We’ve lived through some epic crossovers – think Batman meeting Superman or the glorious fusion of peanut butter and jelly.
But hold onto your capes because we’re about to unveil the greatest crossover of all time: Net Promoter Score (NPS) surveys meet the innovative world of Natural Language Processing (NLP).
It’s like the Avengers assembling to reverse The Snap but in the realm of customer feedback analysis.
Prepare for a fantastic ride as we explore how this dynamic duo shakes up the customer experience game and leaves traditional methods in the dust.
What is Natual Language Processing (NLP)
Natural Language Processing (NLP) is an AI field focusing on the interaction between computers and human language.
Important disclaimer: don’t confuse Natural Language Processing with Neuro-linguistic programming. The latter represents a psychological approach that analyzes strategies used by successful people and applies them to reach a personal, individual goal.
NLP enables computers to understand, interpret, and generate human language in a valuable and meaningful way. It encompasses various language-related tasks, such as text analysis, speech recognition, machine translation, sentiment analysis, etc.
The roots of NLP can be traced back to the mid-20th century when researchers began developing early computer programs that could understand and generate human language. However, these programs were rudimentary and rule-based.
Fast-forward to the 1990s, when significant advancements in machine translation brought on the emergence of chatbots and early sentiment analysis tools.
In the early 2000s, NLP found applications in search engines, social media analysis, and information retrieval.
Today, NLP faces a new revolution with the advancement of deep learning models. These models achieved remarkable results in various NLP tasks, including:
- language translation,
- sentiment analysis, and
- natural language understanding.
NLP works by breaking down language into its constituent parts, such as words and phrases. It then applies algorithms and models to analyze and generate human-like responses.
In essence, NLP combines linguistic analysis with machine learning to enable computers to understand and work with human language. With its capabilities, NLP has become a fundamental application technology – ranging from search engines to virtual assistants.
Using NLP for Open-ended Survey Questions
Open-ended questions inviting respondents to provide free-text responses allow respondents to share their thoughts about your brand, using their own words and voicing their true feelings.
However, the richness of open-ended comes with a price; manually analyzing large volumes of unstructured text data is time-consuming and subject to human biases.
Here’s where NLP steps in.
You can deploy the NLP tech to extract meaningful patterns and insights from open-ended questions.
NLP in Action
Imagine you’re a sports apparel provider conducting patient satisfaction surveys. In the past, analyzing the open-ended responses from thousands of customers was a labor-intensive task that took weeks.
NLP was introduced to streamline the process.
NLP algorithms quickly categorized responses into positive, negative, and neutral sentiments, allowing you to find areas of concern.
Additionally, topic modeling revealed that many people mentioned “waiting times” and “payment options” as pain points.
You obtained these results by listening to your customers, quickly identifying their issues, and solving them before customers move on to your competitors.
NLP and NPS – Why Bring Them Together?
However, to get all these benefits, you need to do more than quickly analysing survey responses.
You still need to ask the right questions at the right time.
How can you justify continued investment acquisition without genuinely understanding your retention performance?
The Net Promoter Score (NPS) provides an effective means to address this challenge.
Through NPS, you segment customers into three distinct groups based on their responses to a single question:
“On a scale of 0 to 10, how likely are you to recommend our products to your peers?”
With this information in hand, you classify each customer as a Promoter, Passive, or Detractor, enabling you to understand customer sentiment and satisfaction accurately.
Understanding and satisfying customer needs has never been more critical in our current zeitgeist.
It’s a competitive marketplace, demanding you go above and beyond to capture customers’ hearts and, more importantly, retain them.
In this challenge, the NPS rises as a powerful compass guiding businesses toward success.
However, NPS’s significance extends far more profound.
While the numeric score is valuable, the true magic lies in the open-text comments often accompanying NPS survey responses.
These comments are unfiltered, unstructured expressions of customer sentiment.
NPS responses bridge the gap between company and customer, fostering a sense of partnership rather than a transactional relationship.
You can use these responses to identify pain points and moments of delight, aligning your strategies with customer expectations.
To that end, you should take full advantage of the NPS surveys, ensuring your growth path is crystal clear.
NLP is the key to unlocking deeper insights from NPS data, equipping you to analyze, understand, and draw meaningful insights from unstructured text data. Let’s break down how NLP streamlines the analysis process.
- Sentiment Analysis
NLP models can automatically classify open-text responses into positive, negative, or neutral sentiments.
This categorization gives you a quick overview of customer sentiment without manual reading.
- Topic Modeling
NLP can identify and group similar topics or themes within open-ended responses.
It automatically detects patterns, revealing what respondents are talking about the most, whether it’s product features, customer service, or pricing.
- Keyword Extraction
NLP algorithms can extract keywords and phrases frequently appearing in the text, helping you pinpoint specific issues or trends.
Consequently, your analysts can identify recurring pain points or areas of satisfaction faster and with lower risks of human error.
