How AI Gets to the Root of the Customer Feedback Loop
Contributed article by Chris Martinez and Kevin Yang, co-CEOs, Idiomatic
There’s something so frustrating about taking
the time to provide feedback to a brand and feeling like you’ve fallen on deaf
ears. This is especially true if this is an issue that you know is widespread
and not specific to your interaction with the brand. However, with the number
of ways that customers can now provide input to brands through—support inquiries, social media,
app/product reviews, surveys, forums, and more—brands are often left drinking from a feedback
firehose. Brands can barely process and respond to all the feedback, let alone
understand the bigger picture and identify trends. The result? The loudest
customers get the responses (you know, the ones that eventually start tweeting,
leaving reviews on every forum imaginable,
calling several times a day, and even going so far as to threaten
lawsuits), while slow-burn issues impacting many customers go unaddressed. This
leaves the majority of their customers feeling like brands aren’t listening to
or acting on their feedback, sparking much larger issues for brands to address
when the fire eventually starts.
It’s not that brands don’t want to create tight customer-feedback-to-product
loops or customer-feedback-to-operations loops. It’s that they can’t translate
the millions of customer feedback data points from various digital sources into
easily understandable insights. Most customer service processes still rely on manual analysis,
keyword searches, and survey-centric customer feedback initiatives, which
simply can’t give them the actionable insights they need to properly manage the
continued customer feedback surge.
digital brands like Instacart, FabFitFun, Pinterest and more are tapping
artificial intelligence (AI) to help them stay ahead of the customer curve to
improve satisfaction, cut support costs/complaints/issues and get more from
their customer data. How can you do the same?
the Gap Between How Customers Speak and How You Label/Describe Their Issues
companies have had their support agents label cases. Companies often make these
labels very high level to reduce the options for agents and keep things simple.
The problem with simple labeling solutions is that accuracy comes at the cost
of specificity. AI can make labeling both specific and accurate.
can act confidently to address customer feedback when they have a complete and
real-time view of what customers are actually saying. Machine learning and AI
tools (like Idiomatic) can be tapped to create a custom set of labels for
unique data sets and calibrate sentiment analysis. Knowing there is negative
sentiment associated with the “Login” category is not actionable; however,
knowing that 100 users tried to reset their password yesterday and didn’t
receive the email tells a much more specific story, with a clear problem to
solve. With AI doing the labeling, labels become much more specific without the
heavy manual lift of relying on agents to lead this initiative.
Freeform Qualitative Feedback Into Quantitative Data
way AI can be tapped to wring value out of customer data is by looking at all
interactions from the ground up to assess the root cause of each individual
interaction and identify trends and questions that should be asked, as well as
supplementing product usage data with anecdotal behavioral information. This
could mean looking at how often features are mentioned, and how customers are
describing their problems. One example of this is looking at tweets to identify
sentiment timed with a product launch. On a larger scale, FabFitFun needed a
scalable, data-driven way to translate the voice of the customer
cross-functionally. Idiomatic analyzed and categorized FabFitFun’s text survey
responses and support contacts in real-time–leading to a 250 percent increase
in product satisfaction.
Call Center Connection Chaos
is worse than spending time with a customer service agent and then realizing
you have to be transferred to someone else to help with your questions. When
users self-select ticket categorizations, it can lack the precision needed to
connect them with specialized agents. Now picture you have four million monthly
customer support contacts and users self-selecting their ticket category.
That’s exactly what Instacart was up against.
integrated Idiomatic with Zendesk to categorize support contacts in real-time.
Using AI-driven, real-time ticket categorization can optimize efficiency,
improve the support experience and save costs. Instacart streamlined support
workflows with ticket routing, agent specialization, and spike notifications
and was able to uncover nuanced customer pain points in the process. By routing
tickets to specialized agents, the company was also able to reduce support time
and save $445k in annual support costs by proactively addressing specific
customer pain points and driving down contact volume.
are just a few of the ways that AI can help bring focus, free up agents’ time
for more strategic uses, automate repetitive tasks and enable quicker responses
to customers. These strategies also help companies move away from gut instincts
and fragmented perspectives to draw conclusions from real customer
conversations and data. Instead of focusing on reply times and customer satisfaction,
empowering customer support teams with AI tools and focusing on metrics like
Net Promoter Score (NPS) and reducing customer churn can help focus on the
right metrics and outcomes, a win for the agent, the customer and the company’s
bottom line. When the root cause of a customer issue is quickly surfaced and
addressed, we all win.