Yelp has messy desk syndrome.

I am developing the ultimate food-finding killer app. 

As a result I have become a food app snob. Not a food snob, mind you. I'll still eat anything.

I have checked out dishes on Foodspotting, checked in through Foursquare, bought deals on Groupon, found discounts on (Seattle Weekly) Happy Hours, and largely ignored (Yellow Pages) YPmobile. (Hey I'm honest.)

Foursquare

Foodspotting

Yelp is Goliath. It's the app to beat-- and it's huge. A huge company. And a huge user interface. Bad mouthing Yelp has become a hobby of mine. I have said in my previous post that "no one likes Yelp but everyone uses it" and I have been finding out why this is the case. 

In my own user research, I have found that people really like having options. While they say they like simplicity, simplify until you lack features and your user base will disappear. They will return to the other app that is not designed nearly as swanky but has all of the filters, categories, content, and deals they are looking for. Complexity is necessary in a be-all end-all app. There is no cross-referencing of other apps to find the best burger or the best deal, within walking distance, that is open now, in this hungry world of impulsiveness.

Complexity is the essence of what is to be human. Don Norman's book, Living with Complexity, perfectly answers why the imperfect app has been the perennial purveyor of reporting reviews and leading customers to cuisines.

The apps Foursquare and Foodspotting are nicely minimal and accomplish their respective tasks of linking you to your friends and offering up the best crowd sourced dishes, but come up short because of minor elements that are missing including "Open Now", price range, and a clunky map. If the user says they want an app to be simple, they are lying. "If we build simple devices, people won't buy them," says Don Norman.

Saying that Yelp has messy desk syndrome is actually a form of compliment... 

I am a fan of Al Gore, and look at his messy desk. Mr. Gore is an organized person and/but/therefore his desk reflects the complexity of his life. 

I couldn't resist, but politics aside... 

Norman leads us to understand that like Al Gore's desk, technology is complex as well and for good reason. People are asking for it. They want things that do more. The app I am building needs to do as much or more of the vital functions than its predecessors. One vital omission and it is toast. 

Apple is famous for leaving off lots of features that others have included. But where others have launched MP3 players, smart phones, and tablets and failed, Apple has prevailed well after those products first hit the market and died. The difference lies within A) what was chosen to be added-- the complexity, and B) what was chosen to be subtracted-- we shall call it design. To spell it out, the complexity added was iTunes and the app store. The design is the simple hardware design ethos that only shows the minimal number of buttons needed. All other UI lives on the screen.

An important distinction-- what is complex is often confused with being complicated. It can be true that something can be both, but it is not the general rule. Something complex can also be incredibly enjoyable to use. It can even be simple. The inner workings, whether they be gears or algorithms, need not be exposed to the end user. 

A great example Don Norman shows off in his talk at Stanford is the difference between 3 espresso machines. One is truly an influence to steam punk. It has brass valves that have to be skillfully worked to produce a cup of espresso. The second machine needs to be filled with water and coffee, a press of the button, and periodic cleaning. The third is the easiest with loading a module in and pushing the button. The complexities are the same. The complexity is just loaded differently. The easiest machine for the user is the hardest (read: most complex) for the manufacturer.

I'd say that Yelp was most like the second machine before their latest app release. They are a lot easier and even more useful now. They could still push to be more like Machine 3. But for now, that's where I am aiming...

I am still wrestling with changing the paradigm of how people think of what they will eat next. Typically the first thought when hungry is?-- What do I want to eat? Should I go out to eat? Then thought by category-- Mexican, burgers, Thai, Pizza? Arrive at the restaurant and read the menu. Transfer thoughts from your left brain to your right brain to picture the dish. If there are other considerations like specials, price, ingredients to watch out for, then process those and order. Still not sure? Ask what's popular or recommended. Quite a process-- one we take for granted.

If the process is reversed-- Apply filters for price, specials, ingredients. Look at pictures of food closest to your location that are recommended by others. Spot something tasty. Go to the restaurant and eat it.

Much like choosing your very own next meal, it can be either as simple as walking to the cafeteria and eating the same sandwich every day, or there can happily be many factors influencing the decision, especially for a foodie. Perhaps now both can take the same amount of effort. 

Allow me to demonstrate the power of pictures:

 

I rest my case.

Yelping about Yelp

No one likes Yelp.

But everyone uses Yelp. It’s usually the first site you turn to for finding a restaurant.

If you are not already familiar with Yelp, the crowd sourcing, review-based site has been one of the web’s venerable longtime establishments. Their local reviews are brimming with massive amounts of opinions, way too much data to sort by yourself, so the Yelp machine sorts the most appropriate reviews for you. Relevant, recent, and trusted reviews float to the top, so you read as many reviews as you feel comfortable with.

In a polling of 20 people, everyone had used Yelp in some form or another, whether on the web or via mobile app. Often when looking for a restaurant, the hunger pangs were already starting to kick in for all but 1 of the 20 polled. Therein lies the problem. With so much data to sift through, how do you quickly and effectively find your next meal? In the age-old problem that everyone has several times per day, the question of “what to eat” has been a source of enjoyment, frustration, disappointment, and an occasional pleasant surprise that many businesses have tried to answer for as long as there have been restaurants.

