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manufacturing industry

Why Analytics Presents a Game-changing Opportunity for Players in the Manufacturing Industry

Today data and analytics solutions that leverage artificial intelligence, big data, and business intelligence are defining a C-level agenda that forms the basis for all digital transformation initiatives of players in the manufacturing industry. While manufacturers possess huge volumes of unstructured machine data, not all possess the ability to analyze and extract insights from it. But with a powerful manufacturing analytics strategy, manufacturing industry players can tap into this information, converting data into actionable insights to improve decision-making and drive revenue.

Though the data deluge may seem like a challenge, it’s crucial to note that if analyzed properly, data has the potential to become an organization’s most valuable asset. Moreover, the evolution of data is poised to accelerating owing to the evolution of Industry 4.0 and the automation of manufacturing processes. Which means to gain control of this data, businesses that operate in the manufacturing industry must leverage new technologies and advanced manufacturing analytics solutions that turn data to insights. However, the manufacturing industry has traditionally been slow to adopt new technologies and many organizations may find it difficult to fully benefit from investments in digital technologies. This article highlights how manufacturing analytics can improve operational efficiency, productivity, and create a competitive advantage by offering new business opportunities within the manufacturing industry.

We offer customized analytics solutions to help our clients tackle the most pressing challenges in the manufacturing industry. Request a Free brochure of our analytics solutions to learn more about our manufacturing analytics solutions.

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Challenges Facing the Manufacturing Industry

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clickstream data

Clickstream Data Analysis: Cost-Effective Approach for Businesses to Succeed in 2020 and Beyond

What is Clickstream Data?

Clickstream data refers to the data generated by the users when they perform any activity or when they navigate over a web application. It comprises valuable information for businesses that can help them quantify user’s behavior and get an idea of how effective their website is at driving sales. Also, with the help of clickstream data, businesses can understand the user experience, based on their navigation patterns. Furthermore, by analyzing clickstream data businesses can predict which page customers are likely to visit next, improve their marketing strategy and come up with better recommendations.

Talk to our analytics experts to know how our big data analytics solutions can help you visualize online visitor interactions through online channels.

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CLICKSTREAM DATA ANALYTICSBenefits of Clickstream Data Analysis for Businesses 

Click path optimization

Clickstream data analysis can guide businesses in website traffic analysis that can further help them in tracking the path the user takes while navigating through their website. As a result, businesses can gauge metrics that affect user experience such as the number of pages visited, page loading timings, the amount of data transmitted, and frequency of users.. Furthermore, by gaining such useful insights through clickstream data analytics, businesses can optimize the click path by making minor changes to the website to reduce bounce rates and increase conversions.

Our clickstream data analytics solutions enable businesses to track KPI’s and gain insights into customer buying behavior. Request a FREE proposal to gain in-depth insights into our analytics solutions.

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Market basket analysis

Clickstream data analysis can pave the way for market basket analysis that can give them a better understanding of aggregate customer purchasing behavior. Also, by analyzing market basket, businesses can discover common interests of customers and common paths they take to arrive at the purchasing decision. Such valuable information can help businesses to determine the most productive path a site user can take for researching and buying a product.

Quantzig’s real-time data monitoring and clickstream data analytics solutions can help you observe user activity and determine the impact of your marketing campaigns. Request a FREE demo below to know more.

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Next best product analysis

By leveraging clickstream data analytics, marketers can conduct the next best product analysis (NBP). With the help of this analysis, businesses can analyze what products customers prefer to buy together. This can further help them send real-time offers on such products to the customers to improve customer experience. Consequently, this can improve sales and revenue growth in the future.

Better customer segmentation

Clickstream data analysis offers in-depth insights into how individual customer segments behave. This can further help them to personalize customer experience at every touchpoint by analyzing customer behavior and interests in real-time. Additionally, customer segmentation at the granular level can establish another level of transparency and trust with customers and improve customer loyalty and retention.

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data analytics

Retail Data Analytics: How Data Analytics Will Transform the US Retail Industry

Players in the US retail industry are forced to cut costs to stay afloat during the recession period. Though a pain point, cost-cutting offers a good head start towards achieving the desired sales and profit margins. The recent advances in data analytics and technology have made data analysis much simpler, helping retailers to identify and capitalize on new opportunities in the US retail industry. With retailers across geographies offering a diverse range of offerings use similar promotional campaigns, retail data analytics has turned out to be a key differentiator in the US retail industry, helping players make important business decisions with ease. This has made it crucial for retailers to not only integrate and analyze data obtained from different sources but to also leverage retail data analytics to improve their overall decision-making ability.

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Top Data Analytics Applications That Can Help Players in the US Retail Industry

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Insurance fraud

How Big Data and Analytics Helps Prevent Insurance Fraud?

