Retail analytics software offers flexible, scalable, and advanced solutions to retailers to make informed business decisions. It offers analysis across store operations, customer management, merchandising optimization, loss prevention, and supply chain management. Retail analytics can be applied to the entire retail chain to transform the business with stronger customer relationships, more profitable growth, and unique competitive advantages. Therefore, retail analytics solutions empower retailers to gain deep insights into retail operations, customer behavior, and marketing campaign effectiveness.

Based on their performance in predefined criteria, the vendors are placed into 4 categories for retail analytics software: visionary leaders, innovators, emerging companies, and dynamic differentiators. The top 25 vendors in the retail analytics market have been evaluated. These vendors include 1010data, Inc., Angoss Software Corporation, BRIDGEi2i Analytics Solutions Private Limited, Capillary Techologies, Diaspark Inc., FLIR Systems, Inc., Fujitsu Limited, GainInsights Solutions Pvt. Ltd., Happiest Minds, International Business Machines (IBM) Corporation, Information Builders, IntelliVision, LoyaltyOne, Manthan Software Services Pvt. Ltd., Microsoft Corporation, MicroStrategy Incorporated, Oracle Corporation, QBurst, QlikTech Technologies Inc.. RetailNext, Inc., SAP SE, SAS Institute Inc., Trax, Visual BI Solutions, and Zebra Technologies Corporation.

Vendors who fall into this category receive high scores for most of the evaluation criteria. They have strong and established product portfolios and a very strong market presence. They provide mature and reputable retail analytics solutions and services. They also have strong business strategies. Vendors who fall under this category are International Business Machines (IBM) Corporation, Oracle Corporation, Manthan Software Services Pvt. Ltd., Microsoft Corporation, Fujitsu Ltd., SAP SE, SAS Institute Inc., QlikTech Technologies Inc.,and MicroStrategy Incorporated.

They are established vendors with very strong business strategies. However, they are low in product portfolio. They focus on a specific type of technology related to the product. Vendors who fall under this category are IntelliVision, and Information Builders, and Zebra Technologies Corporation

Innovators in the competitive leadership mapping chart are vendors that have demonstrated substantial product innovations as compared to their competitors. They have very focused product portfolios. However, they do not have very strong growth strategies for their overall business. Vendors who fall under this category are BRIDGEi2i Analytics Solutions Private Limited, 1010data, Inc., Angoss Software Corporation, RetailNext, Inc., GainInsights Solutions Pvt. Ltd., Happiest Minds, FLIR Systems, Inc., and QBurst.

They are vendors with niche product offerings and are starting to gain their position in the market. They do not have very strong business strategies as compared to the other established vendors. They might be new entrants in the market and require some time before gaining a significant traction. Vendors who fall under this category are Trax, LoyaltyOne, Visual BI Solutions, Capillary Techologies, and Diaspark Inc.

Retail analytics includes merchandising analysis, pricing analysis, customer management, performance analysis, yield analysis, inventory analysis, and other applications, such as assortment and order optimization. These applications use retail analytics solutions to analyze data from every aspect of the retail industry and analyze the changing customer preferences. Merchandising analytics software provides analytical insights for huge data volumes of store-based operations.

SPACE PLANNING AND OPTIMIZATION

Space planning and optimization solutions provide specific space planning recommendations for a retail store or a cluster of stores. The space planning and optimization feature in the merchandising analytics solution provides a hard look at future space needs and helps with deciding the right size for the entire retail store network. The adoption of such solutions helps in making precise micro and macro-level decisions, thereby improving the operational efficiency of the enterprise. Forward-thinking retailers are leveraging the huge volumes of data from their overall network to enable effective floor planning and attract a greater number of customers. Predictive analytics and decision optimization technologies drive improved results for all-size retailers at the store level and help enhance sales.

PRODUCT CATEGORY ANALYSIS

The large and SME retailers have recognized probable opportunities in offering goods and services through tightly integrated physical and digital channels. Brick-and-mortar retailers have witnessed intense competition from varied e-commerce websites, which has led to a decline in their growth. In the saturated market, these retailers have shifted their focus from opening new stores to embedding consumer insights into their merchandising processes to enhance their sales and profitability. Adoption of the product category management solution enables retailers to manage each product category and maximize consumer demand.

STOREFRONT LAYOUT ANALYSIS

Storefront layout management is as important as space planning and product category management, to maximize floor space for customer and inventory capacity. Retailers are adopting analytics with customizations regarding the effective management of their floor space, as the smallest amount of unused space could cost the potential revenue of the firm. Thus, it is imperative to strategically design the retail store layout and maximize the overall profit.

PRICING ANALYSIS

Retailers have identified product pricing as one of the most prominent aspects of their growth and have worked accordingly on refining their overall pricing strategy. Despite the fact that predictive pricing models offered by different software vendors can manage their pricing and promotion strategy, enterprises have focused on implementing full-fledged profit optimization tools. Performing smart competitive pricing analysis can provide retailers with quickly accelerated growth.

CAMPAIGN MANAGEMENT

Campaign management solutions primarily focus on increasing customer traffic in the brick-and-mortar stores, which will ultimately lead to increase in in-store revenue and customer satisfaction. The adoption of campaign management solutions increases the effectiveness of the overall brand campaigns and promotion programs. To engage more customers, manage customer loyalty, and withstand the competitive environment, retailers need to adopt cross-channel campaign management solutions, which would enable them to merge customer data from multiple channels and deliver the right message at the right time through the appropriate medium. The historical data also assists in determining how each marketing element contributes toward the organization's overall success, volume impact, and business revenue

LOYALTY MANAGEMENT

Large enterprises have increasingly adopted customer loyalty programs to try and address the challenge of every customer. Retailers adopt strategies to target the mass-market with a general campaign. The majority of the retailers are experts in identifying and knowing their customers to improve their overall effectiveness and enhance their market shares. However, the analytics solutions help them in providing a holistic view of that data. Various parameters, such as loyalty management applications are focused on the analysis of customer-centric data and integrate previously available customer data to segment the customers, based on purchasing history, demographics, and other factors. They help retailers target new customers and enhance customer acquisition abilities. Furthermore, they provide segment-specific data that can help in customer retention by loyalty management and segment-specific offers.

