This step-by-step walkthrough demonstrates how to successfully configure and run your speech and text analytics solution.
The foundation of your speech and text analytics system must begin with a specific key performance indicator (KPI). For this reason, you must ensure that your topics support analysis of the targeted KPI.
Common KPIs include the following examples:
As part of the initial speech and text analytics rollout, answer these key questions:
The KPI you target must be clearly defined and measurable. Make sure you define goals that are realistic and ask yourself what your organization currently does to meet those goals.
As part of the initial speech and text analytics rollout, answer these key questions to identify goals that will be targeted by the speech and text analytics solution:
Common concrete and achievable goals a speech and text analytics solution can help an organization attain include the following examples:
The KPI should have a financial impact and/or be recognized as important to the business strategy.
To help you determine the business strategy impact speech and text analytics will help you achieve, answer these key questions:
The following is a good example of a goal’s bottom-line assessment:
Understand how the KPIs you will target with speech and text analytics are measured today, and determine the baseline from where you will start measuring results.
To determine how to define a baseline and how to measure your speech and text analytics goals, answer these key questions:
The following is a good example of how a speech and text analytics system is tailored and used to set a baseline to measure a specific agent skill:
Upsell attempts are currently self-reported by agents, but they may not be accurate reflections on the true upsell attempt percentages. To measure the percentage of upsell attempts made by each agent during their calls, the Upsell topic will be created to include all the common upsell phrases and it will be run against agent calls. The initial results will be sanity-checked against the current self-reported numbers. When necessary the topic will be altered to include more phrases.
Voice transcription transcribes contact center voice interactions (that is, audio) into text that is stored as speaker separated conversational language. Voice transcripts generated by the system are shown in the Transcript tab as part of the interaction detail. For more information about how to enable voice transcription, see .
Programs are packages of topics that instruct speech and text analytics which business level intents to look for in recorded conversations between interaction participants. Programs are mapped to specific queues or flows and can contain topics of varying languages and dialects.
When voice transcription is enabled a default program is created. The Out-of-the-box topics will be deployed against the newly created program with the topic language determined by the Default Country Code setting (Admin > Account Settings > Organization Settings > Settings). All the interactions will be transcribed and analyzed against this program and its mapped topics.
To create and edit a program, see .
Programs are a package of topics that instruct the speech and text analytics solution about which business level intents or topics to look for in recorded conversations or digital transcripts between participants based on the flows or queues mapped to the program.
Your organization should be setup with a single default program that contains the required topics to be analyzed. A single program can also contain topics from many different languages/dialects. The system will select the correct set of topics from default program based on the detected or provided language.
When you have multiple lines of business, distinguished by flows or queues, a separate set of intents (that is, topics) can be detected for each business. To do this a separate program is created for each line of business. The program should contain topics unique to the line of business, but it can also contain topics used in other programs.
If it is determined that multiple programs are required in your organizations, custom programs can be created on an ad-hoc basis.
It is important to ensure that each program is mapped to the intended queues or flows that will handle the type of interactions for which the program was created.
To create and edit a program, see .
Perform the following steps to enable transcript content search.
To disable topic spotting in the IVR, remove the default program from the speech and text analytics settings, and map the queues to the desired topic spotting.
For detailed steps, see .
To enable sentiment analysis and topic spotting for digital interactions (email, chats, and messages), you must set an expected dialect (language) in the speech and text analytics settings page.
For more information, see .
A topic is made up of phrases that represent a specific intent (for example, cancellation). Each program consists of one or more topics that outline the topics of interest to be detected in the flows or queues that are mapped to the program.
When a topic is included in a program, the system searches for all the phrases included in the topic’s definition, in all the interactions associated with the program, based on the flow and queue mapping.
When one of the phrases is found, it is identified as an event and the topic registers as found at a specific time during the interaction.
To review out-of-the-box topics and decide which ones you want to deploy, see .
Not every out-of-the-box topic will be applicable to your organization. Genesys recommends that you review the out-of-the-box topics list, consider the underlying phrases shortly after deploying them, and decide which topics and/or phrases are worth keeping, require editing or should be removed altogether.
When reviewing these topics, consider both your organization’s vertical and the KPI(s) your speech and text analytics solution will target.
For more information, see .
To support the identified KPI(s) you must create topics that are tailored around your specific industry, business and use case. Identify these topics and ensure that they support a business goal.
Follow these steps when creating custom topics:
For more information, see .
You can add new or custom words to the dictionary from the transcription engine. To do this add a phrase or a set of phrases with the new word(s) into a topic.
Topics help boost the recognition of specific words and phrases during voice transcription. They adapt the underlying language model to look for organization-specific language in conversations.
For more information, see and .
