You won’t be surprised to learn that email, not Twitter, is the preferred form of communication amongst our users. The following scenario has happened all too often: someone uses DemandSpot to find a lead, then tweets them to offer his or her service. The lead replies that they’d like to learn more and… nothing. The User, in this case, hasn’t been checking their Twitter account often enough. The solution is simple – send the DemandSpot user an email to tell them of the opportunity (lead). It’s now available, and you can let us know what kind of events you’d like to receive in an email (and how often) from our settings page.

An even more common scenario is when someone starts using DemandSpot to find leads in their area, but gets tired of coming back to our website to check for new leads. The solution is practically the same – an email search alert. It works pretty much like Google alerts where get an email whenever Google finds new web pages that match your keywords. In DemandSpot you search for leads by location and keywords and we send you an email as soon as we find new leads that match your search.
We created this short how-to video to show how easily you can add a search alert on DemandSpot:

So this post is all about helping our users set their goals (and beat them) by letting you know how well others are doing – and how to do it well yourself. I won’t go into any specific user’s stats – that’s their private business (although I encourage you to share if you care to), but I can lay out some aggregate numbers. Here we go:

• Over 2,000 messages were sent from agents to leads through DemandSpot to date (since early August).
• Those messages were sent by just over 200 agents. The average – 10 messages per agent – is a lie: some sent over 100 messages, and some only sent one or two.
• 42% of messages contained a link (to the agent’s website most of the time). 33% of those links were clicked by the persons they were sent to (yes, we can tell). That’s a huge huge CTR (click-through rate) – more than 10 times as much as you can expect from other traffic sources such as search ads or email marketing. Moreover, we can attach a face, name and Twitter account to those website visitors, so even if they didn’t fill up a form on your site, you can still follow up.
• Most messages try to strike a conversation with the lead. Just over 1 in 6 are successful – success being measured as the lead sending back a (favorable) tweet. Follow-backs are even more common.
• Some messages contain the agent’s email or phone number. Naturally, we can’t measure the response rate for those. I’d appreciate it if some people who tried this could chime in with their results.

It’s extremely important to realize that these numbers are just statistical averages, and do not represent the typical case. Some of our users see over 50% success rate in striking a conversation with leads on Twitter (and then off it), while others fail completely. We did an extensive research, poring over more than 500 messages, to try and identify what makes a successful sales-tweet. I published the results here, and reading them can seriously improve your own results (on DemandSpot and on Twitter in general).

Takeouts:

• Keep on tweeting. On average, 1 in 6 tweets will lead to a conversation. Don’t give up after just one or two.
• Learn and improve. Educate yourself on what works. Some resources include our own research, and @nik_nik’s excellent ebook.

We understand that many agents find it hard to make time for tweeting leads persistently. That’s no reason to miss the opportunity to farm Twitter for leads though. DemandSpot has auto-responders, that do the tweeting and following of leads in your area automatically. Unlike the rest of our service, auto-responders cost money (based on performance), so you get $10 free when you sign-up to give them a try.

Your turn now: let us know what works for you, what doesn’t, and what else we can do to make you successful. We add new features and improvements to our website on a weekly basis and your feedback matters!

It all begins with Twitter Search. Our system runs queries for some keywords (like “house hunting“) every few minutes. We’re running a whole bunch of queries – to match the various ways in which people refer to the process of searching for a house: in addition to “house”, there are also apartment, apt, home, condo, pad and flat. The verb can be huting, searching or looking, in various tenses. Finally, there are some implicit indications – the tweet: “we got pre-approved for a mortgage” makes a very good lead. In short, the idea here is that we do the job of figuring out which queries are worthwhile and then do the tedious work of continuously running those searches for you automatically so that you don’t have to do it. You can roughly tell which expressions we search for by looking at the high-lighted part of the tweets on demandspot.com.
In order to retrieve the tweets in close to real-time, while not creating a heavy load on Twitter’s servers, we change the time interval between consecutive searches dynamically – according to how fast tweets are coming in. So at times when tweets are few and far apart (like night time in America) our queries slow down as well.

