Today Ben Popper published the Top 10 Rules for Crowdsourcing Business Ideas.

We like the list and this article is another great primer for industry executives just starting to tune into the power of Crowdsourcing.  But we found one big omission in the article so couldn’t pass up the opportunity to share what we’ve learned over the last 18 months.

At StyleHop we are helping fashion companies by crowdsourcing the fashion assortment decision. Today, fashion merchants and buyers must use their experience and intuition to determine how many of each dress or skirt to buy until they have spent their budget. Buying too many of a bad dress leads to markdowns and poor gross margin performance. Buying too few of the right dress leads to lost sales.

We set out to help buyers and merchants by asking the crowd which dresses were the best. However, in our first few pilot studies, the crowd never got the answer right. While they were no worse than the merchant’s guesses, they were no better either.   Over time, though, we got better and better at selecting who we let participate in our crowdsourcing studies and created a methodology to screen potential crowd contributors. Now we are seeing predictive success that consistently beats merchant/buyer guesswork alone and sometimes by a very wide margin.

The #1 rule of crowdsourcing we learned (let’s call it Rule #11) is to “Choose your Crowd” – define clearly the expertise you are looking for and define a methodology to optimize your chances of identifying the best-of-the-best for your particular activity.  This doesn’t mean you don’t want diversity of opinion…but you need the opinion to be informed in some way.  For crowdsourcing like ours where we are aggregating opinion, letting just anyone participate simply doesn’t work.

Women’s Wear Daily Technology Editor, Cate T. Corcoran led with this cover story on Monday’s WWD. It looks like Crowdsourcing is finally penetrating the mainstream fashion world. This is consistent with my own experience meeting with fashion executives. Two years ago, they had no idea what crowdsourcing was. Today, they are looking to meet with vendors that can help them think through how to embark on a crowdsourcing initiative.

I had a brief email exchange with Ms. Corcoran and suggested to her that the focus of her story, crowdsourced design, while perhaps more flashy, will ultimately have less impact on the fashion world than crowdsourced assortment selection prior to production. Don’t get me wrong – crowdsourced design can be a powerful tool in building marketing buzz. However, for mainstream fashion brands, it’s just impossible to do much more than capsules using crowdsourced design because the crowd just can’t be counted on to get the merchandising formula right – these tops need to match back to these bottoms and be consistent with this color story, etc.

Crowdsourced assortment selection, on the other hand, holds incredible promise for fashion brands. By involving a predictive panel to help identify which style prototypes have the greatest commercial potential, crowdsourcing promises to help address the number one problem in fashion: 25% of every single dollar in the fashion business is lost to markdowns because we’re buying the wrong inventory. We buy too many of the wrong design (= markdowns) and not enough of the right ones (= lost sales). Crowdsourcing helps merchants systematically improve their hit rate at identifying which dresses are the good ones and which are bad. Crowdsourcing focused on this problem has the potential to remove billions (yes, billions) of dollars of waste in the system due to misallocated inventory investment.

An excerpt from the story in 7/26/10 Women’s Wear Daily:

Major brands and retailers see crowdsourcing as a way to increase customer loyalty, while smaller firms build entire businesses from it.

And in a fashion democracy that already is accelerating at an alarming rate, ultimately anyone could be a designer, creator or manufacturer, with profound implications for the structure of the fashion and retail worlds, as well as the overall economy. Analysts estimate that crowd-sourced and customized products could eventually make up as much as 10 percent of the total market for apparel, accessories and footwear.

Link to Full Text

By Scott Denne

With Netflix Inc. paying out a $1 million prize on Monday to a team of outside researchers that improved its movie recommendation algorithm, two venture-backed start-ups are overjoyed that the “open innovation” model is spreading.

Open innovation “like any big change in business takes time to promulgate,” said David Ritter, the chief technology officer of InnoCentive Inc. “The Netflix prize is a bit of a turning point.”

