How can you identify customer needs and preferences with text analytics?

Text Analytics is essentially a process of deriving meaningful insights from text data, such as a Word document full of Amazon reviews or comments on Instagram posts. It analyzes words, sentences, and documents to understand emotional tone, customer opinions, pattern recognition, and also to identify future trends (predictive analytics). I used these techniques in my first analytics project to enhance a children's education website by examining user feedback and comments. I discovered that one of the puzzle games had high engagement, whereas math quiz games were at the lowest. This helped me in customer retention by revamping the experience using text analytics.

In very simple terms, test analytics is like checking how well something works. For example, if you have a game or an app, test analytics helps see if everything is running smoothly, finding any problems, and making sure users have a good experience. It's like giving things a checkup to keep them working their best.

Consider text analytics as giving summary in short words. This summary is derived from a large set of conversations, comments in your company Instagram page or its like you favourite actor trying to understand if you like the movie or not. This is a statistical process to infer the useful information by applying different set of principles, modelling, classification, summarization, sentiment analysis and many.....

Text analytics, also known as text mining, is the process of extracting meaningful information and insights from text data. It involves techniques from linguistics, data mining, machine learning, and statistics to analyze large volumes of textual content. The goal is to discover patterns, trends, and relationships, making unstructured text data more accessible and understandable for various applications like sentiment analysis, topic modeling, keyword extraction, and more.

Text analytics is a way of finding useful information from texts, like books, articles, or messages. Texts can have a lot of information, but sometimes it is hard to understand what they are saying or what is important. Text analytics helps us to make sense of texts by using computers to look for patterns. Text analytics can also help us to see the information in a different way, like using charts or graphs. For example, text analytics can help us to: - Learn what people think about a movie by reading their reviews - Find out what are the most popular toys by looking at online comments - Discover new facts or ideas by reading different sources Text analytics is like a superpower that helps us to understand texts better and faster. 🚀

To use text analytics on your text data, you have to get it ready first. This means cleaning, processing, and transforming it so that text analytics methods can understand it. Remove things like punctuation, numbers, HTML tags, and stop words to keep only the important stuff. Also, make the text consistent by converting it to lowercase, removing accents, and simplifying words. Break your text into smaller parts like words or sentences. Finally, turn your text into numbers, like counting how often words appear or using TF-IDF scores. This way, the text becomes data that text analytics can work with.

To prepare your text data for analysis, imagine it's like organizing a bookshelf. First, 'dust off' your data by removing irrelevant items (stop words). Then, 'sort the books' by categorizing words (tokenization). Next, 'align the spines' by bringing words to a common base (stemming or lemmatization). Finally, 'label each shelf' by identifying key themes or sentiments. It's like turning a cluttered book collection into an orderly library, ready for exploration! 📚🔍

Some of the steps to prepare text data for analysis are: 1. Collect data from sources. 2. Clean: remove irrelevant info, handle missing values. 3. Split text into words (tokenization). 4. Remove common words (stopwords). 5. Reduce words to base form. 6. Normalize numbers, dates. 7. Eliminate duplicates. 8. Create numeric representations (e.g., TF-IDF). 9. Handle class imbalances if present. 10. Split data into training/test sets. 11. Encode categorical target variables. 12. Explore data for patterns (EDA). 13. Choose relevant features. 14. Create consistent preprocessing steps. There are more steps than this but this covers most of the work. Also, make sure to document steps for transparency.

Performing a final quality check before analysis ensures that the processed text data aligns with the specific goals of your analysis. This step involves reviewing the data to verify that cleaning, tokenization, and other transformations have been applied correctly and consistently. By aligning the data with your analysis goals, you enhance the accuracy and effectiveness of text analytics. This, in turn, enables you to extract meaningful insights related to customer needs, sentiment, or any other objectives you aim to address through the analysis of textual information. It ensures that the data is well-prepared and suitable for the intended analytical tasks.

