In this post, we will cover:
- Three Datasets being used to predict consumer behavior
- Companies cross-referencing data are able to dominate their respective fields
Three Datasets being used to predict consumer behavior
Dataset #1: Amazon best-seller products
The product category, subcategory, as well as other parameters, can be fully customized (e.g., item brand, color, ASIN, etc). When a company identifies a product with high sales volume among competitors, they can quickly work to incorporate this in their catalog.
Here is a sample of what this dataset looks like:
Dataset #2: Amazon best-seller products with the lowest number of sellers
This Dataset takes the previous one to the next level. Knowing which products are best-sellers is a very important insight. But if there are thousands of vendors competing for buyer attention, then the likelihood of making a sale is significantly reduced.
That is where this Dataset comes into play – it highlights the products in your company’s field of interest that have high Sell-Through Rates (STRs) yet at the same time have very few vendors actually selling the items in question. This means that all of the items in this Dataset present a high-impact value proposition for your merchandising team.
Here is a sample of what this dataset looks like:
Dataset #3: Amazon’s most reviewed books
This is a Dataset that takes a simple metric, the quantity of reviews on a given product, in this case scenario books, and enables vendors to make quick and easy value calls. If a book has 3,241 reviews, it is clearly popular and driving people to comment.
Products that receive so much engagement may not necessarily become best sellers on a vendor’s shop. But what they can do, is be a great way of attracting interest in your store both via advertising campaigns as well as through online discussion forums that will help introduce your store to a relevant target audience.
Here is a sample of what this dataset looks like:
Companies cross-referencing data are able to dominate their respective fields
The above-mentioned Datasets provide valuable/actionable insights for companies in the digital commerce space. But when these Datasets are cross-referenced with additional information, they can create a unique competitive advantage, enabling them to become powerful players in their respective fields. Here is an example:
Quantity of reviews cross-referenced with substantial customer feedback
A company can cross-reference the quantity of reviews a product receives with substantial customer feedback articulated over the span of hundreds of product reviews.
Let’s look at this ‘ergonomic gaming chair’, for example. The Dataset shows that this product currently has 30,436 reviews which means that this is a popular item. When this fact is cross-referenced with qualitative feedback, one can identify a common denominator among buyers. As you can see in the product review below, many shoppers complain that the chair is in fact, very low quality and starts to fall apart after an average of 1-2 months of use.
By cross-referencing these two data points, a company looking to expand into the gaming chair industry can now use this information in order to improve the quality of its design. They can then display the fact that their chair is of higher quality on product listings and in advertising campaigns. If they feel confident enough, they can even provide a ‘100% quality guarantee or your money-back’ warranty.
The bottom line
When used creatively, Datasets, especially when cross-referenced, can be a powerful tool for digital commerce businesses. It can help provide the informational advantage that your company needs in order to formulate an effective ‘go-to-market strategy’, help you successfully localize a business, enter a new market or simply grab increased market share on your home turf.
