Yelp’s financials have been fairly strong lately, and with $550M projected revenue for this year and the aim of getting to $1B (82% jump) in 2017, the company will need to be open and aggressive about new revenue steams. Let’s explore how to generate an additional $100M (conservatively) in yearly subscription revenue that would directly monetize (and further diversify revenue streams) Yelp’s most loyal users, generate data that can improve the overall service, and avoid conflict with existing advertising partners. (note: my sister used to work at Yelp as an accountant, but provided no information or insight for this article.)
Yelp Power User Subscriptions
The basic premise is to provide additional functionality for a subset of Yelp’s users and charge them for it, but using the benefits from their usage to positively impact the service for all.
Personalized Recommendations – Replacing “Best Match”, Netflix Style
The problem with Yelp reviews, as with many review systems, is that actual results are skewed. On a 5 point scale, one might think that 2.5 / 5 would be an average venue. However, for Yelp, the average score is more like 3.8. And while Yelp may want people to be forced to dive deeper (Virtually identical ratings mean people have to dive into reviews to understand what’s different, said Vince Sollitto, who heads communications for the San Francisco-based company.) into reviews because these scores make it harder to differentiate among venues, this is not a very user-centric, empathy-driven approach. In fact, this is better for venues and Yelp – the more venues are better ranked, the more open venues will be to working with Yelp on advertising. The more venues are better ranked, the more people will visit them. This is a clear conflict of interest.
I would like to see smarter recommendations with the option of going deeper into reviews only when I want to. If you have ever used Netflix’s recommendations system, you understand how this could work. As a user creates more reviews, the system is able to predict which others users are similar to that user and provide predicted rankings for new venues. Admittedly, this only works if you and other reviewers have a common set of visited locations and would thus work best in places you live in. However, if you visit a new place, Yelp could use your demographic data to create a profile that may match other users in new locations – there are a number of different approaches to predicting responses without historical data, and this would be a very useful data experiment to create value across all users.
A simple story to explain the need for personalized recommendations comes from a friend. She is Vietnamese and went to Palo Alto in California for Vietnamese food. It was not only expensive ($50+ per person) but terrible. Yet, people in Palo Alto love it and review it accordingly. With a personalized recommendation, I would be steered away from this place despite its positive reviews and to a place that people with my tastes enjoy.
Personalized Recommendations – Incorporating External Data
In addition to predicting scores and using that to sort recommended places for each individual user, Yelp should incorporate external data. While user ratings are great, I also want to know what has been featured on TV (Bourdain) or has won awards from professional reviewers. In many ways, that data already exists on Yelp, created by users – for example, search for “Michelin” and you should find a good list of Michelin-places in that city. These metadata should be officially added into listings. Such locations would automatically receive a bonus in the personalized recommendation scoring or include special badges, and users who value (and visit) them would see more such venues in their recommendations.
Normalized Review Scores
Beyond ranking places for the individual user, scores should be normalized over the last one year of reviews using the full 1-5 spectrum. Ever see complaints about a restaurant that changed ownership recently? Or a place that lowered its quality standards after building a strong reputation? How good is this place right now? Current Yelp review scores don’t take currency into effect very well. I want to create clear separation in order to compare places more easily. How much better is this place than the other place?
While the math to normalize is pretty easy, the process (by area radius, venue category?) to do so is a little complicated and would need to be tested to finalize on format.
Getting More Data – Allowing Data Export and Pure Numerical Reviews
Although Yelp has the most user-review data of any source, it creates barriers preventing additional data that could be used for the product features mentioned above. For example, I am very uncomfortable with Yelp owning my data and making money off of it, thus I would rather write on this blog than for Yelp. I do not need Yelp to share money with me, but I would like to export my data (reviews, bookmarks, check-ins) for myself.
In addition, Yelp forces reviewers to write reviews. I prefer the IMDB method which allows both numerical-only ratings and detailed reviews for those who like to do so. To see the stark difference this can make in conversion and user data, my IMDB history has over 1,100 reviews (average of 70 per year) while my Yelp has 2 after eighteen months.
