Trail Run illustration

Nowadays, companies want to be able to test business decisions and ideas at a scale large enough to believe the results but also at a scale small enough to reduce the large investments and risks that come with full-scale execution.

Trial Run helps conduct tests such as altering store layouts and remodeling, loyalty campaigns, and pricing to recommend the best possible tailored rollout to maximize gains. You can now implement new ideas with minimal risk and maximum insight with the power of business experimentation. Trial run helps you:

  • Test each business idea at scale to generate customer insights without excessive spending.
  • Find out why your customers behave the way they do.
  • Learn how your customers will react to your new big idea.


What is Trial Run?

Trial Run is a data-driven, cloud-based test management platform used to test business ideas for sites, customers, and markets. Trial run is built using Azure Kubernetes Services, Azure Synapse Analytics, and Azure Virtual Machines. It is intuitive for beginners and experts alike and helps companies scale experimentation efficiently and affordably.

Trial Run supports the entire experimentation lifecycle, which includes:

Trail Run illustration



  1. Design: Build a cost-effective and efficient experiment that gives you the data you need to proceed with confidence.
  2. Analyze: Work with variables that provide you with targeted and actionable insights.
  3. Act: Use the generated insights to ensure your new rollout provides your stakeholders with the precise ROI.

Trial Run offers valuable support across various operational and administrative departments, including Retail, Consumer Packaged Goods (CPG), and Telecommunications.

Through its scientific and methodical testing approach, Trial Run can uncover fresh perspectives and guide decision-making through a range of tests, including:

  • Marketing and merchandising strategies.
  • Enhancing the in-store experience.
  • Examining store operations and processes.

These tests are carried out at the store operations and process, product, or consumer levels.

Trial Run offers a dynamic, affordable, and modern way of experimentation so you can stay relevant in a rapidly changing business environment. Trial Run also helps you to drive experiments through:

  • Driver Analysis: Identify key factors that are significant in driving the business outcomes
  • Rollout simulator: Maximize the ROI of a campaign
  • Synthetic Control Algorithm: Determine the right number of control stores with appropriate weights to create the replica of the test store
  • Experiment calendar: Avoid overlaps in experiments
  • Clean search: Let Trial Run parse the experiment repository and find entities that are available for a test


    What you can expect from Trial Run

    • Graphical design elements make it easy to use the program as an expert or a beginner
    • Automated workflows can guide you through the process from start to finish
    • Highly accurate synthetic control results with automated matching processes that only require minimal human intervention
    • Experiments at speed and scale without the hassle of expert teams or expensive bespoke solutions
    • Training, troubleshooting, and best practices from the best in the business
    • Easy pilots to help your new idea go live in as little as 6 to 8 weeks

    Trial Run stands out from other solutions by offering a transparent methodology and easily explainable recommendations. Trial Run utilizes a cutting-edge technique called “synthetic control” for matching, ensuring precise results. Trial Run can be used as a SaaS offering that is easily scalable based on demand and can be hosted on the cloud of customer’s choice. With Trial Run software, customers have unlimited test capabilities, enabling them to design and measure numerous initiatives without any restrictions. Finally, Trial Run success is proven in enterprises, with over 1,000 use cases deployed on our platform.

    How do I get started?

    Are you ready to implement cutting-edge technology to help you build cost-effective and efficient experiments that provide you with the data you need to make decisions?

    If you want to achieve successful Trial Run implementation, get started on Azure Marketplace.

    Interested in learning more about how Fractal can help you implement Trial Run, contact us to get in touch with one of our experts.

    NRF event Social post

    This year, Fractal is again pleased to be at NRF at Microsoft booth #4503, presenting its key AI-powered solutions for retail and consumer packaged goods (CPG).

    The solutions showcased at NRF 2024 are:

    • Trial Run, a solution to help execute ideas with minimal risk and maximum insight through business experimentation.
    • Crux, your Generative AI-powered personal analyst
    • Competitive Intelligence, which provides real-time insights into competition pricing and assortment strategies.


    1. Trial Run: Enhance decision-making leveraging business experimentation

    Unlock a world of possibilities with Trial Run, a cloud-based test management solution. The solution enables retail and CPG companies to gather valuable insights into customer behavior and market trends to help retail and CPG companies.

