80% of executives believe that automation can be applied to any business decision, according to a recent Gartner survey. Businesses are automating a wide range of business processes and operations, from simple and repetitive tasks to complex and mission-critical operations.

Companies have been working to become more data-driven for many years at this point, with mixed results – only 26.5% of companies indicate that their organization is data-driven. Automation tools directly impact brand success and are frequently adopted and integrated by businesses to stay competitive within their industry, and enable data-driven transformation. Data-driven automation enables businesses to improve operational efficiency, make better decisions, and deliver an enhanced customer experience.

Automation projects can be a slippery slope – if not executed properly, it can adversely impact data processes, usage, employee confidence and the customer experience. To realize the value of automation, data and analytics must champion data-driven automation as a strategic thread of business DNA, not a tactical one-off project.

As businesses look for opportunities to modernize their processes and optimize operations via data-driven automation tools, they must first develop a meaningful strategy. This requires a well-planned approach that includes clear objectives, appropriate technologies, and the right skills.

The first step in building a strategy for data-driven automation is to define clear objectives. These objectives should be aligned with the organization’s overall business strategy and should be specific and measurable. A scoring methodology can help businesses rate opportunities for automation according to business impact while sustaining an ongoing backlog for prioritization.

Organizations also need to have the necessary tools & skills in place to support their automation strategy. They must carefully consider which technologies are best suited for them – like robotic process automation (RPA), artificial intelligence (AI) or machine learning (ML). Having experts such as data scientists, engineers and specialists on board will guarantee faster results.

In short, data-driven automation is no longer a luxury but a necessity for today’s organizations who are looking to thrive in an ever-changing market.

Check out the full article for data-driven automation usecases and more steps for successful implementation.

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In today’s business landscape, data is the key which separates thriving companies from those falling by the wayside. Whether it is rapidly redesigning products & services to the new realities, closing talent supply-demand gaps, or resetting operational costs based on zero-based budgets – businesses & executives need the ability to make the right decisions at the right time with the right stakeholders.

Despite increasing investments in D&A capabilities to support such needs, a 2023 D&A Leadership Executive survey highlights the gap between the worlds of business & analytics where the overly optimistic assessment from leaders executing D&A initiatives (98% say D&A is providing value) does not match with the progress reported on longer-term initiatives such as driving business innovation with data (only 59.5% say yes), and, creating a data-driven organization (only 23.9% say yes).

So, if you are responsible for D&A capabilities, it can be difficult to separate what are the real, transformational initiatives capable of revolutionizing your business from the ‘noise’ of so many new technologies and trends promising ‘faster and more accurate access to insights.’ Using our experience supporting & guiding leaders at multiple levels, I help you cut through the noise to focus your limited time, effort & resources on the areas that will be most impactful to the goal of using D&A to drive business growth in 2023.

  1. Embrace managed AI to realize the promise of AI for your business.

    McKinsey – who expects a $10T+ return from AI – rightly identifies, success remains elusive for many businesses, with 72% not yet able to take advantage, while an astonishing 85% presently fail to realize any sort of business benefit from their attempts at integrating AI into existing operations! If majority of the AI projects fail, organizations must invest in failing fast. In other words, you must be able to identify the use cases that will achieve specific business outcomes & are feasible & do it in days not months. Rather than DIY, you should consider fully managed AI solutions for business such as Infocepts’ AI as a Service (AIaaS).

  2. Revisit resource allocation models for your D&A projects to get more done with less.

    Companies are facing financial crunch because of on-going economic uncertainty including the very real possibility of a recession in the next 12-18 months. Data shows that companies that come out strong after a recession – i.e., with twice the industry averages in revenue & profits – are proactive in preparing & executing against multiple scenarios backed by insights. They re-deploy employees against the highest value activities in their strategy, weigh trade-offs & take deliberate risks, but without cutting corners on critical projects. You should a) stop paying for legacy technology maintenance, b) fix your broken managed services model, & c) choose on-demand talent.

  3. Help your teams ‘actually’ use your D&A capabilities to increase adoption & realize business value.

    Many organizations have invested in specialized teams for delivering data. But the real value from data comes only when your employees use it. Data fluency is an organizational capability that enables every employee to understand, access, and apply data as fluently as one can speak in their language. With more fluent people in an organization, productivity increases, turnover reduces, and innovation thrives without relying only on specialized teams. Top roadblocks according to industry surveys include lack of trust in organizational data and lack of data skills in employees. You should a) assess your organizational fluency, b) establish a data concierge, & c) introduce interventions to strengthen access, trust, and adoption.

