AI-based forecasting in IBM Planning Analytics
Leading research and design to deliver a minimum viable experience
Project context and goals
Project context and goals
For a financial plan manager, generating forecasts and budgets is typically a tedious and manual process that involves accessing multiple data assets across multiple systems to estimate forecasts.
The goal of this project was to augment users' forecasting tasks by providing statistically accurate forecasts as a starting point.
The business goal in this endeavour was to gain competitive advantage for our product, IBM Planning Analytics, by providing an integrated AI-driven forecasting capability.
My role
My role
Lead designer and researcher
Team
Visual Designer, Product Manager, Development Manager, Development Architect, 6 Developers
Product
IBM Planning Analytics - a financial and operational planning software
Preview chart for forecast
Initial research and early insights
Through a quick competitive analysis, we realized most competitors did not offer a quick integrated forecasting capability.
We interviewed 6-8 customers to map their as-is forecasting process and uncover opportunities for improvement.
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Smaller to medium-sized companies lack statistical and data science expertise, and rely on estimates for forecasts
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Larger companies tend to have in-house solutions to support statistically accurate forecasts
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Most customers forecast bottom-up, instead of top-down, implying that a quick forecast could be valuable to most users
"Hope for something that gives us a baseline, using historical data. We spend a lot of time bringing data from Excel to Planning Analytics...with this I'm looking to improve our productivity more than accuracy [of forecast]"
- A small-sized company
Primary user need identified
User
Need
Outcome
Line of business managers
need a way to apply statistical algorithms to their forecast,
so that the accuracy of their forecast improves and they finish forecasting on schedule.
Requirements scoping workshop for first release (MVP) with project stakeholders
MVP ideation
The cross-disciplinary team agreed that our first release would focus on providing a quick and statistically accurate forecast to our users. Together we solidified the concept through several iterations and created a prototype.
Feedback for prototype
Over 2 conference workshops with upto 70 participants, a remote customer-group session with 13 participants and 5 one-on-one interviews, we improved the prototype based on user feedback.
"I like forecasting at the click of a button”
"I wish it would tell me how forecast was calculated.. in plain language"
"I wish we could ignore some data"
"Will the average user understand [statistical details]?"
User feedback from workshop
IBM Planning Analytics workshop for AI-forecasting at IBM Data and AI Forum - Miami, 2019
Survey results
Final designs for first release
Using historical data, user runs an AI-based forecast for two items
Usability tests
75
SUS score
I planned, recruited and conducted usability tests with 10 customers to evaluate the MVP offering before it was released.
Smaller usability issues were fixed by the team right away, while the larger issues and enhancements have shaped the roadmap.
Retrospective
With the market-trends and IBM's AI focus, it was critical to get this capability into our product early. However, with misalignment of goals, and organizational changes, it was a challenge for the team to collaborate effectively and deliver rapidly. Research findings played a key role on this project in resolving conflicts and grounded the decisions firmly in user needs.
This project was certainly a learning exercise of building mutual trust between Design and Development. We worked together on solutions to communicate more effectively and truly collaborate to deliver the best experience to our users.
Next steps for this project are to expand this forecasting capability beyond the minimum viable experience.