Markdown Analytics

Discount management is a critical part of margin maximization for a retailer. It explains why retailers have heavily invested in discount optimization technologies, which has proven that more granular and analytical approaches to discount pricing result in improving gross margins. Modern retailers deal with a wide variety of products, and the pricing of all these products can vary across multiple channels like mobile, website, and physical store. Data analysts can give weighted importance to all the factors like costs, trends, behavioral analytics, etc., to predict consumer behavior due to changes in price. Big Data analysis tools can analyze market trends from social media sentiment analysis, consumer psychology, inputs from POS systems, online transactions, and mobile transactions to develop markdown pricing that is likely to trigger an increase in demand. Successful markdown optimization generates additional values to the business, such as increased revenues, fattened margins, better sales conversions, and optimal inventory. This optimization also becomes much more crucial when fluctuating product lifecycles, seasonal demand, various assortments, varieties of the customer base, new stores, price competition, and any other factors that may change after the initial decision-making is done are considered.

By combing the power of our flagship framework — — with a decade of experience, we have ensured AI projects are delivered in a timely and robust manner and organizations achieve a successful AI transformation journey with our systematic approach towards enterprise AI and MLOps solutions.  

Our approach to an enterprise MLOps journey begins with a robust data intake and elaborate analysis to frame the problem areas.  The second part includes applying an engineering mindset, identify enablers required for AI initiatives to be successful at enterprise scale, and preparing cognitive models. Finally, these models are deployed and managed throughout the lifecycle of the solution. Throughout the lifecycle, pre-built frameworks are used to push the required transformations ahead. Coupled with a robust uptime for applications we use and near-zero chances of interruption, this means unwavering support all the way for customers. So, what would have typically taken months to deliver can be developed and deployed in weeks, and most importantly — at a fraction of the cost.


Lack of clarity, approach, and tools means higher costs and lower margins: Retailers are almost always under tremendous pressure to both improve margins and manage inventory. They use the same pricing strategy — a ‘peanut-butter approach — across a range of products regardless of the item or store-level performance. This leads to high-cost markdown waves as Excel formulae-based processes can be inaccurate and inconsistent due to a lack of a data science approach.

Fixed percent discount bucket strategy may not work: This strategy can result in more popular items selling out while others may not sell at the same rate. Retailers can end up giving away margin on certain items due to a discount for much longer than the exit date. Fixed price points can also lead to devaluation of the items in the consumers’ minds. 

Markdown optimization for seasonal merchandise can be risky and sensitive: US retailers typically generate about a third of their annual revenues during the markdown period. Thus, sales during this period contribute immensely to overall revenues and margins for retailers, and improvements (or deterioration) in sales during this period contribute directly to the retailer’s bottom line.

Pricing during the markdown cycle is often both ineffective and inefficient: Due to little or no visibility to sell-through or inventory positions, the ability to assess the best items to send to markdown is limited. Largely static store groupings (e.g., climate zones, localization clusters) have zero ability to surgically markdown prices. Markdown impact estimates rely heavily on recent data and merchant assumptions without advanced or consumer-centric analytics to determine the best clearance price. 

Highly manual reporting adds to the inefficiency: This needs high processing time and is limited in terms of information and insights. Material delays in processing time as a result of complex and antiquated systems and/or reliance on third-party vendors act as further deterrents. – Cognitive Solutions

  • Our markdown optimization solution can empower retailers to plan, optimize and execute profitable markdown decisions for short and long lifecycle merchandise. 
  • Our research shows that markdown optimization can capture 5% to 15% of incremental revenue for retailers. 
  • With the help of accurate lift predictions, savings in markdown costs and better planning can be easily achieved. 
  • Reduced inventory disposal fees can be rewarding through improved sell-through rates, increasing working capital.  
  • We can drive higher increased net margins, and consequently, higher revenues and better negotiation on markdown contracts with merchants are a reality.

