At end of the day, there’s no substitute for getting out of your office and meeting people face to face. Conferences, mixers, pitch event, et. al. are all great ways to expand your network but they can be pretty hit-or-miss. You generally only have time to talk to a few people. Bad luck and a handful of long-winded duds can completely kill your evening.
So when I was contacted by Adrian Avendano Monterrubio (@amonter5), co-founder & CEO of PeopleHunt, I was interested in learning more. Their approach is simple in concept: while at an event, you sign into PeopleHunt, join that event, answer a few questions, and they will direct you to someone else at the event who shares your interests. The execution is pretty clean and well thought out. For instance, when the app finds a possible match for you, it pings him and he has 30 seconds to accept. If he ignores or rejects the match, the app moves on to the next possible match on its list.
Clearly, this app will ultimately live or die based on the quality of the matches it makes but before it even gets that chance, there’s a bigger challenge: getting the word out.
In theory, you could use this app anywhere. Waiting on line to get into a movie premier? Set up a quick “event” and see who you meet. But until they have pretty deep penetration, odds are that you would find few, if any, matches at an ad hoc event like that. The PeopleHunt team is smart enough not to roll out with a user experience that is bound to disappoint.
Instead, they are starting with conference and event planners. The planners can tailor the PeopleHunt app to a specific event and announce its availability to their attendees. Planners are a bit of a tough sell, though. They are extremely busy with mission-critical tasks on the days immediately prior to the event. A nice-to-have app is far down their priority list.
There are four ways to handle a situation like this:
- Reduce the time/effort required from the gatekeeper
- Increase the benefits to the gatekeeper
- Bypass the gatekeeper
- Give up and try a different distribution model
We brainstormed for a bit and came up with a few possible solutions from column 1 (e.g., they could set up the event questions for the planners), some from column 2 (e.g., improve and stress the value of the who-met-who data; get more exposure for the event by including the event hashtag in the default text for tweets about who you met), and at least one somewhat edgy idea from column 3 (e.g., scrape event and attendee data from Eventbrite, Meetup, TicketLeap, et. al., set up the events in PeopleHunt, and alert the attendees – all without the organizer’s involvement). I am sure they will come up with several more on their own and, when they have some data on what works and what does not, we’ll meet again.
Like all good entrepreneurs, they asked if I knew anyone who was interested in investing. My advice to them was to wait. They are targeting a real pain point but unless they have data to show that they can monetize their users sufficiently to cover a high user acquisition spend (in this case, promoting to event planners), they need to solve the gatekeeper problem first.
It took a while, but Wizpert really grew on me.
Disclosure: Wizpert is an ERA company. I am a mentor for ERA but have not advised Wizpert nor do I have a financial stake in ERA. As always, I may invest in Wizpert but for now at least, I have no skin in the game.
In brief, Wizpert instantly connects you with experts. Let’s say you…
- are trying to get back in shape but are not sure what exercise routine is right for you.
- have been trying to sleep-train your child but no matter what you do, he won’t go to sleep on his own.
- want to make social media work for your business but you don’t know where to start.
You could spend hours searching for and wading through volumes of free and often contradictory advice on the web but wouldn’t it be great if you could just pick up the phone and get instant, clear, concise advice from an expert in that field? Wizpert thinks so.
There have been past attempts at creating directories of experts but they all generally worked like this:
- You read through a bunch of experts’ bios and try to pick one you think is qualified.
- That expert is rarely available right away so you either try to book an appointment or you go back to step 1.
Wizpert, currently in beta, presents you with a list of pre-qualified experts who are available right now. You don’t need to read though pages and pages of bios – each “Wizpert” has only a 140 character description! – you simply pick an expert from the short list Wizpert provides.
Wizpert is free for now but will roll out paid advice within the next few months. They expect most experts will charge $0.50 – $2.00 per minute. Wizpert keeps 25% of the revenue.
The first time I saw Wizpert, to be candid, I was not exactly stoked. Wizpert needs a lot of traffic and a good conversion rate to succeed. This former often requires a large marketing budget and the latter is a crap shoot. Wizpert actually failed my first screen but stuck in my mind as a dark horse to watch. Fortunately, I ran into the co-founder, Michael Weinberg (@Michael_Wizpert), again recently and I liked what I heard.
Obvious question #1: How does Wizpert recruit experts?
When they open up a new category, they manually recruit the first few experts, focusing especially on experts with highly trafficked blogs. For the next wave of experts, they run a paid email marketing campaign. After that, word of mouth kicks in. In parallel, Wizpert has inked strategic alliances with other organizations of potential experts to help populate certain verticals.
Wizpert currently has over 1,100 experts and the cost per expert acquired is surprisingly low, almost trivial.
But how does Wizpert qualify its experts? Each would-be expert must answer a number of calls and get sufficiently highly rated by users before they can charge for their time. Once approved by Wizpert, the expert’s ranking (viz., likelihood to be recommended to a user) is determined by their rating, availability, and other factors. This winnows out the bad ones and keeps the best ones busy.
Obvious question #2: How does Wizpert acquire users?