Overcoming text pre-processing challenges is crucial for meaningful analysis. Use NLTK or spaCy to remove noise and stop words. Tokenize text into words or phrases for simplicity. Address synonyms and variations with stemming or lemmatization. Balance data using techniques like SMOTE. To manage sarcasm, use advanced NLP models like BERT. For handling multilingual text, use language detection tools such as langid.py or translation tools like Google Translate API. Process large volumes efficiently with parallel processing or sampling. Tackle short text challenges with contextual embeddings or transfer learning. Adopting these techniques ensures clean, structured text data, vital for accurate and actionable insights in text analytics

Sentiment analysis is like a tool in text analytics that tries to figure out the feelings, opinions, and attitudes in written text. It's useful for checking how customers feel—happy or not—and finding areas to make things better. To do this, you can use a ready-made sentiment list like the nltk library in Python. Or, you might train a machine learning model with the scikit-learn library, teaching it to understand sentiments. Another option is to use a pre-made model like the one in the hugging face library, called BERT, which already knows a lot about sentiments.

Few sentiment analysis tools are: Lexalytics Uses NLP to process text and identify emotions in a user's interaction. Intent analysis Detects and understands customer intentions toward a brand, product, service, or experience. MonkeyLearn Analyzes comments and reactions to gauge audience response to content. Talkwalker Analyzes the tone of online mentions from social media networks, blogs, and forums. It can also detect sarcasm. Repustate Analyzes both customer and employee sentiment. Social Searcher Provides insight into positive and negative customer feelings about a brand on Twitter. Awario Monitors what people are saying and how they're saying it to evaluate how customers feel about a brand.

In my humble opinion, delving into sentiment analysis is like navigating a linguistic minefield. Enthusiastic users unleash a barrage of emotions, complete with emojis, slang, and the audacity to incorporate characters from foreign languages, complicating the pristine path to data preparation for AI Model deployment. Clearly, a savvy Gen-Z language maestro is required to decode this emotional rollercoaster. Because, you know, parsing feelings is just another day in the life of a tech wizard.

Sentiment analysis is used for identifying customer sentiments from reviews and social media. In reviews and social media messages, customer's sentiments contain customer's emotions, their opinions and intents. Sentiment analysis is used to identify +ve and -ve sentiments from reviews. Customer express positive and negative sentiments for different product's features. Aspect based sentiment analysis approach is used to link sentiment words with product's features. In sentence, positive and negative sentiments is expressed by adjective, adverb, polar nouns and verbs. We can train a classifier for sentiment analysis. We can also use sentiment dictionary like sentiwordnet. Apart form that, pre-trained transformer models can also be used.

Sentiment analysis has unveiled the profound insights hidden within customer feedback, revealing the emotional underpinnings of their experiences. By analyzing customer reviews, social media mentions, and feedback surveys, businesses can decipher the sentiment spectrum, identifying areas of satisfaction and potential issues. NLTK's VADER lexicon and TextBlob's sentiment analysis tools empower businesses to extract actionable insights from customer feedback. Pre-trained models like Hugging Face Transformers and Stanford CoreNLP provide a powerful foundation for sentiment analysis, while Scikit-learn and TensorFlow/PyTorch frameworks offer flexibility for customized models.

Topic modeling is a powerful text analytics technique that aims to discover and summarize the main themes in a large set of text data. It helps you understand what customers are talking about, their interests, and any issues they may have. To do topic modeling, you can use probabilistic models like Gensim's Latent Dirichlet Allocation (LDA). Visualization tools like PyLDAvis help make sense of the results. Matrix factorization models, such as Scikit-learn's Non-Negative Matrix Factorization (NMF), break down your data further. Additionally, neural network models like the Conditional Variational Autoencoder (CVAE) can handle complex patterns and nonlinear relationships in your text data, revealing insights into topics and words.

Topic modelling can help customer service teams in several ways. By analysing customer feedback, topic modelling algorithms can identify the most common issues and concerns that customers have. This information can be used to improve customer service by creating more informative self-serve content or by helping customers directly. For example, if a customer service team identifies that many customers are having trouble with a particular product feature, they can create a tutorial or FAQ to help customers resolve the issue on their own. Alternatively, they can use this information to train their customer service representatives to better address these issues when they arise .

