Lightspeed x-series

Role

Product Designer

Type

Web app & Mobile

Tools

Miro, Figma

Timeline

6 Weeks

Overview

Overview:

Lightspeed is a one-stop commerce platform that’s built for retailers, offering point of sale, e-commerce, inventory, and reporting features to keep their business running smoothly. This case study explores the successful implementation of a customer data consolidation strategy in the x-series flagship product, highlighting the challenges faced, the strategies used, and the results achieved through the seamless solution.

Problem

Problem:

Several clients struggled with data accuracy and customer management—billing errors eroded trust, duplicate records caused inefficiencies, and missed marketing opportunities hindered engagement. These challenges led to a fragmented experience, making it clear that a smarter, more streamlined solution was needed.

Several clients expressed growing concerns about data accuracy and customer relationship management. They faced persistent challenges such as billing errors that damaged customer trust and missed opportunities for personalized marketing initiatives, like loyalty programs. Additionally, the presence of duplicate customer records created inefficiencies in customer support, leading to a fragmented and suboptimal service experience. These issues collectively hindered customer engagement and operational efficiency, emphasizing the need for a comprehensive solution to optimize data management, reduce errors, and enhance relationships with customers.

Solution

Solution:

We designed and implemented a solution to identify, group, and merge duplicate records, creating a streamlined and accurate customer database. This not only reduced billing errors but also unlocked the potential for personalized marketing strategies and improved customer support efficiency.

Process

We conducted in-depth research to understand the scope of the problem, collaborating closely with stakeholders to design and implement a targeted solution. Iterative testing and feedback ensured a refined system, providing lasting improvements in data accuracy and operational efficiency.

Final Designs

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Merging Duplicate Customers in Lightspeed X-Series

How did I get to the final design?

Empathize

>Define

>Ideate

>Prototype

>Test

Empathize

Discovery Research

We conducted in-depth user research, speaking with customer support teams, data analysts, and end-users to understand how duplicate records affected their daily work. Interviews revealed key challenges with data accuracy and customer relationship management.

We explored the complexities of billing errors, missed marketing opportunities, and support inefficiencies caused by duplicate records. User interviews provided valuable insights into these challenges, along with expectations and preferences essential for crafting an effective solution.

User feedback guided our research, shaping our project's direction and refining our objectives. Insights from interviews were crucial, providing a user-focused perspective that informed the design process and led to a more effective, personalized solution.

We conducted in-depth user research, speaking with customer support teams, data analysts, and end-users to understand how duplicate records affected their daily work. Interviews revealed key challenges with data accuracy and customer relationship management.

We explored the complexities of billing errors, missed marketing opportunities, and support inefficiencies caused by duplicate records. User interviews provided valuable insights into these challenges, along with expectations and preferences essential for crafting an effective solution.

User feedback guided our research, shaping our project's direction and refining our objectives. Insights from interviews were crucial, providing a user-focused perspective that informed the design process and led to a more effective, personalized solution.

Research Highlights

Needs

Users require accurate billing processes to ensure financial transactions are error-free and reliable.
Users need an efficient customer support system that eliminates duplicate records, streamlining assistance and issue resolution.
Users require reliable customer data for effective personalized marketing, including loyalty programs and targeted promotions.

Wants

Users want a user-friendly interface for interacting with the customer database, making it easy to navigate and understand.
Users desire an automated process for identifying and categorizing potential duplicate records, reducing manual efforts.
Users want customizable features within the marketing platform to tailor campaigns based on specific customer preferences.

Desires

Users desire real-time data validation to ensure the accuracy and integrity of customer information continuously.
Users may desire predictive analytics integrated into the marketing platform, offering insights for more effective and targeted campaigns.
Users may desire seamless integration between the customer database and support systems for a unified and streamlined user experience.

Needs

Users require accurate billing processes to ensure financial transactions are error-free and reliable.
Users need an efficient customer support system that eliminates duplicate records, streamlining assistance and issue resolution.
Users require reliable customer data for effective personalized marketing, including loyalty programs and targeted promotions.

Wants

Users want a user-friendly interface for interacting with the customer database, making it easy to navigate and understand.
Users desire an automated process for identifying and categorizing potential duplicate records, reducing manual efforts.
Users want customizable features within the marketing platform to tailor campaigns based on specific customer preferences.

Desires

Users desire real-time data validation to ensure the accuracy and integrity of customer information continuously.
Users may desire predictive analytics integrated into the marketing platform, offering insights for more effective and targeted campaigns.
Users may desire seamless integration between the customer database and support systems for a unified and streamlined user experience.

During reviews with stakeholders and team members, we faced challenges that required thoughtful collaboration. To address them, our development team assessed the necessary technologies and infrastructure for the project.

Challenges

Messy Data Overload: The client’s database was cluttered, making duplicate records hard to track.
Team Resistance: Some customer support members hesitated to adopt the new automated system, fearing it would complicate their workflow.
Integration Challenges: Connecting the automated system to existing databases proved trickier than expected, causing delays.

