Selling Style and Other Intangibles on the Internet
A modified TV set, a phone line and a transaction-processing computer. Throw in a hankering to avoid the boring, weekly grocery run, and new signals lit up in Michael Aldrich’s mind.
This led to the creation of a revolutionary, makeshift eCommerce system all the way back in the 70s. Since then we’ve been selling everything we can, from electronics and movies to fashion, food and furniture, online.
But let’s step back a little further. Before Aldrich began imagining use cases for his novel idea of ‘teleshopping’, Phillip Nelson — a professor of Economics at SUNY, Binghampton — first started observing the basic differences between what he called ‘experience goods’ and ‘search goods’.
These differences laid down the ground rules for product treatment online even before eCommerce became popular. But what’s more important, they also help online businesses understand how shoppers perceive products they can’t touch and feel.
Mark Runs, Bikes and Loves Smoothies
This is a stripped-down version of a marketing persona. If we want to be more specific, we could say that Mark is single and may eat out often. But he’s also health conscious, so he probably prefers simple, single-serve meals whipped up with minimal effort instead.
It’s plausible he uses his trusty blender for a post-run breakfast smoothie every day. Or if he doesn’t already, he might be open to the idea of it.
But once we realize Mark needs a blender, we start to question the nature of the blender itself. How is the blender useful? Why will Mark prefer this particular blender over other blenders? How can you guarantee its quality?
An eCommerce store can answer all of these questions and bridge the information gap by being reliable and transparent about:
- Product characteristics (size, power, durability)
- How the product is sold (secure transactions, flexible deliveries) &
- Trustworthiness of the seller (product guarantees, customer support, return policies)
Mark’s blender is a search good.
Search goods are products whose quality you can judge before you buy or use them.
An online store can remove the uncertainty behind buying a blender or a power saw with a well thought out product blurb, good images and reviews from other customers. Armed with this information, Mark can buy the Super Decimator Blender at the next Cyber Monday and be sure that he’s made the right decision.
On the other hand, things aren’t quite so straight forward with experience goods.
What Elise Really Wants to Know About the Pants
How can you tell if you will like a hotel? Or a new dish? Or a dress? Without staying there/ tasting it/ trying it on, you’re stuck in the dark pit of Product Uncertainty.
Experience goods, by nature, are products that are hard to define, evaluate and quantify unless you buy them and use them. In Nelson’s words, ‘their utility or value can’t be ascertained before purchase’.
If Mark had a friend Elise who wanted a pair of jeans, size and color descriptions would only go so far in helping her make a choice when she’s shopping online. What she really wants to know is how it will look on her and if it suits her taste.
We hit a wall here because it’s hard to say something is ‘just right’ for someone when there are 7 billion different kinds of people on the planet.
In a paper titled Product Fit Uncertainty in Online Markets: Nature, Effects and Antecedents, Hong and Pavlou say that when it comes to experience goods,
‘It is not enough for a product to be described thoroughly and expected to perform well, the product must fit the consumer’s individual preferences.’
When a product listed online cannot show the shopper what he or she is looking for, we’re looking at a severe case of product fit uncertainty (defined as ‘the degree to which a consumer cannot assess whether a product’s attributes match her preferences’).
It’s a tough call for Elise and online stores selling pants.
Fashion and Product Fit Uncertainty
So obviously, it’s hard selling fashion online…or is it?
Fashion eCommerce is one of the fastest growing sectors in the world, and in the last year alone eMarketer predicted that fashion eCommerce would hit $60 billion in sales in the U.S and grab a whopping 17.2% market share of total retail eCommerce.
The problem is not bringing people to online fashion stores. They’re already sold with factors like variety, payment options, delivery convenience, and the ability to track down items they won’t find in local brick-and-mortar shops. The problem is bridging the gap between providing a cornucopia of options and at the same time, narrowing them down to fit an individual.
Stores like Trunk Club and Stitch Fix are trying to overcome product fit uncertainty by ultra-personalizing buying choices. They assign a stylist, take down your measurements, find out what you like and send you clothes every month that you can either choose to keep or send back.
But this is still a manual process. How can true personalization scale to larger eCommerce stores? And are there any tools or processes we can use to help customers evaluate experience goods online?
As Maz Kanata puts it, “It’s a good question for another time”.
Christina writes about artificial intelligence and fashion-tech at Mad Street Den — a computer vision-based AI startup whose cloud-based AI platform offers a wide range of products to businesses across the globe in fashion, retail, IoT, robotics, gaming and more.