DATA TALKS CDP - SaaS Platform
Data Talks is a Swedish start-up developing a SaaS Customer Data Platform that enables users to collect data from various touchpoints and offers tools for segmenting, visualizing, analyzing, and taking actions based on the data.
My Role as a day-to-day in-house UX designer
- work closely with customers, business management, developers, designing complex and data-heavy product in a fast-paced agile environment
- use qualitative, quantitative data and leveraging product analytics to influence decision-making
- take designs from wireframe concepts to high-fidelity solutions with the aim of simplifying customer journeys and product flows
- contribute to product strategy and roadmaps and iterate toward a strong product market fit
- test and evaluate the development implementation of the user experience design specifications
A SELECTION OF PROJECTS I HAVE LED & CONTRIBUTED TO:
Components
Flexible Onboarding for Better Retention
I have made key design decisions to segment the portal into smaller, self-standing products-components in direct response to the business needs. This approach addresses a fundamental change in how new customers are onboarded and provides more flexibility in creating personalized billing plans for our customers, which helps prevent churn.
- reduce the time required for onboarding by automating tasks such as defining data types and attributes
- allow tracking more gradual key performance indicators (KPIs) associated with feature usage and desirability
- provide the flexibility to compose customized portal versions with selected features activated based on user needs
Data Visualization
From Data to Decisions - Visualizing What Matters
How to showcase data in a way that provides insights to users? How to design dashboards that highlight patterns, trends, and outliers? How to help users make the most of their data? These are some of the most important questions I ask myself when designing data visualizations.
For me, much of it depends on the context in which a piece of data is presented. When designing data visualizations, I consider adding similar data aggregations, enabling users to track changes across different time variables, or applying filters based on advanced attributes.
- dashboards visualy highlight patterns, trends, and outliers which in turn aids in tracking continuity and changes in the business
- visualizations serve as a universal language for data communication. Simplified complex information, is accessible to a broader audience, including non-technical stakeholders
Segmentation
Beyond Behavior and Location - Smarter Connections, Deeper Analysis
I was responsible for redesigning the Segmentation Builder, a core CDP feature crucial for initiating effective data analysis and activation.
In its previous version, the Segmentation Builder allowed for clustering profiles solely based on behavioral or geolocational data.
Through the redesign, I have introduced the capability to establish dependencies between profiles, events, transactions, and inventories.
- empowers users to harness their rich data ecosystem, facilitating the creation of diverse segment alterations
- unlocks greater personalization opportunities for users
- lays the foundation for integrated machine learning capabilities, enabling the identification of patterns within existing data and offering recommendations for segmentation strategies based on these insights
Golden Record
Unifying Customer Data While Reducing Manual Work
Before we introduced this feature, data engineers were manually creating a single comprehensive customer profile out of the resolution of identities across different data points.
With the Golden Record feature, customer data is automatically taken from all variations of touchpoints, including integrations, designated files, external systems with various naming conventions, and is cleaned and unified.
- helps to establish consistent workflows and procedures for data manipulation
- reduces the time spent by data engineers on manual data cleaning and consolidation
- results in better data quality and predictability