The insurance industry is witnessing a consistent annual growth but companies in this industry are struggling when it comes to insurance penetration among consumers. Globally, insurance fraud has become a major concern for insurance companies. To tackle these challenges insurance service providers are investing huge resources and manpower in insurance fraud detection.

Data analytics solutions can help insurance companies to reduce exposure to fraud, and credit losses by the timely identification of all insurance risks.

Companies in the insurance industry are under tremendous pressure to ensure adherence to claims decisions while striking the right balance under regulatory pressures, growth of digital channels and proliferating competition. This is the reason they are turning their attention towards big data and analytics solutions to identify misrepresentation, detect insurance fraud, and prevent identity theft. 

Talk to our analytics experts to know how our data analytics and visualization solutions can empower you to identify and tackle fraudulent activities.

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INSURANCE FRAUDRole of Data Analytics in Insurance Fraud Detection

SWOT Analysis

Insurance companies can leverage big data and analytics to analyze their strengths and weaknesses first, to, detect insurance fraud. This can help them to identify gaps in their existing fraud detection framework. As a result, they can improve their insurance fraud detection techniques and modify strategies.

Quantzig’s analytics solutions can help companies in better management of insurance claims, underwriting, actuarial, pricing and marketing functions and insurance fraud detection. Request a FREE proposal to gain in-depth insights into our portfolio of analytics solutions.

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Consistent monitoring

Data analytics helps in identifying new trends and patterns in the process of insurance claims as they emerge. This can help insurance companies to seamlessly update their models and rules in the monitoring system to prevent insurance fraud. With this fundamental approach to insurance fraud detection, insurance companies can continuously monitor the billing and patterns off-line as well to identify abnormal insurance claims.

By leveraging our analytics solutions insurance companies can better understand customers’ credit behavior to determine the level of risk in claims and relevant policies to suit and benefit customers. Request a FREE demo below to know more.

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Data Integration

Big data and analytics play a crucial role when it comes to data integration. It can help insurance companies in building effective insurance fraud detection capabilities by combining data from disparate sources. Also, data analytics helps companies in the insurance industry to integrate internal data with third-party data. By leveraging big data analytics insurance companies can integrate data sources with derogatory attributes such as bankruptcies data, criminal records, and foreclosures into a model and can address the transient behavior. Furthermore, big data analytics can help in uncovering anomalies in insurance billing to identify insurance fraud.

To learn more about how data analytics can help you prevent insurance fraud and quantify predicted loss severity from claims, request more information below.

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telecom analytics

Four Interesting Telecom Analytics Use Cases | Quantzig

The growing popularity of digital technologies and advanced analytics tools have impacted business operations across industries. The telecom industry is undeniably at the epicenter of this technological disruption as such advances have not only resulted in the evolution of new business models but have made multi-device usage and multi-device viewing the norm for telecom companies. Leveraging advanced telecom analytics solutions can help businesses gain unprecedented visibility into core business processes. Apart from improving efficiency and growth, telecom analytics also helps companies to analyze real-time data sets to make fact-based decisions that propel peculiar, conclusive, and action-oriented culture across the organization.

Quantzig’s advanced analytics frameworks and data models coupled with domain knowledge can help telecom companies to jump-start their analytics journey. Contact our experts to know more!

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Four Telecom Analytics Use Cases

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banking analytics

Banking Analytics: Transforming Business Operations in the Banking Sector

Today’s customers have become digitally empowered and that is why there is a need for the banking industry to harness the power of customer data with banking analytics. Banking analytics should be the part of every major initiative in the banking industry ranging from customer engagement, retention, to finance and workforce. Also, to harness both external and internal data banks need to leverage banking analytics.

Banking analytics helps a financial institution to be more efficient about the way it goes about targeting its customers, and therefore, be more customer-friendly.

Furthermore, analytics in banking can help banking firms in operational improvement, cost-cutting, and customer experience improvement. In this article, we have curated a list of a few benefits of banking analytics that can help banking firms to measure customer and product profitability, identify high-potential prospects and customers and improve the ability to target products and services to prospects or customers.

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Benefits of Banking Analytics

Customer acquisition & retention

Banking analytics can help companies to identify high-value customer segments that are most likely to respond. This can further help in expanding the customer base by acquiring the right type of customer. By leveraging analytics solutions, banks can predict which business action will them earn loyalty of their customers. Banking analytics aggregates customer data and offers an in-depth insight into each customer’s behavior. This can further help banking organizations to quickly determine which segments are the most appropriate to target for different products and services. As a result, banking firms can improve relationship with their customers leading to improved customer acquisition and retention.

