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Reach out and talk to customers before they even ask a question.

Innovative clothing rental company Le Tote uses an automated trigger to offer help to customers who are lingering on the checkout page.

The method we chose to get text from one user to another involves loading an iframe on each Facebook page, and having that iframe's Javascript make an HTTP GET request over a persistent connection that doesn't return until the server has data for the client.

The request gets reestablished if it's interrupted or times out.

Even without accounting for the sizeable overhead of spawning an OS process that, on average, twiddles its thumbs for a minute before reporting that no one has sent the user a message, the waiting time could be spent servicing 60-some requests for regular Facebook pages.

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Better numbers and quality reports let you measure successes and stay on target.

For Facebook Chat, we rolled our own subsystem for logging chat messages (in C ) as well as an epoll-driven web server (in Erlang) that holds online users' conversations in-memory and serves the long-polled HTTP requests.

Both subsystems are clustered and partitioned for reliability and efficient failover. In short, because the problem domain fits Erlang like a glove.

By proactively engaging customers during the buying process, Le Tote is able to reduce cart abandonment and increase conversions.

Analytics plays an important role in chat and messaging.

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