Big data and data science have become an everyday part of retailing for many online giants. Companies like Amazon.com, Backcountry.com, Zappos.com, and many others have been actively using big data successfully for years. In the below article we explore the ways in which most ecommerce companies use these tools and how practical benefits derived.
One of the chief motivations for these companies is to develop product recommendations in order to pair customers with other products they are likely to buy through cross-selling or upselling strategies.
If for instance I am in the market to purchase a protein powder for bodybuilding then I may also be sold a creatine supplement as well. If I am in the market for the creatine, I may be also sold a monthly recurring product or supply or a more expensive brand or version of the product. In short, the goal for marketers or data scientist is to provide a reliable recommendation algorithm that will track individual consumer behavior to increase revenues.
Big Data improves shopping experience
In point of fact, the shopping experience becomes much more personalized for each consumer based on the big data and the predictions and correlations that can be drawn from it.
My unique tastes or propensities may not be so unique after all when compared to other shoppers seeking similar items in my category. Data scientist no longer need to intuit consumer behavior or shopping proclivities as data is now gathered and interpreted in highly precise and predictive formats.
Additionally, with predictive modeling companies can then make decisions not only regarding where to invest marketing dollars but also what inventory to purchase and stockpile. This may be a new line of fashion apparel or toy before manufacturers are sold out. Therefore your company may have the healthy stock to offer consumers as other retailers experience back order or do not have adequate inventory to meet consumer demand. Contrarily, data science can help retailers avoid purchasing inventory that is no longer on trend or relevant. If one is manually attempting to infer future trends based on past trends this type of analysis is largely retrospective and not prospective (and therefore of very limited value).
One may have great information of past buying patterns but in an ever shifting and dynamic marketplace this may not be the most useful in producing go-forward information or intelligence. Where many companies go awry is in attempting to manually collect information from various disparate sources and then to make the best decisions based on this limited date and their personal inferences.
In today’s environment, big data dramatically widens the field and is essential in providing decision makers with a holistic or more comprehensive universe of data. When properly interpreted by data scientist, this data creates results that can allow decision makers to make choices with more laser beam precision and to avoid costly mistakes.
The benefits of data science in the ecommerce field are manifold. They allow marketers to to personalize marketing for individual consumers through remarketing campaigns, recommendation engines, couponing, etc. As the field evolves, we are seeing the growth of apps available for many shopping carts that can apply data science to smaller privately owned or owner operated web companies. I encourage ecommerce entrepreneurs to look out of for these and to install the software to their shopping carts.