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Retail Analytics Explained

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Published: Reading time: 12 min
o9 Solutions The Digital Brain Platform
o9 SolutionsThe Digital Brain Platform

Retail analytics is the process of gathering and analyzing data from physical, online, and catalog outlets. This gives businesses valuable insights that help them make informed decisions, optimize operations, improve customer experience, and increase profitability.

This analytical approach extends to enhancing decisions related to pricing, inventory, marketing, merchandising, and retail store operations by employing predictive algorithms. 

These algorithms process data from both internal sources, such as customer purchase histories, and external repositories, such as weather forecasts. These enable businesses to uncover patterns, trends, and correlations in vast amounts of data, improving inventory management, pricing strategies, and personalized customer experiences in-store and online.

Retail analytics also serve the purpose of gauging customer loyalty, identifying purchasing patterns, predicting demand, and optimizing store layouts. For instance, retailers can strategically place items on store shelves based on the analysis of products frequently bought together. It also enables the implementation of personalized discounts for frequent shoppers, aiming to increase average basket sizes and encourage more frequent visits.

Key Insights

  • In the realm of retail, data-driven decision-making is replacing traditional intuition-based approaches through the application of retail analytics.
  • This analytical approach assists retailers in determining optimal inventory levels, staffing allocations, and pricing strategies that attract customers while ensuring profitable margins and market share acquisition.
  • Drawing from diverse data sources such as point-of-sale (POS) systems, in-store video feeds, and systems tracking individual customer purchase and service histories gives businesses a comprehensive dataset for informed decision-making.
  • The integration of artificial intelligence (AI) and machine learning in retail analytics tools enhances their capabilities, enabling trend prediction and personalized offers, and serving as the foundation for pricing and inventory allocation decisions. The inclusion of robust visualizations makes these tools more user-friendly compared to conventional spreadsheets or text-based business intelligence reports.

Understanding Retail Analytics

Retail analytics involves the systematic collection, examination, and presentation of data pertaining to a retailer's activities  This analytical approach extends to the scrutiny of customer behavior, monitoring of inventory levels, assessment of marketing campaign efficacy, and more. 

By scrutinizing diverse data sources such as customer purchase histories, call center logs, and point-of-sale (POS) systems, retailers can extract valuable insights into customer habits and preferences. This enables them to adapt their product offerings, pricing structures, return policies, and even change the physical and online layout of their stores accordingly. 

Analytics empower retailers to make informed decisions regarding promotions, marketing strategies, and staffing levels. Ultimately, the integration of data analytics allows retailers to enhance sales, minimize costs, and improve customer satisfaction and loyalty.

Why is Retail Analytics So Important?

Retail analytics takes the guesswork out of many types of decisions. Experienced employees are often a font of wisdom, but as the baby boomer generation ages out of the workforce, less experienced employees will have fewer insights to share. Even the most experienced and savvy retail executives must wade through a plethora of internal and external data points on factors that include labor strikes, merchandise trends, and weather forecasts. Analytics helps retailers synthesize such data and take steps to anticipate future events.

Retail is a highly competitive retail business complicated by the relative novelty of online commerce, and retail profit margins have always been thin, leaving little room for error. Even slight adjustments in product selection and inventory management can greatly reduce stockouts or, at the other end of the same spectrum, the need for steep discounts. 

Those adjustments, in turn, can have an enormous impact on the bottom line and operational efficiency. For example, fashion retailers can use advanced data analytics to decide which styles and sizes to order for different locations and in what quantities, based on demographic and purchasing trends at each location.

Key Advantages of Retail Analytics

Retail analytics enhances revenue generation, minimizes overhead and labor expenses, and optimizes profit margins through::

Mitigating stockouts and curbing the need for discounts: Retail analytics enables users to discern demand trends, ensuring an adequate product supply without excess inventory that might necessitate significant discounts. For instance, by analyzing the popularity of fashion items driven by social influencers, analytics can project how rapidly demand wanes, preventing overstock.

Enhancing personalization: Analytics empowers retailers to comprehend customer preferences, enabling them to outperform competitors in capturing demand. 

Refining pricing decisions: Data analytics synthesizes various factors, such as abandoned shopping carts, competitive pricing data, and the cost of goods sold, to help retailers set optimal prices. This prevents pricing that exceeds market tolerance or falls below what customers are willing to pay, allowing retailers to maximize profits.

Optimizing product allocations: Analytics aids retailers in strategically distributing products across different geographic regions, distribution centers, and stores, thereby reducing unnecessary transportation costs. For instance, a sports apparel retailer can employ analytics to identify temperature-sensitive sales patterns and allocate thermal undershirts accordingly. This minimizes costs by prioritizing distribution to areas expecting colder temperatures.