Next up, every tweet goes through a “semantic engine”. This is a nifty piece of software that has some understanding of human language. The problem we’re solving here is that search by keywords alone retrieves many irrelevant tweets. For instance, “how’s the house hunting going?” and “good luck house hunting!” mean that someone else is doing the hunting, not the tweeter herself. In fact, search retrieves more irrelevant tweets than relevant ones, including references to the past or future (“last year when we were buying a house”), spammers, bots (tweeting listings from Craigslist for example) and more. We also differentiate between supply and demand (“House hunting in Denver? check this out..” is probably written by a Realtor, not a home buyer).
Last but not least, the semantic engine also identifies location names in the text, and assigns latitude/longitude values so we can place the tweet on a map. This is pretty difficult since many names are ambiguous: Los Angeles is commonly refered to as LA, L.A, but the La in “La Perla” is something else. Another example: Louisville is probably Louisville KY, but could also be Louisville IL… (more on this subject below).

At this point we already did quite a lot, but we’re still far from done! Twitter users provide relatively few details about themselves – name, location and a short bio are all there is, and while professionals usually fill these up, more than half of tweeps don’t bother providing all of them. Nevertheless, it’s worthwhile to know a little more about a person before you contact them. Other social networks to the rescue: we use some clever algorithms to beef up our leads by adding information about them from their other social media profiles. Right now we’re able to find Twitter users on other social media in about 40% of cases – and we keep getting better at this. You can when we’re successful from the lead’s contact info in DemandSpot – if we managed to match someone to their other online profiles, you’ll see a bunch of links to those other websites.
Why is this important? Let’s take location for example. The lead’s location is probably the single most important factor when searching for real estate leads. However, far less than 10% mention where they’re looking in the tweet itself, and less than 50% specify their location inside their Twitter profile. This means that many leads are lost unless we can tell where they’re from. By locating their profiles on other social media, such as MySpace, Facebook, LinkedIn, Digg, Yelp and many more, we’re sometimes able to determine their location after all. Automatically locating these profiles with a high level of accuracy is no easy feat of course – names for example are too ambiguous so we don’t use them, but this isn’t the main focus of this post, so I might expand on this issue at another time.
Another good use for alternate sources of location is disambiguation. In the example above, when someone says they’re house hunting in Louisville and their MySpace profile says that they’re from Illinois, then we make an educated guess that they’re searching in Louisville IL, not KY.
In short, this trick allows us determine pretty accurately the location of another 30% or so of leads – above what you’d be able to get through Twitter alone.

Now comes the last part in this “behind the scenes” exposition. At this point we index the lead information in our database making it easy to search for.

Our website itself is simply a Twitter web-client that takes advantage of all the extra information we’ve collected to make searching for leads fast and simple. There’s more to it – we keep your prospecting history for you, provide analytics and more, but that’s a subject for another blog post – stay tuned.

Now if you’ve come all the way to the end of this post, you must be thinking to yourself that all that I’ve described could be applied to other kinds of Twitter searches – not just house hunting. You’re absolutely right, and we’d love to hear your ideas on the matter. Thanks!

Here’s how:
Go to www.DemandSpot.com, click “Find Buyers” and filter by your geographic area.
What you see now is a list of people who tweeted that they’re in the market for a house. See any interesting ones? Click the “connect with” button, sign-in or sign-up if necessary (it’s free), then click the “follow” button. That’s it. You’ve just followed a lead on Twitter.

But wait, that’s not all. Wouldn’t you love it if you could just follow those leads automatically, without spending any time on this until they actually follow you back or contact you regarding their real estate needs?
Here’s what you need to do. Towards the top of the screen there’s a tab header called “Auto Response”. Click it. Now select the same search parameters as before, and say how much you’re willing to pay to auto-follow a lead. Prices start at just $1 and you only actually pay if a lead follows you back or sends you a reply – you’re paying for interested leads, not for advertising. Finally, scroll down to the “Auto-Responder Type” box and select “Follow”. You’re done. You get $10 free to play with when you sign-up, so the first few leads who actually follow you back or send you a reply won’t even cost you anything.

