Peek behind the curtains and see the type of future we envision with Lancey.

An ugly thing happens when organizations graduate from startup to the scale up phase and beyond. They tend to form a definition of how growth should be tackled and assigned. For software focused companies, this split is a murky space between where marketing and product teams operate. This seesaw of responsibility is what I like to refer to as the growth pendulum.

Marketing/sales (go-to-market) teams get told to focus on acquisition between a mix of lifecycle marketing + acquisition methods composed of paid and non-paid channels. Whatever method you use or your title within this function, the message tends to be clear: increase the pipeline (whether you're product-led or sales-led using SDRs).

Product teams then get tasked with figuring out how to seamlessly onboard, activate, and retain these users that are brought in. Use data to generate hypotheses, launch new features and run experiments. 

So far so good.

Somewhere along the way, this model broke and everyone put their hands up. Marketing and product teams are busy and not the most closely aligned teams (though they should be). And CMOs and CPOs aren’t closely watching what the other team is doing.

GTM teams spend $ on acquisition without actually knowing if it's the right user to attract. And product teams keep tackling churn and retention without a good understanding of which user actually matters.

So what's next?

We need to make it easy to figure out what's happening from a product lens and make this actionable by anyone, autonomously. 

Go-to-market teams can focus their efforts on the right users/leads while the product team orient their efforts on what makes users stick and build a revenue focused growth engine.

We’re building a revenue focused experimentation engine that automatically understands product data across all available sources to align the entire GTM efforts. Lancey automatically figures out what actions are leading users to activate, retain, and convert. It can identify why something is happening, automatically figure out the next best action, and tell you the most effective user segments to target.

Unlike traditional methods of looking at past data to figure out what happens next, Lancey uses deep reinforcement learning methods to actually optimize how your users interact with your product. It can self-correct in real time based on current and likely future actions.

Take for example a user who signs up for your product and completes the onboarding process. They are now faced with multiple different actions they can take. Lancey quickly determines what the impact of all possible actions is and determines the highest impact and most likely path to push this user down based on their unique traits. It then nudges the users using a combination of communication (emails, in-app experiences) and infrastructure (features, pricing) channels.

Every customer is now its own unique entity, navigating, exploring, and converting on an optimal path that Lancey is monitoring.

This is Lancey. Welcome to the future of autopilot revenue focused experimentation.

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