Luvocracy, a recently launched, Pinterest-like social marketplace where people can buy products recommended by friends and other tastemakers, has raised $11 million in funding from Kleiner Perkins, Google Ventures, Marissa Mayer, Ali Pincus, Jim Lanzone, Tony Robbins, CrunchFund*, RPM Ventures and XG Ventures. Kleiner Perkins partner Bing Gordon is joining Luvocracy’s board.
Sharing products has become mainstream thanks to Pinterest and Facebook. But Pinterest lacks the ability to actually buy many of the products curated on the site by friends, and there is too much noise on Facebook to make it a dedicated e-commerce platform for recommendations. Enter Luvocracy, a startup co-founded by Nathan Stoll and Roger Barnett.
Stoll was an early Googler who ran and expanded Google News. The last company he co-founded, social search service Aardvark, was acquired in 2010 by Google. Barnett was the founder and CEO of Beauty.com and the CEO of Arcade Marketing, the largest perfume-sampling company in the world. He is also the chairman and CEO of Shaklee Corporation, the leading natural nutrition company in the U.S.
The premise around Luvocracy is to be the social marketplace where people can buy products recommended by those they trust. At a macro level, the startup is bringing the power of recommendations, which have driven purchase decisions for centuries, into the world of online shopping. Stoll recalls the story of his grandmother, who was a longtime Avon lady, as demonstrating the power of human recommendations. Even today, nearly 90 percent of all online and offline purchases (or 8 billion transactions) start with a word of mouth recommendation.
But there hasn’t been a streamlined way to easily share, discover and buy great products recommended by the friends you trust and tastemakers whose styles you admire.
Once you register for the site, you can check the products you are interested in (i.e. home goods, men’s style, women’s style), import your Facebook friends, and more. You can filter your shown product feeds by trending (by recommendations), the people you trust for recommendations, the latest product added, and featured products and tastemakers. When I first registered for the site, I immediately made my first Luvocracy purchase, a “Shopping Is My Cardio” sweatshirt.
Luvocracy lists a maximum you will pay for the product. To make buying a recommendation dead simple, Luvocracy created a “Buy It For Me” service. When there is something you “luv” from a trusted person, it’s as simple and easy as clicking the “Buy It For Me” button, and Luvocracy takes it from there, locating and purchasing the item and even dealing with any shipping or return issues on their behalf.
The startup will manage merchant returns for you, and offers a 30-day return policy.
Similar to Pinterest, Luvocracy also lets you easily create collections of products that you adore and want to recommend, so that others can discover and purchase from you. All recommenders receive a portion of the sales in Luvocracy (and Luvocracy makes a cut from each purchase) as well.
The challenge Luvocracy will face is to create an audience in a social commerce world that is already being dominated by Pinterest and the most recent up and comer Wanelo. But my immediate impression by Luvocracy is that it could accomplish this among the design-focused audience that e-commerce standout Fab has been able to tap into. The quality of the products posted on Luvocracy is high, and I had not seen most of the items I browsed through on other e-commerce sites (and as my purchase indicates, shopping is my cardio, especially online). Even the user experience itself of Luvocracy and the presentation of products is sleek.
If Luvocracy can maintain this quality and design-focused brand even as it grows, the site could create a loyal (and high-purchasing) user base. Based on my intial purchase, the startup already has my attention.
*TechCrunch founder Michael Arrington is a General Partner of CrunchFund.
Whenever my friends and I get together for a movie party, the toughest part of the evening is collectively deciding what to watch. There’s always one person that shoots a good suggestion down with a curt, “I’m not in the mood for that”. Moviora is an app that aims to make that process a little easier.
It’s a strange thing. Streaming services like Netflix and Amazon Instant Video certainly provide a plethora of video content for our viewing pleasure, but oftentimes the biggest challenge is narrowing down all of those options to just one title.
Netflix offers a list of recommendations by sifting through our viewing history, but it’s far from perfect. I’ve watched a lot of stupid crap, and so have all of my other friends I share my Netflix account with. More often than not, my searching on Netflix will end up in mired in a list of obscure, low-budget B-movie titles that I have no intention of ever watching.
Moviora is an app that attempts to discern what we’re in the mood for watching by essentially playing a game of ten questions. For example, it asks if you’re in the mood for a “drama”, and you narrow the choices down by answering yes or no. Once you’re cycled through these prompts five to six times, you’re provided with a movie recommendation, along with a trailer, some reviews from Rotten Tomatoes, and links to the film on Netflix and iTunes.
