Category: Blog

in-store analytics

A Beginner’s Guide to In-Store Analytics

The rapidly-growing, competitive retail landscape has increased the need for customer analysis across different business segments, especially e-commerce. This is the reason why the demand for in-store analytics has gone off the roof. Today conventional retail businesses have started implementing in-store analytics aggressively to retain their customers. Due to this factor, in-store analytics is now n integral part of conventional physical stores.

At Quantzig, we understand the importance of in-store analytics in retail business. And to help companies in the retail sector to excel in the competitive landscape and stay ahead of the curve, our team of experts has curated a detailed guide to in-store analytics. This guide can assist companies to improve profitability and retain their customers significantly.

Are you finding it difficult to track all the cart and basket values in your store? In-store analytics may hold the key to success. Get in touch with our experts to know how our analytics solutions can help your retail business.

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What is in-store analytics?

In-store analytics is the process of collection of customer data and applying them to operations of the retail store. This can offer actionable insights into their business on both macro and micro levels. Is it sounding too complex? But it is not, provided you have proper retail analytics solutions in place. A proper analytics dashboard offers data-driven insights that make all data easily understandable and quickly applicable. In-store analytics solutions provide pre-built reports that help retail business in identifying key metrics responsible for retail performance. Furthermore, this helps in decision making and devising an overall business strategy that is based on data-driven facts and not on assumptions.

Want to know more about why in-store analytics is important for retail business? Download our free supplement here:

What is in-store analytics important for retailers?

#1: Facilitates better pricing decisions

In-store analytics has the potential to analyze price sensitivity efficiently. It can offer better visibility to retailers into the factors responsible for fluctuations in price. Also, it helps in identifying the points that have a major impact on the volume of sales. Furthermore, this can help in generating additional revenue for the retail business and safeguard them from any future price fluctuations.

#2: Track conversion rates

In-store analytics plays a crucial role in helping retail businesses to make informed decisions by keeping track of factors leading to conversions which is the heart of retail analytics. Also, these insights assist in monitoring retail performance, understanding consumer behavior, and enhancing the shopping environment. Furthermore, advanced analysis combined with data obtained from all these sources enables retailers to improve marketing operations, drive sales and boost the productivity of retail staff.

Reliable data leads to smarter decisions. Our retail analytics solutions help companies to understand customers and their behavior. Based on our analytics solution businesses can obtain profound insights into the buying process of customers and their journey. The results, in turn, can help businesses to drive customer traffic, monitor customer journeys, and translate prospects into loyal customers. Request a FREE proposal to know more about our portfolio of services.

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#3: Understand customer behavior

In-store analytics helps in understanding the behavior of customers. This can further help businesses to send targeted offers and content depending on the response of a customer towards the brand. Marketing operations become more efficient if marketing is done through a targeted approach. This has high chances of inducing sales and thereby, converting a prospective buyer to a loyal customer. 

Our retail analytics guide is here to help you improve sales and achieve higher profits. Download the free supplement now.

Benefits of In-Store Analytics

Benefit #1: Improves the shopping experience

Today customers are well informed, and they research a lot before making any purchase. A retail business if incorporate in-store analytics to their marketing operations, can identify buying patterns of customers and their buying preferences. Consequently, this can help in enhancing the marketing strategy and improving the shopping experience of customers.

Benefit #2: Enhances customer support service

If you are into retail business and want to improve your customer service, the most important way to do that is gaining insights into customer’s response towards your brand. Conducting surveys help but it is not the proper solution because sometimes the customer’s response is not genuine. Therefore, tracking and analyzing customer data can help more in improving customer service and in-store analytics makes this very easy.

Our in-store analytics solutions can help you see how the customer flow translates into conversion. Request a FREE demo to gain better insights.

