ContactBabel CallMiner User Manual
The US Contact Center
Decision-Makers’ Guide 2021
The Interaction Analytics Chapter
Sponsored by
Intelligence from Customer Interactions
CallMiner leverages AI and
machine learning to deliver
interaction analytics that produce
enterprise-wide ROI.
In your fast-paced contact center environment, analyzing
every interaction in order to optimize call outcomes,
customer experience and revenue generation could be a
challenge. But CallMiner interaction analytics makes it easy.
Gain an unparalleled view into conversations, leveraging
AI to help guide agents in realtime DURING their calls, or
to coach them post-call on the best practices that work at
YOUR organization.
Leverage interaction analytics to optimize
performance across the enterprise.
• Identify techniques/agents that produce the best outcomes
• Uncover insights to inform product, marketing, sales
• Maintain compliance through 100% monitoring
• Enhance employee experience with real-time agent guidance
Take a FREE CallMiner analytics test drive starting
with your own phone calls!
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“The 2021 US Contact Center Decision-Makers’ Guide (13th edition)”
© ContactBabel 2021
Please note that all information is believed correct at the time of publication, but ContactBabel does not
accept responsibility for any action arising from errors or omissions within the report, links to external
websites or other third-party content.
CallMiner is a recognized leader in the speech analytics software industry, harvesting key
customer and operational insights from multi-channel customer interactions. Uniting with our
customers and partners, our platform drives contact center efficiency, exceptional customer
and employee experience and significant improvements in top and bottom-line corporate
performance.
CallMiner Eureka offers both real-time monitoring and post-call analytics, delivering actionable
insights to contact center staff, business analysts, and executives. The results include improved
agent performance, sales, operational efficiency, customer experience, and regulatory
compliance.
With over 2 trillion words analyzed annually, CallMiner serves some of the world’s largest call
centers, delivering highly effective, usable, and scalable customer engagement analytics
solutions.
Highlighted by multiple customer achievement awards, including six Speech Technology
Magazine’s Reader’s Choice Awards, CallMiner has consistently ranked number one in
customer satisfaction.
Learn more about our customer engagement and speech analytics solutions to
help your business:
Web: CallMiner.com
Social Media: LinkedIn, Twitter, Facebook, YouTube, Blog
User Community: EngagementOptimization.com
Email: marketing@callminer.com
5
INTER AC TION ANAL YT ICS
On first glance, customer interaction analytics can be seen as providing similar information to
management information and reporting systems: taking masses of data and making sense of what they
mean to the contact center's performance and perhaps even inside the wider business. However, the
vital thing to understand about analytics is that it gives contact centers the answer to 'Why?', not just
'What?'. Why are average handle times so different across agents? Why are customers of this product
upset? Why are people calling the contact center?
Customer interaction analytics solutions offer huge opportunities to gain business insight, improve
operational efficiency and develop agent performance. In fact, the list of potential applications for this
technology is so high that businesses could be forgiven for being confused about how to target and
quantify the potential business gains.
Depending on the type of business, the issues being faced and even the type of technology being
implemented, drivers, inhibitors and return on investment can differ greatly. While an analytics solution
may be implemented to look at one particular pressing issue, such as automating the QA process, it will
further develop over time into looking at business intelligence and process optimization.
Interaction analytics can be used in many different ways to address various business issues. This is an
advantage – it is hugely flexible – but it can also make its message to the market more complicated.
However, depending upon how interaction analytics is used, it can assist in:
• agent improvement and quality assurance
• business process optimization
• avoidance of litigation and fines
• customer satisfaction and experience improvements
• increases in revenue and profitability
• improvements in contact center operational performance, and cost reduction.
Like most contact center applications, analytics can be used to cut costs, but its promise goes far beyond
this. No other contact center technology provides the 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.
This is not to say that the science of customer contact analytics is yet at its zenith. Significant
improvements are still being made to the accuracy and speed of the speech engines, the sophistication
of analytical capabilities, the integration of various data inputs and the usability of report. The
integration of sophisticated AI and machine learning capabilities within the analytics solutions offers the
chance to take analytics far beyond what was imagined a few years ago.
Some of the actionable findings from analytics may seem very simple – the recommendation to change
a few words in a script, for example – but the overall potential impact upon the cost, revenue, agent
capability and customer experience that is possible through analytics is perhaps unprecedented.
6
There are various elements to customer contact analytics solutions, including:
• 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, web chat and social media. 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.
