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Retail Data Analytics

A Full Overview

ScienceSoft applies 35 years of experience in data analytics and 21 years in retail IT to help retail businesses build smart data analytics solutions that increase sales and drive significant cost-saving opportunities.

Retail Data Analytics Software - ScienceSoft
Retail Data Analytics Software - ScienceSoft

Retail Data Analytics: The Essence

Retail analytics is needed to collect, process, and analyze valuable data from every facet of a retail business. Retail analytics solutions include customer, sales, and supply chain analytics; inventory and price optimization; merchandising and marketing analytics for brick-and-mortar and ecommerce stores. Advanced analytics solutions powered by AI and ML provide intelligent insights for real-time decision-making (e.g., personalized product recommendations for online customers) as well as for long-term planning and forecasting that help drive stable business growth.

  • Integrations: CRM, customer service management software, inventory management software, marketing campaign management software, supplier management software, POS software, an ecommerce platform.
  • Users: managers, and specialists in sales, marketing, customer support, inventory, ecommerce, and merchandising, financial analysts.
  • Implementation costs: $80,000–$1,000,000, depending on a solution’s complexity. You are welcome to use our online calculator to get a tailored ballpark estimate for your project.
  • Implementation results: up to 60% increase in the operating margin.

Retail Analytics Solution: Key Features

At ScienceSoft, we develop retail business analytics solutions that match the unique needs of each of our customers. Below, our consultants list the features that are most frequently demanded by our clients in retail industry.

Retail data processing and storage

  • Automated ingestion of structured and unstructured retail data (e.g., customer data, transaction history, sales by product) from internal and external data sources.
  • Cost-effective storing of all data types in a data lake.
  • Batch and real-time retail data processing.
  • Automated data cleansing: adding missing data to customer profiles, removing redundant product details.
  • Aggregating structured data in a data warehouse that is optimized for analytics querying and reporting.
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Retail data analysis and reporting

  • Online Analytical Processing (OLAP) for slicing and dicing data across multiple dimensions (e.g., sales per employee, product bundle, store, brand).
  • Calculating the required retail KPIs: e.g., conversion rate, average order value, foot traffic, inventory turnover/shrinkage, sell-through percentage.
  • Identification of dependencies between multiple variables (demand, sales volume, season, etc.), performing root cause analysis, spotting anomalies, and highlighting potential issues.
  • Interactive dashboards for visual data representation with drag-and-drop functionality, slicers, filters, and more.
  • Scheduled and on-demand reporting.
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  • Automated customer segmentation based on demographic, geographic, behavioral, and other parameters for efficient targeting in loyalty programs, marketing campaigns, and other customer-centric activities.
  • Analysis of CSAT, lifetime value, loyalty, average basket composition, number and frequency of purchases by customer and customer segment, etc.
  • Calculating customer profitability per product, including total profit per customer, total revenue, costs incurred, etc.
  • Retention analytics to identify the expediency of customer retention costs and activities.
  • Analyzing customer movement in brick-and-mortar stores with the help of a computer vision system.
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Supply chain & inventory analytics

  • Supplier KPIs: returns by period, order fulfillment rate, promise day adherence, total transportation cost, share in product category, etc.
  • Analytics-based selection of the best-fitting product suppliers.
  • ML-powered supplier risk forecasting, e.g., prediction of how supplier change can affect product profitability.
  • Monitoring inventory by category (on hand, low stock, aged, perishable, etc.) across all supply channels and stores.
  • Identifying inventory demand patterns per sales channel, physical location, customer segment, etc. for proper inventory allocation and prediction of out-of-stock and overstock cases.
  • Multi-echelon inventory optimization.
  • Modeling different supply chain scenarios to mitigate supply chain disruptions.
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Pricing analytics

  • Automated identification of perceived value to understand the maximum price that customers are ready to pay for the product.
  • Automated calculation of price elasticity in relation to season, customer segment, store location, etc.
  • AI-powered monitoring and analysis of competitor prices and pricing techniques to adjust own tactics.
  • Price optimization to identify the most profitable base, markdown prices per product and brand.
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  • Identifying top selling products and product categories.
  • Tracking sales KPIs like sales growth, average conversion time, sales per rep, lead-to-sale percentage, and more.
  • Analyzing sales performance at fine granularity, e.g., sales per period, payment mode, store, product purchase frequency.
  • Sales forecasting by brand, SKU, product category, season, etc.
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Marketing analytics

  • Promotion analytics to identify the best tactics per customer segment, see how the promoted products affect the basket cost, whether a campaign attracted new customers, and more.
  • Optimizing and planning markdown strategies per brand, product, category.
  • Calculating marketing campaign KPIs: click-through rate, conversion rate, return on ad spend, etc.
  • Evaluating the success of marketing channels based on KPIs and prioritizing the best-performing ones.
  • Automatically calculating product affinity to upsell or cross-sell and prevent product cannibalization.
  • Analyzing and optimizing loyalty programs based on customer satisfaction and retention rates.
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Assortment & merchandizing analytics

  • Identifying optimal patterns for product display in online and brick-and-mortar stores and optimizing shelf space.
  • Calculating assortment performance metrics: product/brand sales per store, margin per unit, product penetration rate across customer segments, etc.
  • Identifying the potential of different products to attract traffic in order to shape the product range.
  • ML-based planning of assortment based on its predicted performance.
  • Identifying products/SKUs/brands to be discontinued/promoted based on the calculated penetration rate, frequency of purchase, customer sentiment, etc.
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Check the Insights You Can Get with Retail Analytics

Make informed decisions on sales optimization with granular sales performance reports, accurate forecasts, and drill-down capabilities

Understand your customers and their journeys to build data-driven marketing, sales, and customer service strategies that resonate with your audience

Identify and target your most profitable customers with customer lifetime value analysis

Continuously track all the essential KPIs to efficiently manage your relationship with suppliers, identify the most reliable ones, and timely spot potential risks

Get reliable insights into your procurement management processes to identify areas of reducing costs and increasing savings

Get a 360-degree view of your order management processes from placement to delivery

Get data-driven insights into your inventory management activities to achieve optimal supply-demand balance

Build a Retail Data Analytics Solution with ScienceSoft

ScienceSoft’s team is a ready to provide comprehensive data analytics consulting and development services to help you implement a data-driven retail strategy.

Essential Integrations for a Retail Analytics Solution

ScienceSoft recommends establishing the following integrations in order to get a 360-degree customer view and comprehensive insight into retail business performance:

Integrations for Retail Analytics Software - ScienceSoft

  • To enable customer segmentation.
  • To calculate customer-related metrics: engagement, satisfaction, profitability rates.
  • To analyze service requests by issue type, location, customer demographics, etc.
  • To identify flaws in customer service, inventory management, delivery, and other processes affecting customer satisfaction and get suggestions on the optimal ways to fix them.

Point of sale (POS) system

  • To identify customer spending habits like the buying time or preferred payment method.
  • To analyze sales performance per store/season/geographical location, etc.
  • To analyze customer online behavior and optimize marketing, cross-selling, and other activities based on the acquired insights.
  • To offer personalized suggestions to each customer based on their preferences.
  • To minimize inventory-related costs and optimize stock levels.
  • To analyze supplier performance and choose the most efficient and reliable suppliers, determine the top-performing products by supplier.
  • To provide a consolidated view of the retail business performance, including sales, inventory, marketing, etc.
  • To assess marketing campaign performance and get intelligent insights for marketing optimization (identifying the most profitable markdown strategies, forecasting the efficiency of targeted promotional activities, etc.).
  • To suggest personalized marketing campaigns at different levels of granularity (by a customer’s product preferences, type of device, income level, location, etc.).

An in-store computer vision system

  • To quickly identify and react to situations like empty shelves or long checkout queues.
  • To analyze customer routes and actions and optimize merchandising and store navigation.
  • To detect possible security gaps (e.g., fraud at self-checkout stations) and timely take preventive measures.

Factors That Drive High ROI of Retail Data Analytics

With 35 years of experience in data analytics and 21 years in implementing retail IT solutions, ScienceSoft has defined the key factors determining the success of retail analytics software.


To make sure the solution will be able to handle the growing volumes of data and new data sources as your business expands.

Focus on security

To protect sensitive customer, business, and financial data against cyberattacks or unauthorized modification by implementing robust security mechanisms for data backup, encryption, authentication, sharing, etc.

AI-based analytics

To enable advanced analytics capabilities, such as intelligent demand forecasting, real-time personalized suggestions for online customers, dynamic pricing optimization, etc.