Solution Approach

Use Case Discovery

We actively engage with our clients to capture the business requirements while observing the problem. In the case of deriving credit scores, traditional model development methods are lengthy, tedious, and often prone to human bias, lack data accuracy in the absence of proper data collection, data models, and data cleansing. We can effectively identify the relevant datasets and formulate a use case-based approach that would solve the business problem or improve/predict actionable insights to mitigate the problem.

We can assist in setting up the required infrastructure – the framework provides out-of-the-box development platforms. All functionalities can be accessed using a Jupyter Notebook — ensuring zero-delay and plug-and-play availability of high-end hardware. Development images configured based on pre-defined templates can be installed on-premises or in a development VM within the infrastructure. This enables authentication using LDAP, seamless project setup using Bitbucket, Jenkins, and Docker (ensuring build and deployment without software compatibility issues).  The project can be started seamlessly with the relevant environments, which are subsequently created automatically.

The framework made available by leverages the latest ML and DL tools while preparing models and includes Pachyderm-based data versioning, deployment using a Kubernetes orchestration system, Kubeflow and Spark-based ML and DL build and deployment, Istio-based service mesh enabled microservice architecture, and ELK based monitoring capability, contributing to reduction in latency time.

Data Engineering’s MLOps framework has different data adapters available through a common catalog of services that simplify interoperability and scalability concerns, enables APIs, and abstracts all the technical complexities from the service consumer. This allows establishing high-end Alluxio and Presto-based rapid, inexpensive data connectivity and data collection from diverse sources (available in structured, unstructured, and streaming formats) coming in at a high velocity and in huge volumes. 

All the data sources are funneled into the data storage layer after proper validation and cleansing. The storage landscape with different storage types and extreme flexibility is built-in to manipulate, filter, select, and co-relate different data formats.

Infrastructure and MLOps Automation

The details collected, project code, data preparation workflows, and models can be easily versioned in a repository (Bitbucket, Git, etc.), and data sets can be versioned through on-premises /cloud storage. These can be added as exploratory variables by using two excellent features of

  1. Data Connectivity Marketplace libraries
  2. Data Versioning

The attributes obtained are used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate, and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis).  Different datasets and their different versions can be easily controlled and stored into xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. MLOps automation allows creating pipelines, train with as much data and as accurately as possible, fastest time to inference, with the ability to rapidly retrain. The xpresso Data Pipeline Management (Rapid Model Training and Experimentation) uses Kubeflow-enabled pipelines. Thus, multiple experiments using different models and datasets can be created, tested, paused, and restarted to gain better insight. 

It is possible to predict optimum markdowns through a modernized/automated tool built on a cloud platform that uses advanced statistical models and ML algorithms. This tool can integrate with multiple data sources and be accessed remotely, helping retailers plan better markdown strategies backed by empirical data from the ML models. 

How can help Retail Organizations transform their journey to cognitive AI solutions is an AI/ML Application Lifecycle Management Platform. enables complete lifecycle management of AI/ML solutions, addressing the AI transformation journey of enterprises on any cloud platform of choice. offers functionality essential for building AI/ML solutions – primarily enabling data scientists to rapidly build predictive and prescriptive models. The platform provides a user-friendly interface to develop, deploy, and manage AI/ML solutions at scale. In addition, supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications.

Key benefits of include:

  • Empowers data scientists to transform AI/ML research into solutions 
  • Improves the productivity of data scientists by enabling them to focus on the business problem, developing algorithms and rapid experimentation of models 
  • Addresses the shortage of skilled data science resources with automated workflows, toolkits and frameworks 
  • Manages AI transformation journey costs without any wastage of R&D efforts 
  • Provides an enterprise-ready and secure environment for complete lifecycle management of AI/ML applications
  • Enables at-scale deployment of enterprise AI/ML applications on-premise, cloud (AWS, GCP, Azure), or hybrid environments

Additional details on can be found at: We can schedule a demo of the platform for anyone interested in learning more.

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