Categories of Retail Data Analytics

The realm of retail data analytics encompasses four primary categories, each serving a distinct purpose in enhancing business insights and decision-making. These categories are:

Descriptive Analytics: Descriptive analytics serves as the foundational layer for more advanced analytics solutions. It addresses fundamental questions related to "how many, when, where, and what." Business intelligence tools and dashboards utilize descriptive analytics to provide regular reports on sales and inventory levels, offering insights into past performance.

Diagnostic Analytics: Diagnostic analytics aid retail organizations by identifying and dissecting issues that may impede their performance. By amalgamating data from diverse sources such as customer feedback, financial performance, and operational metrics, retailers gain a comprehensive understanding of the root causes behind the challenges they encounter.

Predictive Analytics: Retail predictive analytics enables retailers to anticipate future events by considering various variables such as weather conditions, economic trends, supply chain disruptions, and emerging competitive pressures. Employing a what-if analysis approach, retailers can model potential scenarios, such as predicting the impact of different discount percentages on a product or estimating stock depletion based on various potential actions.

Thanks to predictive tools, businesses can use historical data and trend analysis to determine which products they should order, and in what quantities instead of relying exclusively on data from past orders.

Prescriptive Analytics: Prescriptive analytics represents the convergence of AI and big data to leverage the insights derived from predictive analytics and recommend actionable steps.

At this stage, AI algorithms suggest specific actions based on the anticipated outcomes. For instance, prescriptive analytics can empower customer service agents with real-time suggestions for offers, including upsells based on previous purchase history or cross-sells tailored to address new customer inquiries. This integration of technology streamlines decision-making processes and enhances overall operational efficiency in the retail sector.

How Retail Analytics is Utilized

Retail analytics serves as a comprehensive tool for companies to analyze past operational and financial performance, diagnose issues, propose alternative strategies, forecast demand, and provide real-time suggestions for store associates, customer service agents, and others. The overarching goal is to enhance sales, profits, and customer satisfaction.

  • In-store analytics tools leverage data from POS systems and in-store cameras to analyze customer shopping patterns. This aids retailers in optimizing product placements, maintaining appropriate inventory levels, and minimizing theft.
    Example: Video footage reveals customer interactions with displays and POS data evaluates the impact of merchandising on loyalty card users.
  • Customer analytics relies on data from various customer interaction systems, including POS systems, websites, phone logs, and customer service chats. By scrutinizing this data, retailers gain insights into item popularity, reasons for returns or exchanges, and the effectiveness of promotions or suggestions.
    Example: Customer analytics helps organizations discern effective marketing campaigns for promoting new items over the phone versus in chat conversations.
  • Inventory analytics: assesses inventory levels to optimize warehousing and distribution strategies, determining when to replenish items based on inventory levels and projected demand. Example: This can reduce labor and shipping costs associated with excessive safety stock.
  • Merchandise analytics assists retailers in evaluating the effectiveness of product displays, particularly in physical stores, aiming to entice consumers through compelling assortments or offers. It also aids in adjusting prices to maximize profit margins across products.
    Example: By using merchandise analytics, a clothing retailer might discover that featuring seasonal items near the entrance led to a 20% increase in sales.
  • Web analytics tracks the digital footprint of online consumers, helping online retailers make informed decisions about how and where to display goods on their websites, pricing strategies, and effective marketing promotions.
    Example: Heatmaps can indicate which areas of a web page visitors are clicking on. 
  • Demand forecasting predicts the demand for specific online items based on customer interactions such as viewing, adding to the cart, removing items, or abandoning the cart. Though not counted as sales, these actions provide insights into future demand.
    Example: Leveraging demand forecasting, an online retailer might notice increased customer interest in a specific product category based on website interactions.
  • Sales forecasting enables retailers to predict future sales by analyzing actual sales figures and other factors. When combined with demand forecasting, it helps anticipate total demand across all channels, allowing retailers to manage inventory effectively.
    Example: Using sales forecasting, a brick-and-mortar store might analyze past sales data and market trends to anticipate a general uptick in demand for seasonal items.

Tools for Analyzing Retail Data

Analyzing retail data involves harnessing information gathered from diverse sources, encompassing physical stores and online platforms. The following tools play a crucial role in this process:

  • 1.

    Point-of-sale (POS) systems: These enable retailers to monitor and oversee customer transactions. POS systems offer insights into customer purchases, generating comprehensive reports on sales and prevailing customer trends.
  • 2.

    Customer Relationship Management (CRM) software: This covers a range of functions including sales, marketing, customer service, and e-commerce processes. Retailers leverage CRM software to track customer interactions, maintain individual customer data, and identify potential sales, marketing, and service opportunities based on this accumulated information.
  • 3.