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Benefit #3: Prevents theft and burglary

For retail stores, shoplifting is one of the most regular problems that they face. In-store analytics can come to the rescue here. By leveraging in-store analytics, retail stores can identify the spots that are highly vulnerable to shoplifting. Also, the data from the in-store analytics dashboard can help retail stores to strategize their marketing operations and take key decisions in improving security at the store. Consequently, this can help in dealing with theft and burglary.

Benefit #4: Optimizes store performance through real-time dashboards

In-store analytics dashboards offer specific insights to examine the product placement in the store and the optimal floor layout. Also, it helps in identifying frequented and non-frequented areas in the store. The in-store analytics dashboard also offers actionable insights into conversion by section and traffic by section. Furthermore, it helps in identifying profit maximizing patterns and paths. 

Want to know more about the benefits of in-store analytics and how it can enhance your retail store performance? Request for more info below.

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predictive analytics in marketing

Predictive Analytics in Marketing: Key to Drive Business Profitability in 2019

What is Predictive Analytics?

Predictive analytics is an approach that helps in predicting unknown future events. There are many techniques that are used in predictive analytics such as machine learning, data mining, data modeling, and artificial intelligence to examine current data and make future predictions. Also, it helps in finding patterns in both structured and unstructured data sets, thereby assisting in the identification of future risk and opportunities. Furthermore, predictive analytics has the potential to integrate management and technology together to drive better business outcomes. By leveraging predictive analytics solutions, businesses can become dynamic and can anticipate behaviors and outcomes based on the known facts and data and not merely upon assumptions.

How can predictive analytics solutions help businesses derive successful outcome and boost ROI? Read Quantzig’s recent blog to find out.

3 Ways Predictive Analytics Solutions Can Help Businesses Derive Successful Outcomes

Leveraging Predictive analytics in marketing can help businesses refine their marketing strategies and provide personalized services to customers. Want to know how? Get in touch with us right now!

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Predictive Analytics in Marketing Realm

How can predictive analytics in marketing drive profitability for business? Are you thinking the same? The answer to it is, any tool, process or technique that can guide marketers to identify the buying habits of consumers is nothing less than a boon to their business. This is because if the past buying habits of a customer are identified and analyzed well, it can help in projecting the future buying habits, thereby helping in future decision-making based on those projections. Predictive analytics in marketing helps to ensure that these predictions are precise and accurate.

Here are a few things that a business can do when the available data is mined and predictive analytics in the marketing realm is applied:

Analyze and predict the seasonal behavior of customers

Today most of the products and services are sold online. Application of predictive analytics in marketing especially helps in this case. It helps in highlighting the products that are on high demand and those that customers prefer to buy at any given time.

Target the most profitable product category

The second benefit that businesses gain by applying predictive analytics in marketing is that they can target the most profitable products and services.  By administering the technique of artificial intelligence and machine learning, it is easy to identify affluent customers who prefer high-end products. This is an integral part of effective and predictive marketing strategy too.

By applying predictive analytics in marketing businesses can gain insights into new profits streams, better ways to conduct the business, and ultimately lead the game. Request a free proposal to know more.

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Employ the most suitable marketing strategy for winning repeat business

Predictive analytics in marketing can inform businesses about customers who are most likely to be repeat customers. Owing to the high competition, businesses need to allocate resources on targeting such customers that are likely to profit the business the most. And applying predictive analytics in marketing is the best step to achieve this.

How can predictive analytics help in forecasting consumer demand precisely? Read our latest success story here to gain better insight.

Forecasting Consumer Demand with the Help of Predictive Analytics – A Quantzig Success Story

Prioritize customers

Finally, predictive analytics in marketing helps in prioritizing customers. It helps in identifying factors that indicate that a particular customer s most likely to become a repeat customer. It guides to recognize customers who buy the highest-margin products and are most likely to initiate returns.

Use-Cases of Predictive Analytics in Marketing

predictive

Use Case #1: Refine segmentation for better campaigns

Applying predictive analytics in marketing helps in refining customer segmentation and creating customized campaigns. It allows to mine behavioral and demographic data to push quality leads further down the sales funnel.