It is unlikely I will ever be a Yelp Elite because I am not much a Yelp community driver. However, that does not make me a non-active user. I am joining a couple of official Yelp events below soon, but would like to see more, with exclusive slots set aside for paid Yelp users as an added benefit of subscription.
Paid subscribers should have the right to opt-out of ads (but can be on by default) and receive exclusive promotions (offers) for subscribers from businesses. Similar to social media ads on Facebook and Twitter, users would be allowed to vote for or share promotions in order to show interest. As on Google Adwords, advertisers who are just paying to spam users would have to pay more as a penalty for being less relevant. This would create a win-win scenario for both users and businesses who truly care.
I would like to see the ability to review individual plates or meals, not just the venue. Not everything a place serves is equal in quality, and I would like reviews to be broken down into smaller components such as service quality. Some styles of restaurants are affected by lower grades of service, but often I do not care about that. I just want to know what is the best food for a best price, and there is no way to quickly determine this. Yelp should be making this possible!
This service would be provided at $5 per month or just $30 per year for annual subscriptions. Imagine the $5 per month as a perfect solution for travelers visiting a new location (ex. 4 day trip in Chicago) and needing to know the best places specifically for them. It’s not too much different from buying a travel guide. Perhaps only yearly subscription users would have certain features such as the history export, but I think that numerical-only reviews should be opened to all. I use $30 per year as a personal preference that seems reasonable to me but also as a stark contrast to paying the per month fee ($60). Fees would be due at the beginning of any subscription period, providing Yelp instant cash flow, but could be refunded at a pro-rated level. Yelp Elites could be given free subscriptions.
Yelp currently has approximately 150 Million Users (including international markets). To reach $100M in yearly subscription revenue, just 2.22% of these users would need to subscribe – I believe (based my own experience in social networks) that this number could reach 5%. Please note that I have simplified the calculation, not accounting for regional user / wealth populations, single month purchases, future growth, mobile vs. desktop, and new ad product growth for subscribers, etc.
If you are thinking you would never pay for such features, that is ok! You are one of the 98% who would not need to. However, I am one of the 2% who would. 2 out of 100 people is fairly low on the requirement side.
Since Yelp is trying to reach $1B in revenue in two years, they clearly are concerned about their existing sales, which has been slowing in growth the last few years. Although paid subscribers could turn off ads, by keeping them on by default, Yelp would reduce impact on the ad impressions removed. Normalized Reviews could impact businesses, but this would only be available for subscription users and would be a complimentary score to the existing system – most people could still remain confused (yay!) by the overly positive Yelp review system. Yelp’s current display of Google Display Network ads would be minimally affected.
A great benefit of reducing the review barrier and allowing numerical reviews is providing more data that can be used to promote businesses, which in turn helps businesses. In particular, this would help smaller businesses with less than 100 reviews because they have the most to gain from more reviews. (If you are concerned about fake reviews with the numerical-only system, there are different ways to filter and normalize that data as well) Offering advertising access to paid subscribers also creates new revenue opportunities for Yelp and focused opportunities to improve the perception of the business. Paid subscribers are more likely to review and create content for a business and Yelp helping businesses get subscribers in the door first is a more cost-effective method to seed business perception.
Recap and Conclusion
Here is a recap of my proposal:
For All Users:
- Enable numerical-only reviews, with breakdowns for specific aspects, such as service quality and food quality (but not required)
- Enable dish-specific reviews, numerical and tagged text reviews
- Enable personalized recommendations, IMDB-style, based on past review history and incorporate external data such as Michelin and TV mentions – do not show predicted ratings
For Premium Users:
- Normalized reviews for easy comparison, including recency data
- Show predicted ratings for personalized recommendations
- Op-out for ads
- Exclusive targeting from advertisers for promotions, using Google Adwords and Facebook style feedback to penalize spam
- Subscriber-exclusive invite slots for official Yelp events
- User Data Export
- Conservative estimate of $100M in revenue (150M users * 2.22% * $30 /year / user)
- Not including monthly one-time payments for “tour guide” like service
- Long term potential of $225M (even with no further growth of userbase)
Yelp is in competition with Facebook, Foursquare, Google and others for local advertising dollars. Despite Yelp’s data trove, it can do more to get more data as well as create more value to its users through that data. Over the long term, this would create more loyalty lock-in to the service, even without forcibly locking users in (as it does now).
