Big Data Analytics Archives | Quantzig

Category: Big Data Analytics

big data analytics

Big Data Analytics Helped a Client to Ease Data Processing & Improve Service Efficiency by 12%

“Considering the unique needs of our business and the complexities of our data sets, Quantzig’s big data analytics experts did an outstanding job in laying an analytics roadmap.”

About the Client

The client is a leading mobile marketing automation solutions provider based out of Austria that measures mobile app engagement and provides granular, analytical insights to its customers. They also empower companies to send customized marketing messages to meet the unique needs of the end-users across multiple channels such as in-app, push, email, web, and other media.

The Business Challenge

In today’s economically uncertain era, many leading businesses have come to appreciate that the key to better decisions, more effective customer engagement, sharper competitive edge, hyper-efficient operations, and compelling product development is- Data. The challenges faced by business is not due to the shortage of raw materials, but due to the lack of domain expertise and analytical skills to turn the unstructured, huge volumes of “Big Data” into actionable insights.

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The marketing automation segment witnessed accelerating growth only after 2014, around the same time when mobile users first outnumbered desktop users. Today, unstructured online data sets have grown exponentially making it crucial for businesses to leverage analytical methodologies to analyze these data sets. Moreover, the ongoing data deluge signifies that marketers pursuing consumers need to deploy a way to closely understand the end-users of their applications. This is crucial because once they analyze consumers’ mobile behavior, they can hone their core mobile marketing competencies to match their requirements.

The client- a mobile marketing automation company (MMAC), needed a massively scalable big data analytics platform to inform its marketing-oriented customers about how well their mobile applications were engaging mass-market consumers. The company sought a ‘single version of truth’ platform that was also affordable and user-friendly for the application developers, typically marketers who weren’t necessarily data scientists.

The client’s challenges spanned three core areas including:

  • Velocity
  • Variety
  • Volume

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Solution Offered and Value Delivered

As frontrunners among big data analytics solution providers, we exhibit proven big data analytics capabilities in successfully handling the entire lifecycle of big data implementation including deployment, development, maintenance, and support. Having worked on advanced technologies and big data analytics tools that are leading the big data ecosystem, our analytics experts poses the capability to develop big data analytics frameworks that address all the functional components including data provisioning, data management and data consumption. 

We adopted a comprehensive three-step approach to big data analytics that offered a 360-degree view of consumers interactions with mobile apps. The insights also enabled the client to choose what changes might boost usage, increase business, and retain consumers – and improve the ROI of their marketing investments. Also, by deploying visually interactive big data analytics dashboards we offered in-depth insights tailored specifically to each app developer and its offerings.

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Phase 1

The first phase of this big data analytics case study revolved around data cleansing, data aggregation, and data analysis. A detailed analysis of customer data obtained from millions of smartphones helped the client to gain comprehensive insights into the demands of the end-users.

Phase 2

The second phase of the big data analytics engagement focused on analyzing and translating user behavior across a broad range of mobile apps. It also involved analyzing data on time-series information, sequential action, geo-location, and others across all types of applications and mobile devices.

Phase 3

The final phase of this big data analytics engagement revolved around data visualization and dashboarding to help the client analyze user paths while making in-app purchases.


Business Outcome

With the help of our big data analytics solutions, the mobile marketing automation solutions provider was able to improve their service efficiency by 12% and expand their capabilities, beyond supplying its customers with aggregated data about users of their applications. Data visualization and the devised big data analytics framework enabled the client to track and gauge customer engagement rates. Our big data analytics solutions also offered insights on how they could improve the UI of their applications to improve customer engagement.

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transportation data analytics

A Multinational Manufacturing Giant Revamped Their Logistics Workflow Using Transportation Data Analytics

Headquartered in Denmark, the client is a leading consumer packaged goods manufacturer whose private label products are distributed to several retail outlets across the globe. To sustain a competitive edge, the client wanted to deploy the right logistics management systems and efficient processes to transport their products to the end-users. Though they had an extensive delivery team spread across geographies to connect their warehouses and partner outlets, they faced major roadblocks in transporting their goods in a timely manner.

The Business Challenge

Data obtained from every source can help you unearth actionable insights if analyzed accurately, and transportation data is no exception. Having said that, it’s crucial to note that transportation data analytics has the potential to improve logistics management, as well as enables businesses to optimize transit routes and services. Leveraging transportation data analytics to develop route maps can help businesses to optimize the logistic workflow, which in turn, will result in a drastic reduction in congestion levels and time spent in transit.