In terms of support, proper topic modeling and historical data can help you outline the categories/types of tickets your customers are likely to submit. This enables you to build a relevant new ticket page with categories written in your users' language. Another scenario involves retrieving a set of keywords that will be associated with certain agent teams, thus triggering the correct automated ticket escalations. Thirdly, don't forget about your customer feedback data. If the volume of text feedback is high enough, you don't need to manually go through each piece. Repetitive themes will emerge for both positive and negative sentiments.

Topic Modeling algorithms are used for extracting themes and topics from the documents. Multiple algorithms can be used for topic modeling. 1)Latent Dirichlet Allocation(LDA):- LDA is unsupervised algorithm. It extracts mixture of topics, where one topic contains a group of similar words. 2)Non-negative matrix factorization(NMF) & Singular value decomposition(SVD):- SVD and NMF uses matrix factorization approach for finding topics from documents. SVD decompose word-document matrix and creates word-word matrix and doc-doc matrix. Matrix ranking algorithm is used for finding top words from the document. These algorithms find topics on the basis of co-occurrence. 3)RAKE, TextRank and Lexrank are graph based algo used for finding topic.

Topic modelling is the tool at your disposal to comprehensively understand the general meaning of an unmanageable number of samples (up to the hundreds thousands). The most advanced techniques nowadays can involve the combination of encoding and LLMs to first extract meaning of the groups of samples, then explain each topic to us using the LLM. 1. The process begins by using encoders to convert each sample into a vector 2. We can now apply clustering to group all the samples with similar meaning 3. We will have obtained a small number of groups (compared with the number of samples). 4. If we pass the main representative of each sample and the top frequent words in the cluster, we can make the LLM describe the topic of the cluster

Text classification has witnessed a substantial improvement in quality since the introduction of zero-shot classifiers. Traditional models could only output the classes they were trained on (for example, sentiment classifier would usually output Negative, Neutral, or Positive label). This approach would have required a specialized model for each task. Zero-shot models, however, allow you to state the classes without any prior training (for example, you wish to classify an article, and you list 3 potential classes of your choice: finance, medical, education). The zero-shot classifier is able to make a choice within the given classes even if it has not been pre-trained on them.

Text classification categorizes text into predefined labels, aiding in customer segmentation and personalized strategies. Three approaches include: 1. **Rule-Based:** Quick and simple, using predefined rules or criteria. For example, Pandas in Python can assign labels based on keywords. 2. **Machine Learning:** More accurate and complex, employing algorithms like Support Vector Machines from Scikit-learn to predict labels for new text based on learned patterns. 3. **Pretrained Models:** Advanced methods, like Hugging Face's Roberta, use fine-tuned models for specific tasks, leveraging pre-existing knowledge for effective text classification, especially in handling intricate language nuances.

Text classification for customer segmentation involves: Data Collection: Gather customer text data (reviews, feedback). Labeling: Categorize texts into segments (e.g., happy, dissatisfied). Model Training: Train a system to recognize patterns linking text to segments. Prediction: Apply the model to new texts to assign segments. Analysis: Understand different customer groups for targeted strategies.

To identify customer segments using text classification, collect and preprocess customer data, label segments, train a model, integrate it for automatic categorization, analyze segment distribution, refine the model iteratively, and tailor strategies based on insights gained from each identified segment.

J’ai constaté que beaucoup de nos clients utilisent une #BI pour simplifier la classification des données par famille. Dans le cadre de l’utilisation de #Llama2, nous avons pu intégrer directement les données de la BI et de les étiqueter. La pertinence des réponses étaient acceptables et a permis d’accélérer le projet.

Exploratory Data Analysis (EDA) is a crucial step in understanding and gaining insights from text data before applying more advanced analytics and ML techniques. EDA can involve many different things including Basic Statistics/Visualization, Text Preprocessing, Term Frequency Analysis, Document Length Analysis, Sentiment Analysis, Topic Modeling, Named Entity Recognition (NER) and more. The goal of EDA in text analytics is to gain a preliminary understanding of the dataset, identify patterns, and generate hypotheses for further analysis using advanced text analytics techniques. This process helps in setting the groundwork for more sophisticated modeling and extraction of insights from textual data.