The Content Search view displays interactions that contain a transcript of the conversation between external (customer) and internal (IVR, ACD – Automatic Call Distribution, agent, conference, or voicemail) participants, based on filter criteria.
Using the transcription search you can find very specific areas of opportunity that you might not be covering within your set of topics. For example, if you are interested in finding and quantifying how many customers are threatening to pursue legal action against your organization, you can search for interactions where the external participant (customer) stated “I will call my lawyer” or a similar phrase. Based on your query results you could decide to build a topic around this theme and more closely track this type of interaction.
Filter interactions according to the customer’s overall sentiment from -100 to +100.
This score weighs all positive and negative markers at the end of the interaction to provide an indication of how the customer experienced their interaction with the contact center.
By searching for a low sentiment score interactions (between -50 to -100), you can locate interactions where the customer left the conversation feeling angry and/or frustrated. On the other hand, you can search for interactions with a high sentiment score (between 80 to 100), where the customer specifically indicated their satisfaction when finishing the conversation.
Once the interactions are located, they can be analyzed to find the root cause of the customer sentiment, plan your next steps (for example, contact the customer, agent coaching), or reward an agent for his/her work.
Filter interactions according to the customer’s sentiment trend.
The customer’s sentiment trend is determined by comparing the sentiment in the first half or more of the interaction, to the sentiment in the last few phrases of the interaction.
By searching for low score trend interactions (between -50 to -100) you can locate interactions where the customer had a steep sentiment decline between the first half of the conversation and the second half of the conversation. Also, you can search for interactions with high sentiment trend score (between 80 to 100) interactions where the customer expressed negative sentiment in the first half of the conversation, and had their experience changed to a positive one in the second part of the conversation. Once the interactions are located, they can be analyzed to find out the root cause of the customer sentiment, plan next steps (for example, contact customer, agent coaching), or reward an agent for his/her work.
The topic trends view helps paint a story by displaying data in a way that is easy to understand (for example, highlighting trends and outliers).
This view displays the list of topics and the distribution of interactions based on the user selected filter criteria (eg. Date range, media type, etc).
Common Usages:
For more information, see .
Improving agent performance is one key area where speech and text analytics can be instrumental to achieve your organization’s goals. To attain this benefit, it is important to follow guidelines for developing agent training programs in which is used to research optimum agent behaviour, track improvement in agent skill use, and improve a specific performance output for the company.
For more information, see .
To successfully deploy and use a speech and text analytics solution, your organization must be setup to rapidly act upon receiving actionable data. Your organization must also be able to provide feedback to the platform administrators about new business opportunities. These new business opportunities can be added to the solution in the form of topics that will generate new actionable information.
The following roles are suggestions
Roles and profiles have proven to be key for successful speech and text analytics deployments. Roles may change based on the nature of the project. The roles listed below are necessary for complex projects requiring enterprise wide analytic and process reengineering efforts as well as agent level interventions.
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With speech and text analytics users within your organization can gain deep insight into all interactions that are occurring in your contact center.
The following represent several use cases that are valuable to your business.
Follow the three steps below to extract the data discussed in the three use cases above.
Turn the audio from the contact center voice interaction into structured data (for example, text). The structured data can then be used for large scale analysis, accomplished with voice transcription. For more information, see .
Define key topics of interest within conversations. The topics of interest will depend on the KPIs (Key Performance Indicator) and use cases you are targeting.
For quality managers and supervisors, decide which agent skills or behaviors you want to track. For example:
Most objective evaluation criteria and some subjective evaluation criteria can be measured by detecting phrases within transcripts.
For business analysts, understanding why customers contact you is the starting point of any improvement program. The reasons are usually specific to your business and require an internal discussion to gain consensus on what needs to be tracked. For example are:
In addition to defining contact reasons, you should also create topics around specific products or services that your organization provides. This combination is an important part of understanding your customers.
For risk managers, detecting phrases that put the business at risk is most importance. This may range from language that indicates fraud by the customer or agent, severe complaints or legal action threatened by customers, or specific compliance language that must be communicated on calls. Topics can be created to watch for these markers in conversations.
Review the resulting data in analytics views to make conclusions about performance, and act based on what the data reveals.
Quality managers and supervisors should look at the Topics tab in the Agents view to isolate top and bottom performers on measured skills and behaviors, so that they can isolate opportunities for coaching or recognition for each specific agent.
Business analysts should look at the Topics Trend view to see if there are any unusual trends in call reasons, or mentions of products or services as defined by topics. It is often useful to look at this information according to handle time so you can see what types of call reasons resulted in longer calls. These call reasons should be assigned process improvements to speed up these interactions.
Risk managers should periodically view trends for key topics, or perform ad hoc searches to investigate any concerns raised by the business. Identifying high risk interactions where there may be complaints or inappropriate agent behavior that should be investigated or mitigated enables improved customer protection.
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