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Ness updates its iOS app, a personalized dining guide for you

Erica Ogg — Thu, 18 Apr 2013 02:00:03 +0000

A restaurant that has four stars on Yelp might be a big hit with a lot of people, but there’s no guarantee that you in particular will like it. What if you can’t stand Cuban-Japanese fusion? For you, that restaurant should be ranked zero stars. That’s the premise of personalized dining recommendation app called Ness that’s debuting a new version of its app on Wednesday.

Ness first came out with its free iOS app in 2011, but has gone back and improved the ability to predict what users will like. The app is a slick, beautifully designed way of helping you determine where to eat by asking you a few questions to get an idea of what you like and don’t like. It then adapts and learns from how you continue to use the app. Think of it like Pandora or Zite, but for eating out.

The subject happens to be dining , but what Ness is up to is really about maximizing the accuracy of personalization. With its own algorithmic models that use machine learning technology, Ness takes the user’s location, time of day, stated preferences and behaviors within the app (ignoring things, dismissing things) to basically build a dining guide for you. Its determination of how much you’ll enjoy the place or the cuisine is represented by a percentage grade out of 100.

Time of day is an example of a key indicator for the new Ness: if it’s mid-day the app will automatically search for good lunch places. If it’s nighttime, it looks for dinner spots.

It also detects your location to determine how far you’d have to go for food. If you’re in a densely packed metro area during the work week, you’re probably just looking to grab something close by, so Ness will search within a few-block radius for food options. If you’re in an office park in a suburb, Ness knows you’re going to have to drive a couple miles to find lunch, and will recommend places accordingly.

Ideally, this is supposed to happen automatically, without the user having to constantly remind the app of these search constraints.

What you don’t do is also important for Ness’s algorithm: if you search for coffee but never click on Starbucks when the option surfaces always opting instead for a local coffee bar, Ness will start to assume you don’t like chain coffeeshops in general and stop showing you them.

Using that approach to personalization, said Ness Computing founder and CEO Corey Reese, “takes longer to pick up that signal, but the benefit is you [the user] don’t have to do any thinking to make it work.”

Like recommendation services in other genres, like music or news content, the idea of Ness’s more personalized approach is not to keep you in a bubble of your established tastes only. It wants to understand what you like and point you toward places that you’ll enjoy, but not the same places all the time.

“We’re focused more on the moment of delight: ‘Oh, here is a place I had not known about,’” said Reese. “It’s less about making sure you don’t have a crappy meal.” In his mind, that’s more of what Yelp is for — ensuring a minimum level of quality.

“We’re really trying to find those hidden gems that you may not know about.”

The release of the 2.0 version of the app on Wednesday is just one step, he tells me. After the service understands what you like, the next step is for Ness to understand what it is about a place that makes you like it — the ambience, the food, the location, the staff, etc. But you’ll have to wait for improvements in that area in future updates.

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Consumers could pay higher switching costs in a data driven world

Stacey Higginbotham — Thu, 11 Apr 2013 17:55:18 +0000

How many people do you know that only fly on one airline, even if they hate it beyond reason? They put up with poor service or inconvenient flight times all because they’ve racked up status or miles with the company. They can’t switch or they start over from square one with a new airline that might only be marginally better; the switching costs are too high.

As we bring more data into all aspects of our shopping lives, consumers may suddenly face the same conundrum in restaurants or stores. As data collection and predictions improve, stores are better able to anticipate the value of a customer and reward them accordingly. This leads me to wonder two things, how does this change customer service? And will it drive consumers to greater loyalty by making the cost of switching providers higher?

I spoke yesterday with a company called Swipely that provides a payment system to small businesses. The company is now managing more than $500 million in annual sales — a number that has grown 100 percent the last three months in part because its customers get a side order of data with the payment processing.

One of the metrics Swipely offers is an estimated customer lifetime value (based on demographic data and the amount the customer has already spent). Seeing this gave me a bit of a chill, in part because it took the concept of customer service and turned it from a basic expectation to a cold calculation based on your potential worth as a customer.

