Case study
Retail & CPG
Case study
Retail & CPG
15%
Fewer stockouts
20–30%
Increase in order value
15%
Fewer stockouts
20–30%
Increase in order value
CUSTOMER CHALLENGE
Turning customer data into meaningful connections starts with understanding the customer journey.
→ Difficulty targeting the right audience due to fragmented customer data across systems.
→ Poor engagement, low lead generation, and rising opt-outs from irrelevant campaigns.
→ No unified view of customer insights for upsell or cross-sell.
CUSTOMER CHALLENGE
Turning customer data into meaningful connections starts with understanding the customer journey.
→ Difficulty targeting the right audience due to fragmented customer data across systems.
→ Poor engagement, low lead generation, and rising opt-outs from irrelevant campaigns.
→ No unified view of customer insights for upsell or cross-sell.
OUR SOLUTION
Building a unified, AI-powered data foundation to turn fragmented customer information into actionable marketing intelligence.
→ Implemented JIVA-Gen AI Orchestrator to create a knowledge graph-powered data fabric.
→ Integrated and cleansed 2M+ records from 6 sources; enriched with ZoomInfo data.
→ Enabled natural language search and segmentation across structured and unstructured data.
→ Trained 35+ marketing analysts with prebuilt queries and change management support.
OUR SOLUTION
Building a unified, AI-powered data foundation to turn fragmented customer information into actionable marketing intelligence.
→ Implemented JIVA-Gen AI Orchestrator to create a knowledge graph-powered data fabric.
→ Integrated and cleansed 2M+ records from 6 sources; enriched with ZoomInfo data.
→ Enabled natural language search and segmentation across structured and unstructured data.
→ Trained 35+ marketing analysts with prebuilt queries and change management support.
Results
Building a unified, AI-powered data foundation to turn fragmented customer information into actionable marketing intelligence.
76%
improvement in customer data quality
76%
improvement in customer data quality
Results
Building a unified, AI-powered data foundation to turn fragmented customer information into actionable marketing intelligence.
76%
improvement in customer data quality
76%
improvement in customer data quality
FAQ
Frequently Asked Questions
FAQ
Frequently Asked Questions
Why does JIVA use a Unified Data Fabric instead of a traditional data pipeline?
Why does JIVA use a Unified Data Fabric instead of a traditional data pipeline?
How does JIVA’s dual knowledge graph improve context, accuracy, and trust?
How does JIVA’s dual knowledge graph improve context, accuracy, and trust?
Does JIVA require data migration into a new platform or lake?
Does JIVA require data migration into a new platform or lake?
How does JIVA handle enterprise security and access controls?
How does JIVA handle enterprise security and access controls?
Can we build our own custom workflows and automations on JIVA?
Can we build our own custom workflows and automations on JIVA?
How does JIVA connect to our core systems and microservices?
How does JIVA connect to our core systems and microservices?
What is JIVA’s pricing model?
What is JIVA’s pricing model?
Why does JIVA use a Unified Data Fabric instead of a traditional data pipeline?
How does JIVA’s dual knowledge graph improve context, accuracy, and trust?
Does JIVA require data migration into a new platform or lake?
How does JIVA handle enterprise security and access controls?
Can we build our own custom workflows and automations on JIVA?
How does JIVA connect to our core systems and microservices?
What is JIVA’s pricing model?
Impact
See JIVA in Action for Your Industry
Simplifying enterprise data challenges to deliver faster, smarter outcomes.
Impact
See JIVA in Action for Your Industry
Simplifying enterprise data challenges to deliver faster, smarter outcomes.
Engagement
USe cases
contact
marketing@jktech.com
Engagement
USe cases
contact
marketing@jktech.com
