The retail market has been the backbone of the global economy for many decades. With rapid globalization and digitalization, it has become the centerpiece of the world economy in no time.
However, with each passing day, the fundamental process of supplying to meet customer demand has been changing.
Retail economics is becoming complicated as consumer demands are highly dynamic, the market is fluctuating and unstable, and the narrowing profit margins are pushing new constraints and restrictions on the market players. However, retailers can use a Business Intelligence solution to an
analyze sales patterns and consumer behavior and make effective business models and strategies to enhance their business operations and revenue.
This solution is known as Retail Data Analytics.
Well, it is software that enables retailers to avail an in-depth insight into all aspects of their retail business. It offers them thorough analytical data and precise analysis, that can assist them to take strategic and more informed decisions.
It allows them to analyze retail KPIs (key performance indicators) such as the average transaction size, total sales volume, rate of return, and retail conversion rate with a single click.
It allows the managers and analysts to overview the organization’s real-time performance across all the business units and regions.
Retail analytics solutions also help retailers in identifying and analyzing customers’ data and uncover which product is selling well, and which product is performing less, along with their sales figures based on demography.
It also allows them to evaluate shopping patterns and determine consumer trends, formulate effective sales strategies, and offer better consumer loyalty programs.
A retail analytics solution can be integrated with various other software, such as retail management software, retail operations solution, and retail POS systems to fetch the data in real-time.
A retail analytics solution must have the following capabilities:
Retail analytics works in multiple ways involving sales evaluation, customer analysis, assessing historical data, and many more. Here is how Retail Analytics works:
This analysis helps retailers to know about their best-selling products, and provides them with information about the customers who have spent the most, what is their characteristics, profile, and spending pattern.
This crucial information will assist retailers with optimizing their product placement, and marketing initiatives and improve their service delivery to their customers.
Here retailers can use AI/ML, Big Data technologies to capture and analyze customers’ movement and behavioral patterns via IP cameras placed around product displays and shelves. Retail Analytics fetch the data and measure the gender, attention, and mood of the customer.
It breakdown the data into multiple aspects to offer a wide range of insights about product offerings, advertising, and store layout to improve customer loyalty and engagement.
Technology allows you to fetch customer behavior, but you can’t completely ignore customer feedback. Your business must listen to your customers, fetch their feedback and take corrective actions.
Retail Analytics helps you gather this information from multiple sources and then transform it into actionable insights with the help of its effective algorithms.
We have four types of Retail data analytics available, and each one has a different use case and advantages. Let’s understand each one of them in detail.
It acts as a foundation for more complex and refined types of analytics, including the other type of analytics mentioned in this blog. Descriptive analytics addresses the fundamental questions of “what, when, where, and how many. It provides a plethora of power BI dashboards that offer weekly reports on inventory and sales levels.
It enables retail enterprises to determine and analyze underlying issues that may be impacting their productivity and performance. It amalgamates data from various sources, such as financial performance, operational metrics, and customer feedback. It helps retailers to gain a thorough insight into the root causes of issues they are experiencing.
It helps retailers to foresee future events based on numerous variables, such as economic trends, competitive pressures, emerging technology trends, weather conditions, and supply chain interruptions.
This analytics follows what-if analysis, which enables enterprises to understand and predict the action if a certain condition is met or not met.
It uses emerging technologies such as Big Data and AI to perform analysis and predict recommended outcomes. It suggests various offers to the customer care executives, which they can pass on to the customers.
It fetches various insights based on the customer’s purchase history, which enables organizations to offer a personalized experience, improve cross-selling, and enhance overall customer satisfaction.
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A retail analytics solution can single-handedly transform the way your enterprise functions and performs sales. It comes with a plethora of tools, that can help you improve revenue and margins, and reduce operational overhead and labor costs without much effort.
Here are some important benefits you can expect by implementing the Retail Analytics solution for your organization:
Retail analytics solutions can help you comprehend consumer demand patterns so that your organization can have adequate product inventory in hand so that you must not resort to offering steep discounts to sell your excess inventory. It helps you to track the dynamic of demands driven by your marketing campaigns.
Retail Analytics enables retailers and businesses to analyze their customers’ preferences and purchasing patterns, which helps them capture more customers by catering to their demands.
For example, by analyzing a customer’s purchasing history, a fashion retailer can send a personalized alert to its customers who have shown interest in a specific type of clothes.
Retail Analytics allows retailers to set the optimal prices for their products by considering various factors, such as competitive pricing information, cost of goods sold, and abandoned shopping carts. This helps retailers in maximizing their profits by setting the right prices to capture more customers than their competitors.
Retail Analytics enables retailers to efficiently allocate their products in numerous geographic regions, stores, and distribution centers. It not only reduces the needless logistics cost by also decreases the overall operational overheads.