That’s not to say that clerks or managers at restaurants will suddenly turn into jerks for customers who don’t shop there often, or will play it cool with new customers until the vendor determines how much that individual might be worth to the business. Heck, in many ways that already happens: shopkeepers often assess your likely purchasing habits by your demeanor, dress and other physical attributes.

But putting a number on your worth as a customer and cloaking it as data turns it from a hunch into something that feels rational and scientific, even if the algorithm behind that metric is unproven. And believing they are behaving rationally can drive businesses to make really inhumane decisions about humans. In an ideal world this data would be used to reward loyal shoppers (perhaps in the same way the Ritz Carlton rewards its loyal customers already), but what happens when this experience trickles down from a high-end hotel to a local toy store?

It’s one thing to eat every week at a nearby Mexican restaurant and have the manager know your name and set aside a table for you. It’s another when based on a few visits to an establishment, you’re lumped into a certain class of customer based on predicted spend than gives you substantial benefits. Can vendors offer enough perks to those “whales” that they might ignore a lapse in service to keep their free queso coming?

Does this added layer of data start to boost customer retention to the point where it’s harder for another restaurant to win business? This may seem almost silly, but in the cell phone industry things like early termination fees, contracts and (formerly) the inability to port a telephone number created such high switching costs that there are concerns about those practices making the market less competitive.

I would like to think that better data in loyalty programs wouldn’t distort the market for local goods and services, but I am curious if it will. Human nature is such that a free bowl of queso and someone knowing your name can overcome mediocre enchiladas and the occasional service lapse.

And if you’re opening a taqueria down the street getting those customers in the door might prove to be more expensive and have little to do with your fabulous tomatillo sauce.

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How to compete with Facebook in 2013

Mark Mulligan — Fri, 04 Jan 2013 17:47:31 +0000

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Foursquare takes on Yelp with 10-point rating system

Ryan Kim — Mon, 05 Nov 2012 22:02:24 +0000

Foursquare has been pitching itself as a local search tool but until now, it didn’t have a rating systems for places like Yelp’s five stars or Google’s Zagat 3o-point scale. But now, the location-based service is introducing a 10-point rating system that will give users a better idea about how good a place might actually be.

With the latest update to the iOS app, Foursquare will now include the score on places when a user conducts a search through Foursquare’s Explore engine. Users can also check out the scores online at Foursquare’s recently beefed up website.

In a not so-subtle dig at Yelp, the company said in a blog post that users don’t have to put up with a bunch of 3.5 star ratings. Foursquare comes up with its score by looking at tips left by users, likes, dislikes, popularity, check-ins and it also weights signals more heavily for local experts.

The ratings help boost Foursquare as a local recommendation tool, giving people a number they can latch on to. It’s part of Foursquare’s evolution into a local search engine, which it hopes can ultimately compete with Google and Yelp for users and advertisers. We’ve noted that Foursquare recently opened up its website to non-members, turning its home page into a Google-like search tool. And it previously added menus and OpenTable integration as well.

The fact that the new score is a combination of a bunch of user behavior and algorithmic work means it might be harder to game the ratings, as sometimes happens on Yelp. But it also means some merchants might be unhappy with their scores or may not understand how to improve them. We’ll have to see if Foursquare’s 25 million users start trusting its ratings the way they might with Yelp stars. But I’m looking forward to the system because I see so many 3.5 and 4-star places on Yelp, I’ve started getting blind to those ratings.

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Friendster founder launches social-news app, but will it fly?

Mathew Ingram — Thu, 13 Sep 2012 20:01:39 +0000

Even if he never achieves anything world-changing again — which he is certainly hoping to do — Jonathan Abrams will always be remembered as the guy who founded Friendster, the very first web-based “social network.” Launched in 2002, a year before MySpace and two years before Facebook, the site became a superstar among digital early adopters but lost its way and was overtaken by its younger competitors. Now Abrams is hoping to reverse that chain of events with a new startup called Nuzzel, a socially driven news-filtering service he launched on Thursday. But while Friendster suffered from (among other things) being too early to the social party, Abrams’ new venture could suffer from the exact opposite problem: the social-news market is so saturated it may be difficult for Nuzzel to get much traction.

