Hypothetical question: If you were going to order cheese from an online grocery delivery service, how would you search or browse for the cheese you want?
Would you go straight for the kill and type “Hook’s Four-Year Cheddar,” one of Wisconsin’s finest? Would you keyword search “cheddar” and browse by bestsellers, sharpness, price, color or region? Would you base the choice on pictures of cheese, pairing recommendations or reviews? Would you just buy whatever cheddar is on sale (no shame in that)?
How Do You Distinguish Your Cheese From All the Other Cheese?
I raise these questions because if you sell physical goods online, you’re on the opposite end of this exchange. You are trying to predict what information, data and content a buyer needs to distinguish your goods from others. If you sell through an online marketplace, your brand competes against goods with similar characteristics and value propositions. That is surely the case with cheese (value prop: happiness).
The strength of your brand, marketing strategy, publicity and reviews may set you apart online. But a search with broad results or filtering options erodes those advantages. That is why I want to discuss the role of structured data in ecommerce in this next installment about product information management (PIM) and digital asset management (DAM).
Since I’m writing from Wisconsin, l will make my point with one of the greatest substances known to humanity: cheese. Whether you sell apparel, electronics, outdoor gear, power tools or artisan foods, the points here are salient. The metadata describing your product must reach an increasingly digital buyer. And the metadata describing your content must make it searchable and reusable for your marketing team and its partners. Combined, both types of metadata will help you differentiate your product offering from the competition’s.
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The Complexity of Digital Cheese
What we retrieve from searching, browsing, and filtering ecommerce products depends on complex information architectures. That architecture is the output of a question that marketplaces and sellers collaborate to answer: When you turn a physical good like cheese into digital information, what data is relevant? Here’s a potential answer (forgive me cheese pros, some of these are layman’s terms):
- Type – cheddar, gruyere, mozzarella, manchego, etc.
- Origin – country, state or province, region, town, protected designation, etc.
- Milk source – cow, goat, sheep, etc.
- Milk attributes – grass-fed cows, hormone-free, skim, etc.
- Added ingredients – nuts, wine, honey, coffee, etc.
- Fat content – a percentage of the cheese
- Flavor – nutty, sweet, smokey, creamy, etc.
- Color – white, yellow, orange, blue, etc.
- Form – shredded, pre-sliced, block, curds, etc.
- Aging – X months and/or Y years.
- Volume by weight – how many ounces of cheese?
- Temperature – does it require refrigeration or not?
In a store, context and packaging provide much of this information (e.g., if it’s sold in a cooler ...). Online, though, metadata stands in for context.
Someone has to code this metadata into a complex ERP platform (and hopefully deliver it safely into a PIM system) before marketers and ecommerce teams can use it to develop content. There could be even more data, though, if the cheese is made for one retailer, one distributor, or a particular global region with a different culture and language or unique labeling requirements.
So, if you sell 100 cheeses worldwide using the 12 attributes above, you’re managing at least 1,200 data points. Since the average shopper isn’t an ACS Certified Cheese Sensory Evaluator® (i.e., a cheese sommelier), and COVID-19 killed in-store samples, the cheese metadata is crucial.
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The Expression of Digital Cheese
Product information shapes what content creators will make. Their task is to translate those 12 variables into a rich product description that makes people want to click "add to cart." What photography or videos would help? A video interview with the cheesemaker? A guide to pairing that cheese with wine, meats, crackers, etc.?
Just as metadata makes the digital product findable online in a marketplace, a separate set of metadata makes content searchable, browsable and reusable in a DAM system. There is overlap but also significant difference between these types of metadata.
For example, for any photo representing your Wisconsin cheddar, you could tag that term: “Wisconsin cheddar.” If you use a DAM+PIM system, you could attach all the Wisconsin cheddar shots to all the Wisconsin cheddar product entries. Simple.
However, great content goes beyond the product shot. You might need images of trendy people eating cheese at a cocktail party. You may need a family enjoying shredded cheese with their chili. You might want action shots of the cheesemaker at work in a rustic setting. Those images could work for many different types of cheese you sell. I mean, why make 30 cocktail party shots for 30 types of cheese? In fact, wouldn’t it be advantageous to show the guests enjoying a variety of your cheeses?
Using a DAM system or comparable tool, someone in your organization has to create a vocabulary for describing cheese content. Maybe they tag the setting (urban; cocktail party), emotion (pleasure; appreciation), subtext (sophisticated; fancy) demographic (young professionals; hipster), and other attributes. By searching or filtering these terms, your teammates find the best image for their need, whether it’s an Instagram post or shot to accompany an online product listing.
Notice: With PIM, you think about how a customer will distinguish one product from another. With DAM, you think about how social marketers, email marketers, retail partners and other teammates will find images to represent one product, multiple products and the brand essence.
Related Article: DAM Governance Practices for the Long Haul
Melting it all Together
The choice of how to categorize and tag your product metadata and content metadata isn’t just a technical issue. It is strategic. It’s a process that requires you to survey the landscape of marketplaces, sellers and SEM keywords.
What keywords and browsing filters pull up your competitors? What do their headlines and subheadings look like on Amazon versus Kroger websites? What data points distinguish the bestselling cheeses from those that are getting some unplanned aging on the shelf?
That market research — not just the technical jargon used by engineers and product development — should shape your product metadata.
The same goes for content metadata. What are you competitors doing, and is it working? When you A/B test images on social media, email marketing campaigns, or on ecommerce sites, what attributes seem to engage people? What images are downloaded and reused most often by your marketing team, salespeople, and partners in PR and advertising?
DAM+PIM is a melting of two processes: One that physically differentiates products, and one that emotionally differentiates them. And if this feels a bit overwhelming to implement, you know what could relieve the stress?