Customer Service and Unstructured Data –
Unstructured data is the key to unlocking the hidden but especially appealing potential of Customer Relationship Management. However, the drawback to unstructured data is not something that can ignore. Customers interact with companies through various forms of communications for after-sale services, generally categorized as customer service. It is not easily searchable to immediately resolve customer queries since it is stored outside of a Relational Database (RDBMS). This is an undeniably clear distinction between structured and unstructured data.
Why is Unstructured Data Important for Efficient Customer Service?
At present, upwards of 80% of a company’s data is unstructured. Although unstructured data can appear quite daunting, it is far from impractical with the right tools and analysis. Each interaction with a customer holds at least one valuable piece of insight into customer preferences. These insights from media, networks, and, most importantly, customer interactions are invaluable from an organization’s point of view. The preferences can further radicalize the pathways to customer service and communication.
There is no one way for a customer to communicate with a company to avail of their customer service options. Restricting those options to understand customer preferences better is surely a step backward. Therefore, analyzing the unstructured data that is stored away in the form of unexplored files allows a company to understand and divert customer communication to a designated department or individual to save time for both the employee and the company.
UiPath, Re: infer, and Enhanced Customer Communication –
UiPath undeniably understands the importance of tools that analyze unstructured data since they recently acquired Re:infer. Re: infer is an NLP (Natural Language Processing) company that uses ML (Machine Learning) to analyze unstructured customer communication data. This specifically involves mining data to build a framework that designates a path to incoming customer communications.
The first thing to remember is the practical applications Re: infer through communication mining offers. The processes adopted by Re: infrared –
- Customer Support Emails–
Every time a new email arrives, it is analyzed to either create a unique circumstance or add to the existing ones. This allows the company to automate its responses based on previous customer communication. Another course of action is customer service people learning and crafting their responses accordingly.
- Customer Support Emails: Triaging –
Fielding incoming customer emails and forwarding them to the respective departments is certainly an intense task. Re: infer not only auto triages incoming emails but also extracts information, categorizes, and assigns priority to the same. This frees up both the email channel for other productive communication and valuable employee time.
- Customer Experience: Insights –
80% of companies believe their customer service is superior. But, as low as 8% of customers believe they receive said superior service. The rather overwhelming gap between the both can be staggering, but it does not matter who is right. The gap has surely resulted in a loss of business for companies, so it is time they gear up and dig into the unstructured data they have lying around! Re: infer has a solution for the above problem too. Re: infer analyzes customer expectations, opinions, and feedback at every stage of their journey with the company. This develops valuable insights that allow companies to automate customer communication for immediate correspondence. This also drastically increases the chances of retaining the customer.
- Enhancement Through Automation –
When a customer communicates with a company, Re: infer extracts value from the message through mining. It understands the various semantics of unstructured data enough to convert them to actionable data. It significantly increases the amount of data that is available to a company when it decides to take action based on previous and even real-time customer preferences. These insights add value to a business that could not have happened if the unstructured data had been left unstructured.
Categorizing immediately allows a company to arrive at something as trivial as a single message. The automated updating of real-time data and the automated communication with a customer reduces the time required for the communication to complete, analyze the feedback, and categorize, understand and implement the necessary changes as seen fit.
It comes as no shock that the integration of analyzing unstructured data to a company will yield benefits from areas that have since long been stagnant. An automated enterprise is undoubtedly a happy enterprise that includes both the customer and the business. Re: infer and UiPath have seen its potential, and it is no exaggeration to say this is a step towards revolutionizing customer relationship management.