Online retailing is a huge industry, but also a very difficult one for retailers. What makes some Internet stores stand out from the pack? Part of the answer is data quality, which undergirds everything that successful online retailers do.
Data and Online Stores
When you think of Internet retailers the names that first come to mind probably include companies like Amazon.com and eBay. These businesses rose from obscurity to become some of the largest retailers on the planet – and to pose an existential threat to non-digital retailers. (For a detailed look at how pioneering online retailers have developed, I recommend Brad Stone’s book The Everything Store.)
The reason that companies like Amazon have grown so wildly successful is that, from the start, they built data into their DNA. They use data to decide what to sell. They rely on data to drive product recommendations. They leverage data to track inventory, engage customers and run promotions.
Their competitors – which include both traditional retailers born before the digital age and online stores that failed to leverage the value of data from an early date – either didn’t do these things or came late to the Big Data party.
Companies like Walmart are now catching up with the likes of Amazon by learning to embrace the power of data-driven online retailing. But Amazon is still in the lead, thanks to its head-start in mastering data.
So, the short answer as to why some online retailers succeed while others struggle to surpass tiny revenue streams is data. But there’s a little more to it than that…
Data Quality: The Key to Online Retailing
There’s more to successful online retailing than simply embracing data-driven models. Data won’t drive your business effectively if it’s inaccurate, inconsistent or missing.
In other words, to use data effectively in the world of online sales, you need data quality. Data quality means the ability of a given data to serve its intended purpose.
Consider the following ways in which data quality is essential for successful online retailing:
To make product recommendations when users visit your site, you need to know where visitors are located, something about their personal characteristics and preferences and ideally, their browsing or shopping history. Just one kink in the data you have about a visitor can disrupt your product-recommendation engine.
If your data set records the visitor as male but she is actually female, for instance, you may end up displaying gender-based product recommendations that completely miss the mark.
Managing inventory by ensuring that you have enough of the products your customers will buy, but not so many that you are paying to store more than you need, is crucial for operating cost-effectively. Using data analytics, you can predict what your customers will want to buy and how buying patterns change seasonably, then adjust your inventory accordingly.
However, if the datasets that your analytics are based on contain inaccurate data – if, for example, the dates of historical sales were entered incorrectly due to a coding error – you’ll end up with inaccurate analytics results and wasted money on inventory.
One of the biggest challenges for online retailers – especially when competing with brick-and-mortar stores – is shipping products quickly to customers after they have purchased them. Inaccurate or incomplete shipping is your worst enemy for fast product delivery.
If a customer makes a typo when entering his address, or leaves out a zip code, you need to correct it before shipping.
Accurate product reviews
When people can’t see and touch what they are buying before making a purchase, it becomes especially important for them to have access to accurate product reviews. In most cases, these reviews are written by your customers. To ensure accuracy and weed out fake reviews, you need accurate and consistent data.
With quality data, you can cross-check to ensure that a customer who reviews an item bought it, for example, and that product details that are included in a review correspond accurately with those of the product.
Successful online retailers don’t just collect data. They ensure data quality so that they can count on their data to do its job.
When you need to achieve data quality, Syncsort can help. Syncsort’s data management products include data quality tools that automate the process of finding and correcting errors and inconsistencies within the data sets that power your business.
Download theBook The New Rules for Your Data Landscape to learn about the new rules that are redesigning the relationship between business and IT.