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Data-driven retail stores are more in control of their business, manage their inventory better, and are able to provide their customers with superior experiences.
Can you accomplish that without an effective data management infrastructure? With the complex challenges a modern merchandiser has to deal with, such as rising customer expectations and shifting trends in consumer behavior, it is essential to have an AI-powered data management infrastructure in place.
Moreover, with the competition the merchandising business is facing, becoming data-driven might be the only option. Not utilizing data, on the other hand, might result in missed growth opportunities and untapped potential for increasing your revenues.
The Infrastructure Required for Data Collection and Management
Collecting the Data – The Touchpoints
For collecting data that holds insights into your customer’s buying behavior, preferences, and psyche, touchpoints play a key role. They are the points of contact between a business and its customers.
How customers interact with the touchpoints reveals a lot about how they think and feel about the business as a whole or some particular offering. In the case of a store, customers interact both digitally and physically with the business.
Digital and Physical Touchpoints in a Retail Store
Some of the major digital touchpoints include the company’s website, its social media handles, digital channels for handling inquiries and complaints, and points of sales.
The physical touchpoints would include store shelves, product packaging, sales representatives, and checkout counters.
Collecting Data from the Touchpoints
Company Website:
Social Media:
Digital Communication Channels (Inquiries & Complaints):
Store Shelves:
Point of Sale (POS):
AI - the Game Changer in Data Collection
When it comes to the company’s website, AI-powered web analytics can be used for tracking website traffic, user behavior, and demographics, identifying patterns and trends. AI algorithms can interpret data from cookies, enabling personalized user experiences based on past interactions. Lastly, you can employ AI-driven chatbots for real-time customer support and data gathering during interactions.
For social media channels, you can utilize AI to analyze social media metrics, audience demographics, and post-performance data for optimizing marketing strategies. You may also implement AI chatbots on social media for interactive and efficient customer engagement and feedback.
Use AI-powered sentiment analysis to gauge customer satisfaction and identify potential issues from inquiries and complaints to enhance the effectiveness of digital communication channels for handling inquiries and complaints. Moreover, you employ AI algorithms to categorize and prioritize customer inquiries, streamlining customer service processes.
To understand how your customers interact with the products at the store shelves, utilize AI-based computer vision to track customer movements and interactions with products, providing insights into customer behavior.
Utilize AI to analyze transaction data from the POS system, identifying customer purchase patterns and predicting demand.
The Power of Data in Retail - Case in Point
A prominent office supply retailer maintains its competitive edge by effectively handling data collected from Online and offline touchpoints.
Employing a robust big data platform, the organization connects data from its offline catalog, website, customer call centers, ERP systems, and fulfillment systems. This strategic approach enables the company to outperform other office supply retailers by efficiently targeting specific customer segments and allocating internal spending to enhance the productivity of various departments.
Managing the Collected Data
To manage the data effectively, the business needs to set up a database integrated with touchpoints, and a tool to interact with that data, such as a data management dashboard.
1- Setting Up a Database
What are the data requirements?
Identify the types of data your business needs to store, such as product information, customer details, sales transactions, inventory levels, suppliers, etc.
What is the database's purpose?
Is it primarily for inventory management, sales tracking, customer relationship management, or a combination of these and other functions?
How will the data be organized and structured?
Plan the database schema and define the relationships between different entities. For example, how will products be linked to suppliers, sales to customers, etc.?
What database management system (DBMS) will be used?
Choose an appropriate database management system that suits the store's needs. Common options include MySQL, PostgreSQL, Oracle, Microsoft SQL Server, or NoSQL databases like MongoDB.
How will the database handle scalability?
Consider the potential growth of your merchandising business and ensure the database can handle increased data and user demands over time.
Ensuring scalability:
Implement appropriate indexing on columns frequently used for searching and filtering, which improves query performance as data size increases.
While setting up the database, ensure that your infrastructure has room for both vertical and horizontal scaling.
How will data integrity and security be ensured?