Use Case #2: Improves content distribution strategy

Sometimes even the good content fails to drive business and the reason behind this is an ill-defined content distribution strategy. By applying predictive analytics in developing a marketing strategy, this problem can be tackled head-on. Using predictive analytics in marketing makes it easier to analyze the types of content that resonate most with customers of certain behavioral or demographic backgrounds. Furthermore, this helps in distributing similar content to such customers sharing the same demographic or behavioral habits.

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Use Case #3: Precise prediction of customer lifetime value

Artificial intelligence and machine learning can make predictive analytics in marketing more efficient. It can enable businesses to gauge the historical lifetime value of existing customers that match the backgrounds of new customers. Consequently, this can help in making a fair and precise estimate of the lifetime value of new customers.

Use Case #4: Better insight into the propensity to churn

Protecting your bottom-line becomes much easier by leveraging predictive analytics in marketing. How do you ask? By analyzing and learning from the mistakes committed in the past. By applying predictive analytics in marketing, businesses can analyze the behavioral patterns of previously-churned customers. Furthermore, this can help in identifying the warning signs from current customers. Consequently, businesses can take measures to plug such customers into a churn-prevention nurture campaign.

Use Case #5: Optimization of campaign channels and content

By leveraging predictive analytics in marketing, businesses can optimize their campaign channels as well as the content. With the entry of new customers in the business pipeline, there is an availability of their data which can be utilized for the various purpose. These purposes include identification of most suitable marketing channels, content type and even data and time to target specific and potential customers.

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Customer experience management

Customer Experience Management: Revamping Customer Engagement Effectively

FY 2020What is Customer Experience Management?

Customer experience management is a concept that deals with the conscious and subconscious perceptions of the customer about a particular brand. Such perceptions are the results of all communications and interactions with that particular brand during the customer life cycle. Customer experience management helps in meeting and fulfilling customers’ demands effectively in real-time. It is an approach that helps in discerning customers’ preferences and delivering them with personalized services. Consequently, this improves customer loyalty, retention, satisfaction, and advocacy. The personalized experiences entice customers to evangelize others about your brand, thereby, acting as a perfect medium for advertising. Additionally, customer experience management helps businesses to extract actionable insights about customers’ behavior from all channels and touchpoints across the organization with precision and speed.

Customer experience management can help companies in predictive analysis and continuous tracking of customer behavior. Want to know how? Get in touch with us now!

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Why is Customer Experience Management Important?

Today’s business environment is highly competitive and hyper-connected. Companies to stay ahead of the curve, need to understand their customers and their needs completely to enhance customer service and experience. This is where customer experience management can help. An effective customer experience management can do the following:

  • Reduce costs by lowering customer churn rate
  • Enhance customer loyalty through valued customer service and interactions
  • Boost revenue through word of mouth sales as well as sales from existing customers
  • Strengthen brand image by offering a personalized customer experience
  • Turns new customers into potential and loyal customers
  • Increases customer advocacy
  • Assist in staying ahead of the competition

Challenges Businesses Face in Customer Experience Management

Challenge #1: Creating steady customer experience across all the channels

Today, customers are well informed and have high expectations from the brands. They expect to receive good service levels from different channels and at the same time they desire for a consistent brand value proposition.  But when it comes to channel proliferation, it makes it tough for businesses to ensure such consistency across all channels and this emerges out to be one of the major challenges in customer experience management.

Quantzig helps businesses to quantify and forecast the value of individual customers across various dimensions. Our analytics solutions help companies in identifying and curating high-value customers to develop effective marketing strategies and maximize profitability. Request a FREE proposal to know more about our portfolio of services.

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Challenge #2: Integrating brand and channel experiences

Customers demand integrated channel experience, but it is challenging for the businesses to offer due to the hurdles like legacy processes and technology barriers. Also, organizational territorialism acts as a major obstacle for businesses to establish a proper customer experience management system.

