7 conversation analytics features to turn you into a customer listening pro
August 22, 2020
Diving into the depths of all your customers’ conversational data can feel daunting. We've spent more than a decade working with medium to enterprise-level businesses across a range of industries to turn them into voice of customer (VoC) listening leaders. Now, we’ll share seven of our most potent conversation analytics features to help you become a customer listening pro yourself.
“To innovate their way beyond a post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, research VP at Gartner.
Three of these critical variables Gartner suggests companies analyze so they can “predict, prepare and respond in a proactive and accelerated manner to a global crisis and its aftermath” are conversation analytics variables. They include audio analytics, speech analytics and text analytics from customer calls, customer chatbot conversations and customer support case emails. Taking these unstructured customer conversational data points and mining them for insight sets companies up to be data-driven VoC leaders at a time when listening to our customers is essential.
Let’s look at seven simple, yet incredibly powerful, customer conversation analytics features we use inside Tethr. Take structured and unstructured audio analytics, speech analytics and text analytics from your customer conversations, transform them into actionable insights and transform yourself into a customer listening pro.
Seven customer conversation analysis features Tethr uses for customer listening
To throttle the power of customer conversation intelligence platforms, like Tethr, to their fullest capability and to deepen your understanding of your voice of customer data, our customer success team has more than a few recommendations.
Use this robust set of call and text analysis features to:
Visualize, organize and share your voice of customer insights across the organization
Refine your unstructured VoC data using category exclusions, determine multi-step processes and drill deeper into conversational data subsets
Unify insight across your existing set of BI tools organization-wide
Visualize and organize your CX insights to share across the organization at every level: CXO to agent
1. Set contextual filters on specific reports and dashboards inside a folder of CX insights
create a single analytics dashboard for business-driven effort,
organize it into a folder with any other dashboards or standalone reports you want to include,
then apply filters to your folder, and across the entire dashboard and any reports.
Finish by sharing custom contextual views of each dash with multiple senior execs, managers and anyone on their teams.
Filtering your customer conversation analytics reports and dashboards by context frees you from the time-suck of needing to build handfuls of unique reports customized to each manager and team’s needs. Instead, repurpose the same analytics dashboard you’ve already constructed once for your Customer Experience team, your Customer Service and Sales teams, your Product team and your Marketing team. Give each team answers to your customers’ most pressing questions and pain points with your business with low-to-no effort.
2. Set real-time instance filters on individual reports on an analytics dashboard
Once you’ve got your single analytics dashboard for business-driven effort and you’ve refined the view for each team of stakeholders, open up the dashboard to quickly adjust the display variables on multiple reports across the dashboard so you can compare unique attributes across each.
To refine your customer feedback data even a step further our Customer Success Manager, Amanda Lucio, shares this tidbit: “When I’ve built dashboards with each report set to the last two weeks using folder filters, and I want one report in the dashboard to show a month-over-month trend (to give the data context), I use Tethr’s instance filters on that dash.”
What’s happening is that you’re refining the view of a report instance on your dashboard — by date, agent, customer, interaction type, team, location or any other custom metadata — rather than editing the analytics dashboard itself.
3. Create user-specific filters when sharing folders full of customer conversation insight
Next, refine your business-driven effort analysis a step further: by user. With user view filters you're able to build one set of conversation analytics dashboards or reports as your "absolute truth" to make sure everyone measures the same data, and give each person you share it with a customized view (so they’re only looking at their own team, location, for example).
So, let’s say you’re sharing your folder full of business-driven effort dashboards with the five managers of the teams mentioned above (CX, Customer Service and Sales, Product and Marketing). Effortlessly customize the view by user’s needs rather than create five separate folders for each team lead.
Now that your managers are on the same page about the voice of customer insights that matter to them by business outcome, team, location and individual — empower the entire organization. We cannot understate the power in transforming disparate customer voice, speech or text data into conversation analytics and then customer conversation intelligence — it’s customer listening pro-level work. And Tethr helps you drive it all home by bringing each team’s point of view and their unique set of actionable insights together.
Refine your unstructured VoC data using category exclusions, determine multi-step processes and drill deeper into data subsets
4. Refine your reporting using the exclude function on categories
Our customer success managers will be the first to tell you how powerful Tethr is. But, looking at your entire voice of customer dataset can often be overwhelming and messy. That said, being able to build categories in Tethr that are well-refined is incredibly important.
Categories help turn the structured and unstructured audio, speech and text data that make up your conversation analytics into traceable events either happening or not happening across your customer calls, chats and customer support cases.
Tethr’s customer success team transforms raw customer feedback data using the exclude function to refine existing categories.
Category exclusion allows you to:
see what the category may not yet be detecting that you can still add to refine it (also called negative auditing).
exclude agents or build categories to refine a report.
exclude custom labels you’ve created on a customer interaction.
A few examples of how category exclusion helps you refine your unstructured customer conversation analytics:
4a. Exclude a keyword or phrase in your audio or text from a category, also known as negative auditing:
As one of our long-time Customer Success Analysts, Lauren Gray, explains, “You can build a category in Tethr and exclude it to uncover other phrases or expressions of the same concept.” This answers the question, what is the category not yet detecting that I can still add to it?
