How I Designed an actionable insights experience for Sprinklr Insights
Enhancing how Sprinklr clients manage and act on insights by reducing manual effort, enabling easier consolidation, and making insights more actionable — ultimately improving efficiency and decision-making speed.
Product Designer
In Development
Customer Experience Management (CXM) / Enterprise SaaS
To standardize Insight management, improve tracking, and enable impact measurement
July 2025 - Aug 2025
Product Designer (Me), 2 PMs, 4 Engineers
Problem statement
Sprinklr clients rely on insights to guide strategic decisions across marketing, product, and customer experience. However, their workflows for managing insights were fragmented — spread across multiple tools, heavily manual, and inconsistent in format.
This led to:
Time-consuming insight collection and reporting
Low reusability of previously shared insights
Difficulty in turning raw data into actionable recommendations
Outcome
The new insights experience is a strategic addition to Sprinklr’s existing product ecosystem, designed to help clients efficiently gather, interpret, and act on insights. Built from the ground up based on user research, it introduces a structured workflow that reduces manual effort, improves access to historical insights, and enables more actionable reporting. By integrating seamlessly with existing tools, this solution empowers clients to make faster, smarter decisions — without relying on external workarounds or fragmented processes.
+40%
Anticipated Insight to action conversion rate
-35%
Anticipated Reduction in Insight Discovery Time
+45%
Anticipated Increase in Summary Engagement
Discovery
Identified Core pain points with support from UXR
Strategised on a Phased roll-out roadmap with PM.
Development
Collaborating with VP-Engineering team to define & ship consistent experience across Sprinklr Insights.
Collaborating with Hyperspace team to create new component and add to Hyperspace Design system.
Ensuring implementation working directly with engineers and doing Design QA.
Context, before we begin
What is Sprinklr Insights?
Sprinklr Insights is an AI-powered consumer and brand intelligence platform used by enterprises to analyze and act on customer feedback. It captures data from dozens of social media, digital, and traditional channels to help companies understand customer sentiment, market trends, and competitor activity in real time.
Funnel for Insight Generation
As a Brand/Marketing Manager, Sprinklr Insights platform is crucial for viewing what my customers are talking about by brand, campaign, crisis, etc. Following is the workflow for Insights Generation.
Our funnel for Saving, Sharing and Actions on the insights generated currently is broken and not usable enough to make the platform sticky for our users.
Why Insight manager is required?
✅ Business Goal (for Sprinklr)
To increase product adoption, reduce support overhead, and drive long-term client retention by enabling clients to manage insights more effectively—turning data into decisions and positioning Sprinklr as a strategic partner.
✅ User Goal (for Clients)
To easily curate, organize, and act on relevant insights without being overwhelmed—enabling faster, more confident decision-making and better collaboration across teams.
User persona/User Journey
Vineet - Quality Program Manager
Aisha - Social Media Analyst
Prioritized Pain points
Fragmented Insight Discovery
Users struggled to locate relevant insights quickly due to scattered sources and lack of contextual filtering.
Low Actionability of Insights
Insights were often passive or generic, making it hard for analysts and social teams to take meaningful action.
Over-reliance on Support Channels
Users frequently sought help to understand insights.
Limited Engagement with AI Features
Sprinklr AI summaries were underutilized due to trust issues or unclear presentation.
High Cognitive Load
Users had to manually sift through large volumes of data, leading to fatigue and slower decision-making.
Insight management - Explorations
We explored a unified insights experience that serves diverse roles without fragmenting the interface. Our goal was a scalable system balancing simplicity and depth. We tested models for insight curation, transparent AI summaries, and seamless workflows from insight to action—while using personalization that adapts to user behavior. These explorations shaped an intuitive, flexible, and impactful experience.
Iteration - Before v/s After
Before
The experience was designed around a single insight at a time, focusing on clarity and simplicity. While this worked for straightforward use cases, it limited users who needed to analyze multiple insights in context or perform comparative analysis across themes.
After
We expanded the design to support multiple insights workflows, enabling users to view, curate, and act on several insights simultaneously. This shift unlocked richer storytelling, faster decision-making, and better alignment across teams—without compromising usability.
Before
After
Proposed Design - MVP (Phase 1)
Explore happy flow bringing in seamless experience.
Every Insight Tells a Story — Choose Yours
This is where the journey begins. Users identify the insights that spark curiosity or demand attention—whether it's a single standout trend or a cluster of data. This moment sets the foundation for exploration, helping teams shape narratives and uncover what truly matters.
Turn Insights into Action
Once an insight is chosen, it’s time to respond. This step transforms understanding into action—allowing users to assign ownership, set timelines, and add context. It’s a seamless bridge between discovery and execution, designed to drive outcomes with clarity and speed.
Manage and take priority action on What Matters
After saving an insight, users arrive at a centralized space where action plans live and evolve. Here, they can track progress, share outcomes, or retract actions—all while staying connected to the original insight. It’s a dynamic hub for ownership, visibility, and momentum across teams.
Where Insights Live and Evolve
This space brings saved insights to life. Users return here to revisit context, share findings, or retract actions—all while staying connected to the original narrative. With collaborative threads, task visibility, and historical entries, it’s a living record of how insights spark action and shape decisions.
Next Steps - Phase 2
Summarizing with AI
Once all relevant data, widgets, and messages are gathered around an insight, users can trigger AI to generate a summary—making it easier to share, present, or act on the insight with clarity and speed.
Conversational Prompts for Action
Instead of navigating filters or widgets, users engage with Copilot through natural prompts like:
“What’s driving positive sentiment this week?”
“Which campaigns need attention?”
“Summarize performance across regions.”
This conversational layer makes insights more accessible, especially for non-technical users.
Unified Dashboard Intelligence
Users can select multiple dashboards, and Copilot will scan them to surface key patterns, anomalies, and opportunities. The AI layout highlights areas that require action, helping users focus on what truly matters.
Instant Insight Creation
From the Copilot view, users can instantly convert AI-highlighted issues into actionable insights—complete with context, collaborators, and suggested next steps. This turns passive observation into active decision-making.
© 2025 Ankur Kushwaha