I welcome all comments and thoughts below!
Over the Winter Quarter, our Technology and Innovation Strategy class at Kellogg culminated in a final research paper. The paper looked at the shuttering of Google Glass and what Google’s next steps should be. As part of this, I got to look deeply into the current state of Virtual Reality, which I have been following and waiting for (hello Oculus!) since I was a child, and Augmented Reality. I will be posting portions of the paper (it’s quite long) in digestible chunks here over the next week. Our team was comprised of Melissa Caldwell, Raghu Chirravuri, Olga Gordon, Jeff Hoffman, and me, Michael Nguyen.
To see all of the sections, see my tag virtual reality.
Google Glass: A Brief History
The idea of Google Glass was born out of a brainstorming session about the future of computing by Google’s founders and several key executives in 2009. Google decided to pursue a portable computing technology that could be attached to the body or worn on glasses. A team of developers, scientists and researchers was recruited and the project was placed inside its own experimental lab called Google X. By 2011, there were conflicts within Google X over issues such as privacy and appropriate public use cases. Sergey Brin argued for the release of the Glass prototype despite agreement among the project engineers that the design was not ready. Brin argued that the uses and societal issues caused by the introduction of Glass should be discussed transparently in a public forum with the users and pushed for the immediate release of the prototype.
Glass was publically announced in April 2012. An estimated 2,000 units were pre-ordered. In the spring of 2013, Glass was launched exclusively at “Basecamp” stores located in New York, San Francisco, Los Angeles, and London at a $1,500 introductory price point. Although there was significant interest and hype surrounding the product release, Glass was introduced with bugs and suffered poor reviews stemming from short battery life and poor screen and camera quality. At first, sales were by invitation only and mostly extended to journalists, developers, and tech media.
Google did not accurately predict the societal backlash against Glass, including privacy concerns. Months before Glass was released, states started passing laws to prohibit use of wearables while driving. People began to associate the device with invasion of privacy. Google responded to concerns by releasing an etiquette guide in February 2014, approximately a year after Glass was released.
Many developers abandoned their Glass projects citing lack of consumer demand and support from Google as the main reasons for halting their operations. The developers who continued to create products for Glass have pivoted their strategies for enterprise application rather than consumer use. In January 2015, Glass stores were shuttered and the project’s key employees were reassigned to different areas of Google. Google’s release of Glass mirrored their software releases in that they provided access to the device and garnered feedback from users. However, this strategy backfired as the device was a very expensive prototype, prompting many early adopters to publish negative reviews publicly.
Aside from the failed marketing launch, Google did not address all of the impediments required to adopt this new technology on a large scale. The team never found a “killer” mass-market app or use case that would encourage mass penetration, leading to consumer and developer confusion over how to actually use this new technology. Although Google has announced that Glass is dead, there appear to be signs that Glass is being repurposed for enterprise uses or concept redesign as the project has been moved to the leadership of Tony Fadell and the Nest team, known for its Internet-of-Things connected home devices such as smart thermostats.
Glass for Med
We recommend re-launching Glass with a medical focus in order to establish a foothold where it can gain new users, learn about how consumers will use its devices, and the ways to work with developers to improve device functionality. This strategy coincides well with Google’s focus on using technology to improve quality of life for people and can help lean away from the negative connotations of Glass, rebranding it as a product that enables the good in society, working with doctors to revolutionize the healthcare industry.
Before its shift in focus, Google Glass was in the early stages of the adoption curve with hospitals and doctors – the most tech savvy among them were using the technology. There are a myriad of applications for medical professionals; examples include helping guide surgeries, documenting and transmitting audio and video of procedures, and allowing increased access to patient information. Many hospitals have been willing to adopt iPads to replace clipboards and folders and we see Glass adoption as an extension of this technological shift. Glass will prove superior to the iPad as it is more sanitary and can be operated hands-free. Some hospitals like Massachusetts General and Beth Israel Deaconess have already shown interest in figuring out the best ways to use the technology.
