Building an AI-powered consumer data platform and boosting the effectiveness of conversational campaigns.
My Roles
Product Design
UX Design
UI Design
Design Sprint Workshop
UX Research
User & Customer Personas
User & Customer Journey
UX Strategy
Interaction Design
User Testing
UX Design
UI Design
Design Sprint Workshop
UX Research
User & Customer Personas
User & Customer Journey
UX Strategy
Interaction Design
User Testing
Enable customers to collect, unify, and organize data from different sources to create a unique and comprehensive profile for each consumer. The goal is to allow companies to use this information efficiently to enhance the customer experience, personalize interactions, and optimize marketing and sales strategies, primarily through their main conversational channel — WhatsApp.
Main outcomes
+44%
+25%
Increase in average engagement with audiences generated by the solution.
Increase in average conversion compared to the conversion rates of previous audiences.
Note: For reasons of preserving their strategic information, the clients did not provide the ROI data.of your campaigns
Overview
Companies face various challenges in managing and utilizing consumer data. One of the main issues is data fragmentation, with information scattered across different systems, making it difficult to build a unified view of each customer. Additionally, data quality often poses a challenge—duplicate, inconsistent, or outdated records compromise the accuracy of analyses and directly impact personalization strategies.
When it comes to segmentation, the complexity increases even further. Creating customer groups effectively requires well-structured, accessible, and constantly updated data. However, a lack of organization leads to generic segmentations, reducing campaign effectiveness and increasing acquisition costs.
Furthermore, the difficulty in measuring campaign impact and attributing revenue to each touchpoint makes the optimization process even more challenging. Without clarity on the performance of marketing actions, making strategic decisions and improving customer acquisition and retention efficiently becomes significantly harder.
Problem space
Companies with large volumes of first-party data face barriers to transforming it into personalized experiences at scale. The lack of a single, consolidated view of the consumer directly impacts brands' ability to execute more effective conversational marketing strategies.
Symptoms and Obstacles
• Difficulty obtaining and managing accurate and integrated data.
•Lack of visibility into customer behavior and journey.
• Inefficient lead acquisition process with low conversion rates.
• Low ROI in proactive notification campaigns.
Problem Hypothesis
"I don’t have 360º structured data in a single view of my consumers that allows me to scale close and personal conversations."
Problem immersion
Experience of those who are there at the end of the flow
Before thinking about how our users could increase the impact of conversational marketing campaigns, which data would be relevant for them to achieve those goals, and how to provide the best possible experience for using that data, we understood it was essential to first comprehend the end consumer’s experience with WhatsApp as a purchasing channel. That’s why we conducted a series of interviews with end users who were already using the app in this context. The insights gathered at the beginning of the discovery process were extremely valuable, as they helped us define our hypotheses for customer research and understand which types of data were truly relevant for improving the consumer experience at this stage of the journey.
After listening to people who use WhatsApp as a shopping channel, I were able to draw a Persona and map the consumer journey in greater depth. This allowed us to identify friction points and real opportunities for brands to significantly improve that experience. From there, it became clear how to deliver a 1:many journey with the same level of proximity, relevance, and accuracy as a 1:1 experience. With this understanding in hand, we already had evidence of the opportunities within the context of consumer data and behavior, allowing us to move forward in building a product that helps our users identify the specific needs of their leads and customers, creating more personalized and efficient interactions throughout the entire funnel.
Customer Persona
Customer Journey
Research with experts
Initial mapping of data types
Since the company has a well-structured services area, it was easy to talk to specialists who work directly with clients daily and understand, from their perspective, which data clients value the most and which other sources those data are typically integrated with.
To do this, I conducted a qualitative survey with 39 specialists from Blip’s service teams and made some very important discoveries:
The majority of data requests (98%) originate from client demands, with 84% of cases involving specific data requests. Still, the teams take a proactive approach: 88.2% identify opportunities and gather data on their initiative, and 74.5% analyze specific information to guide these opportunities.
This research helped the team make an initial prioritization of which data should be integrated into the solution.
