Automation strategy for Salesforce Marketing Cloud (SFMC) workflow
Timeline
August 2024 - Dec 2024
Tools
Figma, Axure, Miro, SLDS Design System
Highlights
UX + Product Strategy, Research, Concept Testing
Challenge
Salesforce Marketing Cloud (SFMC) is built for enterprise level teams - but small businesses? Not so much.
Without dedicated marketing staff, SMBs often find themselves lost in SFMC’s complexity. Our challenge was to reimagine how non-experts could plan, create, and launch high-impact marketing campaigns - quickly and confidently
Solution
We used the current AI capabilities to help SMB's move from idea to execution through a highly tailored workflow
Impact
78% increase in idea-to-execution rate
Lower learning curve for SMBs to start marketing campaigns without extensive training
CONTEXT
What does Salesforce Marketing Cloud do?
SFMC helps businesses strengthen relationships with their customers through multi-channel (Email, SMS, WhatsApp) communication.
However, while its current product suite houses over 10 products, it lacks one tailored for a major market: small business owners. We led efforts to make the current marketing campaign creation experience accessible and tailored to SMB needs.
COMPETITIVE ANALYSIS
Establishing value proposition through market research
Lower Barrier to Entry →
Simplified marketing workflows for SMBs, making Salesforce accessible to non marketers
AI Differentiation →
Strengthen Salesforce’s AI capabilities (Einstein AI), positioning it ahead of competitors
10
iterations
and concept testings
78
%
boost in idea to execution rate for SMB's
DOMAIN RESEARCH
What does the current SFMC ecosystem look like?
SFMC is a very complex ecosystem with multiple tools interacting to do ONE thing - execute marketing campaign. To target our efforts, we mapped out how SFMC currently executes campaigns, how the data flows and what dependencies occur within these workflows
STAKEHOLDER ANALYSIS
How are the current users using SFMC?
This is how different enterprise teams collaborate to execute large scale marketing campaigns
USER RESEARCH
Why do SMB's struggle with this ecosystem
Unlike enterprise teams that work within structured office environments, SMB owners and small teams are constantly multitasking, handling operations, sales, and customer engagement while planning their marketing efforts.
What is their mental model? 🧠
Marketing planning doesn’t happen at a desk, it happens on the go, when they see an ad, hear customer feedback, or get a new idea. Their mental model is reactive rather than strategic.
Existing SFMC tools demand extensive configuration and prior marketing knowledge, making automation inaccessible to SMBs
EXPERIENCE STRATEGY ✨
We reimagined the flow to look something like this
Finding inspiration is the starting point for all our target users. But, without past data they don't know what will work and inspirations can be drawn from anywhere, there's too many ideas to go after. And, The current ecosystem doesnt allow converting inspirations -> successful campaigns. So let's help SMB's plan, create and execute high ROI campaigns without expertise on the go
SECONDARY RESEARCH
What I learned about human-AI workflow 🤖
We found out that AI driven automation can feel impersonal, and trust was a key factor. To build that trust, we established three guiding principles:
AI should support and augment human decision making capabilities, not replace human agency
The capabilities and limitations of AI should be clearly communicated
User should comprehend and trust the output created by AI
Multilayer Personalisation and
Memory Scaffolding
As the users engaged with SF, we wanted the AI to understand their tasks and preferences to generate more specific interfaces.
AI can act as memory scaffolding. Instead of starting from scratch each time, AI should remember past preferences and refine suggestions accordingly.
This goal was also created to differentiate it from it's competitors
TECHNICAL CONSTRAINTS
Can the current ecosystem support this?
Salesforce's Einstein AI provides predictive analytics, NLP, and content personalization.
Content Builder stores, modiefies and deployes AII generated marketing materials
IDEATION 💬
Some insights that drove our feature prioritization
EVALUATIVE RESEARCH
How we carried out our concept testings
To shape and evaluate our concepts, we conducted several interviews with Salesforce Admins and early startup owners.
Feedback from design team
Regular sessions critiqued for relevance and usability
Iterative Process
For fine tuning features, making the app more user focused
Result
Clearer workflows, smarter AI suggestions that meet startup needs
Key insights from concept testings
Many SMB users lacked technical expertise in marketing automation and struggled with manual campaign setup
Implemented a feature that lets users describe a marketing goal in plain English. Using NLP, this is refined into a structured marketing goal.
IMPACT ⚡️
64% of users preferred this approach over manual configurations, leading to a higher completion rate of campaign setup
Users were not looking for automation alone, but for inspiration on what campaigns to run
Implemented campaign templates inspired by successful past campaigns, allowing SMBs to draw insights from industry benchmarks
IMPACT ⚡️
Users reported a 33% reduction in time spent brainstorming campaigns, improving marketing efficiency
Users struggled with translating ideas into actionable steps
Implemented auto-generated task flows, breaking down campaigns into concrete, manageable steps
IMPACT ⚡️
82% of users felt this significantly reduced cognitive load, leading to faster campaign execution
Final concepts in action
Learning and growth
Finding the best solution involved learning a lot of new concepts and skills, getting guidance where needed, taking on tasks and going out of my comfort zone. The first phase of the project focused on understanding the main problem I was tackling. This meant going out of your way to learn more about the product itself and have a sense of their pain points and goals.
I learned how to be a better storyteller when presenting my work in team design critique sessions, in order to get as much valuable feedback as I could. After creating numerous iterations and an interactive prototype for the features, I validated and tested my assumptions.
Throughout the project, we followed an agile framework, cycling through research, design, testing, and iteration. Our biggest revelation was that agile is not just a process; it's a mindset. Rather than treating research, design, and testing as sequential steps, we learned to blend them fluidly, letting our findings continuously shape our decisions. From managing stakeholders and synthesizing research to presenting to leadership and receiving peer feedback, this project was pivotal in my growth as a designer.
Copyright 2024 by Priyanvada Darshankar