When I answered “yes” to drama, “no” to comedy, “yes” to romance, “yes” to action, and “yes” to adventure, it provided me with Crouching Tiger, Hidden Dragon, which is actually a movie I haven’t watched yet and wouldn’t mind seeing at all.
It doesn’t always provide you with the perfect choice, but there’s an addictive quality to the ten questions aspect and it’s certainly a lot better than aimlessly meandering through Netflix’s movie titles.
See the original post: Moviora Plays Ten Questions To Help You Pick A Flick
Toronto-based startup AppHero launched version 2.0 of its service today, with a brand new interface and big behind-the-scenes changes to how it offers up recommendations to users. AppHero is on a roll, fresh off funding and a spot on NBC this morning, but the app discovery space is a crowded one, and competitors like AppGratis have more international experience and are aggressively targeting the North American market.
But AppHero founder and CEO Jordan Satok thinks that his company is offering up one of the most comprehensive app recommendation engines available for iPhone and iPad owners inundated with a flood of new apps every day. In an email exchange, the young founder was kind enough to share a lot of behind-the-scenes information about how AppHero determines which titles to recommend with which users. As such, we’re able to share with you a very detailed synopsis of the recipe for AppHero 2.0′s secret sauce.
Surprisingly, much of the final process of determining which apps are and aren’t worthy of inclusion in AppHero’s recommendation still falls to human hands: Satok’s specifically. He says he “personally looks” at the crop of apps that remains after AppHero’s sophisticated, automated “app review” process tackles the 772,177 current (as of this writing) active apps in the App Store.
“We’ve built some algorithms that based on around 50 different factors (such as what people are saying about an app, how frequently it’s been updated, and what the other apps the developer has are like), is able to automatically filter out all the junk,” Satok says. “Think about this like a spam filter for apps.”
Once Satok has run a fine-tooth comb through the results spit out by its collection of filtering algorithms, the startup begins the task of making personalized recommendations. While Apple doesn’t allow third-party developers to see which apps are already on a user’s phone, AppHero (with a user’s consent, and in ways that sidestep Apple’s lack of direct access APIs without running afoul of their rules) is able to determine “with a high degree of accuracy” what apps they already have. That’s crucial to the process, since it prevents duplicate recommendations, and provides data that can be used to draw conclusions around general categories of interest (i.e., if a user has a recipe app and a kitchen timer, they’re probably fans of cooking).
AppHero has built tech around natural language processing techniques to help it automatically understand when apps offer similar functions. The engine can determine the general function of a particular app (i.e. Real Racing is a car racing game) and then group that with other similar titles (like Need for Speed Shift, for instance). Satok says grouping apps in this way proves much more accurate and useful in formulating useful recommendations than sticking to Apple’s own App Store category and sub-category divisions.
The core of the recommendation magic comes around its ability to analyze tweets, Likes and other aspects of a user’s social graph, which they volunteer when they sign in to their social network profiles on AppHero.
“I can’t go too deep into how we do this, because it’s very core to our business,” Satok explained, “But it’s unique tech because most other recommendation engines (Netflix, Amazon, etc.), are only able to do product based recommendations (i.e., you watched Lord of the Rings so you’ll like the Hobbit), but we’re able to do recommendations across different media types (i.e., you’ll like the TechCrunch app because you’ve liked an article from techcrunch.com on Facebook).”
Other key ingredients of the AppHero 2.0 recipe include improving the text descriptions that appear for each app, by taking not just the top-most content from App Store summaries, but instead finding the meatiest content in the hopes of better articulating to a user what an app actually does. The startup is also geo-tagging apps in its database where appropriate, so that titles relevant to a person’s location will bubble up, and they’re making sure to disambiguate between similarly named spots, so you won’t get recos for apps appropriate to London, England when you live in London, Ontario.
Satok says there are a lot of other projects in the works at AppHero to try and continuously improve the quality of its recommendations. There’s plenty of room for more than one solution in the app discovery space, but users will go back to the service that gives them the best recommendations out of the starting gate, and the underlying tech is the key to winning that race.
Today, mobile app company Alike has announced that it has been acquired by Yahoo and will be joining the Yahoo Mobile team.
Alike allows you to recommend nearby venues that are your “favorites.” This is similar to Facebook’s approach in tagging the things around you that you’re interested in. This could be a great move for Yahoo in pulling together like-graph type information to build out more content on its site.
