Post-pandemic, the increase in consumer demand presents once-in-a-generation opportunities to retail industry players. In 2021, retail sales1 increased 14.1%—the highest growth in the past two decades. US consumers spent more than $1 trillion on retail products as compared to 2020. And this increase seems to be continuing.

Capitalizing on Top Retail Trends – The D&A Scope

Leveraging post-pandemic changes in consumer behavior and expectations requires retailers to innovate data-powered product and processes. Here we present a roundup of retail industry trends and how data and analytics (D&A) initiatives can support them.

Invest in Retail AI Solutions to Improve your Digital Presence

A strong online presence is essential today, whether a business is service-based, brick-and-mortar, or strictly ecommerce. A 2020 eMarketer study2 reports that we spend nearly eight hours each day doing digital activities. Today out of the total retail sales, online retail and ecommerce accounts for about 19% while cutting-edge technology further accelerates online sales.

Retail AI solutions are redefining face-to-face communication and selling. The future for ecommerce looks increasingly more promising as multiple players enter the space and expand their global presence. And compared to traditional retail businesses, it’s easier for companies to scale online while realizing low associated costs.

From a consumer angle, 69% of new buyers came from Asia Pacific in 2020. One US fashion retailer sought to expand its business there, as well as in Europe and the Middle East. Infocepts helped it elevate eCommerce sales with a cloud-native, sales intelligence platform it then rolled out in 53 countries. Providing real-time insights to all its sales associates, the company’s new, data-driven solution helped it generate an additional revenue stream of $50 million plus.

Provide Omni-Channel Experience by Lowering Time-to-Insights

Customer behavior is changing, with their expectations being higher than ever. Easy access to information, multiple buying options, and rapid technological advancements provide them with more ways to shop online. An omni-channel approach focuses on improving the consumer experience while engaging them via more platform options. Infocepts has helped many companies worldwide to implement D&A solutions based on modern retail business, selling, and product strategies. These include:

  • BOPIS (buy online, pickup in store)
  • BOSS (buy online, ship to store)
  • View in your room (with the help of virtual reality apps)
  • Try before you buy

While consumers shop from the comfort of their homes, a unified, omnichannel experience can come as close as possible to that of a brick-and-mortar store. It all starts with your enterprise having a single source of data. Such coordinated handling of data, insights, and the market scenario enables it to quickly respond to changing customer behavior.

Wrestling with its legacy data system and lack of real-time analytics, one retailer was challenged while executing such a strategy. Upon engaging Infocepts, we rejuvenated its omnichannel operations with real-time data integration by way of our reusable Real-Time Data Streamer (RTDS) accelerator, which fast-tracks real-time intelligence for clients. Our solution quickly optimized inventory, shipping, order fulfilment, and workforce management for the client’s distribution center, reducing its costs by $3.6 million in three years.

Navigate Workforce Shortage Proactively with Best-in-Class Analytics

COVID-19 sent massive disruptions through global markets and workforces, with 87% of retailers reporting recruiting difficulties in 2021. The combination of rising customer expectations, heavier work demands on associates, and the new ways of working post-pandemic have changed how retailers are able to fulfill their staffing needs. Data and analytics can address such challenges by –

  1. Analyzing historical workforce data
  2. Predicting future needs to better match customer demand with real-time decisions

Another of our clients had issues with scheduling, staffing, and payroll management in its global retail locations due to limited visibility across workforce management data points. We helped the client improve efficiency and productivity by integrating its workforce management with store operations data. This helped optimize daily operations, and a variety of metrics and dashboards improved staffing, scheduling, and payroll management across its worldwide locations.

Optimise Inventory Planning with Real-Time Analytics

Post-pandemic, big retailers rushed to build up inventories amidst soaring consumer demand and supply chain bottlenecks—in some cases going so far as to rent their own cargo ships. Shortly thereafter companies tried selling off excess inventory. In the US, inventories rose $44.8 billion for companies on S&P consumer indexes; retailers were paying more for storage while prices of goods dropped.