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The manufacturer’s delivery team was complex and comprised of hundreds of employees and partners. The use of outmoded logistics tracking systems made it difficult to track, update, monitor, and deliver products in a timely manner. Also, the outdated legacy systems were difficult to maintain and proved to be of no use in logistics management. Such factors along with the ongoing loss made it crucial for the client to replace the legacy system with a more efficient logistics work flow.

Solution Offered and Value Delivered

The CPG manufacturer approached Quantzig to leverage its expertise in transportation data analytics and drive significant improvements in the logistics work flow. Our transportation data analytics solutions helped the client to gain in-depth insights into their logistics routes through advanced backend dashboard that provided real-time insights based on transportation data.

Through our transportation data analytics solutions we helped the client to revamp their current logistics workflow and achieve the desired level of efficiency. In addition, it’s essential to note that transportation analytics improved their logistics and route planning capability and helped them achieve huge savings on maintenance and renewal.

Ensure the efficient transportation of people and goods using advanced strategies built from the insights of transportation data analytics.

Benefits of Transportation Data Analytics


master data management

Harnessing the Power of Master Data Management to Enhance Spend Visibility and Compliance in the Manufacturing Sector

The client is a leading manufacturer and distributor of high-quality construction equipment based out of Europe. The company is well-known for delivering high-quality construction equipment and services all over the globe. In today’s complex business scenario, establishing a data governance program may seem like an uphill task even for well-established companies. The client, a multinational conglomerate in the industrial and manufacturing sector was no exception. With business units spread across Europe, the client faced several master data management issues that left a measurable impact on their business operations.

The Business Challenge


enterprise data management

Enterprise Data Management for an Industrial and Manufacturing Company | Quantzig

Learn how our enterprise data management solutions and data visualization dashboards can open a new world of possibilities for your organization.

The ever-growing need to enhance compliance, risk management, operating efficiencies, and client relationships have prompted leading players across industries to focus on enterprise data management. This requires businesses to adopt a comprehensive approach to data analysis. A well-structured enterprise data management system helps businesses to bring most of these functionalities under one umbrella, helping them establish appropriate standards of conformity, data integrity, and reliability by increasing data efficiency and throughput.

At Quantzig, we understand the need to deploy robust data management systems to tackle the challenges that arise in today’s complex business landscapes. Our customizable enterprise data management solutions have helped several players across industries. This success story is a classic example of our data management capabilities.

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What is enterprise data management?


healthcare analytics

How Healthcare Analytics Helped a Client to Unravel Many Facets of the Oncology Drugs Segment to Successfully Launch a New Product | A Quantzig Success Story

The client is a leading player in the specialty drugs market based out of the United States. With several years of expertise in drug development, they were looking at leveraging healthcare analytics to make a remarkable entry into the oncology drugs segment through the introduction of a new oncology drug.

The Business Challenge

The healthcare industry today is amidst major tectonic shifts. The ongoing regulatory changes coupled with far-reaching patient protection and affordable care act in the United States are prompting leading players to change their status quo. Besides the increasing costs of regulatory compliance, healthcare players are also facing rising research and development costs. Though the increased regulatory supervision has turned out to be a major burden. Such regulatory requirements are putting in place a solid foundation for healthcare analytics, which, in turn, can be used to drive major organizational transformations. By using this opportunity organizations can extract additional value from patient data and leverage it to create a competitive differentiation.

Though the specialty drugs market continues to exhibit a massive sales growth with the introduction of more targeted therapies, it has become increasingly challenging to monitor and understand how therapies are being utilized by physicians. This challenge is further compounded by the complexity of the oncology drugs market where treatment decision options are highly personalized to each patient’s genetic profile. Facing similar predicaments, a leading pharma company approached Quantzig to leverage its healthcare analytics expertise to successfully launch their new oncology drug.

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Top Challenges Faced by the Client

Problem Statement 1

The client needed to adopt a sophisticated analytics-based approach to gain detailed insights into physician and customer journeys. Limited insights into market dynamics and understanding of customer decision making prevented them from gaining a complete view of the market.

Problem Statement 2

Though the new product launch strategy looked promising, the client had to gain better insights on stakeholder interactions and process data, as multiple stakeholders were involved in the oncology drugs segment.

Problem Statement 3

The company also needed to develop a post-launch analytics strategy to assess the marketing effectiveness and commercial success of the new drug. This proved to be a major challenge as it required the client to devise a long-term, holistic approach encompassing enterprise-level capabilities, technology, analytics, and data management skills.

