Generating Insight From Text Analytics

Introduction

Text analytics is increasingly becoming an important tool for brands seeking to understand the vast landscape of unstructured data. This technology enables brands to sift through large volumes of text to extract actionable insights, a task that was once insurmountable due to the sheer scale of the data involved. By utilising advanced techniques such as topic modelling and sentiment analysis, brands can gain a competitive edge through a deeper understanding of customer sentiment, market trends, and brand perception.

What is Text Analytics?

Text analytics is the process of converting unstructured text data into meaningful data for analysis, using statistical pattern learning and natural language processing (NLP). NLP techniques are applied to the text data to understand and interpret the human language within it. This includes tasks like tokenisation (breaking text into words or phrases), part-of-speech tagging, and entity recognition. The goal is to convert unstructured text into a structured format that can be analysed algorithmically.This technique allows for the extraction of valuable information from text sources, which can include customer feedback, online reviews, survey responses and website content. The insights uncovered from text analytics can inform decision-making processes, strategic planning and customer experience improvement efforts.

Applications of Text Analytics:

• Sentiment analysis evaluates the emotional tone of text data, identifying whether the sentiment is positive, negative, or neutral, thereby providing a direct measure of customer sentiment towards a brand or product. This is crucial for understanding overall customer satisfaction and shaping communication strategies.

• Topic Modelling applied to platforms like Trustpilot are rich sources of customer feedback. Topic modelling algorithmically uncovers recurring themes or subjects in large text datasets, enabling brands to identify dominant conversation topics among their customers. This insight is invaluable for tailoring content strategies and understanding market needs.

• Aspect analysis or aspect-based sentiment analysis, takes sentiment analysis further by dissecting customer opinions about specific aspects of a product or service, such as quality, delivery or price. This granular view is essential for targeted product improvements and marketing messages.

• Text classification categorises text into predefined groups, streamlining the analysis of customer feedback and identifying common issues or queries, thereby enhancing customer service efficiency and responsiveness. For example, text classification can automatically sort customer feedback into categories like 'billing', 'product issues' or 'technical support', allowing for quicker and more focused responses from customer service teams.

• Trend analysis in text analytics identifies patterns and changes in customer sentiments or topics over time, offering brands foresight into evolving market trends and customer preferences. This information is key to staying ahead in a dynamic market. For example, trend analysis might reveal a growing interest in eco-friendly products among consumers, enabling a brand to adjust its product development and marketing strategies accordingly.

• Customer journey analytics uses text analytics to map out the customer experience at various touchpoints, providing insights into customer satisfaction drivers and pain points. This understanding is crucial for optimising the customer journey and enhancing brand loyalty.For example, analysing customer feedback might show that users often experience difficulties during the online checkout process, indicating a critical area for improvement to enhance overall customer satisfaction.

• Predictive analytics uses text data to forecast future trends and customer behaviours. This foresight enables brands to anticipate market needs, identify potential risks, and plan strategically, ensuring they remain competitive and responsive to their customer base. It involves extracting features or attributes from the text that are relevant to the predictive model. This could include the frequency of certain words or phrases, sentiment scores, topic classifications or trends over time. The model is trained using a portion of the collected data, where the outcomes are already known. For example, in predicting customer churn based on historical data where the topics customers discussed and their sentiment towards them, led to churn or retention.

• Analysing open-ended survey responses: Surveys often contain open-ended questions that can yield insightful qualitative data. Text analytics can process these responses at scale, providing a quantitative lens to qualitative feedback. This approach can help brands understand the nuances of customer needs and preferences, informing product development and targeted marketing campaigns.

• Website content analysis: A company's website is an important touchpoint for customers. Text analytics can evaluate the content's effectiveness by identifying the most engaging topics, the clarity of the communication and the alignment of the content with customer interests and search behaviours.

Below are some examples of the insight that can be generated through text analytics. Hover over each image for details.