Fine-tune channel and inventory allocations and minimize
returns
Success story
Here is a summary of a client success story that leveraged
the power of AI-driven analytics to help them get answers from
their data and enable them to take deeper and dynamic control.
They were able to improve their decision intelligence and
ultra-fine-tune their sales and marketing inventory allocations
based on their returns analytics.
Client / Pain:
A B2C e-commerce company in India on a high-growth
trajectory over last few years
Frustrated by piece-meal cross-sectional dashboards that
were sluggish, unwieldy, and time-intensive to manage.
Had limited success in collating data across multiple
sources, formats and data structures.
Limited visibility on loss-drivers of products deployed
across various channels
Existing IT team overloaded with issues backlog
How JetFerry.ai helped:
Multi-dimensional drill-down-able dashboards were
available lightning fast enabling smarter decision-making.
Data integration, collation and unification enabled a
deeper and more complete holistic perspective.
Delivered answers via data visualizations that heightened
visibility on "loss-drivers" and highlighted alternative
opportunities.
No additional IT skilling or expense incurred. After 1
session, existing reporting specialists felt comfortable with the
self-service capabilities of the platform to help them with their
deliveries.
Bonus value: Built-in mobile support was an added
dimension to their customer delight.
Key Take-aways:
Heightened loss-drivers visibility and re-allocation
recommendations: +50% increase
Enhanced Dashboard relevance: +30% increase
Existing reporting team was able to upskill to the
self-service platform within 1 session
1 week: From initial data sharing to the reporting team
recommending use of the self-service platform to management.
+1 day: Reporting team ability to create dashboards with
no assistance. True Self-service and leveraging AI-driven
capabilities.
AI is a powerful enabler!
An awesome entry-level BI solution for those whose data is
too big for pre-BI environments, but lacking an enterprise-budget.