Talking to the right people
Minimizing risks with a Customer Discovery Program
We had a very clear premise about who we needed to talk to: it was essential to engage both end users and decision-makers within companies. We needed to understand the users’ pain points in the context of combining data from multiple channels to create segmentations, while also connecting with decision-makers to grasp the business challenges they faced and the potential they saw in a solution capable of eliminating the existing frictions in this process.
To deepen this understanding and ensure the relevance of the solution we were exploring, we decided to adopt an approach called a Customer Discovery Program, a technique discussed by Marty Cagan in his book Inspired.
We then selected six companies representative of our target market to collaborate with us throughout the product discovery and development process. These partners helped us identify pain points and opportunities, provided ongoing feedback, tested early prototypes, and validated the core functionalities of the product.
This close collaboration not only ensured that the product addressed real market needs, but also resulted in satisfied customers willing to serve as references for future implementations
Client workshops to accelerate discovery
As part of the Customer Discovery Program and to strengthen the understanding of the problems and generate solutions more aligned with market needs, I conducted co-creation workshops with strategic clients throughout the discovery process.
These sessions were organized in a structured way, divided into 3 days, one per week, and included practical activities such as understanding the processes related to consumer data, creating segments, mapping pain points, identifying desired gains, prioritizing features, validating user journeys, and building medium-fidelity prototypes. Working closely with marketing and operations teams from the clients, we were able to primarily:
These sessions were organized in a structured way, divided into 3 days, one per week, and included practical activities such as understanding the processes related to consumer data, creating segments, mapping pain points, identifying desired gains, prioritizing features, validating user journeys, and building medium-fidelity prototypes. Working closely with marketing and operations teams from the clients, we were able to primarily:
• Deepen the understanding of the real-life contexts in which consumer behavior data is used.
• Accurately map pain points and desired gains.
• Co-create ingestion and segmentation flows more aligned with the operational realities of each company.
• Validate value hypotheses even before building prototypes.
• Strengthen customer relationships and engagement with the future solution.
This collaborative approach allowed us to accelerate alignment between what the market needed and what the product should deliver, ensuring that the CDP was built based on practical insights rather than assumptions.
It is important to note that this approach aims to allow us to build a product that addresses the pain points of the participating customers very well. The most important thing, however, is that the solution addresses a market pain.
It is important to note that this approach aims to allow us to build a product that addresses the pain points of the participating customers very well. The most important thing, however, is that the solution addresses a market pain.
Summary of the Immersion and Co-Creation Workshops with Clients
Mapping Out the Data That Matters
User Persona
As a direct result of the co-creation workshops with clients, we were able to consolidate the information gathered into a representative persona of our end user.
The persona was built from real insights about behaviors, needs, pains, and expectations identified during the practical dynamics. It became a fundamental guide to inform design decisions, prioritize features, and, most importantly, to later tangibilize the user journey
Validating the journeys with the users
As the workshops with users and decision-makers progressed, it became possible to visualize who they were, what the day-to-day life of the solution's users was like, and most importantly, what their journey could look like for data integration, segmentation creation, and campaign delivery. With this, I gathered all the insights obtained and structured a user journey to tangibilize the main actions, needs, business rules, doubts, emotions, and critical touchpoints of the professionals who would interact with the solution.
With the journey ready, it was possible to validate it directly with user representatives from each of the companies that participated in the workshops.
For this validation, I met with user groups from each company on different days, where I presented the journey to them and asked them to note their feelings (pre-defined feelings) in relation to each feature, touchpoint, and stage of the journey. At the end, I consolidated all the results from the companies in the document shown below, ensuring that it accurately reflected:
• The real challenges faced by users in the data management and activation process.
• The expectations regarding a smoother, more integrated, and intelligent user experience.
• The main opportunities to reduce friction and generate value at each stage.
The journey became a central piece in the development of the CDP, serving as a reference map to guide interface design decisions, functionality prioritization, and the overall user experience construction.
Consolidated User Journey Validation
Data Flow representation in a CDP
The image below represents the data flow in a Customer Data Platform (CDP) ecosystem, starting with data sources such as Business Messages, Blip data, website, CRM, and database. The data is then organized, standardized, enriched with artificial intelligence, and unified to create a consumer view. This process allows the unified data to be used to create personalized and active audiences across different destinations, such as social ads, active messaging campaigns, email marketing, API, and data warehouse, optimizing marketing and engagement strategies.
