Solving the data assortment problem of digital merchandising

5 min read
March 14, 2023

The Intellyx take on Nacelle and the modern approach to headless and composable commerce - Part 2

Written by: Jason English, Partner & Principal Analyst, Intellyx 

Before e-commerce, merchandising used to be the ultimate game-changer in retail.

Advertising and arranging an assortment of goods in a store, from the display window to the organization of product categories, to the planogrammed end caps and shelf spaces arranged from top-shelf to discount pricing — that’s what used to set one retailer’s brand and top-line revenue apart from the next.

Today’s digital merchandising is in an entirely different league. It’s like the difference between playing 2-D checkers and 4-D chess.

The customer customization conundrum

Today’s consumers and business-to-business customers expect a digital experience that instantly offers products and services tailored to their needs, with an array of options and accessories delivered as quickly and cheaply as possible.

Confoundingly, this level of customization is hard to achieve because the sellers and suppliers needed to fulfill each transaction hardly resemble yesterday’s retail value chain. 

Virtually any product has a deeper digital footprint than its basic specifications and price. E-retailers can partner, resell, promote or make offers based on the metadata surrounding each product and its suppliers, as well as the metadata surrounding its potential buyer and their perceived preferences.

However, as an industry, we haven’t gotten any smarter than our forebears about merchandising just because we’ve ‘gone digital’ or started talking about metadata. Retail chains have always maintained their audience metrics, sales histories and seasonal trends to help plan the assortment of SKUs and arrangement of each year’s offerings. That hasn’t changed.

Instead, the rate of change in customer preferences and demand is now happening much faster than any conventional merchandising plan can support.

Why is it so hard for today’s digital merchandiser to overcome the physical world’s limitations to reach customers with relevant options wherever they are?

Just-in-time manufacturing and marketing

The first wave of eCommerce in the late 1990s brought the supply chain concept of just-in-time manufacturing to the digital world. Companies like Dell thrived based on their ability to configure products to order with the help of a collaborative supplier network rather than maintaining huge inventory stocks or excess reserved capacity.

Fulfilling customer demand with build-to-order computers was a huge leap forward in efficiency and agility. Dell could market and feature products on their website that customers were likelier to buy, with order-to-promise dates that their manufacturers and logistics networks could deliver on. 

But while this use case was certainly customer-centric, it was only valid for a vertically integrated high-tech value chain, where the enterprise buyer could dictate the specifications and data exchange standards at the center of the hub.

There are still modern merchandising lessons an enterprise could take away from this pre-Y2K just-in-time revolution, even if it’s becoming clear that only the world’s largest companies would be able to achieve leverage over their entire value chains, much less control the data within.

The data challenge of digital merchandising

A modern enterprise can focus on gathering more suppliers and vendor partnerships, hiring and retaining the best employees, or even a better website or storefront design – but none of these initiatives will make an impact if the data isn’t ready to fulfill the customer’s needs.

Have you ever noticed how a major convenience store chain like 7-11 generally arranges its assortment of products on the shelves in similar places, even if the store sizes and layouts are different? 

They have retail merchandising down to a science – so much so that vendors supplying chips and soda receive purchase notifications and stockout alerts, then come in and fill their inventory from a truck stacked in each store's order and each shelf spot to be replenished.

If that sounds too complex to pull off, now try digital merchandising—where the ‘digital shelf space’ for displaying combinations of products is theoretically limitless, but the customer’s attention span is severely limited.

Data about a product now becomes the stand-in for a product in digital merchandising. Some product data is easily standardized; for instance, a UPC barcode or number would scan as the same product in almost any warehouse or store shelf.

In this world, many of the ‘products’ on display are not physical products at all – they may be different configurations of items, digital files, passwords, or tokens that give you access to a different price or a purely digital service such as a warranty or insurance. The data variations that can constitute a ‘product’ are infinite, as are the variations of how that ‘product’ can be displayed for different customers.

Applying a canonical data model for interoperability

With so much data variability, a commerce application should separate business logic and data from the presentation layer so that digital merchandising workflows can responsively present the right assortment of products and product options to customers.

Fortunately, many e-commerce companies and software vendors are improving as they publish their own APIs and addressable services for partners, preferably by applying a canonical data model

A canonical data model can break down all of the elements of a given product’s data and metadata from its sources into discretely defined attributes that can be discovered and searched across multiple dimensions rather than providing a strict categorization model or hierarchy that could prove inflexible to advanced queries or recommendation engines.

That’s great from a flexibility perspective, but there’s still a variability problem to address, as sophisticated customers will continue to demand new features, and ask for more details in every purchasing decision. 

The different catalogs, business process, automation and transactional vendors that feed an e-commerce app may have differing views on how to split the canonical data atom into its component parts.

The Nacelle Composable Commerce platform takes a novel approach to this problem. Its single GraphQL API sits on top of a highly performant database system that processes and normalizes. Despite being a single GraphQL endpoint, merchants can request data across all commerce and content domains in one query.

The Intellyx Take

Instead of arranging products in store windows and shelves hoping for foot traffic, digital merchandising arranges data in real time to meet the needs and preferences of customers, wherever they are.

The good news? A new value chain for commercial data has been in the works this whole time. Partners are breaking down their old data silos, from content suppliers enriching product catalogs with attribute data to transactional vendors confirming complex orders.

Companies are deploying headless e-commerce sites that separate the visual concept of a customer storefront from a highly responsive, canonical data layer that serves up exactly what the customer needs.

Copyright ©2023 Intellyx LLC. Intellyx is solely responsible for the content of this article. As of the time of writing, Nacelle is an Intellyx customer. No AI chatbots were used in the production of this article. Image source: “Steel cans with no labels on store shelves”

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