Enterra Solutions is a leading-edge startup specializing in advanced analytics and artificial intelligence. With a strong focus on Trade Promotion Optimization, Enterra helps businesses in consumer goods and retail automate complex decision-making processes and streamline operations. Their innovative, data-driven strategies enable clients, including top U.S. food manufacturers like Nestlé, to drive operational efficiency, foster innovation, and achieve competitive advantages in their industries.

Our team operates within an agile framework, fostering close collaboration with an off-shore development team. The team lead and our client serve as product owners, overseeing the project scope, prioritization of features, and guidance of the product development process.

This structure emphasizes internal alignment within the PwC team, ensuring that our approach and capabilities are well-coordinated before presenting product updates to the client. By managing expectations proactively and delivering above-and-beyond results, we aim to meet client expectations but also strengthen our relationship with Enterra Solutions, setting the stage for future collaborations.

Our primary users are regional and local account managers responsible for managing various product groups based on their specific roles and responsibilities within their organizations. Despite their similar functions, the scale and scope of their management duties vary depending on their position.

These account managers are tasked with creating or modifying trade promotion plans based on market analysis, often derived from "alerts" provided by Enterra systems.

However, they face a significant challenge: navigating and interpreting overloading amount of fragmented market data to identify overarching market challenges and actionable insights.

The Intelligent Agent is strategically integrated into the most critical points of the user journey to enrich data with context and provide actionable insights. By offering recommendations on three distinct approaches to address market challenges, the Intelligent Agent enables account managers to streamline their strategy planning and decision-making processes, significantly enhancing their efficiency.

The platform's sitemap is designed to provide flexibility, allowing users to freely explore diverse information or follow the Intelligent Agent guided workflow.

While users can follow a general process—analyzing data, summarizing insights, creating plans, and reviewing post-event analysis—the Intelligent Agent adds a layer of dynamic exploration. It empowers users to dive deeper into specific events or scenarios step by step, uncovering actionable insights tailored to their own market challenges.

This design approach ensures that users not only achieve their objectives efficiently but also gain a deeper understanding of the data, fostering more informed and impactful decisions.

Leveraging the brand's core color, the Agent Panel is strategically positioned on the right side of the screen, making its presence both prominent and intuitive. Emphasizing the Agent’s always-on availability, seamlessly providing supporting information and AI-driven insights without interrupting the user’s workflow on the main view.

The Intelligent Agent is the core functionality of Enterra’s Trade Promotion Management platform. It analyzes and contextualizes data, breaking down complex information and providing users with recommendations on three distinct approaches. These insights inform users on the next steps or how to address various scenarios using all available system data.

To ensure a human-centered experience, the agent is designed to support—not replace—users during decision-making moments. It offers guidance and information when needed, while empowering users to maintain control over their decisions.

How might we design an agent interaction that provides users with proactive guidance and suggestions, while empowering the users to make their own choices during decision-making moments?

We further breaking this down into three actionable design objectives:

• The agent is not the decision-maker and should not take control away from the user.
  
• The agent should be easy to work with and feel approachable.
• The agent serves as a guide and assistant, offering actionable information and deep insights.

Based on above we created our Agent Design Guiding Principles:‍

• The user is always in control, the agent plays a supporting role.

  • Philosophically we believe that the agent should never take over or highlight without user consent first.  
    
  • If the user is going to be navigated away from their initial goal, the agent must ask for the users’ permission.

• Expand knowledge

  • The agent aims to expand users’ knowledge within the tool, and users’ performance.
    Providing traceable data and reasoning of recommendations.

• Intuitive, Simple, Consistent.

  • Interacting with the agent should be intuitive and simple.
    There should be continuity within the agent panel and the conversation. The agent’s UX elements and behaviors should feel stable and consistent as much as possible.

Enterra’s Wargaming system is a standout feature that contextualizes alerts, insights, and recommendations, empowering users to make informed and strategic decisions. By drawing connections between market data and alerts, the system identifies root causes and generates actionable insights. From these insights, it delivers recommendations on three distinct approaches tailored to address specific market challenges effectively.

Navigating Nested Data Relationships

One of the biggest challenges in Wargaming is enabling users to navigate and analyze thousands of interconnected alerts to develop actionable plans. This complexity stems from the nested, one-to-many relationships between alerts, insights, and recommendations:

• Alerts often connect to multiple insights.
  
• Insights can derive from numerous alerts.
• Recommendations are generated from multiple insights and address various alerts simultaneously.

The Consumer Goods and Retail Intelligence section visualizes vast amounts of data through charts. However, the original brand palette, consisting primarily of purple and teal as the primary and secondary colors, posed significant challenges for data visualization.

Users frequently interact with multiple data points across four different chart types—line, bar, stacked bar, and scatter. The limited palette creates difficulty in distinguishing data when more than four data points are present, especially in charts with unlimited data sets, such as line and bar charts.

How might we create a repeatable color palette based on Enterra’s brand colors that works across various chart types, ensuring clear differentiation and visual clarity for multiple data points?

To address this, we established a set of 7 complementary colors that align with Enterra’s core colors while providing sufficient contrast for effective data visualization. Additionally, we reorganized 8 existing colors into a secondary palette to enhance contrast and create a visually distinct set for more complex data representations.

Key Features of the Expanded Palette:

• Contrast and Clarity: The new color combinations provide high contrast to distinguish between key data points across various charts.
• Consistency Across Scenarios: For applicable charts, colors are directly assigned to ensure consistency in visual representation across different use cases.
• User-Centric Design: The palette is optimized to make data visualization intuitive and easy to consume, even when users interact with multiple data points simultaneously.

This expanded palette enhances the platform’s ability to present complex data clearly, empowering users to derive insights efficiently without compromising the brand’s visual identity.

The expanded color palette was implemented across the CG&R section.

Scatter charts and certain bar or line charts with fixed data sets, such as lift curve on comparing four causal types, pre-assigned colors ensure consistency and ease of interpretation.

However, the core challenge lies with trend line charts and bar charts, which can display an unlimited number of data sets, making effective differentiation more complex.

In line charts, users often manage more than four products simultaneously, requiring better differentiation. The solution:

Pros:

• Establishing Guiding Principles: We successfully collaborated with the client to establish guiding principles for AI usage, emphasizing that AI is a tool for humans and that all decisions should ultimately be confirmed and made by users.
• Balanced Experience: The final design strikes a balance, with AI effectively supporting users by surfacing and contextualizing information in ways that are meaningful and actionable.

Cons

• Scaling Challenges: The project began with a small scope that could be managed within a single Figma file. However, as the budget, resources, and scope expanded, maintaining all work within one file led to inefficiencies and technical lag.
• Library Maturity: The component library was under constant updates, and while awaiting approvals, the team attempted to push updates to the development team to save time. However, this approach led to misalignment between design and development, resulting in additional rework and inefficiencies.