Validating mid-fidelity prototype
After defining the main flows of the user journey, we created a mid-fidelity prototype focused on the functional structure and interaction logic, without aesthetic concerns. This prototype was validated with representatives from the participating companies using the Design Critique dynamic, generating feedback on clarity, usability, and efficiency. The validation allowed us to adjust flows, identify user experience issues, and align expectations before moving on to the final prototype. After the adjustments, we proceeded to the high-fidelity prototype, with visual identity, microinteractions, and refinements, ready for the MVP development phase.
Usability testing
After implementing the adjustments identified in the Design Critique dynamic, I conducted a Usability Testing session for final validation before handing it over to the development team. The results were very positive, showing that the product provides an intuitive and accessible journey. Furthermore, users demonstrated confidence and clarity when making decisions, being able to perform the necessary actions to achieve their goals safely and efficiently.
Is it clear to the user that the Sources section is where they will import the data?
• After completing the file import actions, is it clear that all the imported data will be available to use in manual segmentation filters or by the AI model?
• Are the actions the user needs to perform to create a manual segmentation clear enough to allow them to complete their goal without major difficulties?
• Does the flow for generating an automatic segmentation provide enough visibility to the user that our AI model will generate different consumer groups in an automated way?
SUS Evaluation Score
85
*The zone of excellence starts at 80.3
Final solution
Blip’s CDP was designed as a unique and intelligent platform, developed to centralize first-party data from interactions between brands and consumers. The solution integrates data from multiple sources — such as CRMs, business messages, and conversation histories — and uses artificial intelligence to enrich this information, enabling the creation of unique consumer profiles. From this foundation, it becomes possible to generate hyper-segmented audiences and activate them in personalized campaigns with a high degree of relevance and context. This way, communication between brands and their audiences gains scalability without losing the sense of closeness and personalization.
Solution Components
• Ingestion: Upload and integration of structured and unstructured data.
• Unification: ID resolution across distinct sources.
• Enrichment: Clustering using K-Means, profiling with LLMs.
• Smart Audiences: Personas generated based on data and behavior.
• Export and Activation: Triggering through active campaigns (Blip Trigger).
Use Cases
Creation of segmentations for WhatsApp campaigns.
Lead enrichment and funnel optimization.
Reduction of CAC and increase in LTV through more accurate segmentations.
CDP Home
Data import journey
Integrating external data sources
Creating manual audiences
This feature allows CDP users to create highly segmented audiences based on behavioral, demographic, and contextual data unified in the consumer profile. In other words, the user has access to both data imported from other sources and conversation data from the bot. The interface was designed to be simple, intuitive, and flexible—enabling analysts and marketing managers, without advanced technical knowledge, to create precise segmentations in just a few minutes
Creating audiences with Artificial Intelligence
The user has access to a library of artificial intelligence models, each trained with a specific dataset according to the desired business objective. After choosing the most suitable model, the user can add additional properties to enhance the audience segmentation to be created. For each new audience or objective defined, the AI suggests optimized segmentations based on available data, such as recent behaviors and newly imported information
Activating an audience in a selected destination
After creating a segmented audience, the user can activate it with just a few clicks across different destinations, in a simple and integrated manner. The platform allows sending these audiences directly to channels such as Meta Ads (Facebook and Instagram), Google Ads (Customer Match), active messaging campaigns on WhatsApp, as well as Click to WhatsApp ads, among others. This fast, multichannel activation simplifies the work of analysts and marketing teams, enabling highly personalized actions to be executed with greater agility and accuracy, without the need for technical processes or manual exports.
Consumer Single Profile Visualization
The consumer single profile feature provides a consolidated and intelligent view of each customer, bringing together data from multiple sources — such as e-commerce, CRM, social media, customer service, marketing campaigns, browsing data, among others — in one place.
This unification allows for a granular and actionable understanding of each consumer's behavior, preferences, interaction history, and stage in the lifecycle.
Additionally, with a 360º view of each consumer, companies move away from operating in the dark and begin making data-driven decisions. This leads to more efficient campaigns, longer-lasting relationships, and more relevant experiences, with a direct impact on revenue, NPS, and customer retention.
