Lightspeed x-series

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.

Details

Type: Web app & Mobile | Role: Product Designer | 6 Weeks
Tools: Miro, Figma

Problem Discovery

Several clients approached us with concerns about data accuracy and customer relationship management.
They were facing challenges such as billing errors, missed opportunities for personalized marketing like loyalty points, and inefficient customer support due to duplicate customer records.

The absence of targeted marketing opportunities, such as personalized loyalty programs, further hindered our ability to engage customers effectively. Additionally, the presence of duplicate customer records posed a substantial obstacle in our customer support efforts, resulting in inefficiencies and a suboptimal customer service experience.

These issues collectively underscored the urgency of optimizing our data management processes to eliminate errors, enhance customer relationships, and streamline operational workflows. Recognizing the pressing need for a comprehensive solution, our project aimed to identify, group, and merge potential duplicates, ultimately laying the groundwork for a more robust and accurate customer database.

Objectives

Improve operational efficiency by streamlining billing processes, eliminating errors, and enhancing financial accuracy to build customer trust.

Enhance customer relationship management through a robust database, enabling personalized marketing initiatives and targeted customer engagement.

Maximize marketing opportunities through a better understanding of customer preferences.

Goals

Elevate our operational efficiency and customer relationship management to new heights.

Streamline the billing process, eliminating errors to enhance financial accuracy and foster positive customer experiences.

Improve customer relationship management sought to provide a solid foundation for personalized marketing initiatives and capitalize on opportunities for customer engagement.

Establish data reliability and create a sustainable solution aimed to ensure the longevity of our efforts, underlining our dedication to maintaining a high standard of accuracy in our customer database over time.

Research

User Interviews

We conducted thorough user research by engaging with customer support teams, data analysts, and end-users to understand the impact of duplicate customer records on their daily operations. Through interviews, we gained crucial insights into their challenges with data accuracy and customer relationship management.

We aimed to understand the nuanced complexities of billing errors, missed marketing opportunities, and inefficiencies in customer support resulting from duplicate records. User interviews provided a qualitative understanding of these issues, as well as insights into user expectations and preferences crucial for an effective solution.

During this research phase, we used feedback from users as our guide. It helped us understand our challenges better and shaped our project's direction. The insights we gathered from user interviews were crucial. They not only helped us define our objectives but also informed the design process that followed. Essentially, this research and the interviews gave us a user-focused perspective, steering the project towards a more effective and personalized solution.

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.

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.

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.

Research debriefing and solution brainstorming sessions

Solution Discovery

During these reviews with stakeholders and team members, we encountered challenges that required careful and coordinated resolution.

To help overcome these challenges, our development team checked what technologies and infrastructure we needed for the project.

Challenges

A Huge amount of Messy Data: The client's customer database was all over the place, making it hard to find duplicate records.

Some Team Pushback: Some of the customer support team didn't want to use the new automated system at first because they thought it might make their job harder.

System Connection Issues: Connecting the new automated system with the client's current systems and databases was trickier than we thought, which slowed us down

Solutions

They examined factors such as our data volume, the need for quick data processing, and the scalability of our system to accommodate future growth.

Our machine learning experts developed specialized models to find duplicate records. They prepared the data, created new features, and trained the models using historical data.

We created a detailed plan to improve data cleanliness and organization. This involved addressing missing information, standardizing data formats, and identifying anomalies.
As a result, the data became much cleaner and more accessible for our duplicate detection programs.

To overcome customer support resistance, we communicated the benefits of the new system, illustrating how it could save time and simplify their tasks. We provided support throughout the transition, actively listened to feedback, and refined the system accordingly. Over time, the team adapted to and even embraced the changes.

To address system connection issues, we assembled a dedicated team of experts. They collaborated closely to resolve any issues and ensure seamless data flow between systems.

With a clear problem statement in mind, I began solution discovery by mapping out existing user journeys on a board. This helped validate pain points and use cases. Collaborating with the Product Manager, we crafted an improved user journey, incorporating our solution ideas to address each pain point.

The next step involved running cross-functional brainstorming sessions within our team. These sessions generated a variety of potential solutions, including manual data cleansing, automated processes, and the ability to merge groups of duplicates.

By using the MoSCoW principle, I ensured that our solution was closely aligned with the client's core needs and priorities. We focused on the most critical aspects first while allowing for potential future enhancements. This approach helped us create a well-balanced solution that delivered maximum value to our client's operations.

MoSCow

Must
It was clear that the core requirements were to identify and merge duplicate customer records accurately.

Should
We determined that improving data accuracy and enhancing customer interactions were critical, so features related to these aspects were considered "should-have."

Could
While not immediately critical, features like automated duplicate deletion and advanced reporting were considered "could-have" options.

Wont
We identified certain features that were not within the scope of this project, such as data source migration or complete CRM system overhaul.

After evaluating the feasibility, scalability, and impact of different solutions, we opted for an automated solution that involved machine learning for duplicate detection and merging.

Existing journey map

Intended journey map

Design

Sketches, Wireframes, Hi-Fidelity

Usability test

Lastly I conducted some usability tests with users previously interviewed and team members across the company. And presented prototypes to stakeholders to gather feedback and validate our proposed solutions. This iterative process allowed us to refine the concept.

Test goals

Evaluate Navigation Efficiency: Assess users' ability to navigate the customer database interface for tasks like accessing records and validating data.

Test Feature Discoverability: Determine how easily users discover and understand new features such as automated duplicate identification and real-time data validation.

Measure Task Completion Time: Quantify the time users take to complete essential tasks like identifying duplicates and customizing marketing campaigns.

Gather Feedback on Predictive Analytics: Collect user feedback on the understanding and utility of predictive analytics features, ensuring alignment with user expectations.

Iterations

Test results

A few feedback regarding messages, which later on with the help of the content team we iterated thru some reviews.

They really liked how the interface looks, it's clean and modern. It's easy to figure out where things are."

The options provided makes it easy to find stuff quickly. I can go to different parts without getting lost.

The Merging modal works great. It’s fast, which saves me time."

Overall Thoughts where: They could could tell the team put a lot of work into making the interface easy to use.

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

Conclusion

In summary, the Lightspeed x-series project addressed critical challenges in customer data consolidation, resulting in improved operational efficiency and customer engagement.

Through collaborative efforts and a research-driven approach, we developed an automated solution that prioritized essential functionalities while allowing for future enhancements.

Our user-centric design and iterative process ensured a user-friendly interface and effective problem-solving. As we conclude this project, we celebrate its success in laying the groundwork for enhanced customer relationship management and operational optimization, setting a precedent for excellence in the retail industry.