Developer & Data Infrastructure Platforms
These two case studies will look at CRM data cleaning, routing, and reporting at 6sense - and how we approached GDPR research and design for enterprise customers at Informatica.
CRM Data Cleaning Workflows
Canvas-based decision tree tool that allows Ops users the ability to keep their CRM records accurate through automated enrichment, de-duping, merging, routing, and more.
Data Privacy
GDPR and CCPA were the acronyms of the year in 2019 as everyone scrambled to figure out how to stay in compliance. We talked with several large enterprise customers to evaluate how to help them adapt.
Data Visualization Approach
These examples from 6sense and Oracle show my approach to analytics and dashboards. Simplicity, relevance, proper context and actions, and personalization are all covered.
Case Study 1: 6sense Data Workflows
Data Cleaning and Routing Automation
Project Summary
6sense provides industry-leading predictive analytics that allow platform users to push “the right content to the right audience at the right time“. Most platform tools are aligned around that core benefit.
However, those benefits are all based on accurate data in the CRM and 6sense database. CSMs continually shared customer reports of inaccuracies (duplicate records, missing data, stale data, etc.)
The Data Workflows product was developed to provide customers with the ability to identify and rectify those types of problems as they occurred through automation.
Understanding Needs
Through 1-on-1 meetings (virtual and our annual conference) and through CSM feedback, we learned from MarketingOps users that:
Accurate data was a problem for most users, since the CSM was constantly being updated through different sources (including automated updates through opportunities).
Most customers had some solution in place currently but were not satisfied with it.
Most users highlighted the need to save money on data cleaning since it was a continual process. Licensing with existing solutions was becoming cost prohibitive.
Deeper Dives
Aggregation of all feedback led to high priority themes we could target for the first release - to begin laying a foundation for future enhancements.
Enrichment (ie. filling in the blanks in the database where there are empty or outdated fields)
Automated discovery of duplicates and associated merging logic
Constant visibility for users with notifications by email or Slack
The ability to test flows (with previews and approval workflows) when setting up a new strategy
Ideation
Side-by-side with our Audience Workflows product (omni-channel marketing automation) we explored different patterns that would provide a balance between simplicity, flexibility, and scale. The decision tree pattern was the only format flexible enough to adapt to any customer scenario imaginable, from continual branching logic to enterprise scale.
Monetization Model
Like Audience Workflows, Data Workflows was offered free with any platform package and only billed users on specific actions taken (credits). Many of the automation features within Data Workflows stood to be a new revenue stream for the company.
Unlike Audience Workflows, which primarily focused its functionality around Marketing, Data Workflows was foundational to the entire platform and needed to work seamlessly with both Marketing and Sales Pillars.
Providing Clarity
Figma
After seeing the extent of features I would be designing, I organized Figma files based primarily on which logical groupings of features would grow over time (new nodes, integrations and templates).
Everything else was divided by logical grouping to help other designers navigate through the team folders.
Mockup Libraries for PMs and Engineering
With so many moving parts (80+ nodes, 30+ templates, etc.), I kept the team organized by creating Figma libraries, each with a Table of Contents that was organized with a hierarchy that matched the app layout.
Figma links became a defacto standard for each UI-related Jira ticket (so engineering would always have clear reference - and always from the latest version).
Alpha and Beta Testing
Through alpha, beta, and usability testing, we gained insights into how users thought about, and approached, workflow strategies - addressing pain points long before GA release.
We were also able to better understand which personas typically performed different tasks and where certain tasks became more of a collaborative team activity.
Foundational Knowledge to Build On
Users loved:
the visual decision tree format and the ability to view, edit, and automate entire data strategies in one place.
the fact that the app laid a foundation for solving a platform-wide pain points around inaccurate data.
More attention was needed for:
better guidance and visibility (eg. being able to see the underlying logic that drives certain tasks).
“fast-follow” features, such as Push to Email, Push to Slack, and simplified setup for
Case Study 2: Informatica GDPR
Research and Approach
Project Summary
I joined Informatica at the beginning of 2019 to help rethink their security product for better adoption and better integration to the cloud platform.
Almost immediately after starting, product and business leaders saw that the deadlines for adhering to GDPR regulation was right around the corner - and many enterprise customers were not ready, asking Informatica for help with compliance.
Discovery and Take-aways
With GDPR regulations being new to everyone, we needed three core pieces of information quickly:
What exactly are the regulations calling for - and what do we need to know to help customers become compliant?
Where are enterprise customers at in their journey to compliance?
What users within an organization will be responsible for compliance?
By talking with companies onsite, at the RSA security conference, and at virtual Zoom follow-up calls, we learned that:
Customers had some plan but most were behind in implementation.
Most companies were establishing a Data Privacy Officer role or allowing leaders in existing roles to wear that hat, such as Data Analysts.
Customers continually asked for automation. No new tools.
Team Collaboration
The biggest challenge to the team, resolved through collaboration and workshops, was mapping out cross-product flows across different data governance products. Compliance is rarely relegated to one individual - and is typically part of a larger workflow - with multiple touchpoints.
We also needed to consider how the functionality could be sold as a stand-alone solution, using ServiceNow templates and other third-party integrations.
The data privacy solutions at Informatica are now embedded within Data Catalog functionality and workflows.
Final Product Strategy
While we wanted to offer a third-party option for customers (or at least simple methods of API integration with workflow tools like ServiceNow) most effort was placed on data privacy as a value ad to the cloud platform, the company’s best selling suite of products.
Because of the speed that large enterprises would need to adapt, selling Professional Services hours for initial onboarding was critical.
Data Visualization
Approach
Simplicity and Relevance
The success of our Customer Lifecycle Management app became a double-edge sword. That success had more leaders wanting to influence changes to the dashboard for their own department’s needs. With several Ops leaders adding to the mix, the end result became an experience that was visually overwhelming, inconsistent in format, and prevented at-a-glance insights.
I setup a short series of meetings with PMs and Ops leaders to discuss a different approach. It would require users to make tough decisions on what metrics are actually needed immediately after login and whether charts are actually critical for this type of dashboard.
Through those discussions, I was able to recommend groupings that better organized the main themes of the dashboard. I suggested a default filtering and sorting (hiding the advanced filters by default) and used a simple, consistent format for drilling to deeper details.
Actionable Data
6sense Data Workflows dashboards needed to be able to accommodate two primary use cases. As a user, I can see what needs attention and
drill to research more record-level details, or
immediately create a workflow that will automatically be oriented to that particular problem (de-duplication of accounts, enrichment of leads, etc.)
Being able to launch a workflow directly from the dashboard helps users complete the circle from creating a flow > to measuring success > to creating or fine-tuning a flow for even better success in the next published run.
Simple Format. High Value.
Oracle CSMs monitored customer usage from two perspectives:
What’s going on right now?
Current month usage?
Has the customer come close to (or exceeded) monthly limits?
What are the trends I need to know when working a renewal?
What is the average usage (general trend)?
How often were they over 80% usage (upsell potential)?
Which services are getting the most usage?