In the past, we lived in a retail era where data was scattered and fragmented. With the rise of digital transformation, companies began to leverage data to enhance operational efficiency. Eventually, we will be surrounded by data, using it seamlessly in every aspect of our lives. A wide range of data can fully depict customer behavior, clearly reveal market trends, and optimize resource allocation, enabling businesses to make more accurate strategic decisions.
Big data is powerful, but small data also holds significant value. Many experts have highlighted the importance of data utilization. However, after extensive research, the author noticed that many people in the market are still confused about data collection and analysis. Readers often express concerns that the role of data analysis in the system architecture of IoT applications is not prioritized, leading to solutions that are just collections of hardware or automation tools, with little real intelligence embedded.
First and foremost, the author wants to clarify that there are multiple sources of data. Networked devices are just one way to collect information. It's not only IoT applications that can gather data.
For example, traditional cash registers also accumulate data, but they rely on paper tapes, making access difficult and limiting detailed business analysis. Only when cash registers were integrated with industrial computers did data become more accessible for further analysis. However, due to the need for fast checkout, current electronic cash registers still have limitations in the data fields they can collect. As a result, many retailers attach additional devices like credit card readers, mobile payment terminals, and barcode scanners at the checkout counter. Yet, even with these additions, retailers still lack insights into foot traffic, customer movement patterns, dwell time, or attention at different points in the store.
Recently, with the development of hardware like GPUs and advancements in visual recognition software, collecting such data has become more feasible. Additionally, improvements in data analytics technologies have made computing and storage more efficient and affordable, allowing data scientists to shift focus from structured data to unstructured data. For instance, speech and text data can now be analyzed. Traditional customer service calls, for example, can be transcribed into text and then analyzed using natural language processing (NLP) to identify customer sentiments. These conversations become part of the customer profile. To reduce human workload, chatbots powered by data technology have emerged. Customers can interact with chatbots through websites or in-store kiosks, asking questions about products or services. Not only does this streamline data collection, but chatbots can also recommend products based on the conversation flow. In contrast, human customer service representatives may provide more personalized assistance, but their memory is limited. They can't easily recall thousands of product details or locations, let alone instantly access a customer’s profile to tailor recommendations effectively.
Unstructured data can also come from online discussions, media reports, or social media comments. Viral content online is now a must-know for marketers. Without understanding fan engagement or search engine optimization, a company's voice in the market may fade. To adapt to the new digital marketing landscape, social listening and social media monitoring (SL) have become widely used. Even top-tier manufacturers are learning that market intelligence (MI) is no longer just about copying and analyzing reports. NLP-powered MI systems allow companies to track industry trends, discover new products or technologies, and monitor public sentiment or media coverage of their brand or competitors. This enables faster responses and better decision-making. Moreover, skilled data scientists can develop competitive monitoring tools that combine SL and MI insights.
With the integration of these technologies, businesses are now better equipped to understand customers, anticipate trends, and respond swiftly to market changes. The future of retail lies in harnessing the full potential of data—both big and small—to create smarter, more personalized experiences.
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