Before he started Friendster in 2002, Abrams had a couple of earlier startups that were also early to their respective markets, including a social-bookmarking service called Hotlinks, which the Canadian-born entrepreneur started after working for Netscape in the late 1990s — but it launched five years before Delicious became the hot social-bookmarking tool, and it eventually perished in the dot-com crash. Abrams then launched an event-planning site similar to Evite called Socializr, but it too failed to get much traction and was eventually sold in 2010.

Trying to solve the social-information overload problem

At that point, Abrams had moved on to running a San Francisco nightclub called Slide and also an office-sharing and entertainment space called Founder’s Den — but he says he was always interested in trying to solve the emerging problem of information overload that comes with social networks like Twitter, which he had begun using as his main source of news:

“I started shifting my news consumption away from RSS and places like MyYahoo and started using Twitter more and more, but I found even if I checked it a couple of times a day I felt like I was missing so much stuff. I needed some tool that could help me manage everything.”

Instead of checking out one of the other services that were trying to provide filters and recommendation services, such as Zite or Summify, Abrams — a programmer who got his start working for Canada’s famous Bell Northern Research labs — decided to just put together his own, and what became Nuzzel was born. Users log in with their Twitter and/or Facebook profiles and the system’s algorithms go through a user’s activity streams and pull out the news articles that have been shared or recommended by the most number of followers. The service also uses these semantic signals to generate recommended content that hasn’t been explicitly shared by anyone in a user’s social graph.

In my initial use of the service, it came up with some good recommendations and some popular articles, and the site is well-designed and moves quickly — both of which are impressive, considering Abrams has no employees whatsoever, and put the entire site together himself. Users can filter the articles by time or by the number of friends who have shared them, and each post or article or piece of content comes with related tweets below it, any of which can be retweeted or interacted with. The most obvious use case for Nuzzel is the one that the tagline at the top of the service’s home page describes: “News You May Have Missed” — in other words, a catch-up tool for those who don’t have time to read everything.

The market for social-news filters is super-saturated

Abrams is right that this is a market that needs serving, but the challenge is that there are plenty of others already doing so: there are “personalized newspaper” services like Paper.li, apps like News360, Pulse or Zite, and even a service from the grandpa of social recommendations: namely, Digg, which was acquired by New York-based incubator Betaworks and merged with its News.me news-filtering app. Twitter has a stake in this particular game as well, after acquiring Summify, and there are newer dedicated filtering services like Nova Spivack’s Bottlenose and a startup called Prismatic.

The Friendster founder says that he doesn’t want to take the route that some others have taken by trying to make the system too complicated — he says that he isn’t interested in “having it become some kind of PhD thesis in machine learning,” but simply wants to solve a problem for users in as simple a way as possible. And he says he hasn’t tried many of his competitors, apart from News.me (which he says he liked in many ways). “I’m just really focused on my vision, which I think is a little different,” he said, adding that he is hoping to raise some seed financing so he can hire some staff.

The problem for Nuzzel, however, is that while Abrams may think his vision is a little different, the service itself looks very similar to about half a dozen other apps and services that do fundamentally the same thing, and in some cases have had months or even years to develop a following. That’s going to be a difficult thing to overcome, even for the founder of the world’s first social network.

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Wine site Snooth builds a virtual sommelier for Springpad

Kevin Fitchard — Tue, 21 Aug 2012 13:03:31 +0000

When relaxing with a glass of Bordeaux the last thing most people want to do is math. But for wine website Snooth there’s no more perfect pairing than a fine wine and a complex algorithm. Snooth is working with note-taking service Springpad to match the perfect wine to the recipes its customers clip for their online recipe boxes.

For those of you unfamiliar with Springpad, one of its niftiest features is the ability to “spring” culinary ideas from websites and store them as structured recipe data in your account. The advantage of that approach is that Springpad winds up with gobs of parsable data on ingredients, cooking techniques and instructions that companies like Snooth can act on. Every time a customer clips a recipe, Springpad relays a copy to Snooth’s servers, which then spit out detailed lists of wine recommendations that Springpad embeds at the bottom of the saved recipe.

