Improving Lead Prioritization with Data Science in LogMeIn

LogMeIn is a SaaS company based in Boston, Massachusetts, offering a range of remote connectivity services for collaboration and remote access, such as GoToMeetingLogMeIn or LastPass.

The Challenge

Releasing a free version of a product is a popular way of attracting and onboarding new users. The idea is that at least some of the users will love the product enough to pay for a more feature-rich version.

But very often, those users who would be willing to part with their money also need something else: a push. If contacted directly with a good offer – they will happily convert to a paying customer. If left alone – they will keep hanging on the free version or worse – switch to the competition.

Typically, it makes no financial sense to try to convert everybody registered as a free user.

“Ideally, we would be able to identify a small cohort of the most promising prospects and target them with a conversion campaign.“

Nina Jensen, Senior Data Scientist in Logmein.

Logmein’s Marketing Data Science team decided to do just that with the help of Predictive Analytics.

Nina points out a significant roadblock on projects like this:

“We analyse usage data, CRM data, demographics, interactions with the website and with the chat-bots. The main problem is – this data does not immediately fit into Machine Learning algorithms.“

The Solution

This is where Xpanse AI comes useful.

Unlike the older generation Machine Learning tools, Xpanse AI digests relational databases with non-aggregated signals, events and transactions and delivers models within minutes.

Xpanse AI takes all this data and autonomously searches for patterns much more thoroughly than the manual approach and much faster. It delivers months-worth of work within minutes,” says Nina.

It’s a game-changer for any company looking to accelerate their Data Science delivery.

Xpanse AI allows me to iterate with business problems and deliver solutions much faster instead of being bugged down with data wrangling, ”