Part of the sales process for Twitter consulting is convincing a new client that Twitter is more than just another way to broadcast their message. You have to show them that what appears to be a random stream of tweets is really a collection of highly qualified sales prospects. By aggregating Twitter users as well as their Tweets, you can extract a great set of sales leads along with their contact info. One way to quickly demonstrate the value of tweet aggregation is to deliver an Excel spreadsheet of sales prospects that meet the client’s needs.

When you aggregate tweets from the Twitter streaming API, it also returns the complete account profile for each user. You can data mine this collection of users to extract highly targeted lists of users, along with their geographical location and home page URL.

The free 140dev Twitter framework is an example of the code you will need to do the tweet aggregation. The schema for the MySQL database it creates shows you it has a table for all the aggregated tweets, which links to the list of tweeting users. Since all of this data is collected for a specific set of keywords, you can then extract personal details on the users who tweet these keywords the most with a simple SQL statement:

SELECT count(*) AS cnt, users.screen_name, users.name, users.location, users.url
FROM tweets, users
WHERE tweets.user_id = users.user_id
AND users.location != ''
AND users.url != ''
GROUP BY tweets.user_id
ORDER BY cnt DESC
LIMIT 1000

The 140dev framework’s example database collects tweets for the keyword “recipe”, so this query gives us the most active tweeters in the food world. Here are the results in phpMyAdmin:

You can then export the results from phpMyAdmin to an Excel spreadsheet, and email it to your client. This gives them solid data in a familiar form. Twitter doesn’t deliver email addresses, and doesn’t even collect phone numbers, but you do get each user’s home page URL. This can be used to gather other contact info, a task that is easily farmed out to people on freelance sites like Mechanical Turk.

So the next time you want to convince a client that Twitter is not just a bunch of kids talking to each other, you can just create a tweet aggregation database for the client’s industry keywords, let it collect data for a few days, and pull out a list of targeted users.

I went to the OpenCoffee meetup in Cambridge the other day. They all recognized the importance of Twitter, but don’t understand how to make money from it. We are exactly where we were in 1996 with the World Wide Web when I helped start Andover.net. Great point in the cycle.

So here is my quick 5 point pitch on how clients can benefit from integrating Twitter into business and marketing models. But first keep in mind that you don’t make money “from Twitter”, you make money “with Twitter”. Meaning that Twitter is a lever for improving your other efforts, but you don’t get cash handed to you directly by Twitter users on Twitter. Anyway, here’s my list:

1. Putting keyword targeted tweets on pages in the right way is great for SEO. Google loves tweets. This will increase the page’s search rank, getting a lot more first time visitors.

2. Datamining of tweets lets you find the right people to follow in Twitter for your market. This can be used very effectively to build a big follower list. This list becomes profitable when you tweet messages with URLs you want people to click on. Think of it as free Adwords.

3. Follower lists are also essential if you want to make people do something in the real world, like contribute money, or go to an event. Twitter will be huge in 2012 election.

4. If you have a database of tweets, you can datamine it for sales leads. You can give sales people the Twitter accounts and home page URLs of people who tweet a lot about the products the salesperson is selling. The best part is that the salesperson can see exactly what prospects say about their products and competitors before contacting them.

5. You can also datamine a tweet database for sentiment trends. This is valuable for PR and customer service. It gives you a real-time read on how effective the rest of your communication program is.

There is an interesting thread on the Twitter development list about the need for a “good citizen” rank. This problem is approached in a literal engineering way, which says there are good and bad users. There are plenty of Twitter behaviors that could be seen as “bad,” but the beauty of Twitter is that it is totally opt-in. I only see the people I choose to follow. So the only meaningful criteria is whether I or my client wants to read an account’s tweets. Automating that selection must be made within the context of a specific area of interest. I believe that my mention algorithm is a good way of solving this problem.