Today, real-time, data-driven decisions enable companies to revamp their supply chain by achieving speed and accuracy in inventory management. During changing customer demand and other external trade factors, Infocepts helped another retailer exercise real-time decision making for inventory planning. Including multiple leading and lagging indicators, our solution integrated real-time data along with consumer behavior to predict demand.

Be Agile with Data-driven Intelligence at Every Step

Data-driven decision making is essential for growth and strategic planning in any organization. But simply using data is not enough. Agility—the ability to quickly react amidst constant change within your decision-making framework—is also critical for success.

For example, Amazon Go (still in beta), uses 3D camera technology and RFID3 to automatically detect which products customers are picking up, permitting them to leave the store without a formal checkout process.

The following technology innovations are already in use (or in beta):

  • Cameras and Wi-Fi measure the flow of instore traffic, thus helping retailers schedule their workforce.
  • Augmented reality (AR)/virtual reality (VR) coupled with eye tracking can predict how customers react to retail displays. This helps to position products better so as to increase sales.
  • AI for retail operations includes customer analysis, demand prediction, inventory optimization, and competitive research to help companies stay ahead through data-driven decision making.
  • Robots as sales assistants, drone deliveries, smart mirrors, devices with voice-based search, and digital beacons combine to provide cost savings while enhancing the shopping experience.

Armed with real-time data analytics, retailers will be able to act more quickly, test multiple concepts, and constantly improve sales and customer experience with agility.

Infocepts recently collaborated with Neumont College of Computer Science in Salt Lake City to implement Power BI reporting and its functionalities using Microsoft’s HoloLens 2. It lets users see product data in the 3D HoloLens world. Read about it here: Rethink Retail Analytics with Real Time Data.

Promote Interoperability with Evolving Industry Standards

For systems to be interoperable, they must be able to exchange data and present it in a way that is easily understood by users. Data interoperability is extremely important for growth in retail. The fusion of multiple data points offers huge value in operational insights and the ability to create collaborative workflows to improve process efficiencies. Organizations that use data to accelerate growth realize a faster ROI from multiple investment areas within their business.

A well-known global research firm offers a comprehensive view of consumers and the market through analytics to hundreds of its client retailers and packaged-goods manufacturers. But due to multiple legacy and out-of-the-box reporting tools within its infrastructure, its challenge was to streamline insights to provide a consistent user experience. Infocepts helped it create a unified portal to access 200+ analytical insights across 30+ applications.

The centralized portal enhanced data interoperability to improve overall effectiveness of its analytics ecosystem. After just three months, the company realized 50% higher engagements and interactions with data insights.

To remain competitive and excel, today’s retailers require help from strategic D&A experts. Infocepts helps them establish data interoperability, resulting in a single source of truth for organizational decision making.

Take Advantage of New-Age Retail Trends

The post-pandemic retail surge and rush to find innovative ways to better manage customer expectations will continue for many more years. Retailers who use data assets to transform their products, services, and business model will thrive. In order to accomplish this, you must promote a culture of data-driven decision making, prioritize D&A investments, and closely monitor your ROI. Partnering with Infocepts—a specialized D&A consultant that is highly rated by industry leaders, has deep technological expertise, and comes with a broad spectrum of reusable solutions—can prepare you to address your today’s challenges while preparing you for tomorrow’s.

Want to learn more? Talk to us to accelerate your retail transformation with a data-driven strategy and cutting edge solutions to improve your business outcomes and customer experience.

References:

  1. https://nrf.com/blog/4-things-retailers-need-to-know-inflation
  2. https://www.insiderintelligence.com/content/us-adults-added-1-hour-of-digital-time-2020
  3. https://arstechnica.com/information-technology/2017/04/how-amazon-go-probably-makes-just-walk-out-groceries-a-reality/
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Data is a valuable asset in this hyperconnected digital age; the world is producing more of it than ever before. Each day, 2.5 quadrillion bytes of data is generated by internet users alone. Data-driven intelligent automation is essential in making sense of this enormous amount of information. It helps enterprises transform data analytics and unlock new growth opportunities to stay ahead. And it helps you draw more value from your data to make profitable decisions.