For example, a sports merchandise retailer can utilize analytics to determine the effect of local weather on the sale of thermal undershirts, which allows them to allocate more such items to a store that falls in a geographic area with the specific weather condition.
Retail analytics comprises advanced technologies like Big data and Data mining to examine the huge dataset and fetch meaningful business insights.
It helps businesses to make efficient strategies to improve their workflows, which ultimately optimize their sales performance, improve their revenue generation, and recognize market trends and customer preferences to drive business growth.
Retail analytics solutions aided with AI (artificial intelligence) can simply transform the way businesses interact with their customers. Here are some important Retail Analytics software market size and statistics.
Here are some most valued Use Cases of Retails Analytics implementation :
If you want to optimize the in-store and online experience of your customers, then you must understand how they move through the retail space, offline and online.
Various retail analytics solutions allow you to identify the point of interest, customer traffic density across floorspace and on the website, and their navigation patterns, that too in real-time.
Such Behavioral analytics helps retailers to make data-based and relevant decisions to re-design the in-store and online customer experience.
Instead of depending on the best practices, retailers can make changes in the floor layouts or website layouts to match the customer’s behavioral patterns.
This allows retailers to significantly improve the effectiveness of commercial and web spaces.
Retailers have been using analytics to track their customers’ behavior to offer personalized product recommendations.
Amazon is the biggest beneficiary of this use case, and now modern technologies such as AI/ML, and AR/VR are also providing a level playing field for small and medium-sized enterprises by deploying these analytics to measure their customer’s purchasing behavior.
By evaluating customer purchasing behavior and forecasting future demands, retailers can provide highly personalized product or service recommendations to improve in-store and online shopping experiences.
Personalized recommendations can significantly enhance customer engagement, which ultimately boosts the sales and revenue of a retailer.
It is important to optimize the balance between demand and supply to upkeep your inventory management. Professionals can now leverage massive datasets and retail analytics solutions to make stocking decisions.
For example, Kroger which is one of the largest grocers in the USA has been using an in-house analytics team to examine customer behavior and economic trends to accurately predict demand for years.
Retail Analytics enables retailers to anticipate inventory requirements by using real-time customer flow statistics to anticipate inventory needs. It allows retailers to get the data and insights immediately, without relying on historical data to predict demand.
This model also helps organizations where customer behavior is unprecedented or unpredictable, which we have seen during the COVID pandemic. Inventory optimization enables managers to discover trends and respond to their customers’ needs appropriately.
It has been an important aspect of the business from the beginning and increasing users of Big-data and analytical solutions have allowed retailers to have a better knowledge of it. Now, retailers can utilize multiple inputs to design and drive their pricing tactics.
These involve everything from conventional concepts like competitor pricing and cost of product sold to modern analytics such as real-time customer behavioral patterns and weather forecasts.
This crucial data can help retailers to predict customer price tolerance, and ideal sale duration, and capture various other components of their pricing strategy.
From display optimization to online and offline promotions, merchandising has established itself as an effective process to sell retail products.
We can transform business performance drastically by combining customer behavior analytics and store-level data with AI/ML-driven analysis.
While by using AR/VR in shopping experiences, we can add another tier of actionable data for retailers, which helps them to devise their display strategies quickly.
AR experiences allow retailers to improve their product placements, signage, product displays, and other promotional collateral. This allows retailers to restate their campaigns cost-effectively and quickly before allocating substantial resources.
Retail analytics solution development involves the use of various modules to analyze and extract insights from retail data. Here are some key modules used in retail data analytics solution development:
It stitches together disparate data, store it, and properly handle it, so that organization can use it for further analysis. It improves retailers’ capability to drive their operations effectively and make correct decisions to improve sales and revenue.
This module could be used to collect and analyze retail data, such as inventory, pricing, sales, etc. to determine trends, and customer behavior, forecast sales, predict outcomes, and make well-informed business decisions.
This module allows you to fetch and combine the data from a variety of data sources, then analyze it to gain important insights into the procurement, distribution, and processing of goods. It helps in streamlining the overall supply chain and inventory workflows.
It enables users to collect customer-specific data from different sources and analyze them to attract and maintain their customer base. This module allows businesses to draw crucial insights, develop useful products, deliver improved services, and make profitable and successful business decisions.
This module enables retailers to use various metrics and tools to understand the impact of pricing activities on the business. It helps them in analyzing the profitability of product price points and devise an optimized pricing strategy to generate maximum revenue.
It helps the user to capture sales data and track sales performance. Sales professionals can use this module and associated metrics to set objectives, forecast future sales, enhance internal processes, and predict revenue effectively.
This module helps retailers utilize the data to analyze the success and effectiveness of their marketing initiatives. Marketing analytics enables your business to muster consumer insights, improve your marketing goals, and avail enhanced ROI (return on investment). Marketing analytics can help both customers and marketers alike.
