How to Manage Analytics at Retail Stores?

Store owners and managers know that the key to running successful business operations is to manage analytics at retail stores proactively.  It is simplified to manage analytics at retail stores with the emergence of big data and advanced analytics capabilities. 

The global retail analytics market will expand to $25 billion by 2029 with a compound annual growth rate (CAGR) of 24.0% during the forecast period, according to a report. This data highlights how effective retail analytics is. Many businesses are finding it difficult to adapt to the growth of eCommerce and changes in customer behavior. Retail companies are nowadays constantly striving for more efficient ways to do that. The simplest way to do that is—retail analytics!

These retail decisions are based on hard data, not on speculations, which helps businesses make smarter business decisions that should result in better customer satisfaction and ROI. However, how exactly can retail analytics help? To get this answer, keep reading this blog!

What is Retail Analytics Management?

Retail analytics is the process of using retail operation management software to collect, analyze, and extract insights from retail data to make informed business decisions. With retail analytics, retailers can optimize both the front end and back end of their businesses. 

Retail analytics is also a very useful tool for measuring loyalty, predicting demand, identifying shopping patterns, and optimizing store layouts. This helps a retailer make personalized suggestions to its customers or offer discounts that might encourage customers to repurchase from the same store. 

Retail analytics also provides insights into customer behavior and shopping trends that will become very useful in making informed and improved decisions regarding the following different aspects. 

• Pricing
• Inventory
• Marketing
• Merchandising
• Store operations

These can be done by applying predictive algorithms against data from both internal sources (such as buyers purchasing records) and external repositories (such as weather predictions). Moreover, retail analytics isn’t only about surface-level data points; it’s all about data mining, deep data, and multiple data sources. 

Benefits of Retail Analytics 

Here are some key benefits of retail analytics management in the retail industry. 

• A better understanding of customers’ shopping patterns by analyzing their behavior and preferences.
• More personalized marketing and improved shopping experiences.
• Better inventory management through demand forecasting and trend analysis.
• Reduced out-of-stock issues and improved supply chain efficiency.
• Optimized pricing and promotions using data modeling and testing.
• Improved profitability by aligning prices with consumer demand.
• Performance tracking across different sales channels (stores, online, mobile) is essential for a comprehensive view of your business’s success. To maximize your results, explore our insights on Mobile Integration for seamless and synchronized operations.
• Better decision-making for store locations and marketing strategies.
• Reduced theft and loss by identifying and addressing problem areas.
• Overall improved efficiency and profitability in retail operations.

Retailers can better understand customers, streamline operations, and strategically position themselves to thrive in a complex retail marketplace with these insights.

Types of Retail Data Analytics Management

There are primarily four types of retail data analytics: descriptive, diagnostic, predictive, and prescriptive. Let’s take a closer look at them.

1. Descriptive Analytics

Descriptive analytics is a type of analytics that tells you “what happened in the past.” It is the foundation for more complex analytics types that tell you exactly what is going on in your business in real time. Descriptive analytics is widely used on sales and inventory levels, and data is collected from inventory systems, point-of-sale (POS) systems, enterprise resource planning (ERP), etc. The results are usually presented in dashboards, reports, bar charts, and other visualizations that can be easily understandable. 

2. Diagnostic Analytics

Diagnostic analytics helps retail organizations understand why something happened in the past. They analyze and identify the root causes that may be hindering organizations’ performance. Data analysts use data from multiple sources, including customer feedback, operational metrics, and financial performance, to identify patterns, trends, and connections that explain why things are happening the way they are. They are an excellent way to diagnose problems by combining statistical analysis with algorithms and machine learning to identify areas for improvement.

3. Predictive Analytics

Predictive analytics is a form of business analytics used by retailers to predict future outcomes based on several variables. Analysts used machine learning, artificial intelligence, and intelligent communication to analyze weather, economic trends, supply chain disruptions, and new competitive pressures to find out “what’s next?” for instance. 

Retailers use this approach to map out what the possible results are if we offer a 15% discount on an item rather than 20%. So, basically, predictive analytics is all about “forecasting,” which uses descriptive and diagnostic data to make exact future performance predictions.

4. Prescriptive Analytics

Prescriptive analytics is the most advanced analytic method, which suggests to us, “How can we make it happen?”

