Retail analytics empowers businesses to make robust sales, marketing, planning and pricing decisions to gain a competitive advantage. Business intelligence tools give retailers a bird’s eye view of business performance, sales and marketing efforts to boost revenue and drive profits.

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What This Guide Covers

• What Is Retail Analytics?
• Analytics Types
• Examples
• Benefits
• Important Metrics

What Is Retail Analytics?

Retail analytics is a process that accumulates and analyzes information on products, sales, inventory, pricing and more to discover trends and patterns, predict results and make informed decisions.

Why Is It Important to Retailers?

Retail business analytics gives retailers a glimpse into the performance of their stores, products, vendors, customers and suppliers.

Companies leverage analytics to create robust yet diverse customer segments based on age, demographics, preferences, buying patterns and more. Segmentation helps businesses target specific regions to promote and sell their products and services while delivering an improved customer experience.

Analytics Types

Let’s look at the types of retail analytics:

Descriptive Analytics

This analytics method organizes data to convey a story about business performance. It combines data from multiple sources, including POS terminals, ERPs, inventory management systems and more, for a valuable look at past and present performance.

It gives retailers a performance summary of business processes and actions, including transaction history, inventory needs and promotional successes. Users also leverage descriptive analytics to gauge direct mail campaigns to determine response and conversion rates, cost per acquisition and customer lifetime value.

With the advent of big data analytics, retailers use tools like Google Analytics to track websites and determine traffic sources, page visits, time spent on specific pages, links opened and more.

In essence, descriptive analytics defines what is happening within a business.

Diagnostic Analytics

The diagnostic approach empowers retailers to leverage data and answer the “why” of specific business problems or opportunities.

It uses statistical analysis, algorithms, regression analysis and machine learning to delve deeper into datasets to find trends, patterns and correlations between variables. Diagnostic analytics finds outliers and anomalies while flagging potential issues as they occur.

Historically, statisticians sifted through data manually, applied statistical models, scouted for patterns and found causal relationships between data points. At present, with millions of data points generated every second, humans can’t conduct diagnostic analytics without machine learning and AI.

Predictive Analytics

Predictive analytics uses findings from descriptive and diagnostic analytics to predict future outcomes. Accurately predicting what happens next requires understanding what’s already happened and what caused it.

It detects clusters, segments and exceptions within datasets and uses advanced algorithms and statistical techniques to predict trends. In the past, retailers used Excel to compile data, apply generic statistical models, and forecast trends and patterns.

However, with the ever-increasing complexity of retail business, it isn’t possible to generate accurate forecasts manually. In response, predictive analytics uses advanced statistics, mathematics, machine learning and intelligent automation to consider correlations between factors like price, demand, products and inventory for robust decision-making.

Prescriptive Analytics

Prescriptive analytics tell retailers what to do next to get desired results. This analytics strategy uses what will happen next and provides the necessary actions to get there. Retailers can make incremental adjustments concerning changes in consumer sentiments, demand, supply stocks, inventory and more.

For example, retailers use analytics to anticipate opportunities and recommend precise timing for promotions and services to boost foot traffic, basket size and other KPIs.

There are multiple approaches a business can take to generate expected outputs:

• To see which conditions generate the highest profit, run simulations or what-if scenarios on finite conditions, including assortments, pricing, allocation, inventory, and more.
• Use retail-specific algorithmic AI to recommend the best possible profit and ROI outcomes.
• Train the machine learning algorithm to identify clusters and patterns that produce the best results.

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Examples

Find New Store Locations

Retail business analytics helps users find optimal locations for new stores. Manually studying land costs, predicting local demand and analyzing neighborhood demographics is challenging and time-consuming.

Starbucks leverages geospatial analytics to find lucrative locations. It analyzes a wide range of factors such as traffic patterns, average consumer income, population density, proximity to public transportation and other nearby businesses to determine profitable locations.

Personalize Marketing

Analyzing consumer behavior reveals their likes, dislikes, preferences, choices and purchasing power. Retailers use consumer data to target specific niches, provide a seamless consumer experience and boost profits.

With the right automation technology, marketers can identify specific channels to frequently interact with consumers and provide revenue-driving, personalized shopping experiences.

Businesses that dedicate time and resources implementing personalized marketing strategies get ahead of the competition by achieving brand loyalty and boosting customer satisfaction.

Manage Shipping and Logistics

Shipping and transportation costs often account for a significant portion of a product’s final price. Retail business analytics makes it possible to determine optimal shipping frequencies and quantities that meet demand while minimizing costs.

Route planning capabilities determine the fastest route considering traffic congestion, weather, distance and drop-off points. Moreover, intelligent fuel consumption, vehicle condition, tire pressure and driving style monitoring can reduce costs.

Identify Cross and Up-selling Opportunities

Detailed analysis of customer journeys can help retailers identify cross-selling and up-selling opportunities. Leverage predictive analytics capabilities to sell the right products to customers at the right time. It forecasts which customers are likely to buy certain products based on needs and preferences.

For example, based on previous purchase history, Amazon suggests book recommendations to someone who is fond of self-help literature.

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Primary Benefits

Optimize Operations

Retail business analytics offer profound insights into consumer behavior. Tracing consumer shopping patterns and dwelling times can optimize retail operations for physical and online stores. Based on consumer behavior analysis, managers can:

• Ensure optimal product placement for maximum attention.
• Improve delivery service quality to boost customer satisfaction.
• Establish eye-catching store layouts to entice customers.

Boost ROI

Measure and enhance ROI across different aspects of business management. Store managers can amend marketing campaigns to keep up with changes in purchase patterns.

Analytics lets them focus on campaign-specific strategies to streamline marketing initiatives that attract customers. This process includes assessing customer loyalty, program viability, seasonal promotions, discounts, one-time offers and more.

Enhance Customer Loyalty

Fortify customer relationships with the brand. Retailers can pass on the right information to customers to ensure a seamless buying experience.

Customizing marketing content while tracking purchase history and preferences lets them showcase relevant products to target audiences. Making customers feel recognized and valued augments brand resonance and affinity.

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Important Metrics

In-store Analytics

In-store analytics refers to the systems and processes retailers use to measure what’s happening inside the store. Common metrics used for this analytics strategy include dwell time, foot traffic and conversion rates.

Inventory Analytics

This process analyzes inventory levels to determine the exact amount of stock needed to fulfill consumer demand while mitigating excess inventory risks. Metrics associated with inventory management include stock out rate, inventory turnover rate, GMROI (gross margin return on investment) and sell-through rate.

Web Analytics

Measuring and analyzing website traffic is vital to successful business. Users who run online stores should monitor website traffic to assess visitor numbers, demographics, preferences, source channels and more. Web analytics analyzes how traffic changes after launching an innovative advertising campaign.

Some key metrics include page views, conversion rate, bounce rate, time on page, average page load time, average session duration and more.

Customer Analytics

Evaluate customer behavior to identify, attract and retain profitable customers. The goal is to create a unified view of customers to promote relevant products and services.

It constitutes metrics like customer retention cost, churn rate, satisfaction score, lifetime value and more.

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Conclusion

Retailers are investing an incredible amount of time and resources in retail analytics to eliminate guesswork. Measuring consumer behavior and sales performance is getting easier with the advent of retail analytics solutions.

Is there anything else you would add to this retail analytics guide? Let us know in the comments below!