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’ lives. If I own a bike shop, I typically only have a short time to interact with a customer around the purchase of the bike, and may not see that person again for years, if ever. If I’m running a bike-sharing service, the number of transactions during those years will be many times greater. Each transaction is an occasion to put my brand in front of the customer. The increased interaction allows me to glean more information, to improve service, and to make timely, relevant personal offers. I can offer different bikes for different occasions, or for various members of the family and visitors. I can enable the customer to choose a favorite bike remotely, and offer repeat customers heavy discounts during low-use periods. I understand better how to address a family’s needs—and how to enrich their life experiences. Simply put, your business is first in line to amaze your customers, to win again and again. In some instances, new gear will come on the market, and people may be unsure about spending money on it just yet. When a Mesh offering is added to an existing product sales model, new customers can try before they buy. They become early purchasers and enthusiasts of new product lines for your market.

Build Trust. Grow Base. Refine Offers.

That’s the kind of leverage a Mesh model uses to expand traditional repertoires.

The virtuous circle of trust enables you, as the business owner, to rapidly and frequently interact with customers and prospects, and their friends and families. You learn more about what services or products they want and how to deliver them. Bike sharing, for example, has become one of the fastest growing forms of transportation, especially in Europe. But in the initial offerings, problems emerged. A common one is that customers often want to pick the bike up in one location and return it to another, which presents a logistical challenge to the service. In Paris, which has one of the most advanced bike-sharing programs, users often left the bike at the bottom of a hill, so they wouldn’t have to pedal up. Solutions include having people pay a premium for dropping the bike in a different location or adding a small motor that helps them power the bike up a hill. Paris is testing solutions with groups of users—and like a good Mesh enterprise, they are also sharing their results with bike-sharing services in other cities.

In the case of the clothing exchange company thredUP, the owners revised their core offering—they started out focusing solely on men’s shirts—after repeated interactions with their customers revealed a greater opportunity in exchanging kids’ clothes. They revamped their home page to focus entirely on children’s clothing—a move they learned by listening to data and feedback from their customers.

A new San Francisco chocolate company, TCHO, takes a novel approach to engaging with, and learning from, its customers. TCHO produces “beta editions” of its dark chocolate. Based on customer feedback and continuous flavor development, new versions of the chocolate emerge as often as every thirty-six hours. Version 1.0 went through 1,026 iterations in a year.

CASE STUDY:

thredUP

One November morning in 2008, James Reinhart stood in front of his full closet, but couldn’t find a thing to wear. His predicament sparked an idea: What if he could find a guy his size willing to swap shirts? Consignment and secondhand clothing stores are fine, James thought, but they aren’t convenient for busy people who want to swap clothes. Driven by the desire to create an efficient and convenient clothing exchange, James and his cofounders Oliver Lubin and Chris Homer started thredUP, the Internet-enabled clothing exchange platform. Its concept: “out with the old-to-you, in with the new-to-you.”

Initially, the company imagined using price points to facilitate swaps. But what a headache: How would thredUP accurately determine a shirt’s value? It became clear that using basic measurables—such as brand, size, and condition—would help thredUP arrange shirt-for-shirt swaps. So in early 2009, two hundred men in