There are countless recipes on the web — Springpad customers alone have clipped 1.3 million of them — so rather than hire an army of sommeliers to suggest pairings for each of them, Snooth has taken a Pandora-style approach to categorizing them, said Rich Tomko, the CEO of Snooth Media. It has developed a proprietary algorithm that assigns recipes to one of 2,000 flavor profiles based on their ingredients and techniques used to cook them. It then assigns each flavor profile to three of its 1,400 different wine profiles, creating nearly a million different possible pairings, Tomko said.

“It’s not as easy as it sounds,” Tomko said. “Barbequed or grilled chicken will have a completely different taste than [sautéed] chicken, and they would match with completely different types of wines.”

If you’ve been to Epicurious, MyRecipes or Fine Cooking, you may already be familiar with Snooth’s wine recommendations. It has indexed the recipes from half a dozen partner sites in order to offer up recommendations to their users. But with Springpad, Snooth is venturing out into the larger web to parse recipes on the fly. As long as Springpad can recognize and clip the recipe — the service has trouble with food blogs and other nontraditional formats — Snooth will be able to push wine pairings, Tomko said.

NYC-based Snooth launched in 2007 with the aim of becoming the primary wine resource on the web for the growing masses of wine consumers. Snooth isn’t after the wine elite. It’s targeting the 22 million or so core wine consumers who drink wine two to three times a week. Today the site has 1.1 million registered users and 300,000 newsletter subscribers. It has raised a total of $2.6 million, all from angel investors. It has an advertising-based business model, and according to Tomko, it expects to turn a profit this year.

Snooth isn’t the only company to apply big data techniques to recipes. Yummly, which recently raised a $6 million Series A round, has been parsing the recipes of the web to extract nutritional and flavor data, which it then uses to power its semantic search engine for food. Like Snooth, Yummly is trying to predict how a dish will taste. But rather than apply that data toward wine recommendations, Yummly wants to create a service that allows cooks to find recipes by conceptual notions of sweet and savory, bitterness and saltiness, not just by ingredient lists.

Featured photo courtesy of Flickr user Davide Restivo

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Food Network goes local, builds its own meal finder app

Kevin Fitchard — Thu, 16 Aug 2012 15:03:35 +0000

Food Network junkies now have a digital outlet for the pangs of hunger the network’s popular TV shows induce. FN is launching its own restaurant finder and recommendation app for the iPhone and iPad.

Called On the Road, the app shares many features with popular foodie apps Yelp, Urbanspoon, Nosh, and Forkly, but it’s fundamentally a different animal. Rather than crowdsource information, reviews and photos, all of the content in the On the Road is drawn from the Food Networks own TV and magazine programming.

That may seem like a limiting factor, but the Food Network has a lot of programming. A search for restaurants near my north side Chicago home yielded 23 businesses within a two-mile radius – ranging from upscale establishments, diners, bakeries, and hotdog and burger joints.

While the app can be used to simply find a random place to eat, most of the features are built around FN’s programming. Separate sections are included for each of FN’s TV shows, and you can search by chef as well — many of FN’s personalities bridge multiple TV show. So if you like the show “Diners, Drive-Ins and Dives” you can bookmark the restaurants Guy Fieri features on the show or you can recommend them to friends through built-in Twitter, Facebook and email buttons.

If you really like “Diners, Drive-Ins and Dives” you can use one of the app’s key features to generate a “road trip” highlighting every one of the taquerias, greasy spoons and fried chicken joints appearing on the show in a particular metro area or region. Food Network is trying to tap into peculiar emerging phenomenon: the foodie road trip. It’s included several pre-defined road trips with the app, either centered on particular cities like New York or San Francisco or on regional cuisine, such as Texas BBQ. But the app also allows you to easily generate your own tailored road trips and has a built in badge-based reward system for customers who complete those treks.