Very cleverly. Each expert can put a Wizpert widget on his blog that their readers can click on to get live expert advice. If he is online when they click, he gets the call and makes some money. If not, Wizpert queues up some experts who are available. In fact, having a Wizpert widget on their blog is almost like a seal of approval or badge of honor, so much so that some experts have asked if they can put one on even if they intend to take no calls themselves.
In effect, each expert Wizpert acquires brings with him a slew of potential users. So other than a small Google AdWords campaign to kickstart each a new category, Wizpert spends virtually nothing on user acquisition.
With the specter of a money pit marketing budget put to rest, that leaves conversion rates. In the month since beta launch, Wizpert says that nearly 1 in 4 users who come to their site contact an expert and over 80% of these users rate the experts with whom they spoke positively. Many of these users have made multiple calls.
What these numbers will look like when Wizpert activates the payment mechanism is a legit concern. The categories that are currently performing best for them include Health & Wellness, Parenting, Relationships, and Social media. These are all categories where people are used to paying for advice offline so it’s not a crazy bet that this will stay true for online.
So ultimately it comes down to one irreducible question: Will people pay to speak to Wizpert’s experts? There’s only one way to find out.
If you have ever bought or sold a home, you are familiar with real estate comps. All real estate sales have to be reported to the government so your broker can simply enter an address into any one of a number of databases (e.g., Zillow, ACRIS) to get a list of similar nearby properties and what they sold for.
Leases are different. There is no reporting requirement for leases and there are no comprehensive databases of all lease transactions. Your broker has to rely on whatever data his company has collected on the leases his company handled or ask other brokers to share their comps with him. As you can imagine, the comps he gets are incomplete and time-consuming to collect. CompStak hopes to fix all that.
CompStak is a give-get system. Brokers enter the lease comps they have in exchange for points they can use to get the comps they will later need. They can also buy points if they need more comps. CompStak will also sell this information to non-brokers for whom comprehensive, real-time data could be extremely valuable (e.g., hedge funds investing in real estate).
CompStak sailed right through my first cut. It targets a large but neglected market that is in many ways still stuck in pre-internet practices. And at first glance they are trying to replicate something that already transformed a similar market (viz., real estate sales).
I’ve never worked in real estate so my first step was to network to and talk with a lot of commercial real estate lease brokers. Their reaction to CompStak’s value proposition? They want it but doubt it at the same time. Here’s why:
- Data collection and integrity
- Incremental value of the data set
The brokers I consulted were unsure if many brokers would share their comps this way. Landlords generally require confidentiality clauses so sharing comps in a way that could be traced back to the broker could result in lost business or even litigation. Also, many large brokers consider their internal database of comps to be a competitive advantage and have a blanket policy of not sharing comps. Furthermore, it is of a pain to enter comps; some large firms insist that their brokers enter the comp in their system before paying them for that transaction to insure compliance. To avoid this extra effort, many brokers might simply buy points as needed or pay a flat monthly fee for unlimited comps (if given the option) rather than enter their comps in to CompStak. And as if that were not enough, the data that does make it into the system could be suspect; landlords might try to game the system by entering inflated lease rates or understated concessions (e.g., buildout allowances) so as to influence future leases.
CompStak’s founder, Michael Mandel (@brokerednyc), says that many comps are added by 3rd parties such as brokers that the landlord’s or tenant’s brokers informally shared a comp with, entities that are considering purchasing a property and who received data on all its leases as part of the due diligence process, etc., which might get around confidentiality clauses. At the same time, CompStak tries to get multiple comps for a particular lease to ensure completeness and accuracy. It’s also worth noting that CompStak is currently in beta and Michael believes he already has comps for over half the lease transactions in Manhattan. His success so far partly addresses my concerns on data collection. That said, only time will tell if they continue collect enough accurate comps after the initial “let’s give it a try” phase passes.
The second set of concerns revolves around the value of the data. For brokers, comps are only the start of the process. They are useful for setting clients’ expectations but at the end of the day, what matters most is what competing offers are actually on the table. Comps may have minimal impact at this point. Granted, you could make the same argument for residential sales but the brokers I spoke to felt that the greater complexity of commercial leases (there are many more factors than just price per square foot) meant that the lease offers they receive are less informed by comps than as is the case in residential sales.
For non-brokers, the question is how much incremental value CompStak can provide over existing data sources . Most properties for lease list their availability on CoStar. Since landlords remove properties as they are filled, at the very lease CoStar has some data on the lease and the last asking price. Combining those quasi-comps with historical data on discounts off asking in the final contracts for which they have data (note: CoStar has recently been trying to collect final contract data to turn these into true comps) and adjusting for recent trends in asking prices in that market could (in theory) get a real estate investor pretty far. At the same time, firms like CBRE Econometric Advisors (formerly Torto Wheaton) provide detailed market outlooks and forecasts for the most of the large markets. Since they are owned by the largest commercial real estate services firm and presumably have access to their internal database of comps, their data is based on a significant representative sample of the lease market. While their more detailed reports come out quarterly, they also offer “strategy services” for substantial clients who need more detailed or real-time data. The big unknown is how much additional accuracy and/or timeliness CompStak can provide. If real estate investors do not come to perceive CompStak’s data as substantially better than what’s available, CompStak will be seen as just another data point and may end up competing with established data sources on price… which is generally not a happy place to be. My sense is that this second issue will ultimately depend on how well CompStak does at meeting the first challenge.