Once all that is done and there’s a constant supply of leads coming your way, you can go ahead and follow smart and influential people in the industry who tweet tips and insights on how to turn those leads into buyers. Best of Luck!

All of that feedback has been awesome, and we just completed the first major change to demandspot.com following it – we now let you search both sides of the real estate market – demand and supply.

The next step for us, following your feedback again, is to launch additional categories in DemandSpot. The idea is to use the same system to help our users find and connect with people looking for, well, almost anything. Some of the best suggestions we got so far include helping employers find tweeting job seekers (and job seekers find the best employers), helping people buy and sell used cars as well as used iPhones and any other kind of electronic device.

We’re already hard at work at making some of these suggestions happen, but what we’d like most of all is to hear more from you!

So, which category should we launch next that would make you happiest?

Don’t be shy! Here are some of the ways in which you can let us know:

1. Comment on this blog post. Everyone else sees it and we could get some discussion going.

2. Tweet us @demandspot (or DM if you’d rather keep it private)

3. Use our GetSatisfaction forum

4. Email us at contact@demandspot.com

Thanks in advance. The community that’s building around DemandSpot means everything to us, and we truly appreciate your feedback.

There’s the old TinyURL, the new market leader Bit.ly, HootSuite, a Twitter client, has its ow.ly, WordPress, where this blog is hosted, now offers short links at wp.me, and there’s even a service that lets you create short URL with your own domain (awe.sm). My personal favorite, for the record, is the oddly named Cli.gs.

The real value of these tools however is not in shortening. It’s in analysis.

Before the advent of URL shorteners you couldn’t tell what actually happened after you posted a link somewhere. If the link was to your own website, you could get some idea by looking at your website analytics, but it was hard (and sometimes impossible) to tell which link placement exactly was driving the traffic. If you linked to somewhere else you were completely out of luck.

Now check out this neat trick: take some bit.ly link and add a plus sign to its end. For example, if your link is http://bit.ly/lbhsd try http://bit.ly/lbhsd+

You can see the number of clicks, where they came from (on the web and in the world) and when.

What’s the value of being able to analyze your link performance you ask?

For one thing, it’s a no-nonsense measure of how influential you really are. On Twitter for instance, you could have thousands of followers, but how many of them are actually reading your tweets? By following the statistics of click-throughs that your tweeted links generate you get a much better idea on the level of attention that you actually receive.

If you’re using Twitter as a marketing tool by tweeting links to your website, blog, products, listings etc., measuring click-through statistics is a must: it tells you how effective your Twitter marketing really is.

In DemandSpot we use this mechanism to track how well you do in reaching out to leads. If you include a link in your tweet to a lead we shorten it and then check whether it was clicked by a person in the same geographic location as the target lead was. You can’t pull this trick off by using bit.ly alone – we had to write some more software code for that, but more on this issue in a future post.

How it’s done

For the technically curious, here’s a short explanation on how URL shorteners measure clicks.

When someone clicks on a short link, say http://bit.ly/lbhsd, their web browser goes to that link’s target – on bit.ly’s server. When you click a link, your browser requests it from the target server (bit.ly in this example) and sends it some information about itself, such as your IP address (your geographic location can be inferred from your IP), type of device (PC, Mac, iPhone) and operating system (Windows, Linux) and the version of your browser (IE, FireFox, Chrome). Bit.ly then records that data in its database and responds to your browser with a “redirect” to the original URL. In effect it’s telling your browser that the web page you asked for isn’t there, but someplace else – at the original long URL.

The statistics you see later are the summary of all the records that bit.ly has collected from people’s clicks.

