Consumer Goods, the Data and Semantics

In our last blog post, Big Box Power Driven by Data Management, we discussed the burgeoning power of data in the retail environment. The retail industry has embraced and implemented aggressive data management programs that capitalize on their growing databases. From customer satisfaction to inventory management, retail thrives and grows by the way they depend on accurate and timely data. One of the challenges retail faces is they have highly complex data usability problems with product and unstructured data. This unstructured data can be a significant roadblock to their operations.

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As we look at the retail environment, we find that products or services are divided into three categories:

  1. Convenience Goods:  Inexpensive frequent purchases, involving little effort needed to purchase them. Examples include fast food, toiletries and confectionery products.
  1. Shopping Goods: Products those consumers do not buy as frequently as convenience goods. They usually cost more than convenience goods and consumers expect to have them for longer, so they will do some research prior to purchase.
  1. Specialty Goods: Products with unique features or branding. Consumers do not compare them with other products as the goods have features unique to them. Instead they will spend time searching for the place selling the product they want. Consumers are often prepared to travel to purchase their product and pay a premium.

Being intimately familiar with which category their products fall into, retailers can access and implement strategies that support the sale of that product. Having this knowledge is critical in that understanding the category allows them to implement the proper marketing and advertising strategies. Without proper data management plans, these efforts would quickly lose any effectiveness.  In working through the process of defining and categorizing their data, semantics can be a major issue. In a recent interview with Nathan Krol, Founder and President of Data Ladder, Krol commented on the topic:

“Discovering semantic meaning at the record level with semi-structured data in an automated fashion is very important. Putting together separate data sources is emerging as a needed business solution, so understanding semantics and context is critical to this function.”

Clearly, proper and effective data management is critical to the retail environment. Within that, understanding the proper categories of their goods allows retail to effectively marketing and sell their product. It is crucial to their success that unstructured data is managed properly. Data Ladder and Product Match 2.5 software is the right solution for your retail business. This solution is quickly adept at solving highly complex data usability problems with the product and unstructured data. You will find that Data Ladder is the right partner, with the right solution, for right now!

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Merging Data from Multiple Sources – Challenges and Solutions