Recently the 11th edition of Contact Babel’s “US Contact Center Decision-Makers’ Guide” the largest and most comprehensive study of all aspects of the US contact center industry was published. Customer interaction analytics is a key area of focus in the report, as their survey showed that only on average 30% of contact centers are using interaction analytics, predominately speech, in their businesses. Even though 90% of respondents said they are recording calls. Those are a lot of calls not being listened to and utilized for better customer and agent experiences!
But for the over 31% of businesses who have plans to implement a customer interaction analytics program in the next year or so, Contact Babel has broken down some of key terms and vocabulary you will be getting familiar with.
7 Elements to Customer Contact Analytics Solutions
Speech engine: a software program that recognizes speech and converts it into data (either phonemes – the sounds that go to make up words – or as a text transcription, although there are solutions which directly recognize entire spoken phrases and categorize calls based upon the occurrence of those phrases)
Indexing layer: a software layer that improves and indexes the output from the speech engine in order to make it searchable
Query-and-search user interface: the desktop application where users interact with the analytics software, defining their requirements and carrying out searches on the indexed data
Reporting applications: the presentation layer of analytics, often in graphical format
Business applications: provided by vendors, these pre-defined modules look at specific issues such as adherence to script, debt collections etc., and provide suggestions on what to look for
Text analytics: this solution combines the transcription of customer calls with other forms of text interactions such as email and web chat. It then uses natural language processing models along with statistical models to find patterns
Desktop data analytics: a solution that gathers metadata from agent desktop and CRM applications – for example, account ID, product order history and order value – and tags them to call recordings or digital records, enabling deeper insight.
Like any technology, customer contact analytics has its own descriptive language, and some of the more common words or phrases someone researching this industry.
5 Speech Analytics Term Definitions
Categorization: the activity of grouping conversations according to user-defined topics, such as complaints, billing issues, discussions of specific products, etc. Agent capability can be viewed by these categories, suggesting specific training needs as well as identifying any required changes to processes
Discovery: requiring a transcription-based solution, analytics will dig out phrases and words that are showing up in noteworthy patterns, showing how they fit together and how they relate to each other, discovering trends automatically
Metadata: non-audio data, which may be taken from CRM, ACD or agent desktop applications, which is tied to audio recordings or other interactions, improving the ability to correlate, discover patterns and pinpoint specific types of interaction
Search: if the analytics user knows what they want to find, the search function can return a list of calls with these words or phrases within them. Speech-to-text / transcription applications return the sentence or whole interaction so that the user can see the context as to how this has been used, offering the opportunity to run text analytics on top of this as well
Closed-loop analytics: where also known as “closed-loop marketing”, this activity involves tracking the entire customer lifecycle (i.e. connecting the initial contact all the way to the sale, and into ongoing support and post-sale activity), in order to draw actionable insights about how elements of the customer lifecycle impact upon sales success and marketing effectiveness. From a perspective more closely focused upon the customer experience, “closed-loop” refers to the continued, iterative use of automated alerts, follow-up of issues (e.g. through call-back) to support root cause analysis, and the identification and resolution of suboptimal processes.
No other contact center technology provides your business with this level of potential insight that goes far beyond the boundaries of the contact center, and can offer genuine and quantifiable ways in which sub-optimal business processes can be improved. Download the complete chapter on customer interaction analytics in from the “US Contact Center Decision-Makers’ Guide (2018/19 – 11th edition)” here.
This blog is excerpted from the “US Contact Center Decision-Makers’ Guide (2018/19 – 11th edition)” published by Contact Babel is the major annual report studying the performance, operations, technology and HR aspects of US contact center operations. Taking a random sample of the industry, a detailed structured questionnaire was answered by 222 contact center managers and directors between March and July 2018. Analysis of the results was carried out in August and September 2018.