CROSS-SELL/UPSELL AND POINT OF SALE

Cross-selling/upselling and point of sale (PoS) analytics solutions complement and extend the capabilities of marketing and customer analytics solutions. One of the biggest challenges for retailers is to target the right customer with the right offering. For the right positioning of the offering, retailers analyze customer buying patterns, history, and demographics, which is the primary reason for the high growth in this business function. The adoption of these solutions helps retailers manage daily store operations and understand customer requirements.

CUSTOMER MANAGEMENT

The customer management and analytics portfolio enable the retailers to deploy personalized engagement on data-driven insights about customer behavior. The solutions are designed to create ideal responses for every single customer interaction. The key requirement for understanding consumer behavior is getting quantified data on their real-time activities. Vendors provide consumer management analytics solutions that help in consistently tracking the quantity and frequency of product purchases, frequency and duration of customer visits, price sensitivity, mostly preferred areas of purchases, customer response to promotional activities, and other such activities.

CUSTOMER SEGMENTATION, RETENTION, AND ACQUISITION

Customer churn is the reality that each enterprise must deal with. Retail analytics software vendors are involved in developing a proactive approach to reduce customer churn and manage customer retention. However, the extent of retailers’ customer retention tactics often comes down to generic loyalty programs, nurture campaigns, and discounts.

FRAUD AND RISK MANAGEMENT

The nature of fraud and risk management is reactive. Retailers keenly adopt fraud and risk management analytics solutions to increase their detection rate and reduce the overall risk. Fraud and risk management for retailers uses different analytic software solutions provided by different vendors with customized features, such as the core SAP Fraud Management product to cater to retail-specific customizations.

DEMOGRAPHIC ANALYSIS

Retailers, irrespective of the industry they cater to, are exposed to numerous challenges, ranging from a demographic shift in operations to changes in customer behavioral patterns and preferences, from shortages in supply to seasonal changes in demand for products, and so on. The adoption of retail analytics software that deal with huge volumes of historical and real-time data, such as predictive analytics solutions, helps retailers to statistically forecast such dynamic changes in the external and internal environment of the enterprises, thereby enabling them to make the right business decisions.

PERFORMANCE ANALYSIS

Performance analysis is a critical application in any retail analytics software, as it can gauge the performance indices of both high-performing and low-performing brands. Moreover, the software can help measure the capabilities of a workforce and determine how much business they are able to generate for the company. The performance analysis is subdivided into workforce optimization and top-performing categories and product identification.

WORKFORCE OPTIMIZATION

Workforce optimization is one of the novel ways to enhance the overall efficiency of the workplace as well as the workforce. Retail chains could use analytics to monitor CCTV footage of the retail store and thereby deploy the right number of salesforce, where the crowd intensity is maximum. Employee attendance could also be monitored using analytics, where the track of check-in and check-out processes of employees can be integrated within the retail analytics software.

Further, as tracking technology evolves, the analytical capabilities of the software can be extended to measure the effectiveness of the workforce. The behavior of the workforce and the sales conversion rates can be closely monitored using the retail analytics software.

TOP-PERFORMING CATEGORIES AND PRODUCT IDENTIFICATION

Analytics can be used to recognize the top-performing categories and products that would be helpful to increase the store traffic. In e-commerce paradigms, the top-performing categories could be displayed on the website’s landing page, which will be instrumental in attracting customers. Similarly, leveraging the power of analytics and tapping on customers’ past shopping behavior, similar top performing products could be directed to them, ensuring a net increase in the basket size of the customer.

Moreover, after identifying top-performing categories and products, the stores could decide on pricing them differently. Demand forecasting and inventory management of such top-performing brands could also be capitalized using advanced analytical techniques.

YIELD ANALYSIS

Demand analysis is one of the key application areas for retailers. Retailers want to estimate the consumer demand for a particular product or service. Demand analysis is one of the most important considerations for a variety of key business decisions, such as sales forecasting, marketing and advertising spending, expansion planning, and numerous other manufacturing decisions. Involving predictive as well as prescriptive analytics could help to forecast the demand properly. Involving prescriptive analytics could also be instrumental in identifying business areas, which are high in demand and need special attention. Marketing and advertising initiatives taken by a company are directly related to demand analysis.

BASELINE FORECASTING

Baseline forecasting is an important application to estimate the future demand using past or historical data, and tools, such as machine learning and AI are used to predict a demand accurately. Baseline forecasting helps retailers to meet with their inventory objectives.

SALES FORECASTING

Applications for sales forecasts play a vital role in any retail organization, as they help retailers to minimize product stock-out and hence optimize inventories. These applications consider the historical data and other dynamics, such as season, promotional influence, and price changes for forecasting sales.

MARKET BASKET ANALYSIS

Market basket analysis or affinity analysis is a technique where retailers try to identify the products that are likely to be bought together. For instance, a shopper buying jeans would also be interested in buying a matching t-shirt. With the introduction of electronic POS systems, rich amount of data is generated, which categorically reveals a lot about a customer’s shopping behavior as well as the ongoing market trends. Recommendation engines with their ability to predict consumer behavior based on past purchases, online searches, and past shopping patterns can be used to significantly increase sales conversion by the retailers. Market basket analysis is an inexpensive way of cross-selling and upselling products and improving customer experience.