    Whether it is pricing, promotions, or store layouts, Trial Run empowers companies to make data-driven decisions for a seamless customer experience and increased revenue.

    2. Crux: Your generative AI-powered personal business analyst

    Crux Intelligence Copilot enables decision-makers to quickly gain insights about their KPIs to make informed decisions more easily and faster.

    This Generative AI-powered and voice-enabled solution allows users to interact with their data easily by asking questions in natural language.

    3. Competitive Intelligence: Real-time insights on your competition’s strategies

    Stay ahead in the retail game with Competitive Intelligence. This solution delivers real-time insights into your competitors’ pricing and assortment strategies.

    By harnessing online competitive information, organizing it, and presenting it in an actionable format, Competitive Intelligence empowers retailers to make smarter pricing and merchandising decisions for sustainable and profitable growth.

    Learn more about those solutions or book a slot for a private demo with our team at NRF here:

    7 ways implementing MLOps can transform your business

    Machine learning operations (MLOps)

    As per Gartner, only 50% of machine learning models reach production, leading to an efficiency challenge for many organizations. To improve the rate of machine learning model deployment, many organizations have begun to adopt MLOps.

    MLOps is the concept of applying DevOps principles to ML (Machine Learning) workflows and helping increase the deployment rate of ML models to production by leveraging a set of practices, processes, and tools per the used case scenario.

    The MLOps process includes several stages: data preparation, model training, testing and validation, deployment, monitoring, and feedback loops. The main goal of MLOps is to increase the machine learning process’s efficiency, accuracy, and reliability while ensuring the models perform as expected in real-world environments.

    MLOps typically involves using various processes, tools, and technologies, such as version control systems, containerization platforms, and continuous integration and deployment pipelines, to automate and streamline the machine learning workflow. The tools and practices enabled by MLOps can help reduce the risk of errors, improve collaboration, and ensure that machine learning models are updated and optimized for better performance over time.

    Let us look into the 7 key benefits of implementing MLOps

    7 key benefits of implementing MLOps-illustration

    1. Increases productivity

    MLOps practitioners leverage various tools and practices designed to help streamline and automate machine learning development and deployment processes. It can include automating data preprocessing and feature engineering, managing model training and evaluation, and deploying and monitoring models in production.

    By implementing tools designed to automate and standardize development and deployment processes, organizations can reduce the time and effort required to develop and deploy machine learning models, allowing data scientists and engineers to focus on higher-level tasks.

    This results in the faster and more efficient delivery of high-quality machine learning models, ultimately driving business value and improving productivity.

    2. Faster deployment and easy monitoring

    MLOps methodologies can help organizations accelerate modeling processes. They can also help facilitate machine learning models’ seamless construction and deployment by helping leverage automated systems.

    Commonly used MLOps tools can help with automatic system monitoring systems, which can be used in the continuous monitoring of models in production, allowing for quick identification and resolution of any issues. These tools help organizations improve the speed and quality of their machine learning deployment, leading to increased productivity and better outcomes.

    3. Budget and cost management

    Implementing MLOps can help ensure efficient resource usage and cost control by using tooling designed to monitor usage patterns, identify bottlenecks, and scale resources based on demand. These tools can help estimate and track costs before, during, and after experimentation.

    4. Reproducibility and versioning

    Organizations can leverage MLOps policies to enable a structured approach for practitioners and data scientists to track and manage changes, enable versioning, and provide a history of edits and versions created.

    Versioning aids in deploying models in production and enhances the reliability and scalability of machine-learning models.

    5. Reliability

    Leveraging MLOps methods and tools can result in more reliable ML pipelines by minimizing the scope of human error and providing real-time data insights. MLOps can improve the dependability of machine learning models, ensuring their consistency and accuracy in production.

    By continuously monitoring model performance and dependencies, teams can promptly identify and address issues, increasing the models’ reliability.

    6. Collaboration

    MLOps best practices and policies are meant to break down silos between teams, allowing them to collaborate, share data more efficiently, and seamlessly integrate their workflows. This collaboration can lead to faster and more efficient model deployment and a more streamlined machine-learning process.

    With MLOps, organizations can ensure that their machine learning projects are connected and working together efficiently, leading to better outcomes and improved productivity.

    7. Monitorability

    Through MLOps, organizations can get insights into model performance and retrain the model continuously to ensure it gives the most accurate output. MLOps enables practitioners to do this by providing guidance on best practices for successfully implementing automated monitoring systems. These monitoring systems facilitate constant model monitoring and allow stakeholders to identify any issues or anomalies that may arise quickly.

    Identifying problems and irregularities can help to improve model performance and reduce downtime, leading to better outcomes and a more efficient deployment process.

    With MLOps, organizations can ensure that their machine learning models always perform optimally, improving productivity and better business results.

    MLOps with Fractal

    Fractal has a successful track record of delivering ML projects for clients leveraging our in-house MLOps methodology. Our secret lies in using a reliable methodology designed to remove uncertainty, foster consistency, and enable the quick realization of value, as well as continuous packaging, validation, and deployment of models to production—partnering with us for MLOps implementation grants you access to this proven methodology.

    Getting started with MLOps

    Organizations looking to implement MLOps can leverage the same proven methodology we use in-house to deliver projects for clients successfully.

    Contact us to get started.

    Three pillars of the retail industry: Replenishment, allocation, and transportation 

    The retail industry is one of the most dynamic and fast-paced sectors, comprised of various stakeholders engaged in selling finished products to end-user consumers. In 2022, the U.S. retail sector was estimated at more than seven trillion dollars. The sector is projected to continue to grow, and by 2026 U.S. retail sales are expected to reach approximately USD 7.9 trillion. With the increased demand for consumer goods in different sectors and the ever-increasing choices of products at low costs, investments in the retail sector have also grown over the past few years.

    As there is always an element of change in this industry, new challenges are encountered daily. Take product stockouts, for example. Suppose a customer walks into a grocery store to purchase items of their favorite brand but discovers the product is unavailable. Frustrated by this, the customer chooses to either buy another brand or postpone the purchase; both scenarios are unfavorable to the business. The brand image and sales of the product are damaged because of this out-of-stock issue. The out-of-stock situation occurs when the inventory of a particular product is exhausted; this causes a problem for suppliers and retailers.

    Multiple reasons could cause the product stockout, such as inaccurate inventory data, lack of demand forecasting, or an unseasonal spike in purchasing. Many of these underlying causes of stockouts can be avoided if the business implements adequate processes to be carried out every month.

    To avoid situations like the stockout example above, retail companies need to develop a methodology for streamlining the following operations:

    3 pillars of retail industry

    1. Replenishment
    2. Allocation
    3. Transportation

    These three operations create the three pillars of the retail industry that help monitor real-time insight into customer behavior and understand their buying patterns, hence strengthening the retail foundation.

    1. Replenishment

    Replenishment refers to a situation where the amount of stock left in the store is counted so that the right products are available in an optimal quantity. It is considered an essential aspect of inventory management as it ensures that the right products are being reordered to meet the customer demand.

    In operational terms, the efficiency of store replenishment has a significant impact on profitability. The effectiveness and accuracy of store ordering affect sales through shelf availability and storage, handling, and wastage costs in stores and other parts of the supply chain. By optimizing demand forecasting, inventory management, and setting order cycles and order quantities by making them more systematic, the gains obtained are significant, often amounting to savings of several percent of total turnover.

    For companies that must manage many SKUs, one of the most effective ways of making store replenishment more accurate, efficient, and cost-effective is by using a replenishment system specifically tailored to business operations. When many different products need to be managed, manual ordering is highly labor-intensive and expensive; this results in companies using the replenishment system.

    An efficient replenishment system reduces process costs, improves inventory turnover, and provides higher service levels. The system constantly monitors the stock, sales, and demand while considering the forecast changes in demand and adjusting the replenishment orders. The company can control its inventory to ensure long-term profitability by recognizing the sales frequency, value, or profit margin. The replenishment system calculates the safety stock level for each SKU separately and sets them to meet the service level targets with efficiency, considering the predictability of demand.

