OpenTable Null Search Can Be Improved

In an earlier post, I wrote about null search results as they pertain to a couple of news sites. Today, I noticed a sub-optimal user experience in the OpenTable iPhone app when I searched for a restaurant that was not found. Here is what I saw:


While it does make sense, to a point, that restaurants that are not part of the OpenTable network be omitted from the search results, this is certainly a missed opportunity and can be improved. Before outlining a different user experience, lets take a quick look at what the Yelp app shows for this restaurant:



In the Yelp search results, we are able to find the restaurant we’re looking for along with a list of other restaurants that are similar. Furthermore, in the Yelp restaurant page, we see some important pieces of information pertaining to the restaurant. And this leads us back to how OpenTable can improve their user experience.

At the very least, they should have a record of the restaurant and some important information pertaining to the restaurant i.e. address and phone number. With the phone number, the user will have the option to Call (similar to Yelp above) and get in direct contact with the restaurant in order to make the reservation.

Looking beyond the restaurant information, another feature that may be beneficial is to add a list of several restaurants (that are in the OpenTable network!) that would be suggested to the user instead. With the current user experience, the user is stuck, can’t make progress with the restaurant they’re looking for, nor are they pointed toward the direction of another comparable restaurant.


Yelp, QR Codes, and Verified Purchases

One of the most common complaints that local businesses have about Yelp is the existence of unfair, or even worse, completely false reviews. In the current Yelp ecosystem, any user can write a review for any local business at any time. Thus, it is possible for a user to review a business that they interacted with many years ago (what if their recollection of their experience isn’t entirely accurate at this point?) or to review a business they never even actually used (what if the user’s personal bias leads to them leaving a one star or five star review even though they were never even an actual customer?).

Yelp attempts to mitigate this issue by automatically filtering out reviews that don’t meet a certain standard. By its very nature, any filtering algorithm will yield false positives (reviews that are genuine but where mistakenly filtered out) and false negatives (fake reviews that were not filtered and left on the site). In order to improve such an algorithm, it is vital to constantly feed useful data to the underlying model. Useful data is data that is known to show a high correlation with bad reviews (so the algorithm is more likely to filter the review) or with good reviews (so the algorithm is more likely to not filter the review).

A new piece of useful data, and a new input for the filtering algorithm, can be the concept of verified purchases. If Yelp somehow knew that for a fact, on a given date X, the user made a purchase at the local business for the amount of Y dollars, this piece of information would be invaluable. One way of achieving this product functionality is to partner with local businesses in order to have them add QR codes to the receipts they give their customers. Upon scanning the QR code with a user’s Yelp app, the application will automatically have access to:

  • The business name
  • The date of the purchase
  • The total purchase amount

For example:


In order to evaluate the usefulness of such a feature and to be able to estimate whether or not it will be adopted and eventually succeed, one needs to consider what value such a feature brings to the table for the key parties.

Why would users use this?

  • Access to more deals and access to better deals. Since the local business knows that you’ve already spent money with them (and exactly how much), they are more likely to give you deals to keep you as a customer. Loyalty counts. The Yelp platform will allow any local business to have a loyalty program that has the potential to be as game-changing as the Starbucks Gold Card.
  • Giving more weight to your reviews. As a user, it feels great to know that the site that tracks the reviews considers my review as an important one because they know it comes from a verified purchase.

Why would local businesses do this?

  • More customer engagement. As a business, you know a lot of your users love Yelp. If you can give your users a way to stay engaged with you as a business while channeling that love through Yelp, why not? From your end, you can use the Yelp platform to offer deals to customers who scan the QR codes and check-in or write reviews and tips on Yelp.
  • More data for local businesses. Many small businesses know they are sitting on heaps of data pertaining to the habits of their customers, but they really haven’t done anything smart with the data. Yelp can serve as the platform that leverages this data and makes smart recommendations to the local business as to how best to keep their existing customers engaged and loyal.

Why would Yelp do this?

  • More Yelp engagement. This gives users another reason to use the Yelp product. This aspect is especially vital in new cities where Yelp is building a treasure trove of new user content from the ground up.
  • More data for Yelp. This gives an opportunity for Yelp to build out features (some free and some paid) for local businesses using the purchase data to see how customers are reacting to the business’s promotions.
  • More accurate reviews and a better Best Match algorithm. When a user uses a QR code to draft a review, Yelp has two important pieces of data. First, they know that this user actually completed a transaction with this business – this is a verified transaction. Second, they know how much money the user spent for this transaction. This is important because Yelp can then tweak their review algorithms to give more weight to verified purchases as well as give even more weight to purchases of higher amounts.

Restaurants Appear Deceptively Closer in Yelp

Like almost every other SF inhabitant, I have a passion for great food. A byproduct of this hobby is that I’m often on Yelp scoping out new places to try. Other than gauging the quality of the dining experiences by reading reviews, I’m also on the lookout to see how far a restaurant is from my current location.

Recently I noticed a discrepancy between the distance as called out by Yelp search results as compared to the driving directions I received when using Google Maps. Take the following example. While I was in Dillon Beach, CA, I found a restaurant in a nearby city called Barley and Hops Tavern. According to the Yelp search results, this dining establishment was located 10.3 miles away.


However, when I queried Google Maps for directions between my current address and the restaurant’s address, I received a significantly different result in terms of distance:


According to Yelp, the restaurant was 10.3 miles away, but according to Google Maps, it was at least 16.2 miles away. Who was right, and who was wrong? Could it be possible that one of these sites is using incorrect data to calculate the distance or has a bug in their distance algorithm.

The funny thing is – I think both sites are correct, but for different reasons. The reason why the distance on Yelp looks significantly shorter is that Yelp is computing the distance as the crow flies. In other words, while Google is computing the driving distance (which takes into consideration turns and zig zags in different directions), Yelp is considering the distance on the map if you were to physically draw a straight line between the two points.

In terms of what’s best for the user, the expectation is to see how far the two points are – not for geographical curiosity – but for the purposes of planning a trip from point A to point B. That being said, the Google way (not computing as the crow flies) of calculating the actual driving distance is ideal and the preferred way to go.

Yelp User Photos: Mobile vs. Web

As mobile/tablet user experiences become more widely used versus their (archeological) non-mobile/tablet web counterparts, there may be a divergence of functionality for the same feature on the same site for the different platforms.

Consider Yelp user profiles and the corresponding user photos. In the iPhone Yelp App, I can browse to the user’s profile page and view all of their photos without being a Yelp member:


However, when I try to do the same on my laptop, I see the following:



Here, Yelp is asking me to log into my account, or register as a new user in order to view this user’s photos. Not sure if Yelp is making this distinction between iPhone and WebApp on purpose or if this is a bug, but let’s assume it’s on purpose. They may be making the bet that users are more willing to register and/or login in the non-mobile web flow as compared to the iPhone App flow. Why? Most likely, the user has a keyboard in front of them and can get to the next step more quickly than using their touchscreen device to login or worse, register for a new site. It’s an interesting strategy, but one that will may yield a drop-off in logins or registrations as users flock from their computers to smart phones and tablets.