    Business Intelligence (BI) tools: Retailers utilize BI tools to distill information gleaned from extensive and diverse datasets. These tools focus on tracking key performance indicators such as customer loyalty, inventory turnover, sell-through rate, and days on hand. BI tools facilitate the creation of reports, easily shared with executives and decision-makers.
  • 4.

    Inventory Management Systems: This software aids retailers in monitoring stock items, tracking inventory levels in warehouses and distribution centers, and forecasting demand. Additionally, it helps retailers determine optimal storage locations for items, minimize transportation costs, and ensure the availability of goods to meet customer demand.
  • 5.

    Predictive Analytics: This analytics approach leverages data from past transactions, communications, and other actions to predict future trends and behaviors. The four primary types of retail analytics—descriptive, diagnostic, predictive, and prescriptive—are employed to identify growth opportunities and new customer segments.

5 Best Practices for Retail Analytics

1. Emphasize Customer Data Utilization
Organizations should rely on both explicit and implicit customer data to discern trends and gain deeper insights into customer preferences. Leading retail analytics practitioners integrate data from loyalty programs, e-commerce, POS systems, and external sources, creating a comprehensive view. Customer data, spanning demographic, transactional, behavioral, and psychographic dimensions, offers valuable insights. This process often begins with broad demographic information, and retailers differentiate between existing "customers" and potential "consumers," employing consumer data for targeted marketing strategies like lookalike modeling.

2. Harness Visualization Tools
Leverage visualization tools such as charts, graphs, and dashboards to facilitate a better understanding of data and make informed decisions. These solutions, commonly found in Business Intelligence (BI) software, empower business users to access analytics without relying on IT for generating reports, fostering quicker decision-making.

3. Integrate Data from Various Sources
Analyze multiple data sources, including sales data, historical customer data, and inventory data, to gain a nuanced view of retail businesses. Correlate in-store analytics with merchandise attribute analytics, for instance, to optimize physical store layouts. Inventory analytics ensure sufficient stock to support sales strategies and employing a unified platform for retail analytics helps avoid discrepancies arising.

4. Monitor Key Performance Indicators (KPIs)
Regularly track key performance indicators to measure retail performance and identify areas for improvement. Successful retailers often use weekly KPI summaries, employing a balanced scorecard approach. This involves reviewing recent metrics, identifying trends, and conducting in-depth analyses to understand the reasons behind any shifts, such as changes in sales due to stock-outs.

5.  Set Priorities for Measurement
Exercise discretion in choosing what to measure, recognizing that not all available data and analytic tools are equally relevant. Prioritize high-impact opportunities that can bring immediate benefits to the business. Effective analytics address specific business problems and deliver measurable outcomes, guiding decision-makers toward strategic goals, as advised by McKinsey.

The Evolution of Retail Analytics

In the coming years, retail analytics is expected to become more integrated and less talked about. Applications will use analytics seamlessly, often without users realizing it, in a similar way to how smartphones quietly use location tracking to quickly meet user demands.

For business users, the landscape of retail analytics will shift away from the routine generation or review of weekly reports, integrating more seamlessly into their day-to-day workflows. A growing number of individuals will access the benefits of AI in their every day business pursuits, even without explicit recognition.

Frequently Asked Questions About Retail Analytics

1. What are some examples of analytics in the retail industry?

Retailers leverage analytics for diverse purposes such as predicting demand, assisting managers in purchasing and allocating adequate inventory, understanding customer behaviors, optimizing pricing strategies, and informing staffing decisions.

2. What types of data are utilized in retail analytics?

Retail analytics relies on a range of data from both internal and external sources, including customer purchase histories, call center logs, navigation patterns on e-commerce sites, point-of-sale (POS) systems, in-store video footage, and customer demographics.

3. What decisions do retail analytics assist retailers in making?

Retail analytics eliminates the guesswork in retail operations by offering industry executives insights into how much of a specific item to order, where to store it, the optimal pricing strategy, and the patterns of goods that are typically purchased together.

Retail Analytics Best Practices by o9 Solutions

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About the author

o9 Solutions The Digital Brain Platform

o9 Solutions

The Digital Brain Platform

o9 Solutions is a leading AI-powered platform for integrated business planning and decision-making for the enterprise. Whether it is driving demand, aligning demand and supply, or optimizing commercial initiatives, any planning process can be made faster and smarter with o9’s AI-powered digital solutions. o9 brings together technology innovations—such as graph-based enterprise modeling, big data analytics, advanced algorithms for scenario planning, collaborative portals, easy-to-use interfaces and cloud-based delivery—into one platform.


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