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ConverSight.ai
Conversight.ai is a data analytics platform that helps users find their way to pro actively analyze the performance of business data and make better decisions for a better tommorow.
TEAM
CEO, CTO, 2 UI Front end developers, 3 Customer success, 1 Product Manager
MY ROLE
Product designer strategizes user experience with the CEO and product owners till coordinating the execution with the development to customer implementation team
TIMELINE
May 2024 - July 2024
Empowering Supply Chain Demand Planners with easy forecasting of business data using no code tools
Whats the problem?
Business leaders and managers in the supply chain industry often spend much time forecasting sales and requirements products for different locations.
Its a painstaking process of collecting all the historical data and build multiple formulas to see where we are going to go next.
Why does it take 5 days work, 5 different platforms, and 5000 errors???
When will I make business decisions?
How Conversight is helping?
With high end technology, LLMs, conversights builds models to run forecast by creating different configurations helping users to forecast.
Minimal Inputs and Maximum Output
My Goals for building demand forecasting tools
Curating the user experience to build forecast models to a user group who are not tech savy or have been using excel sheets to make the calculations
Breakdown complex forecasting automation models into easy workflow with simple , non overwhelming User Interface
Guiding users throughout while educating about the quality of data they are forecast.
Achievements
50 new customers
Increasing engagment
30%
Revenue growth after the feature release
70%
Reduction in customer implementation time
Outcome
See how I broke down the product into an experience
Building idea to reality
How did I do the research?
We adopted multiple different methods and sources to uncover hidden truths for the opputunities.
Domain Research and Technical understanding from data scientists on how to build forecast models to understand the terms and Navigational attributes.
Created and curated User interviews to understand the user's pain points, needs, and motivations, to derive.
Conversations, and and brainstroming with the product managers, Product owners, Leadership team and developers to curate user and feature requirements.
What did I do?
Wearing multiple faces to collaborate with different stakeholders and understand from their shoes
Product owners
Data scientist
Customer success
Developers
Learned about the company's growth and business unit restructuring, highlighting the need for forecasting in the supply chain.
Discovered product introduction and the team’s expectations for the platform's features and functionalities
Identified day-to-day challenges and pain points faced by case workers and managers, emphasizing the necessity for a more efficient and user-friendly interface
Market Research
What did we discover?
Anaplan
Inventoro
Limited Custom Scenarios: Tools may lack granular scenario-building for niche or complex markets.
Gaps in Real-Time Data: Insufficient integration with dynamic external factors like weather or economics.
c3ai
Tellius
Accessibility Issues: AI insights may not be intuitive enough for non-technical users to act swiftly.
User, Stakeholder Interviews
How we deep-dived into user mindsets and product requirements?
Technical understanding
Learning about Gen AI and LLM capabilities that CS has developed.
Understanding the ability of forecast data models and the technicalities built inside conversight.
Communicating and discussing the requirements for an ideal experience with the product and resource feasibility with timelines.
Conversing with 15 sales managers who forecast regularly with the customer success teams building out the resources
Conversations with Humans
Forecast modelling components, levers, metric
Simplification of forecasting, launching, modelling, guide
Flexibility and guiding factors for the modelling.
Forecast modelling components, levers, metric
Analysis at SKU level, identification on outlier numbers, mathematical model
Restriction on running cost and speed at which the models
Collecting data with the same component from exisiting product
Guided actions, insights, Cta, navigation flow
Seperate Components for scalability
Number of models to build, pricing, cloud solutions
Product understanding
Users
Stakeholders
Developers
Metrics and impact with reasoning along side every product
Showing historical data along side forecast data
Controls for API calls, socket push
Oppurtunities Pondered
What does run configuration do for the users and the platform?
Can we breakdown the steps into multiple tabs that can be switched easily in between?
Which components or features we can reuse for user recall
Once configured, where will users go for implementing the forecast numbers.
Where did I find the weak points?
Evaluating existing systems to find the missing gaps and oppurtunities.
Current flow
The Opportunity
Enhance usability with a clear, step-by-step user flow that provides intuitive guidance and relevant information for non-tech users to create accurate forecasts.
From
Time extensive setup for customer success teams
To
Easy learning curve and usability
Aiming to build a scalable, simple integration framework, for flexibile automated forecasting on data and get better visibility on business data with enhanced visualisations
A better planning process
User Flow:
For a shorter time to value
Strategies and Implementation
Visualizations of different iterations
Layouts, navigation, Infomation overload
Round 1
Technical constraints affecting the navigation
Round 2
Round 3
Cost of model running, security and access and feature prioritisation
Look at the how the product turned out
Give users the actions they can perform easily on top of the configurations
1
Quick Actionable items to perform on top of data and tables
Clear, clean data loaded tables for ease in usability and readbility.
Guiding users and helping them learn about forecasting with simple models along the way
Constant status updates of what the system is doing based on the action given.
Mananging to show a lot of numbers to consume them easily and in order.
2
Navigation made easy and bold ensuring flexibility
Actions and workspace divided in proper ratios making the user feel comfortable
Adding tooltips at every point to convey the action they can perform at any point making the user autonomous.
Navigation through step by step process with flows enriching users in controlling the flow
3
Intuitive actions for the users
Clean layouts, tags, and findings for large sets of enterprise data
Prioritising information flow and keeping them on under clicks.
Forecast Component Designs
Built feasible components for forecasting on top of libraries to reduce development resources.
Key aspects I achieved?
A path of discover , ideation and create while constantly learning and recreating.
Flexible Approach
There is no one-size-fits-all process; it's about adapting and combining multiple methods to solve the problem effectively
User Validation
Frequent testing with real users ensures the product meets their needs and provides valuable insights for improvement.
Stakeholder Sync
Collaborating closely with stakeholders ensures feasibility and alignment with the product roadmap throughout the project.
What did I do for end-to-end development of the project
01
Consistent Framework
Standardization ensures design consistency, making the user experience seamless across various touchpoints.
02
Structured Records
Comprehensive documentation captures key decisions, enabling smooth collaboration and future reference.
03
Cross-team Synergy
Effective collaboration across teams fosters alignment, combining diverse perspectives for stronger outcomes.
04
Scenario Narratives
Storytelling through use cases brings design solutions to life, helping stakeholders visualize the user journey and impact.
HI! Do reach out to me to know more about how I solved this problem for Conversight and created a simple experience for a data complex medium
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