacific region for large regional banks, a chain of luxury hotels, national telecommunications companies and outsourced call centers. Connect with Liam on LinkedIN.