Visual Analytics. The main selection is in the top left, where users can view, modify, or create new views. The chart is configured in the left panel, time settings are controlled at the top, and a corresponding data table sits at the bottom.
Data visualization tool - 60% trial start rate
Priceindx
B2B / SaaS / Web App
Impact
Engagement with the tool exceeded our expectations, especially since it was a paid, niche feature designed for enterprise customers.
35%
Feature discovery rate
20%
Total feature adoption rate
60%
Trial start rate
“It seems to be very useful so far.”
Customer quote
“Insightful. I find it clear and user friendly.”
Customer quote
Customers: 80+. Metrics have been anonymised, rounded and presented without a time frame to protect company confidentiality.
An upgrade prompt shown to customers without a Visual Analytics subscription. The ‘Learn more’ button opens a modal explaining the feature and its capabilities
Examples of chart configuration settings. Segment customization allows sequential quantitative data ranges to be adjusted based on each customers’ specific needs.
Colour customisation. Although the feature includes built-in colour logic based on the nature of the data, customers can customise chart colours if needed.
Context
Gathering and processing large amounts of data is at the core of the service Priceindx provides for retailers and brands.
Since I joined the company, we had released the Table and Scheduled Reports. However, both were focused on product-level data presented in a tabular format. There was no tool for visualized, aggregated data that would allow analysis at different levels.
Problem
At that time, customers had access only to product-level data, but they had long expressed a need for visualized, aggregated data. This type of data enables different kinds of analysis needed for specific decision-making.
Examples of help and guidance. The first touchpoint with the guidance, located in the bottom-right corner, helps customers get familiar with the tool at a high level through functional and educational content. While using the tool, contextual help tooltips provide additional functional and educational guidance.
Solution
We created a data visualisation tool that allows customers to visualise their data in a dynamic and convenient way. It offers different aggregation types, chart options and time frames to choose from. It allows them to see high-level overview of trends and patterns.
Different colour logic is used depending on the data structure - sequential ranges, categorical values, conditional states, or diverging comparisons
Challenges
There had been a need for vizualised aggregated data for a while. Input started to come in as requests for specific combinations of data, such as margin by category, price position by brand, or price changes compared to competitors.
The challenge was to take these fragmented inputs and turn them into a coherent concept.
To make that happen, I first sought to better understand the customers, specifically the roles within their companies that would rely on this tool for everyday decision-making.
Secondly, I got to know everything about the data aggregation and data visualisation. This turned out to be the most challenging part, but it was essential; otherwise, I would not have succeeded.
Constraints
Most of the constraints came from the core functionality itself - aggregation and visualization.
Not every dataset works with every aggregation type. The type of data defines which chart types are meaningful and even possible, and combining multiple datasets increases complexity significantly.
The complexity of the behaviour required us to map out all scenarios to ensure the logic was correct. This meant identifying which combinations were valid and which were not. This step was unavoidable; without it, the tool would not function properly. Errors would appear on the screen, and customers could create charts that were not meaningful.
In addition, business priorities required us to phase the scope. Some chart types and advanced features were postponed to keep development focused and achievable.
Approach
Here, we used the same approach as in the Table project: designing it around the needs of our largest customer. The main reason for this was performance, which was even more important in this project.
But here it was the other way around: we tried to optimize the performance according to design not design according to performance, unlike how we had to approach it in the Table project.
Because of customers’ mental models and their expectations that certain things should work in a certain way, we had to use per-filter live fetching as part of the core functionality.
Secondly, because of the size and complexity of the project, I decided to create an internal audit system. I mapped out all actions against usability heuristics to ensure that the majority of cases were covered and to define what would happen if something went wrong.
Ultimately it helped to reveal the need for help and guidance within the tool and it became a part of my process in future projects as well.
Team and my role
Product strategy officer, product owner, engineers and designer.
As the sole designer, I was responsible for the whole process, from concept to polished UI. I was working closely with the product and engineering teams to shape the solution within real technical and business constraints.
Next project:
Report-making tool - 500+ reporting workflows created