If used correctly, data-driven intelligent automation enables enterprises to achieve unprecedented efficiency, speed, and results. It also enables businesses to accelerate ROI from their data and analytics investments.

What is data-driven intelligent automation?

Data-driven automation differs from process-driven automation. The latter directs automation such that it can’t veer from a predetermined direction. Humans need to intervene whenever an exception (an assigned task that doesn’t fit the established pathway) is detected.

Often described as being an initial digital transformation step, a process-driven system is limited to automating repetitive tasks. Being easy to implement and manage, it lets organizations automate simple processes within a few weeks, such that they quickly realize time and cost savings, improvements in accuracy, and efficiency gains. Thus it is widely used in delivering physical products, streamlining customer services, and managing financial resources. But process-driven automation has significant limitations.

Evolving from process-driven systems, data-driven technology is a more complex form of intelligent automation. It’s more powerful in handling intricate processes because it’s guided by both data and context.

A data-driven system combines robotic process automation (RPA) and artificial intelligence (AI) technologies that includes deep learning (DL), natural language processing (NLP), machine learning (ML), decision-making engines, and deductive and prescriptive analytics. High-quality data is central to data-driven automation because the AI learns from it to enhance processes.

A data-driven approach automates processes much quicker and more precisely than process-driven systems. It can also span multiple systems and data silos. Unlike process-driven automation, advanced AI empowers it to process unstructured data and engage neurological judgement which uses human-like, judgment-based interactions.

More importantly, a data-driven approach can automate non-repetitive processes, thus increasing the scope of automatable processes across an enterprise. It can automate tasks that previously had to be performed by humans. And it makes RPA smarter, enabling it to engage in processes that don’t follow prearranged pathways.

Why should organizations embrace data-driven automation?

Adopting data-driven intelligent automation can help businesses accelerate their ROI from data and analytics investments. It can:

  • Reduce the incidence of manual errors
  • Cut down on human resource spending
  • Increase analytics speed
  • Improve big data analysis
  • Allow data scientists to generate new insights to guide business decisions

The data-driven approach simplifies and speeds up insight generation by automatically analyzing massive volumes of streaming data and quickly identifying patterns. It accelerates the data preparation process, automates report generation, and empowers users to make data-driven decisions.

Additionally, it makes it easier to share findings with multiple stakeholders across the organization. Quick access to reliable insights ensures key users can ultimately drive transformation projects and the business.

Data-driven intelligent automation offers high-value and unique use cases—from predictive analytics and customer engagement to product optimization. What follows are a few key benefits derived from its adoption.

Faster insights yields profitable decisions – Speed is a crucial differentiator in any competitive market. Real-time insights are essential for successfully launching new products or improving services. Data-driven intelligent automation can make sense of large amounts of variables and metrics pulled from multiple sources. Because the full data value chain is automated, users can access meaningful insights from raw data as it arrives, thus helping teams to take timely and profitable actions. For example, it’s particularly useful in managing marketing campaigns, insurance and financial contracts, fraud management, healthcare processes, and more.

Boost productivity – From data preparation to visualization, it can take a lot of time to manage data lifecycles. Automation can significantly reduce this effort, freeing up your data science teams to concentrate on vital issues and core business areas. It eliminates complexities related to monitoring ever-changing variables. And it makes it much easier for users to interpret data, find hidden patterns, spot minute anomalies, and uncover complex insights often missed by manual approaches.

Reduce costs – Because it reduces time spent on data preparation, analysis, and modeling, data-driven automation results in significant savings. SaaS-based platforms enable businesses to quickly scale data analytics without having to make large investments in building and managing AI capabilities in-house.

