Today, customer feedback is a crucial part of understanding what people think about your brand’s products and services.
As we all know, organizations are becoming more customer-focused and trying to compete with both established and new brands. Unstructured text data combined with other customer data can help businesses gain a comprehensive understanding of their customers.
But language is complex. And brands often have vast numbers of customers, each providing feedback at multiple touchpoints. This means there are thousands of interactions to sift through. And it is why text analytics – that is automated and AI-supported – is so vital to businesses today.
What is text analytics?
Text analytics is the processing of unstructured free-text feedback to identify trends and insights across numerous unique user interactions.
Do you want to know what Facebook users say about your brand in their posts? Text analytics can tell you. Do you Want to discover the most-cited pain points in your customer service emails? Text analytics can help here too.
Almost anywhere there is user feedback, text analysis can help you make sense of it.
Chattermill uses tools that incorporate AI and machine learning. These tools allow us to identify languages, understand customer sentiment, and identify the keywords and topics customers are talking about.
The insights found within text analytics can then be paired with other data sources and be used to improve your business.
Benefits of text analytics
We at Chattermill believe in combining data insights from all possible sources. This provides a comprehensive understanding of our customers.
Analyzing customer feedback from multiple sources can provide a single source of truth. These sources include customer surveys, social media channels, product reviews, support tickets, and chat conversations.
Analytics come in many forms. Web analytics is one example. It can help us understand customer behavior by looking at things like site traffic and conversions.
But it is only through gathering unstructured free text feedback that we can uncover what customers really think and feel.
Unifying these feedback channels helps brands link up the reasoning for specific trends – from customer churn to average order values. Why are customers being tempted over to a competitor? Why are they spending less per purchase this year compared to last year? These questions can only really be answered if we are listening to what consumers have to say.
Yet, this Unified Customer Intelligence is only possible at scale if the text analytics part of it is automated. Automated tagging and processing feedback using machine learning algorithms is another benefit of dedicated text analytics software. It frees up resources and time, meaning staff members no longer have to manually sift through and tag this feedback.
They can focus on taking action based on the insight. They can share the insight with those in the business who can best make the necessary changes. The result? Better customer satisfaction.
How can text analytics help companies?
Text analytics tools are helping an increasing number of brands improve their CX.
Goodiebox is a mail-order beauty product service that has customers all over Europe. The company receives numerous support tickets and feedback across many languages each month.
In the past, Goodiebox staff had to tag support conversations manually. They also had to painstakingly categorize relevant information like member satisfaction, action taken, region, and the reason for getting in touch, to uncover any insight and make it actionable.
This quickly became almost impossible to do in a timely manner – especially as the brand continued to grow.
Using Chattermill and our automated text analytics tagging helped Goodiebox overhaul this process.
Our tool meant the business could quickly identify the root cause behind product issues – such as a broken eyeshadow in one of their monthly boxes – and know precisely how many members were affected by it.
While Goodiebox has used topic analysis to dig into specific product issues, Daryl Wilkes of ASOS discussed using sentiment analysis in our CX leaders roundtable on How to solve customer friction in eCommerce.
‘It’s about understanding those contacts but understanding the sentiment behind those contacts, matching up the contact reasons with the feedback you get from your customers,’ he says.
‘What that can do is not only inform you how you can improve the experience at that contact but also what drove the contact in the first place and actually where you can go hunting for the root cause of the friction.’
Text analytics techniques
Sentiment analysis
Do people love your brand, your products, or your services? Or do they hate it? Sentiment analysis digs into the emotional tone behind what the public says about you.
Whether on publicly available social media posts or more candid survey feedback, customers often share their emotions about brands. Businesses must know what is being communicated to address negative feelings and boost customer sentiment.
Topic analysis
What non-emotional topics are coming up within text feedback?
Topic analysis uses machine learning to detect and assign topics or tags to feedback.
It helps uncover things like product issues or points on the customer journey that are coming up frequently and, paired with sentiment analysis, helps brands understand what parts of the customer experience need to be addressed.
Language identification
Another part of text analytics, language identification, identifies the primary language of feedback.
This helps identify where feedback comes from, potentially highlighting regional-specific friction points for specific customers in certain locations.
How to get started with text analytics
Unify your data
Here at Chattermill, unifying data is our bread and butter.
We call it Unified Customer Intelligence, bringing your data across channels into one platform to give that single source of truth.
Understanding how your customers think and feel about your whole organization – across the checkout process, delivery, and fulfillment, as well as during post-purchase support is crucial.
But you can only get that comprehensive, holistic understanding of your customers if you ensure that none of this feedback data is sitting unused in silos.
Analyze your data
Once you have all your feedback data unified, and in the same tool, it’s time to identify trends and look for insights.
Before this can happen, text feedback (i.e., qualitative data) needs to be analyzed at scale and transformed into insight.
With Chattermill, this can be automated. Our AI text analysis software can uncover trends and insight in near real-time – and present it visually through customizable dashboards.
Distribute your insights
The last and most crucial step is to let those within your organization who can act on your findings have access to them.
It might be that your marketing team needs to tweak some promotional messaging, or your customer service team needs to reach out to a customer directly.
But it’s important to ask how they can be best supported to work in an agile manner. And how can your insight be embedded into strategy and company culture in the long term?
Only then can the true value of text analytics for your business and your CX be realized.
Final words
Even in the digital era, text is fundamental to how the internet works and how customers engage with brands.
Right now, your customers are talking about you.
It might be on social media, or it might be on a customer service phone call. But they are likely expressing sentiment about certain parts of their experience with your brand and product – and they may even be referring to very specific issues they are having or things they love.
All of this is potential insight for your brand. But analyzing this unstructured text manually is massively time-consuming – it’s just not feasible at scale.
That’s where an automated text analytics program comes in. It can draw together feedback from across your organization and marry it up with other types of data. It can transform this text into insights, making it much easier for staff across your organization to act upon.
From there, all staff within your business – from CX teams to product designers – can access the all-important single source of truth and are in the best position to take the necessary action to ensure your customer experience is as amazing as possible.