A word of caution:

A very large percentage of clicks aren’t generated by humans. They’re done by bots – software programs that crawl the web. Naturally, those clicks are worth much less – bots are yet to engage in conversation or buy anything on a website. Most URL shorteners seem to be trying to hide that fact. Bit.ly, as far as I can tell, doesn’t discard bot clicks from its analytics data. Cli.gs does, but there’s no way to know how well it does it.

Bot detection is a rather interesting subject, one in which we invested heavily in DemandSpot. I’ll cover it more thoroughly in a future post.

This post summarizes the findings of a comparative research that we conducted over hundreds of “sales tweets” done through the DemandSpot system. Read it to learn how to improve your own results – based on actual facts.

Twitter is fast becoming a tool for business, and accordingly there’s a lot of advice out there, some of it really good. What I couldn’t find however is advice based on a large scale study, rather than anecdotes and good hunches. While I certainly can’t call our study “scientific”, I hope it’s a step in the right direction.

DemandSpot, as some of you may already know, is a tool that lets you search for real estate leads in your area based on their tweets about house hunting (additional markets coming soon…). Once you find leads you can follow them, tweet them a link to your website, or just start a conversation. Once you do, we follow the interactions. We only recently launched this service, but were very lucky to have several hundred real estate professionals use it during the month of August. That put us in a unique position to analyze a large sample of interactions between agents and leads, so we picked a random sample of 500 such interactions and pored over each one in order to figure out what works and what doesn’t.

What we analyzed:

We compared messages that led to further conversation with messages that failed to generate a response. When messages contained links, we compared those that were clicked with those that were not. We tried many variables: the agent’s Twitter profile, the contents of the message, the freshness of the lead and whether the agent followed the lead.

What we found – Tweet-tips for better results:

The single most significant variable is how fresh the lead is. Every day that passes between the tweet saying that the person is house hunting and the agent’s tweet in reply reduces the chance of response by several percentage points. Replies sent within an hour of the original tweet perform the best by far.

This isn’t surprising since older leads are more likely to have already found a house. I also think that it has to do with state of mind – if I’m thinking about my house hunting now, I’m more likely to respond to a message about it. We found that people are generally happy and pleasantly surprised that a stranger had read their tweet and is offering to help.

Now let’s move on to the Twitter profile:

1. A picture’s worth a thousand words: Profiles that use the default Twitter profile picture get no response. Profiles with a personal photo are slightly more successful than those who use a company logo.

2. Complete profiles perform better, so fill in your short bio and location. Most profiles we checked stated clearly in their bio that they are Realtors, but we’ve found no evidence that the actual contents of the bio made a difference.

Next, let’s take a look at message contents:

1. Links work best. Leads are three times more likely to click a link in your message (to your website, your listing, etc.) than to tweet back. For example: “Here’s a cool home search site <your-link>”. We guess that this is because clicking through requires less of an outright commitment. Of course, if you want that click to lead to further engagement, you had better make sure that what’s on the other side of the click is worthwhile…

   (Yes, we can tell if it’s the lead who clicked – not someone else or a bot. I’ll tell you more about this issue in a future post).

2. Call-to-action is very important. This is hard to do in less than 140 characters, but be sure to make it clear what you want the lead to do next. We guess this is another reason that links work – the CTA is “click here”. Another successful strategy is to ask a question, for example: “I have some great listings in KC. How many bedrooms are you looking for?”

3. Lack of context kills many attempts. The nature of Twitter is such that it isn’t clear, like in email, which message you’re replying to. Tweeting “I can help you” sometimes evokes the reply “help me what?” but usually remains unanswered. “I can help you find the perfect home” works better.

4. In the same vein, messages that fail to offer something specific seldom receive a response. “Let me know if I can be of assistance” is inferior to “”I’ve a few houses in Charlotte that you may be interested in seeing: <your-link>”

   Also note how the second example specifically mentions location.

