According to Gartner, 81 percent of businesses compete primarily on customer experience. Whether it is the chatbot giving ridiculously generic answers, the customer service agent
asking for every detail already provided to the voice assist system, or the feedback provided about technical issues or product enhancements that falls into a black hole of data … customers have no patience for customer experience blunders.
It isn’t that brands don’t want to provide a great customer experience; it’s an inability to manage, and quickly react to, the surge of customer feedback that presents the core challenge. Companies are realizing that customer feedback can’t be limited to surveys. Customer feedback spans so many additional channels today – support inquiries, social media, app/product reviews, forums, and more.
Customer feedback analysis is still overwhelmingly manual, which means most companies can’t keep up. Without an ability to understand all of the feedback in real-time, businesses can’t wring all of the value out of customer data and use it to make business decisions. It’s not only a missed business and customer satisfaction opportunity, it's also a reason for customer attrition and higher support costs.
Forward-thinking, customer-centric businesses (as an example, our customers include the likes of Instacart, Thrive Market, FabFitFun and Pinterest) are starting to rely on artificial intelligence (AI) tools to translate the millions of customer feedback data points streaming in from various digital sources into easily understandable insights. But in a business world where we are increasingly bombarded with the latest AI-driven promise of improved business decisioning, what makes their efforts different and more effective? And how can your business benefit from an AI-driven customer feedback initiative as well?
Let’s review five actionable ways to leverage AI and machine learning to crack the customer feedback code:
1. Find the best resource balance. The best customer experience is generally provided when AI and agents work together. Machine learning can instantly recognize what the customer’s question is and trigger an auto response that gets more information that the agent will need to know in order to properly answer the customer’s question. This not only provides the ability to automate repetitive tasks, optimize large amounts of information and act rapidly, but also allows for human supervisors to be quickly looped in to provide the empathy, extra human touch, and/or context when needed. This is especially important when new issues arise.
2. Take ticketing beyond the basics. Most customer service categorization techniques fall short. Forcing users to self-select ticket categorizations creates huge headaches for often already frustrated customers (and missed opportunities for businesses) because the available categories lack the precision needed to connect users with the right agents or don’t fit the problem. Sometimes users become so confused and frustrated by the category options that they don’t even bother submitting their issue … unfortunately, not submitting an issue doesn’t mean they are no longer frustrated. New tools that rely on AI for real-time ticket categorization optimize customer feedback loop efficiency and improve the support experience. Idiomatic’s integration with Zendesk, for example, helped Instacart seamlessly manage a 116 percent increase in contact volume during the pandemic. Analyzing and categorizing support contacts in real-time and assigning Idiomatic’s customized AI categorizations to tickets in Zendesk, Instacart streamlined support workflows and effectively managed ticket routing, agent specialization, and spike notifications. Instacart was able to uncover nuanced customer pain points and make quicker, customer-driven changes thanks to the power of AI.
3. Rely on AI for labeling. Companies traditionally have had their support agents label cases, which often leads to human errors or the labels becoming very high level in an effort to simplify the process. However, reducing the options for agents also limits your potential insights. Tapping machine learning (ML) and AI tools to create custom labels generates unique data sets. Just make sure your ML is not keyword based; when your ML understands language and the relationship between words, it can better calibrate sentiment analysis to help surface missed trends and unify real-time insights (both across the board and via specific channels). An added bonus? Doing so makes the insight actionable. For example, the label would not simply say the issue is “logging in,” it would say “password reset email not generated to login.” The latter is something that’s actionable for your team.
4. Focus on the “why” behind feedback. The best AI solutions will also look at all interactions from the ground up to assess the root cause of each individual interaction. This is the only way to properly identify trends and questions that should be asked, as well as supplement product usage data with anecdotal behavioral information. This could mean looking at how often features are mentioned and how customers are describing their problems. When feedback is quantified, it can be acted on. One great example of this is when Pinterest created a completely new product roadmap that turned feedback into features by using AI to better understand what was really going awry and where tweaks could be made to markedly improve the user experience.
5. Leverage automation in place of anecdotal ideas to review all customer interactions. This will transform freeform, qualitative feedback into quantitative data. One example of this is looking at tweets across product launches to identify sentiment. This helps companies move away from gut instincts and fragmented perspectives to draw conclusions from real customer conversations and data.
A good customer experience isn’t something consumers just prefer; most consumers are actually willing to spend more on products and services from a brand that’s known to offer good customer service experiences. Following these five tips will help you meet the increasingly complex customer feedback challenge by turning your customer feedback into a complete, data-driven voice of customer picture so you can confidently eliminate critical pain points and create loyal, lifelong customers.