This is a guest post by Sid Banerjee, CEO, Clarabridge
We are living in the “age of the customer,” as Forrester Research recently dubbed it. There are more and more communications channels — from company websites to call centres to social media — for customers to interact with the companies they do business with. As a result, they also have high expectations when it comes to having their feedback heard and considered.
For businesses, this era of customer-centricity presents both challenges and opportunities. Acting on feedback straight from the customer’s mouth can directly impact a company’s bottom-line by reducing metrics such as customer churn.
But there are a few steps between customer feedback and that impact. Companies must make sure that they have the technical skills and capabilities to connect to all relevant customer experience data sources, and be equipped to bring all that data together for holistic and meaningful analysis. But even before that, companies must have the right data governance in place, which a foundational piece for any advanced analysis and action.
Traditional business intelligence (BI) data is generally very explicit and structured, focusing on what has already happened. The universe of structured data is vast, including demographic information, purchase history, digital engagement, multiple chose survey responses, and other CRM data. Businesses have had their hands full analyzing and interpreting these data sets for years, but now the big data challenge is becoming increasingly urgent.
Adding unstructured customer data
When it comes to customer experience management, all of this data must be combined with unstructured customer feedback data. This data includes social media comments, online reviews, call center recordings, agent notes, online chat, inbound emails, and free-form survey responses. Businesses need to consider this data alongside structured data for a complete picture of the customer experience. That’s why, in the age of the customer, next-generation experience technology and techniques – like text analytics, sentiment analytics and emotion detection — are not optional.
Data governance is crucial here, as it ensures that everyone is speaking the same language when it comes to the information’s meaning. When you drop in customer feedback and sentiment data on top of historical data tied to transactions and promotions, there needs to be a standardizes process for interpreting it and distributing it. While many companies have processes already in place to manage high-priority enterprise data, it can be challenging to incorporate new streams of large, unstructured data into those rules and processes. Just consider this estimation from Anne Marie Smith, principal consultant at Alabama Yankee Systems: “I would venture to say that if you took the totality of companies that are engaging in some form of structured data cost governance, not even 1%, maybe one-half of 1% of them, are engaging in any form of unstructured data governance, for a variety of reasons.”
Know what you are looking for
One reason that data governance is especially difficult in the omni-channel, unstructured age of the customer is because data governance, at a basic level, requires an understanding of what information and insights you’re looking for. Companies shouldn’t create high quality data for the sake of creating high quality data, but should have their eyes on a specific business goal. When it comes to customer data, one of the big challenges we’ve seen is that folks don’t know exactly what data is relevant in the first place; they aren’t sure what data to listen to and analyze, much less how to consistently work with it to gain meaningful insights that will impact business performance.
The lesson: When it comes to new sources of data, figure out what you want to get from it before you dive in. And then, use industry templates garnered from others work as a platform to start. Basics like data governance make up the foundation for a common understanding of the customer across your business, as they enable high-quality and advanced analysis for both explicit and implicit information, which is becoming a requirement in order to deliver the increased level of attention being demanded by customers.