Challenge #3: Data collection still in the phase of evolution

Most brands today are collecting data through a method of traditional exchange of giving and take which means brands give their consumers regular information for free like updates on new and existing products and collect valuable personal information. However, this exchange is not ideal, and the model needs to evolve. Companies need to understand the need and demands of every individual customer. They need to use customer data wisely to serve the customers with exactly the same information that they want to know.

Key Steps to Getting Customer Experience Management Right

Step #1: Create and maintain proper customer profile

For establishing an effective customer experience management process, you need to understand your customers completely.  This can be done by creating and maintaining a proper customer profile. Such profiles can help in analyzing and measuring the customer journey and customer lifecycle at every touch point across multiple channels. The complete knowledge about customers paves the way for better service and strengthen the relationships. Companies can use structured and unstructured data to create a customer profile. Furthermore, it can help in adding customer value, behavioral analysis, and propensity scores. Creating a customer profile can help in improving customer experience management in three ways:

  • Suggest ways to improve the customer experience at specific touch points.
  • Understand customer demands and what they expect you to do for them.
  • Facilitates better decision making.

Step #2: Personalize customer service

The next step in improving customer experience management is the personalization of customer service. If you know your customers, you can focus on the context in which the customer operates. The data derived from the customer profile can help in maintaining that focus, provided you keep on enriching the existing data with new sources and information. Consequently, this can help in serving customers with insightful offers, relevant recommendations, and services when the customer is most receptive. You need to keep this in mind that today customers are more powerful, have more choices and presence than ever before. Therefore, providing a relevant, timely and insightful message is important and it can prevent them from getting alienated. Also, this drives brand loyalty and improves brand value.

Want to know how customer analytics solutions can help you improve customer experience management in real-time? Request a FREE demo below.

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Step #3: Adopt the approach to deliver the right message at the right time

The third step in customer experience management involves the use of analytics. If you want to serve your customers with the best offers at the right time at each touch point, you need to leverage analytics solutions. Mapping analytics at every specific stage of a customer life cycle can help in delivering the right message to the right place at the right time. Every stage in the customer life cycle is crucial, whether it is the initial consideration, the stage of evaluation, the stage of purchase, or post-purchase. Each stage offers an opportunity to improve customer experience management and to gain more insights into the customer’s journey. Such valuable insights can improve marketing processes and overall business outcome.

How Can Quantzig Help Your Customer Experience Management?

In a rapidly evolving market landscape, customers are more connected and empowered as they have access to information anywhere and anytime. This makes it vital to analyze and forecast customer behavior to stay ahead of the curve. Our customer analytics solutions help businesses get a better understanding of the buying behavior of customers, their lifestyle preferences through real-time dashboards. Precise predictions of customers’ buying behaviors help businesses deliver relevant offers and improve customer experience management. At Quantzig, the consultants and data scientists help companies to build customer analytics capabilities and strengthen vision to discover and drive continuous business excellence.

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IoT analytics

Top 3 Benefits of IoT Analytics That Businesses Can’t Ignore

From consumers in a digitally rich era to enterprises harnessing new technologies, everyone and everything is connected in an internet of things world. IoT analytics has made it possible for businesses to gain actionable insights that can help them improve decision making effectively. Almost all businesses today are looking for real-time data to decide their next step that delivers a competitive advantage. IoT analytics is an analytical approach that involves analyzing data generated by sensors. The potential of these sensors can be deciphered from their ability to collect data about the physical environment. These data can be then combined and analyzed with various other forms of data to identify valuable patterns that can further help businesses in different aspects such as understanding customers behavior, customer sentiment analysis, detecting fraud, and ensuring security.

At Quantzig, we understand the importance of applying analytics to the incoming streams of data and the impact that it can create for businesses when the volume of data is growing continuously. And to help companies understand and leverage IoT analytics for their business operations, our team of experts have highlighted a few important benefits of IoT analytics.

