Data is a foundational concept in commerce. It is arguably the most critical topic related to success. This blog series covers the different types of data that exist, the importance of data quality and ways data is prepared and transferred in e-commerce today — so you can make appropriate plans or changes to your relationship with data to build a greater chance of success. (Read Part I.)
Consumers and business owners alike need to know and understand the impact data can have on our lives and the transactions we are involved in. By breaking the ubiquitous term “data” down into two larger types, we can evaluate the impact on each party.
Today’s blog post will more clearly define the different types of data that exist in the context of e-commerce, primarily from the point of view of the merchant and business owner.
Data Type #1: Consumer Data
In the general discussion of data, ”consumer data” refers to the data, content, or information a consumer voluntarily provides in almost any transaction with another entity. This can come in the form of data delivered from consumer to service organization — think about the data we give up when we search Google, use Waze, watch videos on YouTube, or interact with our iPhone. Massive amounts of personal/consumer data is communicated to these services, much of it unbeknownst to the consumer. This can lead to serious privacy concerns. This data concept is the easiest to understand — we are all consumers, and the majority of those of us in developed countries are online consumers, readily sharing our data to make purchases.
Data in the context of e-commerce is typically part of an intentional transaction. The data that is shared is not only planned, but explicit, and there is a clear purpose for its existence. When a consumer buys a product from a seller, they turn over relevant data such as names, addresses, phone numbers, zip codes, company names, and email addresses. These data elements are relevant to the transaction to ensure the buyer receives the item/service purchased. In the world of e-commerce, this data can be very valuable to your business. It can inform your marketing plans and product stock levels at specific distribution centers, and it can potentially evaluate demand based on the time of year, season, or other related trends.
In addition, there are order-specific data points (which tie into the specific consumer purchasing) relevant to your integration to Rithum and various channels, including, but not limited to: Order Dates, Site Order IDs, quantity ordered, SKU(s) ordered, requested speed of fulfillment, price paid for an item, shipping paid, etc.
Rithum provides many reports around transactional data to help you analyze this kind of data, and in some cases for these reports, we will do the analysis for you.
Data Type #2: Product Data
Product data is the most important subtopic of this post, and we aim to build on this concept even further during this blog series. By definition, product data is the data that often defines the descriptive qualities of the product, as well as its business-specific aspects: titles, descriptions, attributes, cost, sales price, available quantity, association to specific distribution centers, etc.
This data is made available to Rithum directly by sellers and indirectly by suppliers on behalf of sellers. We sometimes find that sellers take this data for granted — either by accepting poor data quality from the source, or not paying attention to the lack of data provided in general, falling short of the basic requirements for a channel. When you have the option to connect to and optimize your business by listing products to a large number of channels, you quickly find that more detailed, descriptive, and segregated product data is more efficiently transformed to meet the varied data requirements of multiple channels to which you end up connecting.
Consider your stock levels and selling prices on products, and product-level descriptive data (e.g., sizes, colors, materials, how it’s packaged). Success in selling relies heavily on the accuracy of these kinds of data points. As a consumer, if I am making a decision on which pair of shoes to purchase, I need to be sure that when I look at a listing (a collection of data) and select a size (more data) and color (more data) for the purchase, that I will ultimately receive the size and color desired. Then, after purchase, the data available from the channel (usually via email), and my credit card company will give me verification the transaction succeeded, and the amount charged (again, more data) should align with the transaction that occurred on the channel (more data).
As a seller, there is a bigger picture — information and knowledge data — available from the raw data. What is your revenue for a given day? What is your sales velocity on a particular product or set of products? Resulting numbers over a time period for revenue and sales velocity can be turned around into information and knowledge that you as the seller can act on.
Ultimately, the quality of this product data can make or break a product listing (literally). Or at the very least, it can lead to, or detract from, potential sales. Rithum provides tools to help transform the data to meet the requirements of the different marketplaces, but starting on solid footing with higher-quality and more accurate data can make all the difference in your ability to logically transform it. The process of product data preparation and transformation takes a lot of work for the channels, but in the long run we find it is worth the time and effort. It not only benefits you via Rithum, but through any other platform to which you are delivering this data, including your own website.
In part three of this series, we’ll discuss in more detail the challenges associated with poor data and some of the approaches you can make to address inconsistent data.