Intelligent automation by Infocepts

Data-driven systems are better at empowering businesses to take full advantage of intelligent automation. Hence new-age companies use data-driven automation to elevate customer experiences, improve processes, prevent waste and fraud in the industry.

While other modernization endeavors are ongoing, many organizations find it challenging to embrace data-driven intelligent automation within the limitations of their IT ecosystem. Infocepts can help. We design intelligent services to automate complex use cases and deliver autonomous insights to users having an immediate need. It leverages cutting-edge technologies—including AI, NLP, ML, natural language generation, computer vision, RPA, hyper automation, and low code—to deliver intelligent services that work perfectly un concert with user-driven demands.

Learn more about Infocepts data-driven intelligent automation solution and how it helps organizations reimagine their operating models and service delivery using AI and ML.

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COVID-19 has forever changed how business is done and what customers expect from modern businesses. To stay relevant in today’s digital era, organizations are using intelligent automation across most of their business processes to revamp operations, service delivery, and achieve desired business outcomes with zero or minimal human intervention.

The potential of robotic process automation (RPA) — bolstered by data-driven autonomous insights based on artificial intelligence and machine learning — can completely change how products and services are delivered and how they are perceived by consumers. Here are some data-driven intelligent automation use cases where we have solved real-world problems and addressed different client goals.

  1. Global market research firm saves millions with automated data-driven insights

    Infocepts helped a global market research company automate its repeatable, high volume, and time-consuming report generation process, saving over $1M annually through the right intelligent automation solutions and the needed skills. Our client is a leading research firm and helps CPG manufacturers and global retailers make key decisions by providing in-depth market demographic insight.

    It offers various business intelligence reports helping over 15,000 users understand what is happening in their target markets, why it is happening, and decide what to do next.

    Its 65+ dashboards comprised of 10K+ elements which were manually customized by developers to meet the respective end-user requirements of each new client. The process needed manual modification of report elements which was error-prone, provided inconsistent user experience, and led to lost opportunities due to tough competition.

    Our innovative solution automated all repeatable high-volume report generation tasks and saved our client over $1M per year. The solution automated complex business processes or workflows which generate and deliver autonomous insights. It leveraged multiple cutting-edge technologies (AI, ML, NLP, Computer Vision, Low Code, RPA, and Hyper Automation) and can now smartly deliver insights based on user-driven demand through continuous monitoring and intelligent automation.

  2. Omni-channel retailer cuts operating costs with custom-built automation suite

    A leading US retailer operating over 300 stores in North and South America partnered with Infocepts to overhaul its complex data pipeline, which consisted of over 130 workflows, more than 800 mappings, and over 600 tables. Any delays in data loading had a direct impact on the timeliness of enterprise BI report delivery, especially the sales reports that served as the basis of inventory planning, marketing, and sales target decisions. The system also relied on manual monitoring, which was prone to errors that directly caused higher ticket volumes and a lack of confidence in the reports. With increasing operational costs and frequently needed cleanup activities, modernization was the need of the hour.

    Infocepts intelligent automation solution helped the client significantly declutter databases with a custom-built suite capable of automatically providing real-time notifications when failures, environment changes, and unusual jobs are detected. The solution used reusable components and pre-packaged scripts which provided flexibility and scalability. Intelligent automation helped achieve a total of 100k USD savings for the first year and the savings gradually increased as the same sized operations team was able to monitor the growing number of data processing jobs.

  3. Managed services automation saves 5,000 manhours annually

    Intelligent automation enabled Infocepts to revamp a 24×7 managed services program for our client – a leading technology company that provides “customer experience software as a service” utilizing speech analytics and AI-powered text. The clients’ services involved extracting actionable insights from diverse customer interaction modes to propel sales growth while ensuring compliance and increasing operational efficiency. The client relied on discrete proprietary applications that ran on different servers, creating diverse environments that became increasingly complex to manage manually.

    Our customized intelligent automation solution helps provide near real-time alerts and updates on server health, eliminating manual monitoring efforts and reducing the time it takes to resolve issues. It reduced the time spent on bug fixing and elevated customer service levels drastically. Infocepts solution provides reliable, round-the-clock monitoring and scalable support—along with a 30% reduction of effort in recurring manual activities.

