Beyond GPT-4: The Distinctive Value of Text and Image Analytics in Sentiment and Topic Labelling
In today's fiercely competitive business landscape, companies are constantly seeking ways to gain an edge over their rivals. Among the various capabilities that contribute to success, unstructured data analytics capability stands out as indispensable for survival in the face of intense competition. This post explores the significance of text and image analytics specifically and argues that no company can thrive without harnessing the power of these capabilities. There is of course also audio and video analytics to consider but once the tech is available to analyse text and images the rest can be handled with voice-to-text and image-to-text technology. More details on this below.
Reasons that make unstructured data analytics a must for your business:
1. Uncovering Insights: Text and image analytics enable companies to extract valuable insights from vast amounts of textual and visual data. By employing sophisticated algorithms and machine learning techniques, businesses can analyse customer feedback, reviews, social media posts, and other textual data sources. This allows them to identify emerging trends, preferences, and sentiment patterns, leading to informed decision-making and strategic planning. Similarly, image analytics empowers companies to understand visual content, enabling them to recognize brand logos, product placements, and consumer behaviour from images shared on social media platforms. The ability to uncover such insights provides a competitive advantage by allowing businesses to stay ahead of the curve.
2. Enhancing Customer Experience (CX): Text and image analytics play a crucial role in enhancing the customer experience, which is a key differentiator in today's market. By leveraging these capabilities, companies can gain a deep understanding of customer needs, preferences, and pain points. Through sentiment analysis of calls, chats, emails and social media posts, businesses can assess customer satisfaction and promptly address any concerns, improving overall customer experience and loyalty. Furthermore, image analytics can identify visual cues and sentiment from images shared by customers, helping companies gain insights into how customers engage with their products or services. By proactively addressing customer needs, businesses can establish a stronger foothold in the market and build long-lasting relationships.
3. Competitive Intelligence: Text and image analytics applied on publicly available information online also serve as powerful tools for competitive intelligence. Companies can monitor competitor activities, track mentions, and analyse customer sentiment related to competitors through textual data. This information provides valuable insights into competitor strategies, product offerings, and market positioning. Similarly, image analytics can help identify visual elements associated with competitors, such as logos or brand imagery, aiding in assessing market share and brand perception. Armed with this knowledge, businesses can adjust their own strategies, differentiate their offerings, and better position themselves to gain a competitive edge.
4. Operational Efficiency and Risk Mitigation: Text and image analytics contribute to operational efficiency by automating processes that would otherwise be time-consuming and error prone. For instance, text analytics can automate the categorization and tagging of large volumes of textual data, reducing manual effort, and improving data accuracy. Similarly, image analytics can automate the identification and classification of visual content, streamlining tasks such as quality control or identifying counterfeit products. By improving operational efficiency, companies can reduce costs, optimize resource allocation, and respond quickly to market demands, ensuring survival in a competitive environment.
Voice-to-text, Image-to-text and LLMs (Large Language Models)
At DMR, we leverage voice-to-text and image-to-text technology to efficiently process all forms of unstructured data through our listening247 platform. This enables us to label the data with custom machine learning models, ensuring the highest possible accuracy, regardless of the original language. In contrast, some vendors offering multilingual text labelling solutions rely on translating everything to English before labelling, which is not an optimal or accurate approach.
Lately, many individuals have inquired about how the DMR sentiment and topic labelling approach compares to GPT-4 or Bard. The answer is: the DMR approach is unequivocally better. For a less biased and more objective perspective, I encourage you to refer to this paper. Here is an excerpt from the paper summary:
“The preliminary study shows that ChatGPT and GPT-4 struggle on tasks such as financial named entity recognition (NER) and sentiment analysis, where domain-specific knowledge is required, while they excel in numerical reasoning tasks.”
This subject deserves its own article with a proper gap analysis between LLMs and the proprietary and custom ML models that DMR creates.
Text, voice and image analytics have become indispensable capabilities for any company striving to survive and thrive amidst fierce competition. The ability to extract insights, enhance the customer experience, gain competitive intelligence, and improve operational efficiency makes these capabilities vital for success. Companies that neglect to harness the power of unstructured data analytics will find themselves at a significant disadvantage, missing out on crucial insights, falling behind competitors, and failing to meet evolving customer expectations. Therefore, to remain competitive in the modern business landscape, organizations must prioritize the adoption and utilization of text, audio and image analytics to secure their long-term survival.
This statement, which I have shared numerous times in previous articles, encapsulates the essence:
“Over 90% of all human knowledge recorded throughout history exists in the form of unstructured data. If your company solely focuses on analyzing and comprehending structured data, it implies that you are utilizing less than 10% of the available data to inform your decision-making processes.”
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