The first step is the most important i.e. to assess where and what data you are collecting for customer sentiment analytics. Making sure that data is available and detailed enough through appropriate customer engagement is essential for the analytics to be productive. Making an inventory and brain storming additional data acquisition pathways helps enrich the data set for meaningful results that can guide strategy.

Imagine your text data as ingredients for a mystery-themed dinner party. First, you 'sniff' each ingredient (initial data exploration) to understand what you have - are there onions (common words) or rare spices (unique terms)? Next, you 'taste-test' (frequency analysis) to find out which flavors (words or phrases) dominate your dish. Then, you 'mix and match' (correlation analysis) to see which ingredients (words) work well together, creating your storyline. Finally, you 'add a secret sauce' (sentiment analysis) to give your dish the perfect mood, be it suspenseful or joyful. With EDA as your recipe book, you're all set to cook up an intriguing, flavorful data feast! 👩🍳📊

Data Discovery Quest: Embark on exploratory data analysis (EDA) to understand your text data's structure, length, and distribution—laying the groundwork for meaningful insights. Word Cloud Expeditions: Utilize word clouds for visualizing prominent terms. Uncover key themes and identify frequently occurring words, guiding your focus in text analytics. Distribution Mapping Odyssey: Map the distribution of text features. EDA frameworks like matplotlib or seaborn unveil patterns, helping discern variations and anomalies within your textual data. Correlation Excursion: Explore correlations between text features. Identify relationships that can shape your text analytics strategy, guiding decisions on which aspects to prioritize.

Exploratory data analysis (EDA) frameworks, like word clouds, frequency analysis, or co-occurrence analysis, provide visual and quantitative insights into text data. These techniques help identify frequently mentioned keywords, associations between words, or patterns in customer feedback, guiding further analysis.

Unstructured data such as text interactions with customers via various touchpoints such as help channels or surveys, have often been overlooked with limited analytics applied. This has been largely a result of the effort required to model and varying accuracy in terms of classification and sentiment. The advancements and ease of access to LLM's have reduced this significantly, opening the door to incorporate more in-depth analytics in an areas that is rich with information.

Data Analytics do not work without a structured planned and emotional intelligence for the dream team who has collected it to create a final customer experience: an army of experts will help organize and understand the data to use properly prior heading on creating the journey of our clientele. Take the happy Holidays journey for instance at Montblanc where clientele wishes is regarded all year long through their personal choice, preferences locally regionally and globally if we are speaking to VIP clients that travel across the globe and always stop by their favorite flagship in Dubai Mall or Costa Mesa…

A simple approach to go about doing the same is by implementing topic modeling by analyzing customer reviews and support tickets. Start with an Identify recurring themes for your products or services using word frequencies or word cloud. This approach not only helps in understanding current customer sentiments but also in predicting future trends. Once you understand the patterns emerging, you can use the same to create targeted marketing campaigns or personalized product recommendations. By acting on these insights, businesses can significantly improve customer engagement and conversion rates.

Text analytics can be a powerful tool for understanding customer needs and preferences, but it should complement rather than replace human insight and intuition. Considering these pitfalls and good practices can help you: 1. Quality of Data 2. Bias in Data 3. Language and Culture 4. Privacy and Ethics 5. Combining Text and Quantitative Data 6. Human Validation

N’y a-t-il que des solutions des grands éditeurs cloud (ChatGPT, Gemini, etc…) pour mettre en place une plate-forme LLM d’entreprise protégée et européenne ? En effet il n’y a aucun sens de concevoir ses propres modèles. Mais mettre en place une architecture on premise avec un orchestrateur et une plateforme API permet de travailler avec les meilleurs LLM du marché (#Llama2, #Mistral, #Bloom, #Falcon …) et d'en changer au fur et à mesure de leurs évolutions, tout en conservant vos données et en maîtrisant votre sécurité. Vous pouvez même choisir votre LLM en fonction de votre stratégie métier, #RSE ou financière. Le choix vous appartient