Deep Dive Input Flow
Demand Driver - Analytic Edge | CX100
Deep Dive Input Flow
Demand Driver - Analytic Edge | CX100
Deep Dive Input Flow
Demand Driver - Analytic Edge | CX100
Background
Industry
SaaS | B2B | Web App
Role
Product Designer L2
Timeline
12 Weeks
Analytic Edge is a C5i acquired company that provides AI driven analytics solutions for marketing & sales effectiveness. Deep Dive is a feature that was added on 2.0 version of the platform. I led the design team tin crafting intuitive user journey and visually compelling design.
Team: 1 Product Manager, 2 Business Analysts and 1 Product Designer and 2 Junior Designers
Background
Industry
SaaS | B2B | Web App
Role
Product Designer L2
Timeline
12 Weeks
Analytic Edge is a C5i acquired company that provides AI driven analytics solutions for marketing & sales effectiveness. Deep Dive is a feature that was added on 2.0 version of the platform. I led the design team tin crafting intuitive user journey and visually compelling design.
Team: 1 Product Manager, 2 Business Analysts and 1 Product Designer and 2 Junior Designers
Background
Industry
SaaS | B2B | Web App
Role
Product Designer L2
Timeline
12 Weeks
Analytic Edge is a C5i acquired company that provides AI driven analytics solutions for marketing & sales effectiveness. Deep Dive is a feature that was added on 2.0 version of the platform. I led the design team tin crafting intuitive user journey and visually compelling design.
Team: 1 Product Manager, 2 Business Analysts and 1 Product Designer and 2 Junior Designers
Problem
The cross-product impact of marketing is not considered, it is leading to data duplication and inaccurate marketing insights.
The business goal was to make data-driven decisions to improve overall marketing ROI and to identify the most effective marketing channels for cross-promotion.
Until now this was not considered at all in the analyses on this product.
Solution
To design a tool that analyses how marketing one product affects the performance of another, diving deep to the atomic level of data.
Deep Dive Flow uses data set originally uploaded but dives to the most granular level in order to identify the effects of cross-promotion.
This also suggests that Deep Dive will be a part of each of the 6 main modules.
Design Challenges
Demand Drivers tool is spread across 6 modules - Input, Review, Modelling, Reporting, Simulation and Planning. The challenge was to find a way to integrate a new feature that would have an impact on at least 3 of these modules.
Another challenge was whether or not to provide this feature to all the users? Should it be only available for seasoned users? How to prompt them on using this feature? Where and how to start this from?
Design Challenges
Demand Drivers tool is spread across 6 modules - Input, Review, Modelling, Reporting, Simulation and Planning. The challenge was to find a way to integrate a new feature that would have an impact on at least 3 of these modules.
Another challenge was whether or not to provide this feature to all the users? Should it be only available for seasoned users? How to prompt them on using this feature? Where and how to start this from?
Design Challenges
Demand Drivers tool is spread across 6 modules - Input, Review, Modelling, Reporting, Simulation and Planning. The challenge was to find a way to integrate a new feature that would have an impact on at least 3 of these modules.
Another challenge was whether or not to provide this feature to all the users? Should it be only available for seasoned users? How to prompt them on using this feature? Where and how to start this from?
The Access
As a team we went through multiple brainstorming sessions as to how to get a user to access deep dive. We came up with 4 possible flows (as mentioned above) and discussed these with multiple users and gathered their feedback.
Finally, we went ahead with the one that proved to be the most logical flow (refer to the user flow below) - To have an ADVANCED SETTINGS button on each screen throughout the platform and group all added advanced featured to this. This would open as a pop-up in the centre of the screen.
The Access
As a team we went through multiple brainstorming sessions as to how to get a user to access deep dive. We came up with 4 possible flows (as mentioned above) and discussed these with multiple users and gathered their feedback.
Finally, we went ahead with the one that proved to be the most logical flow (refer to the user flow below) - To have an ADVANCED SETTINGS button on each screen throughout the platform and group all added advanced featured to this. This would open as a pop-up in the centre of the screen.
The Access
As a team we went through multiple brainstorming sessions as to how to get a user to access deep dive. We came up with 4 possible flows (as mentioned above) and discussed these with multiple users and gathered their feedback.
Finally, we went ahead with the one that proved to be the most logical flow (refer to the user flow below) - To have an ADVANCED SETTINGS button on each screen throughout the platform and group all added advanced featured to this. This would open as a pop-up in the centre of the screen.
Access Deep Dive chevron from Advanced Settings
Access Deep Dive chevron from Advanced Settings
Access Deep Dive chevron from Advanced Settings
Design Decisions
Grouping of advanced features: The features that a novice user might get distracted by are grouped together and hidden under advanced settings
Pop Up Advanced Settings: easily and consistently accessible without cluttering the main interface
Part to Whole: The decision was made to seamlessly integrate Deep Dive as a part of each module
Consistency in Design: To integrate Deep Dive seamlessly into the existing platform, consistency in adhering to design system is utmost important
Interactive Interface: To manage atomic level of the data set, the interface needs to be extremely user-friendly, intuitive and interactive
Design Decisions
Grouping of advanced features: The features that a novice user might get distracted by are grouped together and hidden under advanced settings
Pop Up Advanced Settings: easily and consistently accessible without cluttering the main interface
Part to Whole: The decision was made to seamlessly integrate Deep Dive as a part of each module
Consistency in Design: To integrate Deep Dive seamlessly into the existing platform, consistency in adhering to design system is utmost important
Interactive Interface: To manage atomic level of the data set, the interface needs to be extremely user-friendly, intuitive and interactive
Design Decisions
Grouping of advanced features: The features that a novice user might get distracted by are grouped together and hidden under advanced settings
Pop Up Advanced Settings: easily and consistently accessible without cluttering the main interface
Part to Whole: The decision was made to seamlessly integrate Deep Dive as a part of each module
Consistency in Design: To integrate Deep Dive seamlessly into the existing platform, consistency in adhering to design system is utmost important
Interactive Interface: To manage atomic level of the data set, the interface needs to be extremely user-friendly, intuitive and interactive
Add Key Variables
Add Key Variables
Add Key Variables
Rename Key Variables
Rename Key Variables
Rename Key Variables
Add Objectives to Key Variables
Add Objectives to Key Variables
Add Objectives to Key Variables
Group Deep Dive Measures to different Objectives
Group Deep Dive Measures to different Objectives
Group Deep Dive Measures to different Objectives
Impact
"It was a great idea to add Incremental bucket to define objective. I could instantly co-relate and think of input module incremental bucket."
- NRS | Data Analyst at AE
"The complex problem is simplified to a simple process, easy to understand and get the work done. Could have added more space for the variable selection dropdown to fit 50 character long variables. This would be helpful for sure."
- Client
Impact
"It was a great idea to add Incremental bucket to define objective. I could instantly co-relate and think of input module incremental bucket."
- NRS | Data Analyst at AE
"The complex problem is simplified to a simple process, easy to understand and get the work done. Could have added more space for the variable selection dropdown to fit 50 character long variables. This would be helpful for sure."
- Client
Impact
"It was a great idea to add Incremental bucket to define objective. I could instantly co-relate and think of input module incremental bucket."
- NRS | Data Analyst at AE
"The complex problem is simplified to a simple process, easy to understand and get the work done. Could have added more space for the variable selection dropdown to fit 50 character long variables. This would be helpful for sure."
- Client