Our banking analytics solutions offer a real-time view of business operations and factors that drive costs, revenues, and financial growth in the banking sector. Request a FREE proposal to gain in-depth insights.

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Fraud Detection

For banking and financial firms across the globe, digitization has paved the way for fraudulent activities.  This has made it important for banks to have intelligent systems and tools to deal with fraudulent activities. By leveraging analytics in banking, banks can easily recognize frauds and analyze the suspicious activities in real-time. Analytics solutions that includes data integration and machine learning techniques can help banks to detect fraud by following a pattern through analytics dashboards. Implementing banking analytics can help automate such findings making fraud monitoring more effective and accurate.

Quantzig’s customized analytics solutions can help you understand customers’ credit behavior to determine the level of risk in real-time. Request a FREE demo to know more.

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Cross-selling

Banking analytics can also be helpful in cross-selling of products. This can be done by analyzing the existing customer behavior and their interests for particular products. By compiling data with the help of banking analytics solutions, banking firms can gain valuable insights and determine where to focus their marketing efforts and which segments to target through marketing campaigns. Also, by leveraging banking analytics, banks and other financial institutions can personalize their communications with customers and offer  relevant banking products and services leading to revenue growth.

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data warehousing and data mining

Data Warehousing and Data Mining: Applications and Emerging Trends You Need to Know

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To thrive in an ever more competitive business world, you not only need to mine data for similar patterns and convert them into actionable insights, but you also need to get better, faster, and smarter at it. At the same time, the scope for finding insights will be even greater. So long as you can figure out how to mine and manipulate data, you’ll be able to learn more about your business than ever before. This also helps you to understand precisely how each piece fits together and how every action and business decision feeds into the mix.

By leveraging data warehousing and data mining techniques and investing in the right data mining applications, businesses can gain a competitive advantage and uncover valuable insights to improve customer experience and business growth. Today data warehousing and data mining techniques have become strategically important to many data-rich firms and play a key role in enterprise decision making. Owing to such factors, it is now considered to be a crucial component of the enterprise decision making and this increase in demand has prompted business executives to ask themselves the following questions-
• Should I invest in data warehousing and data mining software?
• Would it be best to hire data mining analysts or consult an external data mining solutions provider?
• How can data warehousing and data mining help tackle business challenges?

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Though there are no universal answers to these questions, it’s crucial to note that the answers depend on the business landscape, industry, and project requirements. To respond with confidence, business leaders must familiarize themselves with the fundamental characteristics of data warehousing and data mining tools. They need to understand that a successful data warehousing and data mining project requires them to select an appropriate problem and then deploy the most suitable data mining technology or select a data mining service provider for that problem.

Applications of Data Warehousing and Data Mining 

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manufacturing analytics

4 Ways Manufacturing Analytics Can Help Businesses to Optimize Their Operations and Reduce Costs

Businesses that can constantly discover new ways to streamline their operations are successful in the manufacturing industry. Earlier manufacturing companies used to invest a lot of time and resources to examine every single manufacturing process, testing and re-testing innovative ideas and finally implementing changes. However, with the advent of manufacturing analytics, today businesses are able to improve their manufacturing operations more efficiently. With the help of manufacturing analytics, businesses can easily streamline their operations by gaining actionable insights that can further help them to fine-tune their production line. In this article, we have highlighted four ways in which businesses can make noticeable improvements in their manufacturing operations using manufacturing analytics.

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Benefits of Manufacturing Analytics

Production schedule and capacity optimization

When it comes to production scheduling and capacity planning and optimization, most organizations tend to have a hard time. They find it difficult to minimize the discrepancies between their capacity and the demands of their customers. Consequently, most of them are under-utilizing resources or are unable to fulfill customer demand. Manufacturing analytics can help companies in the manufacturing industry to identify, quantify, and prioritize margin improvement opportunities by offering detailed insights into SKU velocity, price band, and segment performance, etc. As a result, manufacturing businesses can improve their production schedule and capacity optimization techniques.

Quantzig’s manufacturing analytics solutions can help you streamline your production processes, improve supply chain efficiency, and increase customer service levels. Request FREE proposal to gain in-depth insights into our portfolio of manufacturing analytics solutions.

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Packaging schedule optimization

Manufacturing companies mostly leave their packaging decisions to designers and marketing experts who know how to market and make the products reach to the target customers. Unfortunately, this takes a lot of time and consumes a lot of resources. Manufacturing analytics can help companies to cut down costs by minimizing the number of switches across the production and bottling lines. It can offer predictive scheduling analysis insights that can help companies to gauge resource availability, and resource utilization capacity to make predictions about future workflows.