We propose Google partner with beacon hospitals to roll out the Glass program hospital-wide and connect doctors with application developers to help design more ways to use the technology. Google can act as a facilitator between the developers and doctors to tailor apps specifically for Glass. By focusing on a few hospitals, Google will be able to work very closely with the doctors and developers, creating a feedback loop to help Google have a firm understanding of how exactly people use the technology in a wide variety of use cases and what could be applied beyond medicine. This will also give more people exposure to Glass, where they can directly benefit from use of it, through faster doctors appointments.
Success will be measured primarily in the information that Google is able to collect from the hospitals, doctors, patients, and developers. Feedback from each of these parties will be integral in understanding how Glass can be used more widely, specifically what type of experiences can best leverage the platform as well as ways to train people on the best ways to use Glass. Utilizing the hospital pilots to increment the product as well as seed a network of evangelists more effectively than the Explorers program will allow Google to continue to perform R&D on this AR technology in an outward facing manner while creating new PR and marketing opportunities, helping to repair Glass’s tarnished brand. Over time, Google will be able to gauge the external market for Glass and enter when it feels like both the product and consumers are ready.
3D Content Hub
In parallel with developing niche applications for Google Glass, we believe Google should leverage AR/VR technologies to create a 3D content platform similar to YouTube. Leveraging Google’s strengths in advertising, experience in platform building and their portfolio of assets like search and Android, we see a 3D content platform as the next paradigm in online content. It will be aimed at both businesses and consumers, allowing both parties to create and view 3D content. With content categories like product demos, training videos, and entertainment, this new platform will grow traffic to Google, increase advertising revenues, and attract users to Google’s other products like Android, shopping, and search.
To successfully build and scale such a 3D platform, Google will first need to identify a VR/AR technology that can be used to record and display AR/VR content. This technology could be based off Magic Leap or some other technology currently in development. The next phase will be to build a YouTube-like content platform that enables upload, search and discovery of AR/VR content. Google could use Search, android and Youtube to drive traffic to this new platform and get users engaged. We believe that it is also important to train customers (both business partners and end consumers) on the new technology to make it easy to record and view content on the platform. In addition, Google could seed the platform with content by hiring or partnering with content developers. Incentivizing content creation will produce high quality content and attract viewers to the platform, who will in turn create and share content. Google has done this successfully in the past with YouTube and we believe that they can achieve similar success with this new content platform.
Success in building this new platform can be measured by comparing the size of the captured AR/VR content market. Another indicator of strong growth is the number of content creators and subscribers. As the platform grows and advertising is incorporated into it, advertising revenues could be a measure of commercial success. Lastly, synergies between this new platform and Google’s existing products could be measured by incremental growth in Android, shopping, and search.
The reason that creating footholds in the Augmented Reality and Virtual Reality spaces is important is in how the battle for these technologies mirror the web browser, smartphone, and tablet wars. In the recent technological age, networks effects are key to success, making it imperative to be an early mover to create a seamless and usable experience for users trying out new technologies. This is why each of these competing firms are focused on capitalizing on the next consumer leap in technology usage: they want to set the standard for consumers. These firms moving quickly in the AR and VR ecosystems, as it is clear this is where consumer and commercial technology is going.
We believe that it is crucial for Google to aggressively pursue both VR and AR solutions to be the platform of the future. By utilizing the foundation that Glass built and expanding use within hospitals, we can better understand usability issues and build solutions specific to doctors, but apply learnings more broadly. Additionally, with our capabilities in building developer platforms, we should continue to pursue our content hub as a place where developers can develop content to be consumed by masses. A combination of these moves will allow Google to continue to be the industry leader in the future, a position that will afford us new ways to capitalize on advertising and build a more complete picture of consumers than our competitors.
An insider’s look at the tumultuous launch of Google Glass http://www.businessinsider.com/google-glass-launch-2015-2 Published Feb 28, 2015. Accessed March 1, 2015.