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Most implementations are actually moving in the opposite direction. Previously, there was a tendency to look to aggregate words into phrases to better capture the "context" of a word. Now, most approaches are splitting words into sub-word parts or even characters. With networks that capture temporal relationships across tokens (as opposed to older, "bag of words" models), multi-word patterns can effectively be captured by attending to the temporal order of sub-word parts.

No reason to oversimplify and ignore a lot of important details — they're doing that for you!

Survival modeling is exactly what's needed for these situations. It allows you to (a) consider censored data (i.e., active customers who you know stay for at least X months) and, (b) use flexible survival distributions beyond the standard exponential distribution assumed in the typical monthly churn rate calculations.

Source: Run a data science company and we work on a lot of customer lifecycle modeling projects with companies much younger than yours.

A quantile-based confidence interval from bootstrapping can yield a 100% confidence interval that does not contain 0, i.e., with 100% of cases positive/negative. But that does not (necessarily) mean that there is a 100% chance that the new version is better than the old one. Confidence intervals are not Bayesian credible intervals and cannot be treated as such. (That said, making some certain assumptions about the underlying model can in some times allow one to treat nonparametric bootstraps in such a way.)

Data security is hugely important. Here are a couple things we do to deal with it: (1) We cleanse the data of Personal Identifiable Information (PII) as quickly as possible (i.e., before it touches our servers), and (2) We host our databases behind secure networks and follow best practices with regards to authentication, encryption, etc.

We were all drawn to applying the statistical, experimental, and algorithmic approaches we learned in graduate school (and in our spare time) to a range of problems in industry. Every project has a big learning component that keeps things exciting and fresh.

Our first handful of clients all came from our professional network. I've been a developer and consultant for a long time (shockingly, over half my life!) and so, despite selling my companies and heading to graduate school, I had a bit of a network of other founders who knew me and were supportive of the new venture.

A few lessons learned, in brief: (1) Try really, really hard to be specific about what you offer (even when in reality you offer a lot of different things), (2) Write great proposals -- they become the project bible and really help streamline client conversations, and (3) Understanding a client's data and business always takes longer than you'd think.