Customers are people, and people are passionate. So whether they’re talking about a recent customer service experience or a product they love or hate, open-ended survey questions and reviews allow customers to go beyond a score or a rating and express their passion (or lack thereof) for a brand and its offerings. And that customer feedback — whether direct or indirect — is just what you need to make a real improvement to customer experience (CX).
Fortunately for businesses, little can be kept secret in the digital age. As your customers shop or inquire about your products and services, they provide you with customer signals in a variety of forms. And as long as you collect feedback and leverage this data ethically, consumers are up for it.
As you collect feedback data, one area that you should pay special attention to is text analytics. If done well, it significantly enhances an organization’s understanding of its target audience, allowing it to serve customers better.
Along with enhancing decision-making, text analytics offers benefits such as increased processing speed, integrating big data, better consistency, and cost reduction. This is why the global text analytics market is rapidly growing. By 2026, it’s expected to have a market value of $14.84 billion, up from $5.46 billion in 2020. This translates to a CAGR of 17.35% over the period.
Read on to learn how to leverage text analytics’ benefits and take your organization to the next level.
Customer surveys are an excellent method to receive feedback. However, they’re sometimes monotonous and exhaustive, leading to survey fatigue. As a result, customers may feel less motivated to participate in future surveys. Data suggests that only 9% of respondents complete long surveys.
Shorter surveys, however, generate better response rates. To get at least the same level of insight with fewer questions, companies need to ask more open-ended questions — like, ‘”What else should we know?” — that generate text-based responses.
While feedback scores and ratings provide a barometer of your feedback, scores typically can’t tell you the ‘why’ behind it. Follow-up rating questions can deepen your understanding of the ‘why’ but usually not as well as text-based feedback. Open-ended comments from customers provide the detail needed to identify the root cause of an issue so teams know how and where to improve.
Choose a customer experience management (CEM) software platform that offers native text analytics so there’s no delay in receiving insights. Systems with non-native text analytics force you to wait to understand the root cause of an issue: The system that captures feedback needs to send the data to a text analytics system that, in turn, takes time to analyze the data before sending it back. The added time not only delays insights and actions to improve but also can create more unhappy customers.
Most feedback programs and even review sites have a specific set of questions they ask customers. In order to get a view of customer satisfaction (CSAT) over time, those questions rarely change and are limited to scores and ratings. While the responses can show trends in opinion about the question topics, they can’t show new and emerging trends that aren’t covered by the questions.
Text feedback fills this gap. If a customer doesn’t see a question that allows them to give the feedback they have — for example, if questions ask about hotel check-out times but the guest wants to complain about bill accuracy — open-ended questions allow the customer to still give their feedback, only in text format.
By using text analytics, you can catch emerging issues and act on them before they escalate.
Text analytics help you understand customer demands by finding keywords, themes, and sentiment in feedback comments. Moreover, customer feedback obtained reveals trends and insights. With this wealth of action-oriented insights, you’ll understand your business’ strengths and weaknesses.
For instance, if you manage a hotel and multiple guests complain about a lack of room service, you may utilize this knowledge to improve their experience. But, if guests routinely praise your staff or comfy beds, you may continue to focus on these areas.
Overall, analyzing text-based feedback helps a business understand its consumers’ wants, needs, and expectations so it successfully adjusts products or services, increasing customer loyalty and retention as preferences are met.
One of the key benefits of text analytics is enabling you to make data-driven business decisions, which is essential. For example, unstructured data from open-ended survey questions and evaluations might reveal customer requirements and preferences otherwise unseen.
Text analytics discovers key themes and sentiments in consumer feedback and follows changes over time. You may, for example, measure customer sentiment after releasing a new offering or making modifications to an existing product or service based on customer feedback.
This data guides product development and customer service strategy decisions. Prioritize improvements based on their influence on consumer happiness and loyalty, and track your progress over time.
Beyond great product quality, you must offer excellent CX and employee experience (EX) for your brand to grow. You may solve customer journey pain points by studying consumer feedback and improving product features, customer service, website design, and user experience (UX).
Text analytics also uncovers employee experience improvements. Analyzing employee data such as effort score, engagement, satisfaction, and sentiment helps you discover and address frequent issues. Some areas it can help you improve include training and development, workplace culture, and employee onboarding.
Improving CX and EX creates a virtuous loop that boosts customer happiness, loyalty, and employee engagement and retention. Profits and expansion can result.
Studies demonstrate that firms that focus on customer and employee experience outperform their counterparts in sales growth and profitability. Hence, text analytics is able to boost customer and employee satisfaction.
As with all types of technologies, text analytics has obstacles that may arise. So, to fully capitalize on the benefits, you need to understand and mitigate the potential challenges of text analytics.
Here are some of the challenges you may face with text analytics and how to navigate them.
Text analytics data must be accurate and high quality to be successful. Poor data leads to misleading insights and ill-fated decisions. Some factors that can lower data quality include data duplication, missing data, inconsistent formatting, and human mistakes.
To improve data quality, perform data cleansing. This process involves eliminating unnecessary or duplicate data, fixing formatting errors, and adding missing data. Data cleaning may increase data quality and assure trustworthy analytical findings.
Combining automated and human data processing improves data quality. Manual data processing allows analysts to analyze and verify data correctness, whereas automated data processing quickly handles massive amounts of data. As you fix data quality concerns, you’ll improve text analytics insights and decision-making.
Text analytics needs to work well with your existing systems to achieve optimal results. However, text analytics integration can be a challenge. You need customer feedback, social media, support ticket data, and more. Yet, this data is typically scattered across platforms, formats, and places, making it hard to examine without the best CEM software platform.
As such, IT, data science, and customer service teams must collaborate during integration. This entails identifying data sources, extracting, standardizing, and feeding data into the software platform used for CX.
While performing the integration, ensure data is protected as well. You can achieve this through encryption and access restrictions to secure sensitive data.
Notwithstanding the hurdles, text analytics must be integrated properly to acquire a complete perspective of customers’ behaviors and preferences and make data-driven choices.
Another area where text analytics may face a challenge is uniformity. This is because unstructured text data is hard to analyze and comprehend. In addition, language, grammar, and spelling are not standardized, potentially affecting accuracy.
Therefore, text analytics can be difficult to apply across businesses and domains due to jargon and terminology.
As you can imagine, text analytics is a complicated science requiring technological competence to analyze unstructured data. Also, cleaning, organizing, and converting data for analysis takes time and resources. So, unstructured text data analysis needs sophisticated software and algorithms.
Text analysis commonly uses natural language processing (NLP) methods, which can be complicated and computationally intensive. In addition, such systems find it challenging to comprehend idioms, sarcasm, and irony.
Analysis complexity demands technical skills, software tools, and resources. Many companies may need to hire or educate data analysts with NLP and unstructured data knowledge. A text analytics platform with built-in NLP and powerful algorithms can also ease analysis for non-technical users.
There’s no doubt text analytics offers insights to enhance a feedback program. However, leveraging it is not an easy task. So instead of starting from scratch or doing the work manually, partner with a software provider prepared to unlock text analytics’s value.
Look for a software provider with expertise in empowering top brands across industries to gather feedback, analyze data, and provide actionable insights — using text analytics to sift through large amounts of text-based data. Your platform should let you rapidly and seamlessly evaluate massive volumes of feedback data, uncover patterns and trends, and create improvements for customer experience.
With text analytics from Medallia, here’s what to expect:
Want to unlock the value of customer insights? Download our official text analytics brochure to learn more about our solutions.