Web Analytics, Mass Customization, and Metadata Converge
Not just the what, but the why.
Not just the what, but the why.
You responded more to two column series last year than any other columns combined: the metadata series and the mass customization series. Since return-on-investment (ROI) marketing involves understanding analytics and how well your site and products are performing, let’s revisit these two topics to understand how sales data and analytics are affected by these two separate, yet intertwined, ideas.
An interesting byproduct of mass customization and detailed metadata is a much more granular understanding of trends and style. Products are currently created based on what’s popular and selling well. Although this ensures new products are introduced to an accepting marketplace, it also limits creativity. Also, it can sometimes be wrong.
Imagine a pair of jeans mass-produced by a major brand. It’s the strongest seller of the season. Based on that knowledge, the company creates a new series of jeans based on the same design concept. The original jeans had a different type of pocket and a different type of stitching. Yet the entire new line of jeans fails to find a market when released.
How’s that possible?
Likely, the specific attribute that contributed to the high sales wasn’t known. Though the defining traits of the jeans were new pockets and stitching, perhaps the fabric was what consumers responded to. Hundreds of variables go into making each product. It’s difficult to know, based on sales data alone, which attributes informed the buying decision.
Companies test new products as a matter of course. They ask consumers what they like about the new product. Consumers, however, can’t always tell you why they like something. New-car buyers may say they bought the car because of its mileage or comfortable seats. Trend analysis may show the actual selling points were the handling or look of its fenders.
If the jeans manufacturer in the above example had the ability to mass-customize its jeans, it might have better understood why that initial pair of jeans sold so well. Mass customization allows a new level of meta-data collection about buying habits. The jeans manufacturer would be able to report on the highest-selling pocket type, stitching, material, color, cut, and zipper style. This level of knowledge could enable it to create a new mass-produced line of clothing based on the best attributes of its customized line.
A personalized user experience focused on these attributes can go one step further in creating a customized user experience based on products and their attributes. If, for instance, the user selects a certain color for an apparel item, the personalized user experience can show other products in that color. It could suggest complementary colors. It could even put together a matched wardrobe based on color palettes.
A personalized experience based on this granular knowledge can even help companies that sell only mass-produced products. One of our clients sells jewelry. Though they can tell what the best-selling items are, they couldn’t tell you why. We created an attribute-based search/browse system that allows users to search by attributes, not just product categories. These attributes are normalized across the entire product inventory.
Instead of just browsing for “bracelets,” the user can look specifically at bracelets that are “gold,” “24k,” and “white” and feature “diamonds.” The user similarly can look at all other types of jewelry, filtering by the same attributes.
The reporting difference is huge. Now, the company can see not only what products sell well but also trends in what people like. It can see platinum jewelry across all product lines is selling better than gold. It can see diamonds surrounded by rubies are a fashion trend, as opposed to diamonds surrounded by another stone. It can understand not only what products are doing well but why they’re doing well.
This knowledge helps the business in several ways. The marketing department can better plan promotions and know what to feature on prominent pages of its Web site and catalog. Buyers can better predict what types of jewelry will sell well and maximize its inventory. Jewelry manufacturers get valuable feedback on why people like their products and other products it should manufacture.
Web analytics, mass customization, and metadata combine in an extremely powerful way. They allow companies to understand minute details about customers’ preferences and buying/browsing habits. Companies will understand the ROI of their products’ detailed aspects beyond anything ascertainable simply by crunching SKU-based sales data.
Until next time…