InnoCentive provides a platform for companies to host challenges seeking outside solutions to problems. Prizes and challenges range from a $5,000 reward from a company seeking creative ways to get men to shave more often to a $1 million prize for finding a biomarker for Lou Gehrig’s disease.

One of our StyleHop advisors is very involved with a company that leverages crowdsourcing to create better/faster/more accurate algorithms.  It’s really fun to think about how StyleHop could leverage crowdsourcing to accelerate the iteration of our wisdom of crowd fashion forecasting model.  ~DR

Link to Original Article

September 21, 2009, 10:15 am
Netflix Awards $1 Million Prize and Starts a New Contest
By Steve Lohr

Update | 1:45 p.m. Adding details announced Monday about the extremely close finish to the contest.

Netflix, the movie rental company, has decided its million-dollar-prize competition was such a good investment that it is planning another one.

The company’s challenge, begun in October 2006, was both geeky and formidable: come up with a recommendation software that could do a better job accurately predicting the movies customers would like than Netflix’s in-house software, Cinematch. To qualify for the prize, entries had to be at least 10 percent better than Cinematch.

The winner, formally announced Monday morning, is a seven-person team of statisticians, machine-learning experts and computer engineers from the United States, Austria, Canada and Israel. The multinational team calls itself BellKor’s Pragmatic Chaos. The group — a merger of teams — was the longtime frontrunner in the contest, and in late June it finally surpassed the 10 percent barrier. Under the rules of the contest, that set off a 30-day period in which other teams could try to beat them.

That, in turn, prompted a wave of mergers among competing teams, who joined forces at the last minute to try to top the leader. In late July, Netflix declared the contest over and said two teams had passed the 10-percent threshold, BellKor and the Ensemble, a global alliance with some 30 members. Netflix publicly said the finish was too close to call. But Netflix officials at the time privately informed BellKor it had won. Though further review of the algorithms by expert judges was needed, it certainly seemed BellKor was the winner, as it turned out to be.

But the race was even closer than had been thought, as Netflix’s chief executive, Reed Hastings, explained for the first time at a press conference in New York on Monday. The BellKor team presented its final submission 20 minutes before the deadline, Mr. Hastings said. Then, just before time ran out, The Ensemble made its last entry. The two were a dead tie, mathematically. But under contest rules, when there is a tie, the first team past the post wins.

“That 20 minutes was worth $1 million,” Mr. Hastings said.

The Netflix contest has been widely followed because its lessons could extend well beyond improving movie picks. The researchers from around the world were grappling with a huge data set — 100 million movie ratings — and the challenges of large-scale predictive modeling, which can be applied across the fields of science, commerce and politics.

The way teams came together, especially late in the contest, and the improved results that were achieved suggest that this kind of Internet-enabled approach, known as crowdsourcing, can be applied to complex scientific and business challenges.

That certainly seemed to be a principal lesson for the winners. The blending of different statistical and machine-learning techniques “only works well if you combine models that approach the problem differently,” said Chris Volinsky, a scientist at AT&T Research and a leader of the Bellkor team. “That’s why collaboration has been so effective, because different people approach problems differently.”

Yet the sort of sophisticated teamwork deployed in the Netflix contest, it seems, is a tricky business. Over three years, thousands of teams from 186 countries made submissions. Yet only two could breach the 10-percent hurdle. “Having these big collaborations may be great for innovation, but it’s very, very difficult,” said Greg McAlpin, a software consultant and a leader of the Ensemble. “Out of thousands, you have only two that succeeded. The big lesson for me was that most of those collaborations don’t work.”

The data set for the first contest was 100 million movie ratings, with the personally identifying information stripped off. Contestants worked with the data to try to predict what movies particular customers would prefer, and then their predictions were compared with how the customers actually did rate those movies later, on a scale of one to five stars.

The new contest is going to present the contestants with demographic and behavioral data, and they will be asked to model individuals’ “taste profiles,” the company said. The data set of more than 100 million entries will include information about renters’ ages, gender, ZIP codes, genre ratings and previously chosen movies. Unlike the first challenge, the contest will have no specific accuracy target. Instead, $500,000 will be awarded to the team in the lead after six months, and $500,000 to the leader after 18 months.