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Like any technology, customer contact analytics has its own descriptive language, and some of the more
common words or phrases someone researching this industry would find include:
• 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. Categorization can be done by the business based on their own experiences and
requirements, through using vendors’ out-of-the-box categorizations for common analytics use
cases, or by implementing AI and machine learning to find categories within the business’s data
• Discovery: requiring a transcription-based solution, analytics will seek 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.
8
DRIVERS FOR CUST O M E R INTER A C TION ANA L YTICS
Customer interaction analytics offers huge opportunity to gain business insight, improve operational
efficiency and develop agent performance. In fact, the list of potential applications for this technology is
so high that businesses could be forgiven for being confused about how to target and quantify the
potential business gains. Depending on the type of business, the issues being faced and even the type of
technology being implemented, drivers, inhibitors and return on investment can differ greatly. While an
analytics solution will be implemented to look at one particular pressing issue, such as compliance or
automating the QA process, it will further develop over time into looking at business intelligence,
process optimization, customer experience improvements and revenue increase.
There are various ways to segment the uses of analytics, and it may therefore be useful to divide them
into one of two groups: those that are around solving a specific known problem, and those which are of
a more strategic, long-term nature, although there is some crossover between the two groups.
Figure 1: Uses of customer contact analytics
Problem-solving/issue resolution
Strategic/long-term
Compliance with regulations
Gathering competitive intelligence
Verbal contracts/repudiation
Feedback on campaign effectiveness and
pricing information
Redaction of card information for PCI purposes
Understanding the customer journey
Adherence to script
Understanding why customers are calling
Identifying agent training requirements
Improving contact center performance metrics
Reducing the cost of QA
Optimizing multichannel/inter-department
communication
Identifying and handling problem calls
Deepening the power and functionality of the
workforce optimization suite
Estimating customer satisfaction and first call
resolution rates
Identification and dissemination of best
practice
Predictive routing
Identification and handling of dissatisfied
customers, and those at high risk of churn
Real-time monitoring and in-call feedback
Maximizing profitability by managing customer
incentives
One-off discovery/analysis via cloud
‘Tell-me-why’/root cause analysis
9
USE O F IN TERACTI O N ANALYT I CS
Compared to recording-based functionality which has penetration rates of over 90% in most sectors,
interaction analytics (especially of the omnichannel variety) is still to reach its full maturity, although the
general long-term increase in penetration rates and the enthusiasm shown by contact centers to learn
more about the subject is very positive.
The positive correlation between size and penetration rate is very noticeable for interaction analytics,
which may require significant investments. As importantly, having huge volumes of recorded
interactions and a large customer base to learn from means that business patterns can be identified
more accurately, and any improvements reap correspondingly higher rewards.
Large operations are also more likely to have the budget and resource to use analytics to its potential,
although there is also a significant level of long-term interest in implementing analytics in the small and
especially the medium contact center sectors.
Figure 2: Use of interaction analytics, by contact center size
14%
22%
33%
21%
6%
8%
19%
9%
23%
26%
26%
25%
10%
20%
7%
12%
43%
22%
14%
30%
5%
2%
3%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Small Medium Large Average
Use of interaction analytics, by contact center size
Don't know / NA
No plans to implement
Will implement after 12
months
Will implement within 12
months
Use now, looking to
replace/upgrade
Use now, no plans to
replace/upgrade
10
Against a virtual ubiquity of call recording, the penetration rates of interaction analytics are much lower:
31% of this year’s respondents use it now, with a further 36% stating that they have plans for
implementation.
Respondents from the TMT and outsourcing sectors report the greatest use of analytics this year, with
those in the public sector least likely to be doing so once again. It is probable that the use of interaction
analytics is driven more by contact center size in call volumes than through the requirements of specific
types of business: many of the public sector contact centers are smaller than average, whereas those in
outsourcing and TMT are amongst the highest.
Figure 3: Use of interaction analytics, by vertical market
29%
32%
33%
27%
22%
31%
10%
13%
4%
9%
22%
14%
8%
7%
11%
20%
17%
16%
9%
48%
28%
17%
40%
11%
21%
10%
4%
28%
18%
24%
5%
22%
10%
30%
21%
16%
36%
12%
5%
28%
50%
27%
22%
38%
30%
42%
32%
27%
30%
4%
11%
4%
4%
9%
3%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Technology, Media & Telecoms
Outsourcing & Telemarketing
Manufacturing
Retail & Distribution
Transport & Travel
Services
Insurance
Medical
Finance
Public Sector
Average
Use of interaction analytics, by vertical market
Use now, no plans to replace/upgrade Use now, looking to replace/upgrade Will implement within 12 months
Will implement after 12 months No plans to implement Don't know / NA
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