3 Powerful Ideas for AI-Driven Decision-Making Leveraging Your Customer Service Data
Most business decisions are made using an organization’s structured data, such as sales numbers, revenue, and growth in specific areas. However, this data doesn’t tell you what makes customers tick and how to retain, attract and please customers. The truth is that many organizations own a treasure trove of data in customer service, which could help answer these questions.
Through daily customer service interactions, customers tell you what they like and dislike about your products and services. They’re also giving you hints on improving brand loyalty–something companies pay millions to survey companies to acquire. Unfortunately, this information is mostly unused and certainly not aggregated because of the complexity of the data.
Customer service data is messy, scattered, and highly unstructured due to natural human-to-human conversations. Unlike sales and profit numbers, you cannot quickly aggregate and analyze customer service data. Because of its complexity, it often requires AI to fully unlock its potential, where the data then becomes a gold mine for all sorts of decision-making tasks. Let’s discuss three ideas to put your customer service data to work for your business using AI behind the scenes.
#1: Uncover Hidden Revenue Opportunities Using Support Conversations
When customers interact with your business, they’re often leaving behind massive digital footprints in the form of customer support questions, online comments on social media platforms, and search for answers on your company’s website to accomplish specific tasks. Many of these searches, questions, and comments contain hidden needs that may not be immediately visible in isolation, but trends emerge when aggregated and summarized with AI.
Let’s get specific. Suppose you’re an executive at a beverage company, and customers have been mixing your company’s kiwi probiotic drink with strawberry syrup, creating a completely new flavor. Perhaps this new flavor is an opportunity for a new product line. But how can you be so sure?
With one or two data points, it’s hard to tell if this “need” is a genuine one. So, this is where volume becomes critical. When data from hundreds if not thousands of customer support conversations and online feedback are summarized to extract common themes of discussions, true hidden needs emerge. Say hundreds of customers ask your support agents in different ways if “Mixing the kiwi probiotic drink with strawberry syrup is safe?” A hundred other customers comment in various ways on social media platforms that they “Mixed the kiwi probiotic drink with strawberry syrup, and it was delicious.” When such volume-driven summaries emerge, you start seeing trends, and with that, you get to uncover new product ideas and the potential for new revenue streams.
#2: Guide Product Enhancements Using Disparate Data Sources
After products, services, and feature enhancements are brought to market, it can be challenging for companies to track what goes on after the fact. Are customers happy? Are they complaining about the new product or service somewhere online? What are they thinking and feeling? Sure, you can conduct surveys before a product release, but what’s more important is how people, in real life, are experiencing these products and services. It’s hard to track precisely what’s happening.
That’s because feedback and indirect opinions can come in all shapes and forms. You can get feedback from customer support calls and asynchronous chat conversations, emails, user reviews, customer satisfaction surveys, and even feedback forms. Many even express their thoughts on social media channels, making it that much harder to monitor what customers really think as you don’t have a singular communication channel.
However, as Bill Gates once said, “Your most unhappy customers are your greatest source of learning.” Therefore, these disparate data sources are crucial for better understanding what customers think about your products and services, so you can stop problems in their tracks and find ways to provide customers with what they need and want.
Fortunately, if you collect and centralize data from all these diverse data sources, you can perform customer-driven improvements with the use of AI. With AI, you can summarize complaints to better understand what customers dislike; you can extract areas of excellence to replicate in other products and services; you can extract pain-points and wishlists to develop a roadmap for ongoing product enhancements. The sky’s the limit to the type of insights you’d like to surface. With this, you’ll also ensure that customers are heard and taken seriously, and with that, you have many opportunities to improve your customer experience and brand loyalty.
#3: Understand and Reduce Churn Patterns by Combining Customer Data with Customer Service Data
Customer churn is problematic for the growth of subscription-based businesses. Therefore, preventing customer churn is in a company’s best interest as it’s often more expensive to acquire a new customer than it is to retain existing ones.
To address the problem of customer retention, companies frequently develop models to predict the likelihood of a customer churning. While this helps detect a potential churn event, it does not help understand why when customers end up churning, whether or not the model predicted the churn event. Was it the recent price hike? Slow service? Poor communication? Was there anything specific that led to churn?
One way to understand the why behind customer churn is to analyze your customer service data because a common reason for customer churn is simply a bad customer experience. According to research by PwC, 59% of US-based customers will walk away from a brand after several bad experiences. So investigating your customer service data, in combination with customer data, is paramount to understanding underlying issues of churn, which can help organizations prevent potential churn patterns from happening at all.
Your customer service and customer data can help answer questions such as:
- Did a large number of customers churn right after a negative support interaction?
- Was the interaction with the same set of agents?
- Were the support conversations related to the same topic?
You can extract various insights and draw correlations through leveraging such data.
However, such analysis cannot be easily done manually by a single human. Trying to read customer support conversations and track patterns can be tedious and confusing. Plus, doing this for every customer who has churned is not a scalable model, especially if you have customers running into the thousands. This is where AI can be powerful. You can use it to better handle the enormous amounts of unstructured, semi-structured, and structured data in customer service, helping you extract the necessary insights to create change and prevent churn events.
As you’ve seen in this article, if you run a midsized to a large corporation, you’re probably sitting on a mountain of valuable data in customer service. While this data is messy, scattered across platforms, and not easily digestible, through data centralization and AI, you can help bring order to this data. AI can help you summarize, standardize and augment your data to support all types of business enhancing decision-making tasks. In the end, this can help improve your customer experience, brand loyalty, and revenue potential.
Written by Kavita Ganesan.
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