It is also known as Range analysis, which allows retailers to take an overview of all of their segments. It offers crucial insights for your product management, which allows you to compare your products with your competitors and take corrective actions for improvement.
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Off-the-shelf retail analytics software could be implemented immediately or after a brisk customization process. These tools are developed to address a specific set of business functions or purposes.
Usually, the target audience for such software is global and it addresses the needs of a wide range of industries and domains. Tibco Spotfire, Tableau, and Microsoft Power BI are a few examples of off-the-shelf software. This software is well-tested, frequently updated, and well-supported by the Power BI developer’s communities.
However, off-the-shelf software may not meet all your business requirements, as your business may have some unique set of needs, which require a higher level of customization.
To address such concerns, it is important to develop a custom solution, which can cater to the unique requirements of your business.
Selecting a custom retail analytics solution ensures that all your unique needs are matched and it also offers you a massive competitive advantage in the market.
| Solution Type | Pros | Cons |
| Custom Retail Analytics | i) Tailored to match your unique business needs.
ii) Offer unmatched competitive edge iii) Complete control over the solution development process iv) More flexibility v) Faster updates |
i) High initial cost
ii) Lengthy Development process iii) Risks of selecting technological risks iv) Unpredictability v) Lack of community support |
| Off-the-shelf Analytics | i) Ready-made solution
ii) Rapid implementation iii) Low initial cost iv) Availability of a wide number of vendors v) Wider Community and Developer support |
i) Few options for customization
ii) Alteration in the development process to fit the software design iii) No control over the solution development process iv) unwanted hard-coded features v) Hidden fees and expenditures |
There are many platforms available for building retail analytics solutions. Here are some of the top platforms:
It comes with built-in retail analytics samples, that contain reports, datasets, and dashboards that analyze the data of retail sales across multiple geographies and stores.
It offers numerous metrics such as year-on-year sales, gross margin, units, new-store analysis, and variance to keep a check on sales performance.
It is an enterprise analytics service that fast-tracks data insight across big data systems and data warehouses. It makes the best use of SQL technologies used in Apache Spark technologies and enterprise data warehousing for big data processing, and Azure Data Explorer to perform log and time analytics.
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We follow an agile and incremental development approach to design a full-fledged Retail analytics solution. We kick-start the process by building an MVP (minimum viable product) with core functionalities.
We use the following Tools to develop a successful retail analytics solution:
Technologies to be used:
Arka Softwares is a renowned player in the Retail Analytics solution development space. We can enhance your merchandise intelligence by analyzing your customer behavior, tracking your assortment and inventory, and spotting some missed opportunities.
We boost your sales via effective location planning, which can help you to build strategic and efficient sales promotions.
Our seasoned experts can help you assess the market movement and make correct predictions to make well-informed decisions. As a result, your organization will have better control over the stocks and customer behavior.
It will certainly help you avail better profit margins and offer a path-breaking experience to your customers and help them experience a successful journey. Our consultants can also help you with an in-depth analysis of your products and transactions across multiple channels.
Retail data analytics enables organizations to fetch customer recommendations based on their purchase history and interaction, which results in highly personalized shopping experiences and enhanced customer service. These massive datasets also allow organizations to forecast trends and make well-informed strategic decisions based on in-depth market analysis.
BI and Retails Data Analytics can help you in enhancing your sales with the help of:
● Predict your customers purchasing behavior.
● Discover popular and weak products or services.
● Improved segmentation.
● Pricing structure optimization.
● Task automation.
● Spot-slipping customers.
● Track customer engagement trends.
Your organization may face the following challenges while implementing retail data analytics:
● Accurate data collection
● Developing trust among the customers
● Usage of modern technologies
● Complying With Data Protection laws and standards
● Keeping Pace With the market competition
Retail data analytics is focused on offering actionable insights related to inventory, sales, customers, marketing, and various other critical aspects important for any organization while making crucial and strategic decisions.
You can use an OLAP database for Retail data analytics. It is an online database utilized to prepare reports and analyses that are efficient and faster than conventional databases. An OLAP database allows the storage of multidimensional and aggregated data, with multiple data attributes to perform a thorough analysis of information.
Jeeto11
The app quickly earned over 1,000 downloads within two months of launch, and users have responded positively. ARKA Softwares boasted experienced resources who were happy to share their knowledge with the internal team.
Archithrones
While the development is ongoing, the client is pleased with the work thus far, which has met expectations. ARKA Softwares puts the needs of the client first, remaining open to feedback on their work. Their team is adaptable, responsive, and hard-working.
Service Provider
I started my project with Arka Softwares because it is a reputed company. And when I started working with them for my project, I found out that they have everything essential for my work. The app is still under development and but quite confident and it will turn out to be the best.
