Welcome to Preact!

Today is an exciting day for all of us here at Preact—it’s the day we exit “stealth” mode and show our public face to the world.

A bit of history: a short time ago, in a city far, far away (Los Angeles), two extremely talented product guys named Gooley and Cory launched FolioHD—a subscription SaaS service used by professional photographers to easily share their images. Like all subscription services, they realized that the best way to grow their business would be to increase the revenue from each customer by reducing churn and getting their customers to pay more. Being software geeks, they built some cool software to help them understand exactly what customers were doing and, more importantly, what those patterns of behavior meant.

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Cyclical Metrics and Account Health

A large part of determining account or customer health is building adaptive models of what “normal” behavior looks like and then reacting to behaviors that deviate from these models. In many cases the question can be put simply: “How often does a healthy account or customer engage with feature X?” and “How healthy is an account or customer given their engagement level with feature X?”

One common case is when an account or customer pays for a monthly subscription to a product or service. We might expect a healthy customer to come back each month and engage with the product or service at a level that meets or exceeds their previous engagement. But how can we know when a customer starts to fall below their typical engagement level? Is there a way to be certain (in a statistical sense) that what you are looking at is abnormal?

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Feature Extraction with Redis

One great thing about applying machine learning at Preact is working with fresh data. In the world of academic research, data is stale: learning algorithms are tested against monolithic reference datasets and each new algorithm picks up the same old sledgehammer and tries send the same old weight up to the same old bell. However, when algorithms are applied to new data it needs to be massaged into a form suitable for analysis in a process called feature extraction.

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The Power of Recurring Revenue

OK, great. You are a salesman for XYZ SaaS Company, and you just sold a customer your premium package. Your boss gives you a nice Commission check so you go home and treat yourself to a nice fancy dinner. The deal is done, the money is collected, and you return to work the next day in search for the next big sale. The previous customer is lost in a list of subscribers as you wait until their contract ends and hope to close them on a new one. The time between “contract signed” and “contract expired” is quickly transforming from a period of satisfaction to a span of opportunity.

“What do you mean by opportunity, the deal is done?”, somebody might ask. Recent studies have shown that it can be 6-7 times more expensive to find a new customer than to up-sell a current one. By no means am I saying that you should stop looking for new accounts, but more company time and resources should be allocated in attempt to increase recurring revenue through current ones. Whether it be a mobile gaming company with in-app purchases, a package upgrade of any SaaS subscription, etc…, there is always the opportunity to boost revenue through constant customer engagement and interaction.

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