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Benefits of IoT Analytics

Benefit #1: Helps in identifying inefficiencies

Implementing IoT analytics can enable businesses to turn the big data that they gather into potential revenue. For example, If IoT analytics is used in mining businesses to monitor their processes of production, it can verify accurately whether automated mining systems are performing well or not. If performance is not as expected, it can further help in identifying the part of the process line that fails to yield the expected results.

Request a free proposal to know more about how Quantzig’s analytics solutions can help your business achieve desired outcomes in real-time.

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Benefit #2: Assist in predicting future trends

IoT analytics solutions are leveraged by many industries today to identify future trends. The energy industry is perhaps one of the major industries that is taking advantage of IoT analytics today. Internet of Things is being used in several ways to improve this industry. One such application revolves around tracking the usage of energy to create reliable estimates of demands in the future. Also, analytics in IoT can help in tracking the performance of different business functions. This is a huge benefit of IoT analytics for the manufacturing industry.

Request a free demo to learn how IoT devices and analytics can help your organizations to leverage targeted marketing and promotions.

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Benefit #3: Improves customer analysis and engagement

IoT analytics can help in identifying the trends of customers buying behavior and patterns. By combining Big Data with IoT analytics solutions businesses can track their customers’ buying patterns and their preferences. Also, it can help in sentiment analysis and provide customers the services they are looking for. Additionally, it also enables businesses to promote targeted advertisements to better serve their customers by offering a personalized experience.

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Supply chain analytics

Unveiling the True Potential of Supply Chain Analytics

Supply chains are lucrative platforms for gaining a competitive advantage. This is because of two reasons which include their complexity and their role in defining the cost structure of a company.  With the growing data complexity, most companies are finding it difficult to model their trade-offs of growth and supply chain cycles such as the procurement process. This is where supply chain analytics can help.

By leveraging supply chain analytics solutions, companies can fine-tune their entire supply chain network in ways that was probably not possible in the past. Supply chain analytics can help in effective supply chain management and identification of inefficiencies in supply chain configurations. It has the potential to improve the end-to-end performance of the supply chain in terms of financial, operational as well as managerial aspects. Therefore, it is high time that companies realize the potential of supply chain analytics in a real sense.

The growing complexity and globalization have put supply chains into the limelight like never before.

At Quantzig, we understand the difference that supply chain analytics can create in the supply chain operations of a company. And to help companies deal with the logistics and transportation challenges as well as improve the supply chain strategy, our team of experts has highlighted a few major benefits of supply chain analytics that can act as a guide for companies to understand market volatility, the impact of indirect supply chain cost and improve ROI.

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Benefits of Supply Chain Analytics

Gain a significant ROI

Supply chain analytics has the ability to analyze the structured as well as unstructured data. The insights gained from such data can help companies to respond quickly to customers’ demand maintaining integrity and accuracy.

Understand the risks better

Supply chain analytics can help in identifying the known and unknown risks. Also, it can assist in predicting future risks by identifying trends and patterns across the supply chain.

Improve accuracy in planning

By analyzing customer data thoroughly, supply chain analytics can help businesses to precisely predict future demand. Also, it guides an organization in decision-making regarding production by analyzing customer demand and market trends.

Achieve the lean supply chain

Supply chain analytics can be used by companies to monitor the responses of stakeholders, warehouse, and customer needs for data-driven decisions.

Our supply chain analytics solutions help businesses to tackle challenges such as reduced visibility and inefficient operations across the supply chain and also offer them actionable insights to facilitate quicker decisions for global supply chain management. Request a FREE proposal to know more about our portfolio of services.

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Prepare for the future

Companies are leveraging advanced analytics for supply chain management. Advanced analytics has the potential to process both unstructured and structured data to offer organizations a competitive edge. Also, it sends timely alerts that facilitates better decision making. Furthermore, supply chain analytics can establish a correlation among different sources and minimize risks.