Analyzing the Complex Pharma Landscape with the Help of Healthcare Analytics

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Solution Offered and Value Delivered

To gain the desired insights and to support the information needs of the oncology drugs segment, the healthcare analytics experts at Quantzig adopted a comprehensive three-pronged approach.

Phase 1

The initial phase of this healthcare analytics engagement focused on analyzing patient datasets to offer a comprehensive view of patient journeys. Such an approach not only helped unravel the different facets of the oncology drugs landscape but also helped the client to better understand the current prescribing and utilization patterns, which, in turn, helped them identify physician practices that presented the greatest opportunity for improving patient care.

Phase 2

In the second phase, a dedicated team of healthcare data analytics experts and data scientists were assigned the task of assessing the impacts of prescribed therapies and their clinical outcomes. Leveraging healthcare analytics helped bring about ground-breaking results by offering meaningful insights that empowered the client to further strengthen their drug development approach and improve the marketing effectiveness of the new drug.

Phase 3

The third phase of this healthcare analytics engagement focused on leveraging predictive analytics to gauge the impact of the new product launch.

The healthcare analytics solutions coupled with predictive health analytics offered timely, targeted insights that aided the client’s drug development process. Along with identifying the factors driving therapies, drug development, and treatment selection, the offered solutions also helped the client to identify barriers to utilization. Equipped with such granular insights the client was better positioned to gauge marketing effectiveness and the success ratio of its new product prior to its launch.

Why is Data Analytics Important in Healthcare?



A Leading International Bank Improves New Account Activity and Customer Retention Using Predictive Modeling


The banking and financial services sector has transformed tremendously over the past few years. The recent advances in analytics and predictive modeling techniques have further propelled businesses by offering powerful analytics tools to gain insights into the changing customer needs and behaviors. With the rise in the use of advanced analytics and data visualization techniques, these analytics advances have begun to accelerate rapidly across industries. The potential benefits of these sweeping new advances and predictive modeling techniques are reflected in a variety of areas such as enhanced anticipation and prediction of possible customer churn, improved effectiveness of cross-selling and marketing activities, and greater efficiency and accuracy in anti-money laundering, and other compliance initiatives.

In such a complex business scenario, satisfying the growing customer base turns out to be a daunting task even for well-established banks. Though banks have been adopting various tools to address these challenges, factors such as ensuring long-term loyalty, customer retention, fraud detection, and credit risk management have always been key areas of concern. Facing similar challenges the client in this study realized that predictive modeling would help them address such issues. The client chose to partner with Quantzig to effectively address their challenges and to expand their knowledge of how modern tools and predictive modeling techniques could improve the efficacy of their existing business models.

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Predicaments Faced

The banking client had been running a new customer acquisition program that focused on new customers relocating near branches with a cash incentive to open new bank accounts. Although the program had generated reasonably good results, the bank was anticipating reductions in available marketing budgets and wanted to lower program management costs while improving overall business outcomes.

Our Solution

Our experts worked closely with the client to develop a new predictive modeling process that makes accurate forecasts to best serve their business budget and operation planning needs. The devised predictive modeling process helped the client to identify influential attributes of new responders by categorizing the prospects into five groups based on their probability of response. The client targeted the top five categories that consisted of 60% of the new and most responsive user groups.

The use of a new predictive modeling approach delivered detailed insights and accurate predictions that helped resolve business uncertainties into profitable probabilities. To take advantage of the insights offered by the new predictive modeling approach and to make better, more profitable decisions, the client wanted to deploy predictive analytics models in their operational systems. A new business plan coupled with a robust predictive modeling platform delivered:

  • A collaborative environment and shared framework for problem definition to ensure the analytics is solving the right problem
  • A repeatable, industrial-scale predictive model

Our Predictive Modeling Solutions Can Help You Gauge Business Success

Business Impact

The solutions offered resulted in a stable predictive model with a performance that exceeded the client’s existing system, despite the considerable effort that had been invested in their existing model. Also, it’s essential to note that by focusing on the most responsive, new targets the client significantly increased customer acquisition rates and associated transactions while cutting down on their marketing costs. The predictive modeling solutions also empowered the client to fine-tune the audience based on various criteria to accurately predict acquisition campaign results. This, in turn, enabled the bank to optimize program performance on a continuous basis.

The adoption of predictive modeling techniques offered the following outcomes:

  • Customer acquisition rates increased by 25%
  • New account activity improved by 30%
  • Significant reduction in marketing costs

What are the different types of predictive models?


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