It’s a slickly designed app, but it’s definitely not for everybody. If you love the Food Network, then you’re going to love On the Road. But anyone looking for a more general-purpose restaurant recommendation app might be disappointed. There’s very little information about the individual restaurants beyond location data, a brief description, and a URL; and all establishments are treated equally – there are no stars or reviews.

But that’s the point. In an interview earlier this year, Food Network GM of digital Bob Madden told me that FN’s digital and mobile content strategies are built entirely around the concept of supporting its more traditional TV and magazine programming. Ultimately FN’s chefs are the curators of On the Road’s content and do they do their curating on TV. The app is intended to provide an interactive complement to that programming.

There are exceptions to that strategy though. In April, FN launches an interactive cupcake cookbook for the iPad that contained content unique to the app.

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Foursquare goes into monetization mode with promoted updates

Ryan Kim — Wed, 25 Jul 2012 04:00:06 +0000

Foursquare is finally addressing that age-old question faced by big, buzzy startups: how are you going to make some real money? The location-based social network is rolling out its long awaited promoted updates, an advertising product that allows local and national businesses to push out specials, news and pictures to Foursquare’s more than 20 million users.

While Foursquare has made a small amount of money in the past on things like brand promotions and allowing merchants to claim their Foursquare venue page online, it really hasn’t had a reliable monetization engine. Over the last three years, it has shunned traditional advertising in favor of creating a fun and increasingly powerful product that works for both consumers and merchants. Now, with the promoted updates, it’s leveraging its Explore technology, which suggests place recommendations to users, to match promoted updates to the people most likely to act on them.

So when users fire up their Explore tab on their Foursquare mobile app, they will now see promoted updates from nearby businesses that want to prompt some kind of action from them. The Explore engine will only send out updates to users in the area who have visited, liked or interacted with similar businesses or had friends that did so. Businesses who buy promoted updates will pay on a cost-per-action basis, which could be anything from a user visiting a venue page or unlocking a special to checking in or loading a special to their American Express card. Merchants will be able to select the locations they want to promote and schedule updates, but they must trust Explore to do the personal targeting.

The service will be tested first by about 20 businesses including Gap, Old Navy, Hilton, Hertz, Best Buy and Walgreens. The promoted updates are intended to help Foursquare’s one million merchants drive in new customers, something the company believes businesses are ready to pay for. The paid updates comes on the heels of local updates, a free product Foursquare introduced last week for merchants who want to communicate to their existing customers.

Foursquare’s new chief revenue officer Steven Rosenblatt told me Foursquare has an opportunity to create a robust mobile advertising product, which is a challenge for many online companies, such as Facebook, that are seeing their traffic move to mobile devices. Rosenblatt said Foursquare enjoys an advantage because it was built as a mobile-first company and understands how to craft advertisements that work for its audience. And, he said, promoted updates work well organically in Foursquare because users are already turning to it to find recommendations on places and activities around them.

“This allows merchants to reach a broader audience and it’s exciting for consumers, who uncover things they didn’t know about,” Rosenblatt said. “It just fits smoothly into the experience without interrupting. It’s additive.”

This is a big step for Foursquare and shows how all of its pieces are coming together into a potential money maker. If promoted updates work, they can demonstrate how a business can make a lot of money tapping the local advertising market. This does seem to be a smarter play on mobile, which hasn’t been a great place for traditional banner ads. But will it be enough to justify Foursquare’s $600 million valuation from last year? We’ll have to see, although Foursquare is not done selling business tools to merchants.

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Social-TV apps and consumer behavior

Nicole — Tue, 17 Jul 2012 06:55:18 +0000

A new category of digital media has emerged in the living room: social TV. This relatively new concept can be defined as any application, website or software that allows viewers to interact with television programming and share that interaction with others. Currently social TV occurs in three ways: organically, as pure play or through TV- or set-top-enabled communication. This report will focus on the pure-play aspect of social TV as it relates to content providers, television networks and advertisers. It answers key questions relating to the segment’s growth potential, important companies, their competitors and likely business models for the future.

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Who and what to watch in the new era of the living room

Thu, 31 May 2012 06:55:44 +0000

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