- Contextual Understanding
Going beyond word frequency, NLP can also recognize the context in which words or phrases are used, allowing it to distinguish between positive and negative contexts.
This context awareness improves the accuracy of sentiment analysis.
- Scalability for larger samples
NLP facilitates the analysis of vast datasets in a fraction of the time it would take for manual analysis.
This scalability is particularly advantageous when dealing with large-scale surveys conducted over a vast fraction of your customer base.
Thus said, let’s go down to the most exciting applicability of the NLP and NPS combination.
Using NLP For Sentiment Analysis
Sentiment analysis uses NLP and ML techniques to analyze and understand the sentiments, emotions, and opinions expressed by customers in various forms of textual data, such as product reviews, social media comments, customer feedback, and chat transcripts.
Its primary goal is gaining actionable insights into how customers feel about products, services, brands, and the overall shopping experience.
Applicable to all surveys but crucial with NPS, sentiment analysis can extract emotions and sentiments expressed in open-ended survey comments, uncovering the “why” behind the numbers.
For example, a satisfaction rating of “7” might seem average, but sentiment analysis could reveal that most respondents expressed positive sentiments, making it a positive overall assessment.
Sentiment analysis can also pinpoint specific pain points or issues that respondents highlight in their responses. For instance, sentiment analysis may reveal that many customers are dissatisfied due to a lack of customer support.
This information helps you prioritize developing your CS center rather than overinvesting in areas of little interest to customers.
As you can see, sentiment analysis can help prioritize action items based on the sentiments’ severity.
If you’re using sentiment analysis in recurring surveys, you can track changes in sentiment trends over time. Tracking it helps you understand the impact of changes or improvements made based on previous survey findings.
Moreover, sentiment analysis can validate or challenge your quantitative findings.
For instance, if a survey indicates high satisfaction scores but sentiment analysis reveals a significant number of negative comments, it suggests that the overall satisfaction may be lower than the quantitative data suggests.
Lastly, sentiment analysis can guide the creation of customized responses for survey respondents.
Several methods and tools are available to conduct sentiment analysis on survey interpretation.
Here are some standard methods and tools:
One method is rule-based sentiment analysis, which involves creating a set of rules and patterns to identify sentiment in text.
You define rules based on keywords, phrases, or patterns that indicate positive, negative, or neutral sentiment.
While simple, it may lack accuracy for nuanced sentiment analysis.
Another method, lexicon-based sentiment analysis, relies on sentiment lexicons or dictionaries that contain words or phrases associated with specific sentiments.
Each word in the text is assigned a sentiment score, and the overall sentiment is determined based on the sum or average of these scores.
Usually, analysts combine rule-based and ML methods to improve sentiment analysis accuracy.
For example, you can use machine learning models for overall sentiment classification and rule-based methods for aspect-based sentiment analysis.
Out of the various tools and libraries available for NLP-based sentiment analysis, some stand out.
The NLTK (Natural Language Toolkit), a popular Python library, offers sentiment analysis tools, algorithms, and lexicons.
VADER (Valence Aware Dictionary and sEntiment Reasoner) is specifically designed for sentiment analysis of social media text and is included in the NLTK library.
TextBlob, another Python library, simplifies text processing and offers an easy-to-use API for sentiment analysis, supporting both pattern-based and machine learning-based approaches.
Others worth mentioning include IBM Watson Natural Language Understanding, Google Cloud Natural Language API, offered by Google Cloud, and Amazon Comprehend, a cloud-based service from Amazon Web Services (AWS).
Before selecting your tool, consider your dataset size, desired accuracy, and available resources should be considered.
Why NLP over traditional methods?
Truth be told, NLP isn’t the only survey interpretation method.
However, unlike traditional methods, NLP models enhance sentiment interpretation’s accuracy, efficiency, and depth.
The main reasons you should use it in your analysis include:
- Getting the Context
NLP models don’t just pick up words; they understand how words fit into sentences.
That means they can tell if someone’s happy, even if they’re being tricky with their words. Old methods like keyword research can miss the subtleties and deliver incomplete insights.
- Broader Reach
As eCommerce goes global, NLP is an excellent tool for worldwide sentiment analysis, going beyond the English language.
While traditional models might struggle with languages other than theirs, NLP is more adaptable, giving you broader insights into your customer base.
As an AI-based model, NLP never stops learning.
In fact, it gets better and better with time as you’re training it with your data. On the other hand, traditional methods use fixed rules that can get outdated.
- Greater Bandwidth
Without breaking a sweat, NLP can handle vast amounts of text, like social media posts, reviews, or survey answers.
Manually analyzing all these words would need an army of people and a more significant period than NLP models.
- Real-Time Insights
NLP can analyze text as soon as it’s written. That means you can know what people are thinking right away.
