OptiMind
OptiMind the PC Optimum AI Assistant
Problem Space
Problem Statement
How might we enhance personalized customer experience and increase customer engagement by incorporating AI tools in our PC Optimum App?
Problem Background
The PC Optimum app is part of Loblaw Companies Limited loyalty program. The program was designed to drive customer loyalty by providing personalized offers based on shopping habits and rewarding customers across a wide network of Loblaw’s stores and affiliated brands. Despite its success as one of Canada’s leading loyalty programs, the system relies heavily on general algorithms and lacks true AI integration, which has led to several challenges ie.Non-Customer-Targeted Promotions and Gaps
Research Insights
User Pain Points
Using Questionnaire surveys and telephone interviews, we identified that the PC Optimum app, while effective as a loyalty program, currently lacks the integration of advanced AI capabilities, which limits its ability to deliver a truly personalized and seamless customer experience. As a result, users face challenges such as irrelevant offers, inefficient navigation, lack of proactive recommendations, and limited real-time engagement. This creates a gap between the app’s potential and customer expectations, reducing its overall effectiveness in maximizing value, convenience, and engagement for users. Without AI-driven enhancements, the app risks falling behind competitors that leverage technology to provide smarter and more intuitive loyalty experiences.
Supporting Data
Our research revealed that 76% of the people we interviewed were dissatisfied with the current PC Optimum app, primarily due to its lack of personalization. Many users expressed frustration with the generic offers they received, stating that the promotions often did not align with their shopping habits or preferences. As a result, they felt disengaged and saw little incentive to actively use the app beyond tracking their points balance. Some users mentioned that they only opened the app when necessary—such as to scan for points or check their rewards—rather than engaging with it regularly. This lack of meaningful interaction suggests that the app is not effectively driving customer engagement or fostering long-term loyalty. To increase user satisfaction, there is a clear opportunity to implement AI-driven personalization features, such as tailored offers, predictive shopping recommendations, and spending insights, which could enhance the app’s perceived value and encourage more frequent use.
Feedback
Our preliminary user research to validate this problem with 10 Edmonton PC Optimum customers through interviews and surveys revealed the following findings:
- The PC Optimum App isn’t offering enough value beyond points tracking and scanning
- Among App users, engagement is limited to scanning points and checking balances with little interaction beyond these basic functions
- The PC Optimum App has generic offers not tailored to purchase history
- Most PC customers only use the physical points card (3 of the 10 respondents use the physical card and another 6 declined to respond to the survey as they only use the physical card)
Landing on the Solution
Based on our target users’ pain points, we knew we wanted to work on the following features:
1. Enhanced Personalization – Customers receive tailored offers, recommendations, and reminders based on their shopping habits, making their experience more relevant.
2. Increased Engagement – AI-powered dynamic content encourages more frequent app usage, increasing interaction with offers and rewards.
3. Seamless Shopping Experience – AI-driven predictive analytics can suggest frequently purchased items, helping customers plan their shopping trips efficiently.
4. Higher Savings & Reward Optimization – Customers are notified of the best deals and ways to maximize their points, creating a more rewarding experience.
5. Improved Customer Satisfaction – A more intuitive and personalized experience leads to higher satisfaction and stronger brand loyalty.
6. Proactive Re-engagement – AI can identify inactive users and send targeted offers, helping bring them back into the rewards ecosystem.
7. Convenience & Time Savings – AI-powered chatbots or virtual assistants provide instant assistance, reducing the time spent searching for deals or support.
User Flows/Mockups
OptiMind : PC Optimum AI Assistant User-…
Images
Future Steps
By gathering direct customer feedback, we learned that we can prioritize improvements that align with user needs and expectations, ultimately increasing engagement, satisfaction, and loyalty.
Surveying current app users could help uncover:
• Why they use (or don’t use) certain features
• What barriers prevent engagement beyond points tracking
• What features they value the most and what they wish the app had
• How the app compares to competitors like Scene+ or Air Miles
• The types of personalization they would find useful
Learnings
Product Manager Learnings:
Sibonisiwe Dube
Co.Lab was a very interesting experience for me. I was deeply inspired by the vast knowledge and expertise my peers demonstrated. This experience reinforced the importance of continuous learning and staying adaptable in an evolving digital landscape.
Below are my key learnings as I progress on the path towards Product Management:
1. The Limitations of Questionnaire Surveys vs. Interviews
One of the key insights I gained is that questionnaire-based surveys do not always provide the most accurate or reliable information. This is because respondents may provide conflicting answers, misinterpret questions, or offer responses that lack depth. Unlike interviews, surveys do not allow for real-time clarification or follow-up, making it challenging to probe deeper into responses. Through this experience, I have come to appreciate the value of interviews as a more effective tool for gathering qualitative insights, as they allow for a more dynamic exchange where misunderstandings can be addressed and responses can be contextualized.
2. Leveraging AI for Efficiency and Strategic Thinking
I also learned how to work more efficiently and strategically by integrating AI-powered tools into my workflow. This has allowed me to streamline complex tasks such as developing strategy documents, refining test scenarios, and enhancing overall productivity. By incorporating AI into my current role, I have not only improved speed and accuracy but also gained a deeper understanding of how technology can be a force multiplier in decision-making and execution. This shift has reinforced the importance of adopting a growth mindset and continuously seeking innovative ways to optimize processes and drive better outcomes.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.