  4. Pharmaceutical company uses AI-powered segmentation solution to identify key opinion leaders

    The opinion-leader segmentation process of an American pharmaceutical company did not meet the current market standards and lacked reliability. It lacked modern features like the ability to analyze digital activities, popularity, and relationship matrix of healthcare influencers and professionals. Also, data was manually updated which was error-prone thus affecting classification and risking incorrect segmentation.

    Infocepts automated the client’s key opinion leader identification and segmentation process using an AI/ML-powered intelligent automation solution. Machine learning algorithms made it much easier for the client to identify top-performing, established, and rising key opinion leader segments for different countries. It also provided meaningful and actionable insights on professional credentials, influence circle, interaction metrics, network, and growth variables. It accelerated our client’s medical science decision-making process and enabled our client to formulate effective personalized engagement strategies focused on healthcare thought leaders.

Get started with Infocepts today to learn about our intelligent automation solution that automates data & analytic capabilities using innovations in Data Science, AI, ML or Robotic Process Automation

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A Machine Learning and Operations (MLOps) platform plays a critical role in enabling data scientists to develop and train models to fulfill business priorities. The key asks business leaders have from their data science teams are centered on driving better sales and customer engagement. The MLOps platform provides the environment data scientists need to create and train models that achieve specific business objectives. Platform developers have a wide variety of artificial intelligence (AI) and machine learning (ML) technologies at their disposal. While such a platform lays the foundation for building data science models that can give businesses a disruptive advantage, developers have to comprehend with a problem of plenty.

How to Compose a Harmonious Tool Stack?

Getting out of the abundance syndrome requires developers to tap into their reverse engineering skills. They have to take stock of the key challenges faced by the business to shortlist technologies that can overcome them. Putting the right tool stack together then becomes a matter of matching must-have MLOps platform features with business priorities.

Characteristics of a Robust MLOps Platform

We have listed below few of the many characteristics of a robust MLOps platform.

  1. Scalability
    With a number of data scientists and ML engineers tinkering with multiple models at any given point of time, platform scalability is essential. If the platform is unable to support multiple users, their collective efforts to improve ML algorithms and codes will create a drag and reduce team productivity.
  2. Version Control
    The iterative nature of the tasks performed by data scientists requires them to test multiple models, optimize parameters, and tune features while dealing with a vast amount of data. Data science teams can’t be efficient if they cannot track model versions with changes to parameters, code, and data. Version control frameworks and Git repositories provide the means for tracking model versions and their performance.
  3. Data and Concept Drift Sensitivity
    With the passage of time, data and concept drifts become inevitable leading to inaccurate results. Models can be trained to trigger training and retraining routines when they detect drift patterns in data.
  4. A/B Testing
    The ability to conduct A/B testing is the cornerstone of developing effective data science models. An ideal MLOps platform should enable data science teams to put their models to test with different sets of users. It enables them to deploy models that are either at par with existing models or better than them.

Build an MLOps Platform Your Data Science Team Deserves

In this advisory note – we have defined effective strategies to build a robust and scalable MLOps platform.

Read Now

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In the rapidly evolving world view, there is a need for new forecasting techniques that are resilient and flexible. The change is required more than ever before since, in the current times, an incorrect decision in any of the domains such as logistics, production, inventory, and retail, to name a few, can pose an existential question to any organization.

Challenges with conventional forecasting models

Any brand throughout its business journey makes decisions to its benefit based on the guiding principles of past data. It is well experienced, as the COVID-19 situation unfolded throughout the world, it not only affected normal life, but also changed the behaviour and psychology of humans. Some changes in consumer behavior like shift to online platforms are more permanent than changes like focused spending. These changes were so quick, that businesses which relied only on the past, saw great variance between forecasts and the actuals. In such scenarios, no concrete decisions can be made based on these models as it would have far reaching impact on the top line as well as bottom line of the brand. The common reasons for most of the forecasting models to fail are lack of indicators to factor in sudden events, heavy dependency on earlier sales patterns, confined seasonal patterns and so on. To redefine this conventional approach, it is necessary to account the variables linked with these changes to provide better insights into changing trends with a reasonable accuracy and lead time.

