PROJECT 1

Hey there, you busy recruiters and managers 👋
If you're short on time, here's a quick rundown to get you up to speed. (Readers interested in more detail might prefer to skip this summary and start with the next section.)

DataGuardian (customer name anonymized for privacy) specializes in storing Salesforce data that powers crucial business interactions worldwide. Their latest mission? To transform their vault of data backups into pure gold with Machine Learning, powered by a bit of magic 🪄 from the folks at TensorIoT.

As the sole designer on the project team, I kicked things off by conjuring up a dashboard that's far from your average data display, in an action-packed two week sprint. Leveraging insights from stakeholder interviews and informal user research, I rapidly crafted a dashboard that turned everyday business data into a compelling narrative.

This initiative will arm the sales teams of DataGuardian's clientele with razor-sharp new insights on their existing data. They'll be able to easily spot their most promising sales opportunities, ensuring their actions hit their targets with unmatched accuracy and impact.

DataGuardian: Safeguarding Your Business's Digital Heartbeat

Meet our customer DataGuardian, anonymized here to honor confidentiality agreements. Today, DataGuardian offers a solution to securely store what's essentially the diary of the business world—Salesforce CRM data, where companies jot down every customer interaction, from the smallest nod to the firmest handshake. Yet, the real magic DataGuardian plans to unleash is still on the horizon. Their next move? Transforming customers' data into sharp insights, set to empower businesses to make bold, informed decisions.

Unlocking the Vault of Data’s Potential

To start, DataGuardian concentrated on their clients' sales data within Salesforce — just the first area among many, like marketing and operations, ripe for exploration. Their goal was to pinpoint the best opportunities for their clients' sales team to pursue, turning mere data storage into a rich source of actionable insights.

Enter the TensorIoT team. My role as the sole designer on this project was to distill the complexities of the available data and machine learning predictions into designs for a clear, engaging dashboard. This wasn't just about making data look pretty; it was about crafting a tool that would become indispensable to DataGuardian's clients, enabling their sales teams to zero in on opportunities with the biggest yields.

The QuickSight Quest: Design Discovery to Sign-Off In Two Weeks

In our initial discovery and design sprint, my knack for asking incisive questions and bringing customer ideas to life steered the project, ensuring smooth and successful progress. Here are some highlights:

1. Kicking Off Design Discovery

   To start, I tapped into our sales team's knowledge about DataGuardian's goals and challenges to shape some initial design sketches.

   

   In preparation for our initial discovery sessions, I crafted rough sketches reflecting our sales team's insights and the customer-defined use case as outlined in our contract:

   Ability to visualize inference results via a QuickSight dashboard:

   1. Opportunity Name
   2. SFDC Stage
   3. Close-Win likelihood
   4. Link to SFDC Opportunity
   5. Search & Filter functionality

   This sketch would help ground our conversations, allowing the stakeholders to point to parts of the design while they described what they’re looking for.

   
   

   Next up - my first meeting with the DataGuardian stakeholders, where I learned more about their business requirements and users. Some of my key takeaways from this session included:

   • The target users are business-to-business (B2B) sales reps and sales managers, especially in smaller firms where personalized engagement with customers can significantly influence success.
   • The current user journey involves sales reps guessing whether an opportunity will win or lose, relying purely on intuition—a hit-or-miss approach.
   • For this phase, the dashboard not only had to show how the data translates to dollars, but also needed to look “cool”, so DataGuardian’s customers would be excited to buy it.
2. Creative Solution Crafting

   During our initial discovery call, I realized my sketches weren't compelling or "cool" enough for DataGuardian’s needs. So, I revisited the designs, drawing inspiration from QuickSight's DemoCentral and discussing available data with our developers to brainstorm new graph concepts.

   

   Potential insights for users

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   1. Allows sales teams to quickly identify high-value and high-potential deals, focusing their attention where it can have the most impact.
   2. Enables identification of high-value opportunities that require immediate focus and lower-value ones that can be nurtured over time.
   3. Simplifies understanding of win-loss ratios within a sales portfolio, useful for quick assessment of sales outcomes.

   1. Allows sellers to easily see the proportion of deals in each win-likelihood category, focusing efforts where there’s a higher chance of success.
   2. Tracks individual or team performance across different stages, spotting trends in sales success or areas for improvement.
   3. Illustrates the cumulative performance of sales teams across various categories, useful for assessing overall sales health.

   1. Visualizes the distribution and intensity of sales activities, highlighting areas of high engagement and potential bottlenecks within the sales pipeline.
   2. Provides a clear ranking of sales reps, motivating underperformers and acknowledging top performers for strategy optimization.
3. Initiative in User Research

   Despite typical project constraints like limited time and resources for user research, I identified a chance for valuable user insight right within our team. Given our own sales team perfectly matched the target user profile, I initiated some quick, direct user research.

4. Achieving Final Sign-off

   Through several more cycles of discovery, sketch iterations, and review with both the customer and developers, I honed in on graph choices that narrated a well-rounded and compelling story, enriched by insights from my user research.

Bringing Design Dreams to Data Life

As we moved from design to development, our goal was to make sure the dashboard matched our vision, tackling technical challenges with creativity. I led the effort to keep design and development in sync, frequently reviewing the UI against my designs and discussing discrepancies with developers.

Challenges arose when developers indicated certain parts of my designs weren’t feasible within QuickSight. In those moments, I collaborated closely with the devs, ideating on alternative solutions that continued to meet user needs and deliver value, all while being achievable within QuickSight's framework and our project timeline.

The Big Reveal: A Tool That Sees the Future 🔮

The unveiling of the final PoC dashboard was a momentous occasion, met with enthusiasm and accolades from DataGuardian. Their expectations were not only met but exceeded, leading to immediate discussions for further collaborations between our two companies. The success of the dashboard underscored my ability to turn complex data into an intuitive, engaging tool for decision-making.

Charting the Course: Lessons from the UX Voyage

Mastering an unfamiliar tool and navigating the new, detailed world of sales pipelines proved to be a rewarding challenge. This experience showcased my adaptability and dedication to growing my UX design skills, while also highlighting my quick ability to learn and innovate in new areas.

The success of my designs for this project sparked notable changes within our organization and beyond:

1. My work with QuickSight led our company to adopt it for more projects, enhancing our visualization tools.
2. The success of this project prompted my company to replace an costly, custom internal tool with a QuickSight dashboard, cutting weekly costs by over $600.
3. My dashboard designs drew attention from Amazon's QuickSight designers, resulting in an invite for my team to beta test their new Figma library.
4. After asking me to showcase this solution company-wide, our CEO praised the user research I conducted on the project, urging the entire company to pursue similar efforts on other projects.