M.Y.J. Insight
Management System for Meiyijia Business Intelligence Insights, designed for China's largest chain of convenience stores as a B2B project.
Client
Duration
Meiyijia Holdings Limited
Meiyijia is a leading convenience store chain in China with over 30,000 outlets. It offers a variety of products like snacks and daily necessities, catering to a broad customer base. Known for its strategic locations and advanced retail technology, Meiyijia is a key player in China's fast-moving retail sector.
Tools
My Role
7 weeks (Fall 2021)
Adobe XD Xmind Adobe Illustrator
UX research Product designer High fidelity prototype
Overview
In 2021, Meiyijia planned to integrate advanced collaborative tools and digital strategies to address its digital transformation challenges. This was part of an effort to enhance efficiency across its network of over 4,100 stores. Concurrently, Meiyijia aimed to continue its expansion by opening over 1,000 new stores.
Problem
Meiyijia faced challenges in utilizing their limited offline data for effective decision-making, impacting the digitalization and optimization of operations across their 40,000+ stores. Inefficient traditional site selection methods, based on surveys and expert opinions, hindered their expansion plans, including the opening and closing of around 3,000 stores annually.
Solution
A business intelligence platform was developed for Meiyijia, improving store site selection and operational efficiency. It analyzes user habits and location traits in Guangdong and East China, aiding in the strategic opening and closing of around 3,000 stores yearly. Additionally, the platform enhances the management of over 40,000 stores through big data and predictive analytics for better operational decision-making.
Image from: Customer case on the company's website. Click on the picture to see the details.
Workload Summary
Company Goal
Digital Process Enhancement: Upgrade retail processes through internet technologies.
Sales Data Utilization: Analyze historical sales for performance management and profit forecasting.
Expansion Decision Support: Use data intelligence for guiding new store locations and recruitment.
Operational Efficiency: Integrate digital tools for better management of the store network.
Data-Driven Store Development: Implement intelligent decision-making for store operations.
How might we better utilize Meiyijia's offline data to enhance decision-making for store expansion and operational improvements, transitioning towards a more data-driven, efficient approach?
Research
SWOT
Interview
Competitive Analysis
Market research
Insight
7
Accompanied by our product manager, we interviewed seven users from Meiyijia, including four executives and three store managers. This was to gain a deeper understanding of the company's current situation and to ascertain their actual needs.
3
Dominant Market Presence with Expansion Potential
Adaptability to Consumer Needs and Trends
Digitalization as a Key Growth Lever
Balancing Expansion with Sustainable Growth
Navigating Intense Competition and Changing Consumer Preferences
Interview
2
direct competitor,
indirect competitor
I analyzed three direct competitors and two indirect competitors to identify their advancements and shortcomings in new retail strategies. Additionally, considering Hotelling's Law and Nash Equilibrium, I investigated the potential inclusion of symbiotic brands in our competitive analysis and conducted relevant research.
Concept
User task 1
Users can select the store location information they wish to view through three methods: 'zooming in', 'clicking', and 'selecting in the filter bar'. Upon entering the location analysis mode, they can view detailed information about the location's Customer Persona, surrounding context, and Competitive Environment.
User flow
Wireframe
Usability test
Improvement 1:
Problem 2:
Improvement 2:
The categorisation of competing shops into A, B and C grades is too simplistic and I don't quite understand the size of the gap between them.
Problem 1:
Directly display the competition index of each shop and categorise them according to different competition index sizes.
In the surrounding context, there is only one scenario for customer flow source, and only the top five surrounding environments are displayed, with unclear reasons for their selection.
The surrounding context displays the top three scenarios contributing to the area, providing users with more reference information. The scene list shows the top ten competing stores, ranked based on a combination of penetration rate, contribution, and customer flow scale.
User task 2
After selecting the desired store location details, such as city, region, or specific address, and the number of stores they intend to open in that area, users can click 'Start Site Selection' for the system to provide information on suitable locations that meet their criteria. Users can then download all the generated location details and their information in PDF format.
User flow
Wireframe
Usability test
Improvement 1:
Problem 2:
Improvement 2:
Each time, new site selection results must be generated. If I find interesting points in one session, I have to regenerate them in the next session to view them again, which is quite inconvenient.
Problem 1:
Introduce a 'Favorite Point' feature, allowing users to save their preferred store points in a dedicated favorites list. This would provide the convenience of easily accessing and reviewing these chosen points at any time.
In scatter mode, there are recommended points everywhere, and I need to click on each one to get their information, which is quite tedious.
Consider utilizing a heatmap or other methods to display the differentiation of each point on the map, making it easier for users to intuitively obtain the information they need directly from the map.
1. “City” Store Map Page
Pre-revision:
Before the improve:
Different colors represent M.Y.J. and competitor stores, with numbers in a ring.
Problem:
1) Faint number colors;
2) Cluttered content; The numerical comparison is not distinct, and customers less concerned about exact figures.
2. “Point” Store Map Page
Clicking on a Meiyijia store in “Point” Store Map Page will allow you to analyze the store information and view the store's persona, surrounding scene, and competitive environment.
Data such as customer flow structure, gender, consumption, etc. are simply displayed, but with the lack of relevant reference data, customers can't understand what's behind the meaning of these data.
Comparing the data on traffic structure, gender, consumption, etc. with the average of the province's data highlights the value of the store's various types of data, allowing customers to understand the store more intuitively.
3. Intelligent Site Selection
Final design
The First feature of the system is the 'Store Map', which displays the number of M.Y.J. stores and their competitors. At the city level, the map is presented in an aggregated mode; at the point level, it switches to a scatter plot mode, showing the exact locations of each store, including competitors
After-revision:
Option 1:
Show the total number of people, the store entry rate and the sales conversion rate in the total number of visitors, and mark where each of these figures ranks in the provincial rankings.
Final choice：
Retain distinct colors, using red to represent M.Y.J. Display the comparison of M.Y.J. and competitor store numbers in percentage form, revealing specific numbers upon selection. Place the total counts in the bottom right corner for a clearer view of regional competition
Option 2:
Show the total number of people and the rate of entry to the store in the total number of visitors, and indicate how many places each of these figures ranks in percentage in the provincial rankings.
For stores that are ranked in the hundreds, the percentage will be more intuitive than just how many places they are ranked, and the customer will be able to get a clearer picture of what level the store is roughly at in the province, without having to do the math in their head. Therefore, the final choice is option two.
After the improve:
In the design process, I incorporated a grid display and used color variations to intuitively represent the recommendation index. This approach allows for a direct and efficient presentation of recommended locations, reducing user interaction complexity and eliminating the need for individual clicks to query.
The initial phase of the M.Y.J. project was executed with exemplary precision, yielding significant client appreciation. Internally, the company heralds this product as the pinnacle achievement of the year. Through the integration of BI and AI, it has adeptly facilitated the retail convenience store industry in transitioning to intelligent operations, enhancing efficiency, and augmenting revenue. Specific metrics remain confidential.
The second key feature of the Intelligent Insight System is 'Intelligent Site Selection'. Customers select an area, input desired spots, and the system provides site selection results, including predicted labels, competition index, and recommendation index.
Innovation Point 1: Site Selection Filter:
Depending on the selected area, the system displays tabs: 'City', 'Region', and 'Points'. Choosing a city shows 'Region' and 'Points'; for specific cities or districts like Dongguan, only 'Points' appears.
Innovation Point 2: Grid Display Design: