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Published On:
September 13, 2023

What is Retail Analytics + a List of Top 9 Analytics Software Solutions

The Retail & Consumer Index
Keeping Retail Leaders Up to Date with Customer Experience Insights
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Direct to Consumer

Gone are the days when data played a minimal role in retail. Nowadays, it is a necessity—arguably a strategic asset—that enables companies to compete successfully in an intensely fierce and fickle retail landscape. The rapid digitization of the industry has unleashed an unprecedented amount of consumer data—presenting both an opportunity and a challenge for businesses. 

Retailers must learn how to harness this deluge of information to extract meaningful and actionable insights or risk losing their competitive edge. Fortunately, retail analytics solutions on the market can help make sense of it all. 

In this article, we’ll cover the various types of advanced analytics solutions, dive into some case studies related to these tools, and provide a comprehensive list of many top options so it’s easy to compare them. Finding the right retail analytics solution can be a big relief for retailers keeping up with ever-changing trends and consumer preferences. By combining all the diverse data streams and turning them into actionable insights, retailers can make data-driven decisions that result in better efficiency, increased customer satisfaction, and greater profitability.

What Are Retail Data Analytics Solutions?

Retail data analytics solutions are powerful tools that help retailers make informed decisions and stay ahead of the competition. These solutions allow retailers to analyze customer behavior, sales patterns, inventory management, and other key metrics. 

With these insights, retailers can quickly identify trends, understand customer preferences, and optimize their operations. By using data analytics software, retailers can make better decisions. They can use the insights gained from analyzing customer behavior to tailor their offerings to meet the needs of their customers. This can lead to increased customer satisfaction and loyalty. 

Another advantage of using data analytics software is that it can enhance the customer experience. Retailers can use the insights gained from analyzing customer behavior to create personalized experiences that meet the needs of their customers. This can lead to increased customer satisfaction and loyalty. 

Finally, data analytics software can help retailers improve their operational efficiency. Retailers can optimize their operations and reduce costs by analyzing sales patterns and inventory management. This can lead to increased profitability and a stronger bottom line. 

Overall, retail data analytics solutions play a critical role in the retail industry. They help retailers make more informed decisions, enhance the customer experience, and increase operational efficiency. By leveraging these solutions, retailers can stay ahead of the competition and succeed in today's challenging retail environment.

Types of Retail Data Analytics Solutions

Retail data analytics companies typically provide four primary types of analytics to their clients. Let’s talk about how they differ from one another. 

Diagnostic Analytics

If we think about medical diagnostics, whereby a doctor runs tests to determine why someone is ailing (i.e., to arrive at a diagnosis), the term diagnostic analytics becomes clearer. This kind of analytics involves the analysis of historical and current data to identify the underlying causes and factors contributing to specific performance or operational issues within the retail environment. In other words, it helps explain a certain outcome so the company can make appropriate decisions as it moves forward.

Predictive Analytics

Just like its name sounds, this type of analytics uses historical data, statistical algorithms, and machine-learning techniques to forecast or predict future consumer behavior, market trends, and business outcomes.

Retailers generally find predictive analytics useful because it gives them a good idea of what might be coming next around the corner. After all, having an idea of what to expect can make it easier to make the right call—whether it’s about inventory, consumer preferences, pricing, marketing, or overall business planning.   

Descriptive Analytics

Unlike predictive analytics (which can literally predict future trends), descriptive analytics examines and interprets historical data to show what has happened in the past. The patterns, trends, and insights it uncovers comprehensively describe past (and present) performance but don’t explain the future.

Retail analytics providers specializing in descriptive analytics often analyze metrics like sales figures, customer demographics, inventory levels, and store traffic. Based on this analysis, their clients make informed decisions about consumer behavior, product popularity, operational efficiencies, and more.

Prescriptive Analytics

Of the four, prescriptive is the most advanced data analytics option for retailers. Not only does it address what happened (descriptive), why it happened (diagnostic), or what might happen (predictive), but it also provides recommendations on the best course of action a retailer should take. 

How does it do all this? Analytics companies keep their processes highly guarded; however, some say it’s machine-learning (ML) based. Algorithmic AI or simulations might also power it. 

Whatever the case, prescriptive analytics provide retailers with actionable recommendations and strategies to achieve specific business objectives, enhance operational efficiency, and ultimately improve overall performance and profitability.

Popular Use Cases for Retail Data Analytics Tools

Many retailers have successfully implemented analytics solutions in recent years. The actionable insights derived from the data can be a game-changer. Below we discuss two of these companies and their experiences in more detail.


Major home improvement retailer Lowe’s has embraced analytics to enhance operational efficiency and customer engagement. The company analyzes purchase history, seasonal trends, and regional preferences using data analytics to optimize its product assortment and store layouts. This data-driven approach not only ensures that customers can find what they need but also helps create a seamless shopping experience. 