Retail Data Analytics for a Multibusiness Corporation: a Success Story by ScienceSoft

Design and Implementation of a Comprehensive Data Analytics Solution

  • Storage of structured and unstructured data from 15+ data sources.
  • ~100 ETL processes that support efficient data quality management.
  • 5 OLAP cubes and about 60 dimensions for data analytics.
  • 360-degree customer view across all channels and business directions.
  • Retail analytics of both brick-and-mortar and ecommerce stores.

Costs and Benefits of Implementing Retail Analytics

The cost of implementing a retail data analytics solution may vary from $80,000 to $1,000,000*, depending on the software complexity, the diversity of integrations, and the number of user roles.



A basic solution that encompasses 1–2 business areas (e.g., sales and finance) and enables scheduled processing of structured data like sales results and customer demographics; ensures rule-based analytics, e.g., threshold-dependent product reordering.


A solution of medium complexity that encompasses multiple business areas (e.g., customers, SCM, finance) and enables both scheduled and real-time data processing (including unstructured data like customer service transcripts and social media content); ensures rule-based and ML/AI-powered analytics.


A solution of medium complexity that encompasses multiple business areas (e.g., customers, SCM, finance) and enables both scheduled and real-time data processing (including unstructured data like customer service transcripts and social media content); ensures rule-based and ML/AI-powered analytics.

*Software license fees are not included.

The implementation of business analytics in retail brings:

  • Up to 60% increase

    in the operating margin due to the implementation of big data analytics

  • 10% decrease

    in operating costs thanks to AI/ML-based retail optimization

  • 23x higher chances

    of acquiring new customers compared to the competitors making non-data-driven decisions

  • 85% higher sales growth

    than competitors, thanks to sourcing consumer behavior insights from data analytics

  • 1–2% gross margin growth

    due to analytics-based assortment optimization

Tools We Use to Facilitate ML-Powered Retail Data Analytics

Although ML is not an obligatory element of the solutions we build, it has been in demand recently. So, we’ve decided to share an overview of two tools that can help enable such functionality quickly and cost-effectively. Both tools provide a wide range of features to build, train, and deploy ML models with a high degree of automation and no-code capabilities.

AWS SageMaker


  • 150+ pre-trained ML models.
  • Complete security of ML artifacts due to data encryption in transit and at rest and the user’s full ownership over the ML-produced content.
  • Native integration with Amazon services: Amazon S3, Amazon Redshift, AWS Glue, Amazon EMR, or AWS Lake Formation.
  • Support for real-time, batch, and asynchronous deployment of ML models.


Although SageMaker provides zero-code capabilities for building and deploying ML models, it can still be too complex for data analysts with a limited programming background.


Free tier usage: available for the first 6 months.

On-demand pricing: $0.05–$28.152/hour.

Costs optimization: savings plans can reduce costs by up to 64%.

Azure Machine Learning


  • Supports training and deploying ML models both on-premises and in the cloud.
  • Adoptable scaling of ML learning models with Azure compute for accelerated training.
  • Registries for sharing and finding ML artifacts across different teams.
  • Native integration with other Microsoft services: Azure Synapse, Azure Storage Blobs, Azure App Service, and more.
  • Continuous monitoring of ML models with Azure Security Center.
  • Automated usage cost control with quota limits and automatic shutdown.


The variety of ML models that can be created with this tool is limited and can fail to meet a retailer’s needs.


Free start option with a $200 credit for 30 days.

On-demand pricing: $0.096–$22.032/hour.

Costs optimization: savings plans for reserved instances can reduce costs by up to 73%.

Consider ScienceSoft’s Services to Implement Your Retail Data Analytics Solution

Delivering complex retail solutions since 2003, ScienceSoft offers data analytics consulting and implementation services to help retail businesses quickly respond to market changes and stay ahead of their competitors.

Consulting on retail data analytics

We perform an in-depth analysis of your case to define the most relevant analytics functionality, deliver optimal architecture design and a tech stack, and provide expert advice on security and compliance. You also get a detailed project roadmap with cost and time estimates for risk-free implementation.

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Implementation of retail data analytics

We take care of everything: design and implement a tailored retail data analytics solution that is scalable and easy to evolve in the future. With efficient project management, quality assurance, and after-launch support, you promptly get robust software that fully meets your needs.

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About ScienceSoft

ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 2003, we help midsize and large retail companies leverage advanced data analytics to drive their business growth. Achieving project goals in spite of time and budget constraints, as well as changing requirements, is ScienceSoft's top priority. You set goals, we drive the project to fulfill them.