By Daniel Fallmann
Today, many call centers have their general scripts and rules they follow when a customer calls. However, consumers have increased their expectations regarding their personal customer experience. With increased competition in every industry, organizations cannot afford to lose valuable business because of poor customer experience efforts.
With the help of artificial intelligence and machine learning technology, companies can begin to access relevant data to personalize a customer’s call center experience and make the journey more relevant. Personalization to scripts and approaches from call centers allow customers to feel understood by the brand, building a solid level of trust and loyalty.
To offer unique and personalized experiences, support teams will primarily need access to relevant data surrounding their customer and their journey with the brand. Without doing so, you’ll just be another call center not optimizing the power of connected data.
Gain Entryway into Relevant Customer Data
Access to relevant and personalized data requires the connection of structured and unstructured data sources. Before calling, a customer may have interacted with your product or company hundreds of times. Artificial intelligence allows companies to connect data from all these interactions in a 360-degree holistic view.
From activity on your website (form downloads, support requests) to emails, call transcripts, and purchase information, data can be captured, analyzed, and utilized in the form of customer journey maps. Seeing the full story of a customer gives workers in the call center all the information they need to help a customer quickly and in a personalized manner.
In a recent Gartner survey, 83 percent of respondents reported that their organization struggles to use customer journey maps to identify and prioritize CX (customer experience) efforts. Being in the other 17 percent can play a pivotal role in transforming how people view and talk about your brand—ultimately, coming back to purchase more and increase business.
The connection of scattered data also allows customer support teams within call centers to access information about related problems and other customers with similar buying habits. Doing so helps call centers see what has worked well in the past and can assist them in shaping their approach for future like-minded consumers.
However, not all data is for everybody’s eyes and data privacy needs to be enforced by giving the proper access rights to each department. AI-solutions, like such, give management the ability to restrict sensitive data to support teams, ensuring compliance and privacy standards are always upheld.
How Does This Look in Real Life and Real-Time?
If a customer calls with a problem, innovative AI-based solutions can first identify who the number belongs from, using data that the company has previously collected. Prior to picking up the phone, the support agents’ screen will show them all the relevant information regarding the customer in an easily digestible format. Therefore, they can already begin personalizing their introduction to the call.
When a customer starts explaining why they’re calling in, the agent will already have a full view of different touchpoints that have occurred. Examples of different touchpoints include web activity, form downloads, conversations with chatbots, previous calls, email communications, and purchase orders.
In addition, the call agent will also be able to search for any piece of intelligence they may need to help the customer in the quickest, most efficient, and personalized manner. If this specific customer calls about a broken appliance, searching for that appliance will extract all the relevant data to steer the representative in the right direction.
Call centers can even take it a step further and search for a particular issue with that appliance and then be fed with information from corporate documents or similar customer interactions in a matter of seconds, eliminating long wait times or needing to put customers on hold.
Conclusion
In the digital times we are living in, innovative companies are well underway transforming their approaches to customer service and customer experience. As an example, chatbots are constantly learning from corporate data to better understand and assist with customer needs directly on the homepage of consumer-facing websites. In addition, management teams are using data in real-time to enhance their current CX strategies and business roadmaps.
There is no reason why people tasked with handling customers on the frontlines in call center roles shouldn’t be equipped with this type of technology. After all, the capacity to personalize and organize data makes life easier for your workforce and gives your customers a unique experience to highlight their individual importance.
Daniel Fallmann founded Mindbreeze in 2005 at the age of 23, after he finished his studies in computer science. He has many years of experience in the computer and information technology sector. As Mindbreeze’s CEO he is an example of high quality and innovation standards. From the company’s very beginning, Fallmann, together with his team, laid the foundation for the highly scalable and intelligent Mindbreeze InSpire appliance.