Here’s what Alike had to say about it:
Alike is joining Yahoo! Mobile
At Alike, we’ve spent the last couple of years working hard to build amazing mobile experiences to delight our customers, which is why we’re thrilled to announce some big news: we’re joining Yahoo! Mobile.
We’ve always been passionate about the growing power of intelligent mobile experiences. We believe that distilled information, deeply personalized and made accessible anytime and anywhere, is what makes mobile experiences a part of our customers’ daily lives.
In Yahoo! we’ve found a team as excited about this vision as we are, and who are serious about making it real. We’re super excited to join Yahoo!’s mobile team, where we can march toward that vision faster than ever.
As of today, we will no longer support the Alike Nearby iPhone and Web apps. Thank you to all our customers, partners, investors, and advisors who’ve supported us from day one! We’ve taken a big step on our journey, and we could not have done it without your support.
Looking forward to starting our new chapter at Yahoo!
The Alike Team
When our own Ryan Lawler covered Alike last year, this is how he described the experience:
The app ranks recommendations based on how “alike” they are to your search queries, allows users to see how far away a place is, and lets them provide feedback about whether they think one place is like another. Venue pages provide more detail, including address, phone number, hours, etc., and lets users bookmark them for later.
Many have thought that Foursquare was a perfect acquisition target for Yahoo, and that might still be the case, or the company decided that it liked the Alike app and team much better. At the very least, Yahoo now has a battle-tested, social-experienced mobile team on its staff.
Through a spokesperson, Yahoo has confirmed the acquisition but is not sharing the purchase price at this time.
The Alike team created an app that focuses on personalization — using the restaurants and places you like to find the ones you’ll love. We were very impressed by the team and their approach to building personalized experiences. The entire Alike team will join Yahoo!’s mobile organization in San Francisco and Sunnyvale.
We’re told that the app and site will be shut down, so it will be interesting to see if Yahoo ever unleashes what the team built in a local discovery and recommendation service of its own.
We’ve reached out to Alike’s CEO Maria Zhang for further comment.
A little more than three years after the company was first announced, personalization startup Gravity is doing a big launch today, opening up its suite of APIs so that they’re available to any publisher who wants to use them.
Founded by a trio of former Myspace executives, Gravity has created an “interest graph” mapping different topics, which it then uses to recommend different content to users based on their activity on a given site. As the technology becomes better-acquainted with each visitor, the recommendations should improve.
Kapur lays out a pretty ambitious vision about how personalization is “the future of content,” but publishers can implement Gravity in more limited ways — for example, by using the technology to create a “Recommended for You” widget that’s personalized for each reader.
Until now, the company has been working with a specific group of partner publishers. Those partners include CNN Money, which Gravity’s technology to deliver recommendations in its iPad app, and TechCrunch, which has a Gravity-powered “What You Missed” widget (it’s down and right from this post). Gravity says it already delivers 25 million content recommendations to 200 million users per day. That’s more a reflection of the reach of its publishers than usage from actual readers, but the company also some partners have seen clickthrough levels increase two or three times, with a 300 percent increase in return visits and 40 percent growth in session time.
Co-founder and CEO Amit Kapur (previously COO at Myspace) said it was always part of the plan to open up the platform to any publisher. So why has it taken several years to get to this point?
“I think when you’re trying to do this big and disruptive, you have to be methodical,” Kapur said, later adding, “You want things to happen sooner and faster, but you have to kind of build at the pace that helps you build that amazing product.”
Kapur also suggested that now is the time when “the market is ready to embrace personalization” — as one example, he pointed to Yahoo CEO Marissa Mayer’s recent interview about personalization as the future of search.
The tools will be available to publishers for free. The company will include sponsored stories in the recommendations and split the revenue with publishers (an approach that’s similar to other content recommendation services like Outbrain). Kapur said one of his big goals in the coming months is to grow the sponsored stories program and hire more salespeople. He also sees mobile as a big opportunity — the small screen size means that publishers want to give their readers a more personal experience, and it also means that they can’t rely on standard ad units to make money.
Gravity has raised a total of $20.6 million in funding, including a $10.6 million Series B last fall. Recent hires include former MediaPass sales executive Robert Leon as its vice president of sales and Josef Pfeiffer, previously a senior product manager at the Wall Street Journal, as its director of product.