When you’re running a SaaS business, you need to know what leads are doing during on your website, and during a trial. That’s key to detecting which ads and channels work to attract good leads, where these leads drop off, which features are easily being discovered and tested out, and when these leads decide to convert.
But that would be great for product and engineering teams to know. What do other teams (marketing, sales, customer success and even finance) get out of this?
For marketing, responsible for advertisement and communication, it allows them to conduct their tasks in a much more personal way. If they know that a lead has not tried a certain feature, then it might be a good idea to send them a personal email about the advantages of using that specific feature.
For customer success, responsible for onboarding and technical support, it also comes down to personalisation. If they know someone is struggling with a certain feature, they’ll cover that specific feature during the follow-up call. And even more important, by constantly monitoring engagement, they can predict churn before it occurs.
For sales, knowing how engaged a person is during trial, is a buying sign. They’ll prioritise their lead-calling list to call most engaged leads first.
And even for finance it could have a clear advantage. If an invoice is not paid in due time, they can see if the app is being actively used by that account or not, and make necessary pressure to pay the outstanding invoice, against potential shut-down.
How journy.io contributes to providing better trial information for everyone.
journy.io collects events, ad campaigns, channel information, devices, locations, timings and everything which is done throughout the customer lifecycle. Including what happens during trial.
More precisely, we collect every event with contextual data from every possible source, unify every sub-journeys from every device and browser to get a full timeline of everything every user from every account is doing.
Our journey-comparing engine then takes everything into account to provide a clear view on what makes an event of testing out a feature, being reached; while also allowing to make predictions on which features to come next in the journey.
As such, both native app data, as well as feature events, as contextual and behavioural intelligence derived from that, is made available to all teams, by filling in fields in the tools these teams already use.