So going into my second cut, it became clear to me that CompStak will come down to market acceptance. If CompStak rapidly amasses a large enough share of the transactions in a given market and these comps are reasonably accurate and complete, we’ll see if the incremental value of more comps and time saved collecting comps is enough to get brokers to change their current behavior to make entering their comps into CompStak part of their everyday routine. As is often the case, even the end users are not sure if this will happen.
For investments like this, you have to go with your gut and without any first-hand experience in real estate, I simply don’t have a gut feel for CompStak. If there were someone I knew with a solid track record in startup investing and relevant domain experience to back up his gut feel who was gung-ho on CompStak, I might go along for the ride.
(Just to be clear, I am not a fan of outsourcing my investment decisions but in certain very specific circumstances, where the judgment of other relevant investors informs gaps in my analysis and can be the factor that tips the balance.)
Feel free to agree, disagree, or insult my investing skills Limerick style.
Update: you can view SpokenLayer’s pitch at TechCrunch Disrupt here
What can I say about SpokenLayer? I want this service yesterday. My only question is if this is a viable business.
Full disclosure: In 2005 I spent several months thinking through a business remarkably similar to SpokenLayer.
Let’s say you are driving to work, running, in the shower, or in any other situation where you want to read your favorite newspaper but either cannot or prefer not to read. SpokenLayer will read it to you.
The concept itself is seemingly simple but actually pretty powerful. With SpokenLayer, any provider of written content can now instantly become its own radio station. The publisher gets a new channel to its audience and a completely new vehicle for advertising, with virtually no additional investment of money or other resources.
SpokenLayer automatically converts an article’s text to speech. Quality is pretty much what you’d expect for an automated text to speech program but for short-form, perishable content, this may be good enough. Additionally, they will manually narrate specific news articles as they become popular or as requested by their clients. They also give the authors of the articles (and possibly even readers themselves) the option of submitting a narration.
In addition to revenue from manual narrations, the main revenue model is audio advertising, split between SpokenLayer and the publisher.
The problem is what if they succeed? What’s to stop their largest publishers from in-sourcing this service? Creating a multi-publisher platform, with publisher-level and topic-level pronunciation guides, and an app with great UX is certainly not trivial. But hacking together an ok-but-not-great text to speech engine to glue onto a single publisher’s app is at most a few months work. If SpokenLayer takes off and they start generating significant ad revenue, their largest customers will have a very simple make vs. buy calculation. SpokenLayer will have to slash their share of ad revenues to the bare minimum to make it not worth their while to in-source the service and even that may not work.
- Aggregation: The aim is for SpokenLayer to become the one-stop-shop for this kind of audio content and that as it captures more and more content, network effects will kick in. Publishers who want their content to be discovered will have to be in their network. To me, this feels like a sonic twist on the old portal model… which we know is just kicking ass for Yahoo, AOL, et. al. Kidding aside, the content discovery angle may be valuable for the smaller publishers but the larger ones are already one of the few always-go-to news sources for their audience. So if the New York Times decided to pull out of SpokenLayer and embed text to speech in their own app, their readers would follow them… with is exactly what happened when they cancelled their deal with AvantGo and created their own mobile site and app.
- Curation: There is no doubt that there is a lot of mediocre and irrelevant content out there so there would seem to be some value in creating a Pandora-like stream of relevant articles to listen to. The question to ask is this: Aren’t the publishers already curating this content for us? I read the New York Times World section at least in part because they do a decent job of selecting news that interests me. For SpokenLayer to add value, it would have to do a good job of finding articles that the Times missed or different takes on the same topic; serving up similar articles on the same topic would simply waste my time.
- Specialization: Simply put, it’s difficult and expensive to develop new technologies. On the other hand, the large publishers have the resources and have shown that they are willing to deploy them if there’s enough money on the table. Plus, the lag time between new & hard to commonplace & easy is getting shorter every day….
- Owning the ad network: Creating the audio ad market is not just hard, but requires scale. Once you have it, you have enough impressions to offer up very specialized content and command higher CPMs. If SpokenLayer gets to scale first, this will give them a solid cost advantage against competitors. But again, the largest publishers already have this scale and, if they don’t already have a radio division, can (in theory) cross-sell their current advertiser base into audio ads.
Even though I am not entirely sold on Will’s argument, I put quite a bit of time into understanding the opportunity. If Will makes a strong case that even with just the middle and low end of the market, there is enough there to make SpokenLayer into a big company, I would likely put it even more time getting to know his team, talking to his partners, advisors, and other investors, digging into the terms of the deal, et. al. But since the point of this blog is who passed my first cut (and why), the bottom line is that SpokenLayer made it at least that far.
Whether I invest or not, I want SpokenLayer to succeed… even though it would make me feel pretty dumb for passing on the idea in 2005. 🙂
Feel free to agree, disagree, or insult my investing skills, Yo Mamma style.
AngelList profile: http://angel.co/spokenlayer-1