How RPA and Customer Service Data Drive Business Value
You’re sitting on a goldmine of data. It’s data that could help you deliver better customer services, market better to existing customers and prospects, aid in enhancing existing products as well as new product design and development, stay on top of quality, and ultimately improve your business's bottom line by boosting the overall customer experiences.
What data is that? The kind you process and collect when servicing customers through your support desk or contact center. By tracking and analyzing customers, you better understand your customers, your products, and your business.
Numerous data sources contribute to this store. There’s operational data from the contact center (who contacted you? how often? through what channel? about what?). Live chat data, chatbot data, NPS or other survey data, customer satisfaction scores, emails, social media comments, and information gathered by the customer service team are part of the store as well.
By using Robotic Process Automation (RPA) to bring together all this qualitative and quantitative data and analyze it, you get a good picture of your business and can manage it to achieve better growth and ROI when compared to the competition.
RPA does this in two ways. First, it helps you “get at” this data, integrating data from multiple legacy systems. Second, it helps you analyze it effortlessly by automating the use of sophisticated data analytics with attended and unattended bots and, increasingly, artificial intelligence (AI) and machine learning.
Using customer service data with RPA can reap real value for enterprises of all sizes. Here are five areas where you can apply it: customer support; marketing; sales; product or service improvement, design, and development; quality; and the overall customer experience.
Enhance customer support
Using customer service data to improve the quality of the support you provide to customers is the most obvious application. The data you get from your agents interacting with customers can be analyzed and processed in a continuous loop to generate thousands of metrics. Your hold times, your abandon rates, your first-call resolution rates, and your resolution times all fall into this category.
RPA helps because it automates many of the digital processes that used to be manual in the customer service lifecycle. For example, in the past, agents had to manually call up different systems and either copy and paste or rekey information from one or more into others. This action delayed resolving customers’ questions or issues, extended wait queues, and led to higher abandonment rates. RPA and supporting technologies such as AI, machine learning, and natural language processing (NLP) also made it possible to collect even more information to analyze such as data from chatbots and live chats.
RPA bots could then be created to automate the process of taking this data and analyzing which issues were likely to need escalation or to predict which tickets were most likely to get reopened. Your customer support team could then formulate strategies to address these scenarios.
One key objective in marketing is to move prospective customers through a journey that is generally agreed to begin at “awareness” and end, if a customer is satisfied with his or her experience, with “brand advocacy.” According to McKinsey, effective customer journeys are important: by measuring satisfaction on such journeys, businesses get 30 percent more accurate predictions of overall customer satisfaction than measuring happiness for individual interactions. And satisfaction with customer journeys has the potential not only to boost customer satisfaction by 20% but to increase revenue by 15% while cutting the cost of servicing customers by 20%.
RPA can help with this, for example, by tracking the customer journey through each touchpoint and soliciting feedback from customers by surveys that are triggered by varying stages in the journey.
Intelligent automation—or RPA combined with AI—can also do such things as calculate at what point in time a customer should receive a marketing promotion and send out that promotion using the communication channel the customer prefers. The process of determining channel preference can be managed by a bot created to track all interactions between a particular customer and the customer service team.
By creating an RPA bot that takes information from your customer relationship management (CRM) system and integrates with behavioral and interactive data collected from your customer support interactions, you can apply analytics to see the customers within your current base who are calling in for help, where in the buying cycle they are getting stuck, and also identify where you may be failing to win customers—and even what types of customers you may not be succeeding with and why.
Advance product enhancement, design, and development
Customer support data gives you first-hand information on what your customers think about your product or service. Creating bots that aggregate this data (leveraging quantitative as well as qualitative data) and analyzes the data for patterns can add value to your business.
By using a bot to track the correct metrics through your customer support data, you can discover opportunities for potential new features and upgrades to existing products. You can even identify unmet needs that would warrant the development of a new product or service.
Boost manufacturing or service quality
According to The National Customer Rage Study, 66% of customers experienced problems with the products or services they bought in 2020. And nearly two-thirds of them felt enraged over the issue or how it was handled.
By using a bot to extract and label relevant data from every incoming case, you can more easily spot new and trending quality issues with your product or service. This means you can deal with emerging problems before they blow up. Likewise, you can track which issues are causing the most support contacts and which questions take the most time to resolve, allowing you to develop faster and more proactive service solutions.
Your customer service team can also collect data that can be extracted by bots using textual analysis. Rather than having to spend time and dollars surveying customers, you can have customer service agents ask needed questions while interacting naturally with customers. Their response can give you insights into improving the quality of your products or services.
Don’t miss the customer service data opportunity
You need to understand your customers to be able to interact with them. And you can only accomplish this by having all the customer service data organized in a meaningful and useful manner, always updated, accessible, and available in real-time to professionals throughout your organizations. That way, you can deliver the highest quality experiences–the kind of experiences you, as a customer, would want.
RPA bots can bring all that together, integrating data retrieved from different systems, running the analytics, and putting the results into an easily digestible report for quality managers.