Prescriptive analysts use advanced processes like stimulations, AI algorithms, and tools to analyze data and content to predict possible outcomes and recommended actions. For instance, retailers can use prescriptive analytics to predict demand for products based on purchasing history and seasonal trends.

What are Retail Analytics Management Tools?

Retail analytics uses several tools to capture data from various sources, including physical stores and online platforms. Let’s examine them. 

1. Point-of-Sale (POS) Systems

Retailers use point-of-sale (POS) systems to monitor and manage customer transactions. These are the systems that provide insights about what consumers purchase, generating detailed reports on sales and current consumer trends. 

2. Customer Relationship Management (CRM) Software

CRM is an application that is used to manage sales, marketing, customer support services, and eCommerce processes. Retail analytics use CRM software to monitor customer interactions, identify possible future sales and information about specific customers, and take advantage of marketing and customer service opportunities.

3. Business Intelligence Tools (BI)

Retailers use business intelligence (BI) tools to extract information from extensive and diverse databases. These BI tools focus on tracking key performance factors like customer loyalty, inventory turnover, sell-through rate, and days on hand. Moreover, retailers can create reports using BI tools, which can then be easily shared with executives and decision-makers.

4. Inventory Management Systems (IMS)

Inventory management systems help retailers track, organize, and manage their goods across the supply chain. All retailers need an IMS system to keep track of their products and ensure they have enough supply to meet order demand, regardless of business size.

Best Practices For Retail Analytics Management

Here are the 6 best practices of retail analytics management.

• Customer Data Utilization

Blend and integrate the data from loyalty programs, eCommerce, POS systems, and external sources. It is best to categorize data into demographic, transactional, behavioral, and psychographic points and segment it for better analysis. Distinguish between existing customers and potential consumers and use lookalike modeling to target prospects similar to high-value customers.

• Visualization Tools

Employ charts, graphs, and dashboards to better understand data. Data visualization is a powerful tool for simplifying what the numbers or data are trying to show or depict. Provide direct access to analytics to the business users so there is reduced reliance on IT.

• Multi-Source Data Analysis

Integrate sales, customer history, and inventory data for a comprehensive view. Correlate in-store analytics with merchandise attributes to optimize store layout. Use inventory analytics to support merchandising strategies. Also, consider using a single platform to avoid data definition discrepancies.

• Key Performance Indicators (KPI) Tracking

KPI tracking analyzes both what happened and why it happened. So, implement weekly KPI summaries (balanced scorecard). Compare current metrics to the previous week’s performance. 

• Goal Prioritization

Focus on high-priority opportunities with immediate business impact. Be selective in what to measure to avoid overwhelming decision-makers. Align analytics with specific business problems and measurable outcomes.

• Integrated Approach

Start with a main goal and 2-3 underlying objectives. Use “leading” KPIs to track progress towards these objectives, like increasing customer lifetime value, improving consumer conversion, and optimizing inventory levels. Utilize visualization tools to review progress and drive corrective actions.

Frequently Asked Questions (FAQs)

• How is business analytics used in the retail industry?

Retailers used business analytics tools to make a data-driven approach to managing stock levels. With these analytics, retailers can optimize inventory management, marketing efforts, product allocations, and pricing. Retail analytics use predictive analytics to analyze customers’ past behavior and purchasing trends.

• How to increase KPIs in retail?

Here are a few tips to help you increase your KPIs in retail. 

• Set goals and establish KPIs.
• Share information with your sales associates.
• Standardize task execution.
• Create a sense of challenge.
• Foster a culture of learning.
• Establish a continuous feedback loop.
• Celebrate performance and achievement. 

• What is a retail dashboard?

A retail dashboard is a performance solution that visualizes and generates reports on all major retail KPIs in a centralized interface. It will help retailers analyze the performance of their existing operations and make an informed decision. A retail dashboard is of the of the primary 3 types: strategic, operational, or analytical.

• What is big data analytics in retail?

Big data analytics are widely used in retail to analyze large data sets to find current patterns and trends. They enable companies to create customer recommendations based on their purchasing history, resulting in personalized shopping experiences and better customer service.

• What are the 5 Vs of big data in retail?

Big data refers to a collection of data from various sources, usually characterized by the following 5 vs, such:

• Volume (the amount of data)
• Variety (the types of data such as structured, semi-structured, and unstructured)
• Velocity (the speed at which big data is generated)
• Veracity (the degree to which big data can be trusted)
• Value (the business value of the data collected)