5 C’s of Supply Chain Analytics

Connected

Supply chain analytics makes unstructured data from various social media platform and structured data from the Internet of Things (IoT) accessible.

Collaborative

Supply chain analytics enhances collaboration with suppliers across the entire supply chain through cloud-based commerce networks.

To know how our analytics solutions can help you in quick and accurate decision making, request a free demo below.

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Cyber-aware

Using analytics in the supply chain also ensures the security of the supply chain from cyber hacks and intrusions.

Cognitively enabled

Using analytics in the supply chain improves the capability of the supply chain through automation. It helps in coordinating, collating, and conducting decisions across the entire supply chain.

Comprehensive

Supply chain analytics offers comprehensive and fast insights in real-time as latency is something that is not acceptable in the future supply chain.

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metadata management

A Definitive Guide to Metadata Management

METADATA MANAGEMENTWhat is Metadata Management?

Metadata is like a golden thread that interconnects different functions of an organization. It comprises heterogeneous, heavily distributed and hybrid mixes of data from cloud systems. Metadata management deals with the management of complex data through analytics, operations, discovery, collaboration, and compliance. Metadata management acts as a key enabler in the data-driven decision-making process of a business. For any organization, all the data-driven actions pass through metadata when the customers browse through the website, run a query or make a virtual view.

Businesses need to understand and analyze their data to survive and grow. Metadata management can provide the required processes to gain this insight and succeed. Want to know how? Get in touch with our experts.

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Why is Metadata Management Important?

With the growing volume of data, metadata management has gained huge importance. Although it sounds a little geeky, it is a crucial component of records management and data governance. Here are a few reasons why metadata management is important:

Reason #1: Increases the longevity of datasets

The lifespan of a particular dataset can be very short due to the unavailability of relevant metadata. The growing complexity of data with the increasing constraints of time and funding also contributes to the shorter life span of the datasets. But metadata management can counteract this tendency of data degradation efficiently. 

Reason #2: Facilitates data reuse and data sharing

Metadata management ensures that the complex and complicated data is easily analyzed, interpreted and shared. It provides detailed instructions to interpret unfamiliar data accurately and helps in data integration.

Reason #3: Helps in maintaining historical records of long-term datasets

The datasets may encounter inconsistency in methods and instrumentation during the ongoing long-term projects. Metadata management helps in maintaining the historical records of such long-term datasets, thereby assisting in data governance. Furthermore, metadata management is important for data integration that contains different parameters but shares common temporal or spatial domains.

Wondering how metadata management system can enable you to build an enterprise model based on the metadata developed from various attributes such as your data integration tools and data warehouse? Request a FREE proposal now!

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Reason #4: Assists in improving interoperability and integrating resources

Metadata management makes it easy for humans as well as machines to understand the different resources. Also, it facilitates the understanding of how data is exchanged among many systems with different data structures, operating systems, and interfaces.

Reason #5: Protects resources and their future accessibility

The digital information today has become very fragile and is susceptible to corruption. Metadata management helps in archiving and preserving such information by tracking the lineage of the information and describing its characteristics. Also, it helps in understanding the behavior of the information to prevent its replication in the future.

Steps to Effective Metadata Management

Step #1: Meta repository creation

There are several repository tools available in the market that profess to create metadata repository. But all such tools may not suit your business. Most of these tools focus on one specific aspect of metadata. Therefore, you need to collect a set of metadata about each element of your business and relevant to your organization in the following six categories:

  • Business metadata
  • Core and technical metadata
  • Data quality metadata
  • Search metadata
  • People metadata

Step #2: Identify core attributes

Develop key business queries to get a fair idea about the underlying attributes which is necessary for analysis. It is important to identify and understand such attributes as they play a crucial role in downstream analytics.

Step #3: Acquire the metadata

Once your foundational capabilities are established, the next step that comes is implementing the meta acquisition process. Most of the categories of metadata need proper processes for acquiring it from the people who work in various functions of the organization.

