Enterprise product design · B2B SaaS · MarTech
Making complex customer data easier to access without taking technical control away from advanced users.
Enterprise marketing teams had access to large amounts of customer data, but retrieving the right profile and understanding what had happened with that customer was harder than it needed to be.
Marketers needed a fast, understandable way to look up a customer and review their activity.
Technical users needed something different: control over the data source, query logic, parameters, and fields behind that experience.
Profile Lookup began as a focused MVP for customer search and evolved into a more flexible system capable of handling ambiguous matches, richer customer profiles, and multiple levels of technical complexity.
The Challenge
The platform already contained customer data, campaign activity, identifiers, and behavioral history.
But users did not have one clear place to answer a simple question:
“What happened with this customer?”
For marketers, finding the answer could mean relying on a technical teammate.
For technical users, a basic search experience would not provide enough control over how customer data was retrieved.
The product needed to make routine lookup easier without reducing the flexibility required for more advanced use cases.
Core Question
The answer was not one compromise interface. The two users had different jobs, different mental models, and different definitions of what made the experience easy.
One data model.
Two levels of complexity.
Two Users, Two Jobs
Primary job: Find a person and understand their customer history.
Primary job: Control how customer data is retrieved and presented.
Do not make marketers behave like database users.
Do not take database control away from people who need it.
Research
Competitive research looked at profile experiences across MessageGears, Braze, Hightouch, and adjacent customer-data products.
Product Strategy
Separate the interaction complexity.
Share the underlying model.
MVP
The first version centered on a straightforward marketer workflow.
Users could search for a customer using a unique identifier and optional date parameters.
Technical Workflow
The Profile Lookup Builder gave technical users a separate environment for controlling the data behind the marketer-facing experience.
The goal was not to hide complexity.
It was to put complexity where it belonged.
Validation
Technical users could preview the configured lookup and verify that the correct customer fields were being returned.
Marketer Workflow
The marketer experience intentionally removed technical configuration from the primary task.
Once the customer was found, the interface surfaced identity and profile information first, followed by campaign history.
This allowed users to move directly from “Is this the right customer?” to “What happened with this customer?”
Design Decision
Identity
Profile attributes
Campaign history
Real-World Data
The MVP assumed search parameters would return a single customer. Real customer data introduced ambiguity.
A lookup could return several possible records. The original experience prevented users from continuing with the wrong profile, but it pushed the problem back onto them by requiring another search.
That exposed the next product problem.
New Product Question
Customer identity can contain duplicate names, multiple identifiers, shared email addresses, old records, and partial matches. The product needed to help users resolve identity rather than pretending ambiguity did not exist.
Evolution
The evolved User Lookup experience expanded search criteria to include:
These parameters gave users more context and more control before the system returned customer records.
Identity Resolution
When several records matched the search criteria, the experience surfaced the possible customers rather than ending the task.
Ambiguity should be visible
before it becomes a mistake.
Preserving Context
The workflow preserved the search context while users moved from multiple matches into a selected profile.
This turned ambiguity into a normal decision point instead of a dead end.
Customer Intelligence
The expanded profile organized customer information into meaningful groups.
The experience evolved from profile lookup toward customer understanding.
Information Density
Profiles could contain dozens of business-defined customer attributes. Showing every field on the main profile would make routine scanning difficult.
The design surfaced a useful subset and allowed users to inspect the complete attribute set when needed.
Prioritize what helps users understand the customer. Make the rest available without making it dominant.
System States
If the system is waiting, searching, missing information, or uncertain, tell the user.
Key Design Decisions
Marketers needed speed. Technical users needed control.
Finding the customer was not enough. Users needed context around campaign activity.
Do not hide ambiguity or choose a customer automatically.
SQL and data configuration are necessary, but not part of everyday marketer lookup.
Make the primary profile scannable while preserving access to deeper data.
Cross-Functional Work
I partnered with Product, Engineering, stakeholders, and design peers to align:
Why It Mattered
Profile Lookup addressed an enterprise capability gap while creating a broader customer-data foundation.
Outcome
Profile Lookup evolved from a focused MVP into a broader system for finding, validating, selecting, and understanding individual customers.
The important outcome was not simply a new profile page.
It was a clearer relationship between customer data, customer identity, and customer history.
Reflection
Users should not need to understand a product's technical architecture to complete a routine task.
But advanced users should not lose necessary control simply because another persona needs simplicity.
Profile Lookup became stronger when the product stopped trying to make everyone use the same workflow.
Simplicity by default.
Depth when needed.
Ambiguity made visible.
Working through a complex product problem?
Whether you're shaping a new SaaS product, improving an enterprise workflow, or trying to make complex data easier to use, All Hatched can help turn complexity into a clearer product direction.