Implement measures to maintain data accuracy and consistency, such as data validation and constraints. Additionally, plan for data security, access controls, and encryption to protect sensitive information.
What are the backup and recovery procedures?
Develop a backup strategy to safeguard against data loss and establish recovery procedures in case of system failures or data corruption.
Common backup and recovery procedures:
Implement database replication to have real-time copies on separate servers, providing high availability and faster recovery in case of primary database failure.
Store backup copies in an offsite location or use cloud-based backup services to protect against physical disasters and on-premises issues.
How will the database be accessed and managed?
Determine who will have access to the database and define roles and permissions accordingly. Plan for database administration and maintenance tasks.
How will the database integrate with other systems?
If the store uses other applications or systems (e.g., point-of-sale, e-commerce platform), consider how the database will interact and exchange data with these systems.
What reporting and analytics capabilities are required?
Identify the types of reports and analytics the store needs to generate from the data and ensure the database supports these functionalities.
The Power of Data in Retail - Case in Point
A well-known shoe and accessory company employs big data to tackle the challenges of the busy holiday season. Operating on a service-oriented big data architecture, the brand integrates various data sources related to payment, billing, and fraud detection. This integration project enables the company to provide a smooth e-commerce experience, even during events like Black Friday, when the website traffic is exceptionally high.
2- Building a Data Management Dashboard
The purpose of a dashboard is to present data for the user to extract valuable insights. Hence, to make it easier for stakeholders to interact with data, a data management dashboard must have the following key features;
Customizable Interface:
A customizable interface allows users to tailor the dashboard layout, data visualization, and key performance indicators (KPIs) to their specific needs. Different teams or individuals within your business may have different data requirements, and a customizable interface empowers them to arrange the dashboard in a way that best suits their roles and responsibilities.
Interactive Visualization:
Interactive data visualization brings data to life and makes it easier for users to grasp insights quickly. Charts, graphs, and other visual elements with interactive features allow users to drill down into the data, explore trends, and identify patterns. Interactive visualization enhances data understanding and facilitates better decision-making.
Cross-Device Accessibility:
In a modern business environment, users need access to data on various devices such as desktop computers, laptops, tablets, and smartphones. Cross-device accessibility ensures that stakeholders can view and analyze data regardless of their location or the device they are using. This feature promotes collaboration and responsiveness.
Custom Alerts Functionality:
Custom alerts allow users to set predefined thresholds or triggers for specific metrics or KPIs. When these thresholds are crossed, the dashboard sends notifications to relevant stakeholders, enabling them to take immediate action when critical events or anomalies occur.
User Accessibility Management:
User accessibility management ensures that only authorized personnel have access to sensitive data. The dashboard should have role-based access controls, allowing administrators to assign specific levels of access and permissions to different users. This helps maintain data security and confidentiality.
Using Data in Retail to Excel in Two Key Areas
1. Customer Relationship Management
In merchandising, a business’s relationship with its customers is vital to its success. By leveraging data, a store can foster profitable customer relationships. Data provides valuable customer insights, enabling retailers to create personalized experiences, implement effective loyalty programs, and address customer concerns promptly. By integrating data from various touchpoints, retailers can offer a seamless omnichannel experience.
Moreover, with a vigilant data infrastructure in place, a store can map its customer’s journey to understand their behavior and enhance their service quality based on the insights gathered.
2. Operations and Inventory Management
In Operations & Inventory Management, data-driven demand forecasting and inventory optimization help retailers maintain optimal stock levels, reduce holding costs, and improve supply chain efficiency. Data also aids in optimizing store layout, staffing, and fraud detection, enhancing overall operational efficiency.
Getting Started
To survive and thrive in the retail industry, becoming data-driven is imperative. Using AI, and machine learning, modern data management systems can transform a merchandising store. An effective infrastructure should cover all the major touchpoints and enable the stakeholders to visualize and interact with data easily.
No matter how well you may think your business is doing, by having an AI-powered data management system, it could always do better. To leverage one for your store, get in touch with Cubix – a Big Data development company that excels in creating tailored solutions to help businesses achieve their goals.