Predictive maintenance and scheduling

Manufacturing analytics also helps improve production efficiency through predictive maintenance and scheduling. By leveraging advanced data analytics solutions, companies can improve the efficiency of production lines by predicting the number of goods that will pass or fail a quality inspection. Through predictive maintenance and scheduling, companies can precisely identify risks and failures of their assets and can improve their ability to be responsive to the unique pressures on each machine.

Request a FREE demo below to know how our manufacturing analytics solutions can help you to track all costing activities including resource consumption and final output costing.

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Cost to serve optimization

Manufacturing analytics solutions that leverage advanced data mining techniques, can help companies to gain valuable insights into the actual cost of supplying a specific product to the customer by modeling all logistics activities in the network. Also, it can help businesses to quantify the value of negotiated service levels, shipment exceptions, and other overhead costs. With a robust manufacturing analytics process, businesses can not only gauge the profitability of products, customers, and routes to market but adopt a data-driven approach to decision-making on product mix, service mix and operational changes for each customer.

To learn more about the benefits of manufacturing analytics, request for more information right away!

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Self Service Analytics

Self Service Analytics: Business Benefits You Can’t Afford to Overlook

Businesses are constantly challenged by the growing complexities of data sets as they attempt to cater to the growing market needs and fluctuating customer preferences. They want to be able to trust current, reliable data to follow the right strategic orientations and realign their tactics. More than ever before, organizations today have access to humungous troves of data. But that doesn’t necessarily lead to a surge in business insights. To help build a stronger data-driven culture, organizations are turning to self service analytics. Self service analytics tools offer data access to more people within an organization by allowing them to combine disparate sources of data and create customized analytics models.

Maximizing the value of your data requires giving users the power to create reports that are easy to interact with, understand, and share. The goal is to help decision-makers understand past performance, monitor current performance subsequent to recent decisions and alert them to changes that go beyond a defined threshold for taking an action.

Self service analytics tools empower business executives to generate reports and perform queries with minimal or no support from the IT team. Request a Free proposal for in-depth insights. 

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Benefits of Self Service Analytics

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point of sale

Point of Sale Data Analysis: 3 Best Practices for Retail Businesses to Succeed

In this digital era, the retail industry has been evolving rapidly. The in-store experience has gone far beyond conducting a simple transaction at the point of sale (POS). The functionality of point of sale has enhanced recently and more developments are on the horizon. Today’s digitally empowered customers are expecting better service and experience in real-time. Real-time engagement technologies, such as the Internet, kiosks, digital signage, even mobile apps, are revamping retail and putting consumers in control of the shopping experience. In response, companies that want to conduct business in this next generation of retail are learning to position their point of sale systems at the center of this Internet-oriented shopping experience. This is the reason why point of sale data analysis has become very important for retail companies. With the help of point of sale data analysis, retailers can deliver good customer experience at store levels. In this article, we have curated a list of a few best practices that can help retailers to establish a clear foundation for their point of sale data analysis strategy and remain flexible enough to adopt new solutions as they emerge.

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 POS DATA ANALYSIS

Point of Sale Data Analysis Best Practices

Best Practice #1: Analyze the trend data first

To improve their point of sale data analysis strategy, retailers need to monitor the market trend data timely through effective corporate reports and scorecards. They need to look at both shorter- and longer-term market trends to obtain desired business outcomes. Shorter-term trends can proactively help in fixing issues before they extend into longer-term trends.

Quantzig’s advanced storefront layout analytics solutions help retailers to build a store layout that drives an increase in traffic, average transaction value, and maximize conversions. Request a FREE proposal to gain in-depth insights into our portfolio of analytics solutions.

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Best Practice #3: Conduct distribution analysis

Tracking new items can be done easily and accurately by creating a report that tracks the percentage of stores selling an item. Here, the distribution would only be recognized once the first sale is recorded. Distribution analysis helps in measuring the percentage of stores that carry the particular item. Also, this analysis can help in understanding how quickly the product is getting through the supply chain – from the warehouse to the store’s receiving, then out from the back room and onto the shelf. As a result, retail companies can make changes to their point of sale data analysis strategy and maximize their profit.

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Best Practice #2: Determine stocking schedules at store level

For retail companies measuring out of stock (OOS) lost sales are probably one of the most challenging tasks. But to improve point of sale data analysis, they need to monitor hourly sales data with significant gaps as this could be a strong indicator of OOS issues. If you are only looking at average units per day, you would not see a difference between weekday and weekend sales. Therefore, it is important to look at sales per hour as it helps to identify an OOS issue during what should be a peak time for sales.

By leveraging our point of sale data analysis solutions, you can gain a holistic view of POS transactions and ROS while continuously monitoring end-to-end transactions from payment source to payment completion. Request a FREE demo to know more.

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