The change indicators

Leading and lagging indicators are types of technical indicators that either give an indication of what could happen next within the markets or provide information on what has already happened. Leading and lagging refers to whether the indicator moves before or after another metric, such as price action.

To identify the correct indicators for your business, asking the right set of questions is the key. Do financial, economic or un-conventional indicators emerge as potential business influencers?, Here are some common indicators and their influence area –

Financial Indicators indicate trends in the consumer buying capacity while economic Indicators indicate industrial production rate and how the market is constantly changing. Un-conventional indicators like google mobility index indicate people movement around recreational centers, shops, medical facilities etc. While economic indicators are no-brainers, they lack localization and are typically published after-the-fact whereas indicators like Google mobility are localized and available frequently. Hence considering a right mix of both ensures quick learning and implementation mechanism.

For many indicators, the relationship with sales is dynamic and evolving due to the wavering market conditions. Investigating the trends of these lagging and leading indicators brings out fascinating insights about their true nature and cause of the change in pattern.

What indicators should you consider?

Leading indicators may be able to follow the current market dynamics rapidly and provide foresight in business well in advance. Hence identifying and using the relevant leading Indicators for business forecasting in real-time can significantly improve the model’s predictive power and can provide crucial accuracy gains. These indicators should be well supported by business justifications and not based on spurious statistical correlation.

Real-life examples:

For example, Airline passengers can be an indicator of luxury product sales. As more people visit airports, more is the footfall for the hub of luxury brand stores, leading eventually to a higher demand for these products. There is a time gap between the booking of the airlines and the sales, and therefore, the airline passenger could be a useful leading indicator.

Another example would be lockdown index as an indicator of luxury product sales. As the lockdown index decreases, the number of people traveling increases, leading to higher demand for products at stores. Again, there is a time gap between lockdown index relaxation and travel plan execution, providing enough time for the companies to prepare for upcoming surge in demand.

Practical implementation of such a non-conventional model for a brand retailer, shows some fascinating correlations like increase in stock market’s S&P 500 performance in the past three weeks positively impacts its current sales while increase in lock down index in the past two weeks decreases the current sales. Similarly google mobility indicators like percentage change from baseline in movements amongst parks, grocery stores, and pharmacies also impacts sales.

To identify these leading indicators, one should study correlation pattern between sales and various time lags of these indicators. As this relationship rapidly changes with time, these observed indicators will constantly influence sales showcasing their highly predictive nature. Once the right indicators are identified, data availability could be the next challenge. Data availability challenges can be more often resolved using proper data mining, modelling and feature engineering techniques.

What are the trade-offs:

A trade-off between business guidance and correct statistical models can create desirable forecast with added confidence using model evaluation metrics like accuracy or mean absolute percentage error (MAPE). Creating a transparent framework can offer significant business insights for managerial adjustments to the forecasts and the eventual acceptance of the forecasts in the organization. Since the data required for this type of modelling needs to be recent and real time, the practical usefulness of this non-traditional forecasting framework is limited to shorter horizons. The framework has the potential to impact the business positively given traditional models may fail to cope with the situations arising in uncertain times like COVID pandemic. The accurate predictions will drive in-time decision making that may lead to better product availability and higher customer satisfaction.

To build a forecasting model that may impact your business positively, be observant and identify the events, triggers or things impacting for your business. Analyse your position in market and changing sales pattern within your consumer behaviour.

Infocepts specialises in solving a variety of data science problems using techniques like Predictive Analytics, Forecasting, Cluster Analysis, NLP, Recommendation Engines, Computer Vision and many more.

Get in touch to know more!

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