The customer experience (CX) is the new brand. And businesses can no longer afford to treat it as an option. No matter what niche your industry or services belong to, the opinion of your customers is now a competitive factor. A good review or brand endorsement can bring flocks of new leads while retaining the existing loyalty. But a bad one can cost you more than a fortune and may leave you out of business.
Text feedback is the easiest way to have a one-to-one interaction with every customer. Such feedback allows them to be open and free. Customers can express what they prefer from service, what they care about, and why, without any hesitation. It is where they get to be themselves without being judged and decide what’s most important. Although an increased number of surveys and responses can be a better data set, processing this is not easy. It is time-consuming, painstaking, and difficult to make text actionable. Also, quantitative data metrics like Net Promoter Score, Customer Effort Score, & Customer Satisfaction analysis is done by existing statistical tools. Still, the qualitative side, open answers, or unstructured data can be challenging. Hence it is calling for upgradation. Thanks to advancements in Artificial Intelligence we are blessed with a promising tech: Text Analytics.
Text Analytics is the process of deriving meaning from text. It uses natural language algorithms to finds patterns and common topics. This can help to take practical action based on insights. It can measure things like customer opinions, user feedback, product reviews, etc… Insights driven from data processing can lead to engaging conversations. It answers key questions viz, how a business or entity performing is based on certain parameters, and what are the flaws that people find bothersome. Social media is one of the driving factors that is bringing a large swathe of growth in this. Whether a customer continues doing business with a brand, their posted reviews are there, online.
How to use Text Analytics?
There are several techniques implemented by software companies to get information. Some of them are
• Counting the frequency of keywords helps figure the typical topics, touchpoints, and issues among the customers. E.g. frequency in online orders of ‘chicken lasagne’ means customers love your outlet’s special chicken lasagne as their meal.
• Sometimes a string of words can provide more insight than a single word alone. E.g. ‘camera phones under $300’ search trend means people are looking for phone options with excellent camera features which lie in the budget range of below $300. This gives a clear idea to distributors selling phones about the price preference rather than individual words of ‘camera’ ‘phone’ and ‘$300’.
• Sentiment analytics (or Opinion Mining) is a factor that utilizes Natural Language Processing (NLP). It allows people to scale the magnitude of the feedback based on positive, negative, and neutral word usage as well as the sentiment associated with commonly used words. E.g. If a movie theatre has a rating of 1.5 stars out of 10, users can identify the horrible experience moviegoers might have had there.
• Categorization of received feedback data, filters and finds feedback based on the contents of the open comments. E.g., If you enter tags like ‘Cheap’ ‘well fit’ and ‘comfortable’ on an e-commerce shopping website, you may find the percentage of shoppers who felt that their cloth items were cheap, fit perfectly, and feels comfortable to wear.
So how does Text Analytics boost CX?
AI-based analysis can provide real-time data that empowers organizations to take immediate action and bridge the gap with the users. Via route surveys, one can find more relevant paths or assign issues to relevant departments in a particular business. This lets you interact with them directly and make them feel that their feedback or response is valued. Plus by it allows converting negative viewer or user into a loyal promoter. With open-ended questions, customers are given the chance to identify what is or isn’t to their satisfaction and why. Thus helping in pinpointing the error areas. Further, it complements traditional survey analysis and lets the customer drive the conversation.
By providing continuous feedback, one can discover emerging developments and behavior patterns. This helps to visualize trending feedback categories and resolve them in a prioritized timely manner. It is a fast way to receive pointers when business houses launch new products or services. For the issues that are not resolved instantly, one can use the sentiment and topic analysis to sort issues and generate relevant actions. Additionally, by studying the given suggestions business may run into a gold mine of new ideas on how they can revamp themselves and improve staff to customer interactions.
Using visual dashboards assists tracking emotions allowing to go beyond metrics and keyword topics and start to understand the emotional experience of the customers. This helps watch how changes are impacting certain areas of the business and its correlation with the overall satisfaction metrics. And a visual representation of posed questions or feedback interface gives it a clutter-free look and better interaction with the users.
Every business brand defines its objectivity and assures a wonderful experience. However, it is the consumers who reveal if this claim is valid or not based on their experiences. Although it may differ from person to person, the average outcome is used to frame how it fares its delivery against expectations. Hence it is the need of the hour to invest in an open-ended feedback system. Not only that to be successful companies must know how to assemble and act upon this data to derive actionable insights and boost customer relationships with the brand.