Additionally, the retailer employs analytics to improve customer service by identifying areas where staff training is needed and measuring customer satisfaction. 

Best Buy  

Leading electronics retailer Best Buy, on the other hand, uses analytics to drive customer loyalty via personalized marketing and service initiatives. The business tailors its marketing campaigns and recommendations to individual customers by analyzing customer data, purchase histories, and online behavior. In doing so, consumers find Best Buy’s communications and promotions more relevant. 

Like Lowe’s, the company also uses analytics to improve customer service. The data helps predict peak shopping times so the company can ensure enough staffing and reduce wait times.    

Choosing the Right Retail Data Analytics Tools and Solutions

There’s no getting around the fact that big data is here to stay. Any retailer that wants to succeed in such an intense and unpredictable environment must make analyzing data a core part of its business. Thankfully, many software vendors and retail analytics providers are waiting anxiously to offer up their services. 

Key Features to Look for in Retail Analytics Software

Below is a breakdown of the top features retail businesses should look for in a data analytics partner.

Consumer Analytics

Customer analytics involves understanding customer behavior, preferences, and purchasing patterns. By analyzing customer data, retailers can tailor marketing campaigns, personalize shopping experiences, and optimize product offerings. This can help boost customer satisfaction, increase repeat purchases, and attract new customers.

Luxury brand Gucci’s digital strategy is a great example of a retailer understanding its target audience and effectively reaching it with tailored advertising campaigns.

Inventory Management

Efficient inventory management ensures that products are available when customers want to buy them while minimizing carrying costs. Data analytics can help retailers track inventory levels, demand trends, and stockouts. This leads to improved inventory turnover, reduced overstocking or understocking, and better cash flow management.

Merchandising Analysis

It’s also important that a retail company’s data analytics provider offer merchandising analysis that assesses the performance of products, promotions, and pricing strategies. The vendor should analyze customer purchase histories to determine which items are selling well, how effective the retailer’s marketing efforts have been, and how its product placement and assortment are faring. This information can help boost the company’s sales and optimize profit margins.

Sales Forecasting

Accurate sales forecasting is crucial for inventory planning, resource allocation, and budgeting. This type of analytics enables retailers to analyze historical sales data, seasonal trends, and external factors to make more precise predictions. This generally means less stockouts, increased resource utilization, and better revenue projections.

Data Visualization

Data visualization tools transform complex data into easy-to-understand visual representations, such as charts and graphs. Clear visualizations allow retailers to quickly identify patterns, trends, and insights. This aids in sharing insights across teams, leading to better collaboration and more effective strategies.

Predictive Analytics

It’s especially useful when a retailer’s analytics solution provides predictive analytics. As mentioned above, this type of analytics relies on historical data and statistical algorithms to forecast future demand, trends, and outcomes. Being able to predict customer preferences, for example, allows retailers to be proactive versus reactive. 

Real-Time Reporting

A retail analytics service provider should also offer real-time reporting so its client receives instant access to up-to-date information about sales, inventory, and customer behavior. This allows the retailer to respond swiftly to changing market conditions and spot emerging trends. 

Additionally, an analytics partner shouldn’t be shy about providing its clients with key performance indicators (KPIs) that track its progress toward goals and measure its success.

Integration Capabilities

Integration with existing systems (e.g., point of sale, eCommerce platforms) is crucial for seamless data flow and accurate analysis. When data analytics solutions integrate with multiple data sources (for data collection purposes), retailers can gather comprehensive insights and make more informed decisions based on holistic information.


Scalability ensures that the analytics solution can handle growing data volumes and increasing complexity as the business expands. As a retailer's operations grow, the analytics solution should accommodate more data sources, users, and analytical capabilities without sacrificing performance.

That’s definitely a tall order, but many retail analytics software solutions out there meet the criteria and all the responsibilities, from data gathering to demand forecasting and more. Business leaders must be forthright and select the service(s) that will work best for their companies.

Top Retail Analytics Software Solutions

Without further ado, it’s time to list several of the best retail analytics software providers around. We’ll start with our favorite at the top.