Yelp is based on the basic tenet of trusting real people’s reviews. This is what helps the site’s clout as much as it hurts the user experience. There are so many features, information points, and several ways to filter the reviews, that it’s both overwhelming and ineffective. Do you trust one review or five? What about the negative reviews to round out the mix? But then the negative review starts casting doubt on the restaurant in question. And then the reviews—can you trust them? Well, Yelp sorts them, right?

Crowd sourced reviews are arguably the most unbiased way of obtaining real opinions, trustworthy qualitative data from the common man or woman. But the way the data is sorted can actually create incredible bias, because… there is no such thing as (*ahem) a free lunch. Let me explain. Realize that these are restaurants (which are businesses) interacting with Yelp (a business too (now listed on the public stock exchange)). Money changes hands so that Yelp’s more than 900 employees can put food on the table (*ahem again). Every profitable business needs a business model.

One of those businesses, 125-year old restaurant Fior d’ Italia, known for its great food and service, did not see the need to use Yelp. It had an established clientele and refused to advertise on the site. The owner’s complaint of Yelp was that Fior had 218 posted reviews averaging 2 1/2 stars, with many terrible one-star reviews showing. Unseen were the “filtered reviews” that would average out to more than four stars. The only way Yelp staff could ‘help us’ was if Fior would advertise with them.

How does a customer cut out the bias? Can a diner trust the data or not?

In the end, a diner just wants to eat something—something tasty. That’s all. Well make that somewhere close. And cheap—but not too cheap because then it might not be worth eating. Instantly gratifying thoughts turned into action are what will win in the realm of “then and there”, a market that Groupon Now and others have gone after-- the local market worth multiple billions of dollars.

Funded by a grant from Google, a study turned web site called RevMiner (the name a combination of Review + Miner) attempts to solve for the inefficiency of the massive Yelp data into manageable chunks. As an added beneficial consequence, I think it may have re-democratized the data as well. Jeff Huang, a PHD candidate at the University of Washington, set out to improve the experience of the smart phone app. In the research article, RevMiner: An Extractive Interface for Navigating Reviews on a Smartphone," he first identifies the problem as a struggle to fit content from the big PC screen onto the smartphone touchscreen.

The mechanics within RevMiner use algorithms to summarize reviewer opinions using Natural Language Processing (NLP) into attributes (words that embody the restaurant). If you need a reason why those keywords were used, then a quick hover over the attribute would explain itself. It is a clever way of taking all of the Yelp reviews and making them available at a glance. It’s all of a sudden easy to see what is largely being said about the restaurant with key words. Further still, color coding is used to make the association of what is “good” versus what is “bad” instantly recognizable. With this app, now the qualitative data is suddenly searchable, browsable, and graphical. So RevMiner is much faster in usability than Yelp. And there’s real beauty in that the Review Mining machine removes bias from the order position of the reviews. This truly is unbiased data at its most lucid, yes?

Well, no. RevMiner will admit that there are shortcomings to transforming reviews into non-contextual words. Words have a different meaning when written in a review than when presented in a query. Someone may query the word “cheap” looking for food that is low priced in absolute terms, but within the context of the review, "cheap" was used to describe a good value compared to their expectations. "Few people will remark that McDonald’s was cheap, but people will declare cheap over a $30 3-course special at an exclusive establishment."

Similarly, the word “good” equates to barely positive. A review might say the food was good, but either the service or atmosphere sucked! I noticed that I actually take this into consideration when I see a Yelp endorsement sticker on the outside of a business. Truly I do not know what to expect and I walk in fully knowing that the atmosphere is a tossup. I attended a Yelp elite event as a +1 (accompanying my legit Yelper friend and taking advantage of the free hors d’oeuvres). The event was held at a total dive bar, well-loved on Yelp. Indeed, the typical Yelper likes getting a lot for a little (perhaps similar to a Groupon customer?). "They love us on Yelp" is an award that must take context into consideration. The Yelp star rating system has a meaning in itself.

So there are some self-proclaimed limitations of the RevMiner app. Qualitative reviews that are transformed them into quantitative data is not a perfect carryover. Perhaps what the RevMiner study calls out best are inefficiencies in the Yelp model. In answering the customer's main question "What do I want to eat?," RevMiner solves for efficiency but may lack the effectiveness that Yelp’s opinions bring. Within the study, there are several iterations including using different priorities, word clouds, and color bars for ratings based on key words. All were tested against Yelp to find what the user preferred. In general use, participants preferred RevMiner in 45% of cases, whereas Yelp was preferred 30%. In performing a conjunctive query (using more than one attribute), RevMiner earned 62% versus Yelp’s 27% preference. So there is something there. Perhaps the two can meet in the middle. Perhaps Yelp can be streamlined, RevMiner more engaging, to solve the question we all ask ourselves multiple times per day, every day, "What do I want to eat?"