  4. Inverse your team’s thinking in 3 (counterintuitive) ways to be more responsive to your business needs.

    Large enterprises have multi-year roadmaps to advance their transformative D&A capabilities to include modernizing existing platforms, retiring legacy ones, or building new applications for business. A proven approach is centred around building a business-driven roadmap, using a modular foundational architecture, aligning cross-functional stakeholders, and allocating necessary talent for success. It sounds simple, but the execution is fraught with challenges.

    Patterns such as agile, data mesh, data fabric, and delta lake guide IT leaders for dealing with them, but there are 3 antipatterns in how work is managed that slow organizations down. You are likely experiencing them! If so, you should a) design work for outcomes, not functions, b) choose products over projects, & c) choose teams over staffing. These tactics will increase flexibility in your roadmap execution, responsiveness to your business needs, and effectiveness of your teams.

  5. Reimagine how you bring value to business – move fast with Solutions as a Service!

    Salesforce and Amazon pioneered commercially successful IaaS, PaaS, and SaaS models in cloud computing that gradually shifted significant portions of responsibility for bringing value from the client to the service provider. The benefits of agility, elasticity, economies of scale, and reach are well known. Since then, countless products have emerged in these 3 categories. But they are limiting & constrain clients from going one step further towards productized services, what we call, Solutions as a Service!

    You must combine products, problem solving & expertise together in one easy to use solution. This is inevitable given the sheer pace at which technology is evolving. This new category requires a shift in thinking and will give you a source of advantage like how the early cloud adopters received during the last decade. Infocepts offers many domain-driven solutions in this category to include data products such as e360 for people analytics and AutoMate for business automations.

Moving forward from ideas to actions

D&A leadership will be challenged like never before in 2023. You can either keep doing more of the same, or take a step back, reflect, and lean-in on new & better ways to move forward on your D&A initiatives. To learn more about the transformational D&A initiatives that you must get done, download our advisory note for the full analysis, examples, and recommendations.

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58% of those companies who have implemented AI report increased efficiency and decision-making throughout their teams. Yet only 12% of companies are actually benefiting from this technology.

AI is rapidly reshaping the business landscape and is now poised to revolutionize decision-making processes. AI-supported decisions are changing how businesses operate and enhancing the effectiveness, speed and accuracy of decision-making processes. Many organizations have made great strides towards implementation but it’s a complicated, time consuming process that often leads to failure.

Organizations need to know what challenges they’re up against when attempting to leverage AI-assisted decision-making and the best practices that help them drive widespread value from their data.

Top challenges which organizations face in implementing AI-assisted decisions

Though AI-assisted decision making is a quickly growing field, many data and analytics executives struggle to successfully implement and scale these solutions within their organization. Here are the top challenges you’ll need to tackle:

  • Poor business case for AI
    Without a clear strategy and business case, organizations might not have a good understanding of what they hope to achieve with AI-assisted decision-making, making it difficult to determine if implementation is successful. Also, it can be difficult to integrate the solution into existing business processes, gain support of key stakeholders, measure ROI, and identify opportunities for ongoing improvement.
  • Data quality
    One of the biggest challenges in implementing AI-assisted decision-making is ensuring that there’s copious amounts of high quality data. Poor data quality can negatively impact the accuracy of AI algorithms and limit the ability to provide meaningful insights, leading to greater inefficiencies.

Best practices for implementing AI-assisted decision-making

Implementation of AI-assisted decision-making requires careful consideration of these challenges and a strategic approach to ensure efforts are delivering measurable business value and growth – and placing AI-powered insights into the hands of the organization. Below are a few best practices data and analytics executives should embrace:

  • Develop a comprehensive strategy
    A clear strategy helps align AI-assisted decision-making with overall business goals and ensure that resources are allocated effectively. High performing AI adopters tend to link their AI strategy to business outcomes. Executives need to define their business objectives and where AI can add the most value; assess their existing infrastructure to determine what must be in place to support the solution; conduct a feasibility study to understand the efficacy and cost of implementation; secure buy-in from key stakeholders; and develop a roadmap for implementation, including budget, milestones, and timeline.
  • Foster collaboration and communication
    Data and analytics teams, technology partners, and stakeholders from all levels of the organization should be involved in the design, development and implementation process to ensure all needs and concerns are taken into account. Establish regular communication channels and encourage cross-functional collaboration to facilitate open discourse about the status and progress of AI projects to increase buy-in and ensure alignment on the goals of AI-assisted decisions.

There are many other challenges which organizations face and best practices which will help you realize the full potential of AI-assisted decision-making. Read our ebook to learn more tips.

Check out Infocepts DiscoverYai, an end-to-end solution providing 360° support to take care of all your woes and embed best practices in your implementation process easier than ever.

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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.

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