Messy Data Overload: The client’s database was cluttered, making duplicate records hard to track.
Team Resistance: Some customer support members hesitated to adopt the new automated system, fearing it would complicate their workflow.
Integration Challenges: Connecting the automated system to existing databases proved trickier than expected, causing delays.

Solutions

Optimizing Data Processing: We evaluated data volume, processing speed, and scalability to ensure future growth.
Smart Duplicate Detection: Our machine learning experts built models to identify duplicates, refining data and training with historical records.
Data Cleanup Plan: We tackled missing information, standardized formats, and fixed anomalies, making the data more reliable and accessible.
Gaining Team Buy-In: To ease customer support resistance, we highlighted time-saving benefits, provided hands-on support, and refined the system based on feedback. Over time, the team embraced the changes.
Seamless Integration: A dedicated team worked closely to resolve system connection issues, ensuring smooth data flow across platforms.

Optimizing Data Processing: We evaluated data volume, processing speed, and scalability to ensure future growth.
Smart Duplicate Detection: Our machine learning experts built models to identify duplicates, refining data and training with historical records.
Data Cleanup Plan: We tackled missing information, standardized formats, and fixed anomalies, making the data more reliable and accessible.
Gaining Team Buy-In: To ease customer support resistance, we highlighted time-saving benefits, provided hands-on support, and refined the system based on feedback. Over time, the team embraced the changes.
Seamless Integration: A dedicated team worked closely to resolve system connection issues, ensuring smooth data flow across platforms.

Define

Strategy

With a clear problem statement, I started solution discovery by mapping existing user journeys. This helped validate pain points and use cases. Working with the Product Manager, we redesigned the journey, integrating solutions for each identified challenge.

Next, we held cross-functional brainstorming sessions to explore potential solutions, such as manual data cleansing, automation, and merging duplicate groups. Using the MoSCoW principle, I prioritized the client’s core needs, focusing on critical aspects first while leaving room for future improvements. This approach resulted in a balanced solution that maximized value for the client's operations.

Must

The core requirement was clear: accurately identify and merge duplicate customer records.

Should

We identified improving data accuracy and enhancing customer interactions as critical, so features focused on these areas were prioritized as "should-have."

Could

Features like automated duplicate deletion and advanced reporting were considered "could-have" options, as they weren’t immediately critical.

Wont

We identified features outside the project scope, such as data source migration and a complete CRM system overhaul.

Ideate

Existing Journey Map

Intended User Journey

Design

Sketches

Wireframes

Hi-Fidelity Mockups

Test and Iterations

Finally, I conducted usability tests with users and team members, presenting prototypes to stakeholders for feedback. This iterative process helped us refine the concept and validate our solutions.

Objectives

Evaluate Navigation Efficiency: Test users’ ability to navigate the customer database for tasks like accessing records and validating data.
Test Feature Discoverability: Assess how easily users find and understand new features, like automated duplicate detection and real-time data validation.
Measure Task Completion Time: Track the time users take to complete key tasks, such as identifying duplicates and customizing marketing campaigns.
Gather Feedback on Predictive Analytics: Collect feedback on the understanding and usefulness of predictive analytics features, ensuring they meet user expectations.

Evaluate Navigation Efficiency: Test users’ ability to navigate the customer database for tasks like accessing records and validating data.
Test Feature Discoverability: Assess how easily users find and understand new features, like automated duplicate detection and real-time data validation.
Measure Task Completion Time: Track the time users take to complete key tasks, such as identifying duplicates and customizing marketing campaigns.
Gather Feedback on Predictive Analytics: Collect feedback on the understanding and usefulness of predictive analytics features, ensuring they meet user expectations.

Results

Users appreciated the clean, modern design of the interface, finding it intuitive and easy to navigate. They liked how quickly they could access different sections without getting lost. The Merging modal was also highlighted for its speed, saving them time. Overall, they recognized the team's effort in making the interface user-friendly, with clear messaging and smooth functionality.

Iterations

Feedback from the prototyping phase was incorporated into the design, leading to several iterations.

Conclusion

In summary, the Lightspeed X-Series project tackled key challenges in customer data consolidation, benefiting over 500K users worldwide by enhancing operational efficiency and customer engagement.

Through close collaboration and a research-driven approach, we developed an automated solution that focused on core functionalities while allowing for future improvements.

Our user-centric design and iterative process ensured an intuitive interface and effective problem-solving. This project successfully laid the foundation for better customer relationship management and operational optimization, driving significant value for users and setting a new standard for excellence in the retail industry.

In summary, the Lightspeed X-Series project tackled key challenges in customer data consolidation, benefiting over 500K users worldwide by enhancing operational efficiency and customer engagement.

Through close collaboration and a research-driven approach, we developed an automated solution that focused on core functionalities while allowing for future improvements.

Our user-centric design and iterative process ensured an intuitive interface and effective problem-solving. This project successfully laid the foundation for better customer relationship management and operational optimization, driving significant value for users and setting a new standard for excellence in the retail industry.