The payoff for Netflix? “Accurately predicting the movies Netflix members will love is a key component of our service,” said Neil Hunt, chief product officer.

From Ad Age:

How a Notoriously Closed Culture is Adjusting to Social Media

Posted by Katie Hillier on 09.10.09 @ 10:03 AM

Buzz over New York’s 2009 Mercedes-Benz Fashion Week is growing and the world’s top designers are taking center stage, introducing their long anticipated collections. This is how the fashion industry has operated for nearly 100 years: Designers secretly produce the fashions that they feel are most relevant to their lines, and retailers decide what items are most relevant to their customer base. But the fashion industry is at the verge of a tipping point — one that could change this system forever.

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NEW YORK, Sept. 10 /PRNewswire/ — First Growth Venture Network, a mentoring program for high potential seed and early stage start-up tech companies, today announced the selection of 15 seed and early stage start-up tech companies for its first class, or “Inaugural Vintage.”
Rest of Story

Our friends at Crowdcast outline the limits of prediction markets:  blog.crowdcast.com

I don’t know Brad Feld but I love his Venture Capital blog, Feld Thoughts.  He understands investing in early stage companies is first and foremost a people business.

Here is his latest post on Startup Karma:

Great Entrepreneurs Believe in Karma

I met with an entrepreneur yesterday that I hadn’t seen in a few years.  I originally met with her about five years ago when she was starting her company.  She’d been a very successful executive at a large company and had decided to jump into the entrepreneurial game and re-invent herself.  Her business has grown nicely – and profitably – without having raised very much money.

We mostly just caught up on how things have been going (we email back and forth periodically, but it had been a while since we had met in person.)  After about ten minutes, she asked if she could tell me a story about the first time we met.  Always game for a good story, I said sure.  It goes something like this.

I was introduced to you by someone I had met with who was a close friend of yours.  He suggested that I get together with you and made an introduction.  After I set up the meeting, I went online to learn more about you.  After poking around for a while, I suddenly got scared – I had no idea why I was going to meet with you or why you would bother meeting with me.  I didn’t want to blow my one meeting and waste your time.  I told this to the person that introduced us and he said “don’t worry about it – Brad will give you 20 minutes of his undivided attention and something good will come out of it.”  So I went ahead and met with you.

I was really nervous.  I was uncertain what to talk about and just starting telling you about my business idea.  You gave me some reactions and a few pieces of advice and as some point said “I bet you wonder why I am meeting with you.”  I had no clue, said so, and you responded, “I believe in karma.  When I was starting out as a first time entrepreneur a bunch of experienced people helped me, gave me advice, and just spent time with me with no particular expectations on their part, except to be helpful.  I’ve never forgotten that and want to pass it on.  I have no idea what will come of this conversation, but if I’m helpful to you, you can pay me back by being helpful to another first time entrepreneur after your become successful.” This has stuck with me from the very beginning of my business and I repeat it often.

This story made me smile a huge smile.  I remember all of the entrepreneurs that helped me early in my career, including guys like my dad, Gene Scott, Helena and Chris Aves, Stewart Forbes, and many others.  Whenever I help someone like the entrepreneur above, I’m paying others back for helping me.

Many of the great entrepreneurs I’ve met believe in this and practice it.  It’s not altruistic nor is it selfless as there are huge emotional returns from watching other people – who you’ve helped early in their entrepreneurial career – be successful.  If you are a multi-time entrepreneur, I encourage you to consider a daily (or weekly, or monthly – whatever works for you) karma break to help someone that is just getting started.

“Despite being around for 10 years, the personalized product recommendation market is still in its nascency,” says Mr. Grau.

Jeffrey Grau is the retail e-commerce analyst at eMarketer.  Jeff and I have talked about how social shopping is going to help consumers understand local fashion trends.  While social shopping has pretty much been a disaster so far, I think he’s absolutely right that we are just getting started.  Check out the full article here.