For example, take a financial category like Financial Installment plan. “You might have Financial arrangement or Installment plan as major themes to be detected across your customer interaction volume. If you build a category and exclude it to see what else you can still add to that category to refine it,” shares Lauren.
4b. Exclude an agent from a category:
For example, if a call center agent has switched customer support teams halfway through the month and you don’t want to see them in this month’s report.
4c. Exclude a label from a category:
For example, to focus the category on finding specific audio issues or foreign language calls, you can label those interactions and then exclude them.
5. Understand customer effort across multi-step business processes
Using one of Tethr’s most powerful, differentiating features (pssst … it’s the Conditional category), you can capture and build structured customer insights on one part of a call. Then, follow it up by taking multiple parts of a call (like a sales process) and determining the ordered steps to be executed toward an outcome (like a sales conversion).
Customer Effort, as shown in the conditional category sequence above, can come from behaviors and reactions happening in the interaction between a customer and your company’s agent, like customer Frustration, customer Confusion, Chronic effort on the part of the customer, Long hold times or Missed expectations.
And although these behaviors and reactions (which we consider the ‘whys’ of the customer’s experience) create customer Effort, they also pinpoint the exact behaviors, reactions and processes your business can improve — like closing business process breaks, filling training gaps, agent skill gaps and coaching to change agent behaviors.
6. Graphing subsets of customer interaction data by their percentage of total volume
A lot of companies may understand the outcomes of a customer interaction (like sales conversion or Average Handle Time) and can base their business decisions on them; knowing the rate and volume of a business outcome can help you hone in on its impact.
For example, instead of only knowing the raw number of times something happens on a phone call, in the chatbot or in a customer support case email, you can see what percentage or rate of your total volume at which something happens.
Let’s say you have 275 sales. And of those sales ...
Which sales pitch was used n times and converted n sales:Pitch 1 was used 26 times and converted 10 of those into sales.Pitch 2 was used 91 times and converted 31 of those into sales.Pitch 3 was used 22 times and converted 10 of those into sales.Pitch 4 was used 40 times and converted 20 of those into sales.
Using the subset as a percentage of volume measurement, or y-axis, on your graph you can hone in on the subset of customer interactions which converted a sale (as shown in the example below where our Customer Success Manager, Amanda Lucio, built a category to track sales conversions).
She also built categories to track each specific sales pitch occurring in a customer conversation (Pitch 1 through 4) and then grouped the subset of total customer interaction by those categories to surface which pitch had the best conversion rate, doing some customization of her graph using our limiting and de-aggregation features to show the most important metrics.
So, which sales pitch was most effective? Contrary to what your initial impression might have been, Pitch 4 with only the second highest pitch use and converted totals was actually the most effective sales pitch of the four with a conversion rate of 50%. Using this feature, you can also get at rates of customer Objections, Rebuttals, customer Effort drivers or Frustration drivers happening in your customer conversations.
Or, you can understand how much of the time a specific outcome is happening. For example, with Subset filtering you can identify specific agent behaviors and their percentage rates of occurrence (e.g., Recontact is happening 10% of the time or Advocacy at 70% of the time), so you can identify your own conversation analytics baselines and then set realistic goals for improvement.
It takes a dedicated customer listening pro to bring together disparate customer conversational data, conversation analytics technology, each team’s point of view and their unique set of actionable insights to help an organization move from insight to action. From conversation analytics to conversation intelligence. Once you’ve refined your categories in Tethr and everyone’s on the same page about the customer conversation insights that matter to them, you’ll be empowering your entire organization with the conversation analytics that lead to organization-wide conversation intelligence.
Distribute Tethr’s rich VoC data to unify insight across your existing set of BI tools organization-wide
7. Export every detail of your customer interactions
So, what’s left in a customer listening pro’s toolkit? Well, powering the entire organization up with that same insight you’ve now shared across teams and departments.
Tethr isn’t only built for analyzing audio, speech and text and pulling out their insights in whatever channel they happen to be. Tethr’s flexible report and dashboard filtering and sharing capabilities and its category refinement features discussed above will fine-tune how you engage the entire business around conversation analytics. But, to enable each team and department to go forth and take action on that data is altogether different.
Meet our last conversation analytics feature: the interaction detail export. With literally a few clicks, export all types of customer interaction metadata, including categories and category groups, Tethr Effort Index (TEI) scores, Agent Impact Scores (AIS), QA scores. Make it effortless to share with other business units across your team who don’t have access to Tethr.
Be the champion of unifying conversational data across your existing set of Business Intelligence (BI) tools and align the business around accurate VoC insights so everyone can make sound business decisions by listening to your customers.
Now it’s your turn to try Tethr’s conversation analytics
So, now you know how our customer success team uses seven of Tethr’s most powerful conversation analytics features to really listen to the VoC by transforming unstructured and structured audio, speech and text data into insight.
We’ve learned how these same features can help you empower and enable your entire organization to move from a unified, customer data-driven place.