1. Acumen by G & Co.

  • Company Overview: Strategy consulting and retail digital transformation agency G & Co. relies and acts on the brand sentiment reports and actionable retail insights of its retail analytics software solution Acumen. 
  • Key Features: Real-time dashboard with live consumer sentiment and market/competitor data; customizable target profiles; brand intelligence reports; custom survey research delivered in two days; data from 9K U.S. and 60K global respondents 
  • Pricing Model: Based on needs. Send an inquiry for more information
  • Case Studies: Saks Fifth Avenue, Levi’s, Nike, Burberry

2. Looker

  • Company Overview: Looker is an enterprise platform for business intelligence, data applications, and embedded analytics sharing real-time insights. It is part of the Google Cloud Platform.
  • Key Features: Reports; scorecards; dashboards; predictive analytics; data filtering; data visualization; data discovery; automodeling
  • Pricing Model: Variable platform and user pricing
  • Case Studies: WPROMOTE, Major League Baseball, Subskribe

3. Sisense

  • Company Overview: Sisense is a business intelligence software company headquartered in NYC. It offers pro-, low-, and no-code capabilities.
  • Key Features: A customizable, AI-driven analytics cloud platform called Fusion; predictive analytics; ML models; data visualization; cloud, on-premises, hybrid, and SaaS options
  • Pricing Model: Variable according to needs
  • Case Studies: Skullcandy, Nasdaq, ZoomInfo, LiveHire, MarcomCentral, VT Freight Express

4. Tableau

  • Company Overview: Tableau is a leading data visualization and analytics platform acquired by Salesforce in 2019.
  • Key Features: Dashboards; inventory management; sales trends; data visualization; mobile capabilities; Salesforce integrated workflow
  • Pricing Model: $70/mo for one individual, $140/mo for two, customizable according to the number of users and hosting
  • Case Studies: Mondelez International, REI, Brown-Forman, McDonald’s

5. Microsoft Power BI

  • Company Overview: Microsoft Power BI is an interactive data visualization software product focusing on business intelligence. It is part of the Microsoft Power Platform.
  • Key Features: Next-generation AI tools; data hub; data visualization; Microsoft 365 (Excel) integration; ML models; real-time analytics
  • Pricing Model: From $4,995 per capacity/mo
  • Case Studies: Swiggy, Fairlife, Anheuser-Busch InBev 

6. TIBCO Spotfire

  • Company Overview: TIBCO Spotfire is an AI-based analytics platform acquired by TIBCO in 2007.
  • Key Features: “Hyperconverged” analytics; Spotfire Mods framework; historical and streaming data; geoanalytics; advanced analytics; data science; on-premise, cloud, or hybrid options
  • Pricing Model: Variable according to needs
  • Case Studies: Panera Bread, Macy’s

7. Qlik

  • Company Overview: Qlik is a business analytics platform founded in 1993 in Lund, Sweden, and is now based in King of Prussia, PA.
  • Key Features: Smart visualizations; dashboards; search and conversational analytics; augmented analytics that uses AI; predictive analytics; machine learning; geoanalytics
  • Pricing Model: $30/mo per user or customizable for a larger organization
  • Case Studies: Lush Cosmetics, The Genuine Parts Company, Design Within Reach, Akindo Sushiro

8. MicroStrategy

  • Company Overview: U.S.-based MicroStrategy provides business intelligence, mobile software, and cloud-based services.
  • Key Features: Cloud-agile analytics; HyperIntelligence patented technology; inventory management; supply chain management; mobile capabilities; no-code UI
  • Pricing Model: Variable according to needs
  • Case Studies: Sainsbury, Schwarz Group, Crate & Barrel, Tata

9. NetSuite

  • Company Overview: NetSuite is an integrated cloud business software suite that includes business accounting, ERP, CRM, and eCommerce software.
  • Key Features: Real-time analytics; role-based dashboards; key performance indicators (KPIs); customizable reports 
  • Pricing Model: Variable according to needs
  • Case Studies: Zenni Optical, e.l.f Cosmetics, Lovesac

Retailers Who Want to Win Use Analytics

The way the world is going—with generative AI growing leaps and bounds every day, for example—retail companies simply can’t afford to put their heads in the sand regarding analytics. However, if they take the initiative and dive into all the data, they’ll likely unearth incredibly valuable information that can change their entire trajectory. If they don’t, they’re just shooting in the dark as the competition passes them by.

Given how time-consuming and complicated it can be to decipher all the data streams available to retailers these days, it’s fortunate that there are numerous retail analytics software providers ready and willing to assist. All a company needs to do is select the one that aligns best with its specific needs and business goals. Retailers who embrace this data-driven revolution position themselves not only as leaders in their field but as visionary pioneers—poised to navigate the ever-changing currents of consumer preferences and seize new opportunities with unparalleled precision.

G & Co. is known for its exceptional expertise in the realm of data and analytics, setting it apart from other agencies and consulting firms in the industry. By leveraging the power of data, we empower enterprise brands with strategic insights that are consumer-centric and future-focused, enabling them to thrive in a constantly evolving market landscape. 

With G & Co. by your side, you can trust in our ability to provide you with reliable and real-time insights to inform your decision-making and keep you ahead of the competition. Contact us today to learn more.

The Retail & Consumer Index
Keeping Retail Leaders Up to Date with Customer Experience Insights
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Direct to Consumer

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