From one-off wins to org-wide impact: scaling AI at J.Crew
See Zoe in Action:
“We’ve tried every AI-powered platform out there. But our self-serve users still asked us to verify everything. Zenlytic solves this. Once our end users understand the results, they trust the results.”
Companies making decisions, faster
Whether it’s doubling down on channels that are performing, fixing campaigns that are broken or acting on early warning signals to reduce subscription churn rates – Zenlytic gives us the insights we need to grow.
With LOLA’s previous analytics tools, they lacked the ability to see when and where their customer subscriptions were slipping.
The Zenlytic BI platform allows LOLA to identify exactly what is causing changes in subscription revenue so they can push innovative new features to combat churn. By using Zenlytic to identify, and act on early warning signs of churn, LOLA has seen higher customer satisfaction and a 10% decrease in their chun rate over the last 12 months.
Before Zenlytic, we were wasting 20+ hours per week suffering through excel files and reporting we didn’t even trust. Now the first thing I do every morning is open slack, check Zenlytic, and get to work.
KOIO was manually pulling excel files from a multitude of disparate sources (i.e. Shopify, ad platforms, website, etc) and relying on an expensive third party data consultant to provide analytics and reporting. KOIO’s team couldn’t pull reports or interactively slice their metrics on their own – rendering it impossible to capture important aspects of their business like inventory, specific product sales, acquisition channels and more.
Zenlytic’s data engineering support team was able to organize KOIO’s data pipelines, upgrade their data stack, and get them running on Zenlytic so that their entire team could understand the full story of their brand’s data.
Without Zenlytic, we never would have spotted these patterns. We have 100 x'ed our ability to understand what’s happening in our business.
For a DTC digital bank, security is paramount. Neo-bank noticed flagged accounts and higher default rates, signaling a possible early attempt to circumvent their fraud protection protocols. They needed to identify high-risk users, quickly and reliably.
With the help of Zöe, Neo-bank detected common traits among flagged accounts and found bad applicants in their system. In minutes (not weeks), they removed the users and tightened fraud protection.