Traditional methods involve slow manual work, so you’d be stuck waiting and end up uncovering issues when it’s too late.
- Grows with you
As your business expands, an NLP model can handle more and more work.
Old-school, manual methods don’t give you the same advantage, as they require hiring more people to conduct survey interpretation.
- Long-term cost-effectiveness
Even if setting up NLP can be pricey, it becomes a money-saving investment in the long run, as you don’t need to invest in human resources doing the same repetitive tasks.
Now that you’re familiar with the long-term benefits of NLP in survey interpretation let’s transition to the next level of enhancing your survey results interpretation.
Take Your Survey Results Interpretation to The Next Level
Even considering all the benefits discussed in the previous section, potential pitfalls are still derived from the NPS and NLP crossover.
For example, NPS survey responses may contain ambiguous or sarcastic language that can confuse NLP models. To avoid this confusion, implement context-aware models to identify nuances and differentiate between sarcasm and genuine sentiment.
NLP models can also inherit bias from their training data, leading to skewed sentiment analysis results. To minimize bias, you should regularly review and retrain models using diverse and representative datasets.
Finally, NPS surveys may involve feedback on multiple product or service aspects. Treating all comments as a single sentiment can oversimplify the analysis.
Avoid oversimplification by implementing aspect-based sentiment analysis to understand sentiment on different dimensions.
Integrating NLP tools into existing survey systems
When trying to get the most out of your NPS survey data, there are some best practices to make your analysis more accurate and useful.
First things first, clean up your data.
Get rid of element that doesn’t matter, like extra words, punctuation, or common words that don’t tell you much. This cleanup makes your data easier to work with.
Next, you want to make sure your model understands all kinds of different NPS survey answers.
That’s where training data comes in. Provide content and context with examples from different industries and languages so it can understand different ways people talk.
Think of it as context awareness, helping your computer avoid making mistakes by understanding words in the right way.
Language changes constantly, like how people use new words and expressions.
Keep your model updated and regularly teach it about new language patterns and expressions so it doesn’t get left behind.
Even though NLP models are powerful, they’re not perfect.
That’s why having humans check the model’s work is a good idea.
Now, the best approach for leveraging NLP processing in your business is integrating a software suite meticulously crafted to transform intricate data into straightforward, actionable insights.
As of the current writing, we are in the beta phase for the customer experience module within Omniconvert, leveraging the capabilities of AI-powered Natural Language Processing (NLP).
Discover how our Customer Intelligence platform takes your raw data and turns it into customer-centric insights. Unlock deeper insights from your data, enhance customer understanding, and drive smarter business decisions with Omniconvert Reveal!
With all things said and done, it’s time we look at some interesting real-world applications where NLP takes center stage:
- Customer Support and Chatbots
Imagine having a virtual assistant that not only understands your queries but responds in a way that feels as if you’re chatting with a human.
That’s the magic of NLP: elevating the quality of customer support interactions.
NLP empowers virtual companions to understand and respond effectively, ensuring that your customers receive prompt, accurate, and personalized assistance.
- Monitoring Social Media Conversations
Social media is where the world connects, shares, and expresses opinions.
NLP algorithms enable organizations to dive deep into the sea of social media conversations. They scrutinize discussions, uncover trends, track sentiments, and identify emerging topics.
These insights are invaluable for effective brand reputation management and orchestrating strategic marketing approaches.
- Leveraging Market Research and Competitive Intelligence
NLP is the compass in the vast ocean of data, guiding businesses toward valuable insights.
It plays a pivotal role in analyzing diverse data sources, ranging from customer feedback and online reviews to survey responses.
With NLP, you can gain the upper hand in understanding evolving market trends, deciphering competitor strategies, and unraveling intricate consumer preferences.
- Streamlining Content Analysis and Information Extraction
Have you ever wondered how large volumes of text can be processed efficiently?
NLP is the answer. It offers automation for thoroughly examining extensive textual content, making it possible to extract crucial information and generate structured data.
This structured data becomes a valuable resource for in-depth analysis, shedding light on hidden patterns and trends that would be nearly impossible to uncover manually.
With NLP, businesses transform mountains of unstructured text into actionable intelligence.
In a nutshell – we live in a world where information is currency. In this world, NLP is the key that unlocks its actual value.
All in all, NLP’s true transformative power lies in its strong connection with the NPS – the ability to unlock deeper insights from NPS data.
When used properly, the Net Promoter Score debugs the entire Customer Journey, unearthing insights you never even thought about before.
Insights derived from hundreds or thousands of consumers are at your fingertips.
NLP models go even further and crunch thousands of responses into topics and patterns, allowing you to identify and track any issues in the customer experience.
All these while shifting from traditional, time-consuming survey analysis methods to agile, data-driven decision-making.
Customer satisfaction and growth?
As good as done – as long as you’re leveraging the greatest crossover of all times: the NPS interpreted using NLP-based models.