5. Conversational tweets work much better than tweets that read like advertisements. Read your message aloud; If it sounds like a radio spot then you’re advertising, not conversing. For example, compare: “Looking for a new home? Contact John Smith at 555-123-4567 for the best properties.” with: “Hi, how’s the house-hunting progressing? We have an open house next Monday – <your-link> for details”

   Luckily, we had just one user (out of hundreds) who received responses like “quit spamming me!” – not once, but three times. That agent used advert-sounding messages. We’re sensitive about spam, and promptly banned him from using DemandSpot, but more on that in a future blog post.

As a more general guideline, we’ve found that agents who fail to convey a professional image, as a Realtor and a Twitter user, are consistently less successful in getting positive responses. Such failures include:

1. Bad grammar and spelling.

2. Sending messages that are longer than 140 characters. They get truncated and show that you don’t know how to use Twitter.

3. Sending your email address and asking a lead to email you. I guess that people see no reason to converse via email when you’re both already on Twitter.

Finally, a word on following:

We’ve found that some people prefer to continue the conversation with you in private. Follow them and they can get back to you via a direct message.

We’ve also found that some of our users are reluctant to send out messages to lead since they’re afraid to seem too pushy. A follow is a less obtrusive way to connect and alert the lead to your being a Realtor in their area who could offer some house hunting help. It frequently generates a follow-back – which you can see as permission to send a more direct offer, and on a few occasions even prompted the lead to send the Realtor a message first.

For your convenience, we put a short version of these tips here and linked to it from next to the message box on our website.

Happy and successful tweeting

What we have in mind is something like Google Insights for Search, where you use people’s search queries to figure out what’s interesting. Chart that over time and you can sometimes predict the future (for more information on that, check out Google’s blog posts on tracking flu trends and the predictability of search trends).
The real estate market heatmap that we launched today does something along the same lines, using tweets. We recommend that you check it out first, then come back to read how it works.

Try the interactive version here.

Twitter seems to be just as good (if not a better) indicator of what people think, need, want and care about. With two caveats: (1) unlike search queries, where people use keywords, on Twitter people use language, so it takes some nifty software to figure out their intent and (2) Twitter Search only shows relatively fresh information, so some information needs to be stored somewhere else to enable longer-term analysis.
Luckily, in DemandSpot we already take care of both issues: (1) our software uses semantic analysis to find people who say, in oh-so-many ways, that they’re looking to buy some real estate and (2) we store meta-data on our servers, so it’s still accessible even when you can’t find it on Twitter Search.

So here’s how it works:

1. As you may already know, in DemandSpot we find people who say on Twitter that they’re in the market for real-estate (see our home page). We also figure out where they’re looking (for instance, here’s a list of leads from around Boston).
2. Today we summarized the demand for each location for the entire month of July, then for August.
3. Then we compared the two to figure out which markets are hot – have the highest change in number of leads from month to month
4. Finally we overlayed the results on a map.

A few words on the data, and on necessary caution:

1. The results are based on over 10,000 Tweets per month. While a large sample, it’s still nowhere near the sample size that Google probably uses for its Insights for Search. Twitter is still a small phenomenon compared with Google (or Facebook for that matter).
2. Since Twitter itself is growing month-to-month, it’s possible that the growth in number of Tweets in a local market is the result of new people in that locality joining Twitter over the past month.
3. We only “read” English tweets. We have some data for countries where English is not the primary spoken language, but I wouldn’t make much of it. The Countries that contributed most tweets to our sample over July and August are the US (10′s of thousands), Great Britain (thousands), Canada (thousands), Australia (several hundred).
4. While the US as a whole has lots of tweets, small local markets may have just a few. In building the heat map, we ignored localities where we found fewer than 10 tweets per month (but you can still find them on DemandSpot).
5. We hope that you find this map useful and interesting, but please understand that we give no guarantees as to the accuracy of the results, and won’t be held liable for decisions you make based on them.

So what’s next? That’s entirely up to you!
Don’t be shy, use the comments to let us know what you think, and what’s missing – we’d love to add more features that the community finds useful.