    2. Allocation

    In allocation, the new stock of products is distributed to individual store units to maximize the product’s sales and prevent any stock-out situation in the future. This process enables assigning supplies to support the organization’s strategic goals. Having sufficient stock levels is an essential component for any retail business; with the changing consumer habits, it becomes crucial for the stock to be available in the right place at the right time.

    To meet new and increasing demands, retailers need an efficient process to gather, interpret, and analyze data from customer behaviors and habits, which would help get a more localized and specific idea of what is sold at a larger quantity in different locations. Items that are high sellers in one particular area may not sell well in others, so recognizing and monitoring this can ensure that the stock is allocated to the most needed location. Due to this, an opportunity is provided for the retailers to encourage sales by pushing stock of a similar type that a customer may favor at a particular location.

    3. Transportation

    Transportation plays a significant role in delivering the right stock of products at the right point of delivery. It connects the business to its supply chain partners and influences customer satisfaction with the organization. With the ever-changing customer preferences and as their expectations continue to evolve, the transportation industry is undergoing a dramatic transformation to meet these demands.

    Today, data plays a vital role in shaping the industry’s progress amidst tough competition. Due to the maturation of automation technologies, AI will help the transportation industry to manage drivers and fleet managers. By employing the techniques of AI, fleet and truck adjustments will offer data in real-time, eventually improving the industry’s standard. The safety and retention of the drivers will also increase from these newly acquired standards, and with enhanced access to data, there will be transparent communication between drivers and carriers.

    The average time between goods purchasing and delivery decreases by using real-time findings, making retailers focus on transportation to improve their business performance. The ability to automate insights, alerts, and data exchange more quickly will be the game-changer for this industry.

    These three pillars of retail can be strengthened by adopting in-house solutions and capabilities like StockView for retail, Edge video analytics, and Dynamics 365 customer insights.

    How could these solutions help the retail industry?

    StockView for retail helps retailers reduce lost sales and improve customer experience by automatically detecting out of stock items on shelves. It uses computer vision technology running at the edge to detect gaps on store shelves automatically. It also provides retailers with powerful insights and analytics into stock-out activities at both single-store and multi-store levels.

    Powered by Microsoft Azure Stack Edge, it offers a scalable, flexible, and cost-effective solution that brings the power of the Azure cloud platform down to the individual store, eliminating the need for costly and unreliable data transfers while offering a predictable and consistent TCO (Total Cost of Ownership).

    Edge Video Analytics solutions for retail typically leverage Azure Stack Edge devices, IoT devices, vision AI (Artificial Intelligence), and other technologies in conjunction with in-store cameras or other video sources.

    Insights gained from Edge Video Analytics can allow retailers to swiftly react to in-store customer behavior and product stock-outs while improving security and reducing shrinkage.

    Also, organizations can track store foot traffic, automatically notify employees to open more checkouts due to lengthy queues, and automatically detect product stock-outs. These insights can be used to help improve demand forecasting, optimize supply chains, and detect variances in population preferences down to the individual store.

    Dynamics 365 customer insights give you access to Microsoft’s extensive library of pre-built data connectors to help businesses gain faster time to value. Plus, you can power up your customer profiles with AI by leveraging Azure Machine Learning and Azure Cognitive Services, and Fractal’s library of custom AI models. So, we leverage this solution to maximize the potential of customer data.

    All these solutions and capabilities help understand the customer motivations, preferences, and desires to meet their demands and increase sales effectively, strengthening the retail industry’s pillars.


    To meet these growing customer expectations, retailers should prioritize collecting customer data and analyzing it to support business decisions throughout their value chain. The inventory stocking patterns and shipping routes will shift in relation to patterns informed by this data. Retailers should make a concentrated effort to leverage the data while making critical business decisions, and to remain efficient; they must remain flexible and transform their operations as they capture more insights from their data.

    Over the past 20+ years, Fractal has helped many retail companies make their replenishment, allocation, and transportation operations more efficient by leveraging AI, engineering, and design. If you would like to learn more about optimizing these aspects of your retail business, please contact us to speak with one of our experts.

    Find how Fractal helped a global CPG company to operationalize an analytics engine designed and provide store recommendations for maximizing investments in their Azure environment. Read the full case study here: