Sentiment analysis has become a household phrase in this “era of the consumer” that has ushered in an acute focus for organizations towards customer experience, as both a necessity in business success and a core differentiator. Sentiment analysis in the context of customer experience refers to gaining an understanding of how your customers feel about your products, promotions, brands, or the interactions they have with your organization such as through the contact center.
Traditionally customer feelings have been measured through use of surveys, what Gartner refers to as Direct Voice of the Customer. While asking for direct feedback is a critically important component of measuring customer sentiment, surveys do have several limitations, one being that they only collect feedback from the small percentage of customers that actually respond. The small sample of respondents usually represents a dichotomy of customer groups – the very happy, or the unhappy. The contact center or customer engagement center represents a huge repository of data, that if tapped, could give you a much broader view of your customers’ sentiment, with limitless ability to ask different questions of the data that you are already capturing every day.
What is “Sentiment Analysis” and why should you care?
The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc., is positive, negative, or neutral.
Of course with the advent of speech analytics, sentiment analysis is not limited only to written text, but can also be extracted from spoken word. Contact centers record an estimated nine million hours of calls per day in the United States alone (extrapolated from Pelorus Associates Interaction Recording report). According to The Northridge Group, 48% percent of consumers still prefer to use voice channel for their mode of engaging with organizations, more than double that of any other channel. But of course live support through text channels such as chat, email, and Facebook messenger are growing rapidly. Through all of these customer communications and the associated data collected with them, there is a treasure trove of opportunity to extract and analyze customer sentiment.
The best news is you don’t have to conduct surveys in order to get at this data. While surveys do allow your customers to provide you direct feedback about how they feel, there are several key limitations to relying on surveys to understand customer sentiment.
Interaction analytics begins with raw data – multichannel interactions, such as chat transcripts, social media posts, recorded contact center calls, SMS, emails, and other – and transforms it into structured data that can be sorted, filtered, searched and analyzed to better understand your customer interactions and customer satisfaction.
Notably, interaction analytics do not stand on their own; rather, they’re most often leveraged to boost contact center performance and improve the overall customer experience.
How to Improve Interaction Analytics
The simplest way to improve your interaction analytics is through specialized interaction analytics software, which not only records data, but also analyzes it on your behalf.
The process is simple: Interaction analytics software will evaluate your interactions across all customer communications channels, including contact center calls, chats, and emails, as well as SMS text messages, social media posts, and more. The result is comprehensive analytics that represent a complete picture of your company’s customer interactions.
What’s more, the best software will not only capture your interactions, but it will convert them into an easily analyzed format (e.g. text) and make analysis easy through:
- Performance scoring
- Free-form search and playback
- Contact evaluation for various metrics, including sentiment/acoustics, categorization, and performance scoring
- Evaluation and comparison of key metrics, via data visualization
- Auto topic analysis to determine root cause and identifying outliers
- Measurement of key performance indicators across channels
- Easy extraction of analytics data