Feature Extraction and Data Enrichment

In 2021, retail companies are faced with an abundance of data. This data covers various aspects of their operations, including customer insights and product information, representing a vital resource for modern businesses. Companies can leverage this information for informed decision-making and to refine their strategies. Additionally, they can enhance sales processes and improve customer service. Essentially, data is crucial for organizations aiming to adapt to the ever-evolving digital landscape.

Nevertheless, having vast amounts of data alone doesn’t guarantee value creation based on data; the quality of that data is equally crucial. Incomplete or low-quality data can lead to misleading conclusions and poor decision-making. This blog will delve into the process of data extraction and the advantages of data enrichment in boosting data quality and usability.

Product Named Entity Recognition  

Product Named Entity Recognition, commonly referred to as P-NER, is a technique for extracting information from large sets of unstructured text data. P-NER can classify this data into predefined categories. For instance, consider a television, a product with multiple characteristics like brand, size, weight, and resolution. These attributes are organized into their respective categories. However, P-NER often relies on traditional machine learning techniques and significant manual input, which is not ideal. Utilizing deep learning offers potential solutions, which are elaborated below.

Hybrid Bidirectional Long Short-Term Memory  

Hybrid Bidirectional Long Short-Term Memory, abbreviated as BI-LSTM, is a P-NER implementation comprising three layers: input representation, context decoder, and tag decoder. The first layer assists the model in understanding and accurately interpreting data. The second layer processes images by essentially breaking down the input into various underlying structures and attributes. The final layer performs similar functions for textual input.

BERT  

BERT (Bidirectional Encoder Representations from Transformers) is a language model adept at understanding and contextualizing text. It assigns values to certain words based on the relationships formed between them. For instance, in the phrase “The car is sprayed in a blue hue that reminds you of the Azure,” words like sprayed, azure, and tint relate to the word blue. With training data, BERT establishes associations between these terms, enabling the identification of blue as a feature. This straightforward example demonstrates how BERT facilitates the extraction of product attributes from unstructured text. The accuracy of this feature extraction improves with a larger dataset for training.

Data Enrichment  

As previously noted, high-quality data is essential for extracting product attributes to realize data-driven value creation. While data can be enriched manually, this process is often labor-intensive and susceptible to human mistakes. To mitigate these issues, enriched data must undergo checks for inconsistencies and errors—an arduous task that may lead to inefficiencies in time, resources, and costs.

PowerEnrich.ai  

PowerEnrich is a software solution that adeptly combines data extraction and enrichment, facilitating a cohesive strategy for data-driven value creation effortlessly and autonomously. PowerEnrich extracts data from four key sources: images, text, PDFs, and web pages. By smartly utilizing AI, PowerEnrich can recognize and interpret data irrespective of variations in abbreviations, spellings, or phrasing.

In summary, PowerEnrich assists companies in processing their product data and attributes more efficiently and effectively. Additionally, it allows for more detailed and comprehensive product descriptions, leading to improved product discoverability, increased sales, and an enhanced customer experience.

Interested in learning how PowerEnrich can benefit your business? Please reach out to us  to explore the opportunities together.

By Lieske Trommelen