A properly built metadata management model has the potential to give a 360-degree view of how operating systems in your organization are connected together. Such models can help users to solve issues based on different attribute types and names. Request a free demo to know more about how metadata management models can help in creating custom metadata types.

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Step #4: Create a protocol and maintain consistency

Data keeps on changing constantly with the growth of new business initiatives every day. Such changes result in long-term data instability. Therefore, it is necessary to create a more efficient process to monitor data streams for change. This is the most difficult but important step in establishing an effective metadata management process.

Metadata Management Vs Master data Management

Metadata management talks about the content in the data that is gathered and helps the organizations in uncovering the facts hooked to their data. Through metadata management, users can decode the data in terms of what it represents and derived. Metadata management consists of all the entities and attributes, programs, calculations, reports etc. The nature of metadata depends on what it talks about.

Master data management talks about the data that consists of entities like products, services, people, organizations etc. It usually talks about non-transactional data which usually covers:

  • Internal entities such as employees, departments, and products
  • External entities such as suppliers and customers
  • Information of organization such as reporting hierarchies and the chart of accounts.

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predictive analytics in marketing

Predictive Analytics in Healthcare: A Pathway to Personalized Patient Care

human-skeleton-163715_1280Every industry today is transformed virtually by predictive analytics and the healthcare industry is no exception. Predictive analytics in healthcare is advancing patient care significantly by offering detailed insights into the medical history of patients. However, there are many healthcare organizations that are yet to tap into the full potential of predictive analytics in healthcare. Deploying predictive capabilities in extracting actionable, forward-looking insights from the growing pool of data is still a daunting task for many healthcare companies.

However, realizing the power of predictive analytics in healthcare is highly essential as predictive analytics platforms can help healthcare companies to transform medical wisdom into real-world insights. Leveraging predictive analytics in healthcare can help companies to move into the new era of medicine. Consequently, this can improve hospital care, help in the management of chronic diseases and enhance supply chain processes.

At Quantzig, we understand the difference that leveraging predictive analytics solutions can create in the healthcare industry. And to help companies in the healthcare sector stay ahead of the curve, our team of analytics experts have highlighted the ways in which predictive analytics can improve healthcare and also have explained a few use cases for predictive analytics in healthcare.

Are you looking for ways to gain consolidated insights to mitigate risks and resolution of issues for medical cost management and improved patient care? Get in touch with our experts now!

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big data in banking

Big Data in Banking: Transforming business operations significantly

Companies in the banking sector are working hard today to adopt a data-driven approach to enhance their customer service and expand business operations. But the main challenge lies in the fact that banking companies are disrupted by the immense volume of data assets and the increase in the volume of customers. This is affecting the levels of services offered by banking companies dramatically. This is where big data in banking can play a crucial role in game changer for banking companies.

As the importance of big data in banking continues to grow, companies in the banking and financial services sector have realized that they can use data to track the behavior of customers in real-time. This real-time analysis can enhance the overall profitability and performance of the companies. Consequently, this can boost the growth of companies in the forthcoming cycle.

We, at Quantzig, understand the significant opportunities that companies can unveil by understanding the role of big data in banking. And to help companies excel in the competitive landscape, our team of experts have explained in detail about how big data in banking can act as a catalyst in the process of churning growth. Also, they have highlighted the ways in which big data in banking can guide in simplifying the process of monitoring, improving customer service, and optimizing business operations.

Are you looking for ways to uncover opportunities for cost structure and working capital improvement? Get in touch with our experts now.

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Benefits of Big Data in Banking

Benefit #1: Improves customer experience

Customers today have high expectations while they interact with their banks and credit companies. This makes it essential to focus on the needs of the customers and understand their complex buying journey. Big data in banking can help banking companies to gain a 360-degree view of customer journeys. It provides a central data hub combining customer’s interaction with the company, transaction history, his personal data, preferred service etc. These insights provided by big data analytics can help in customer segmentation and thus, serving customers better and building a customer-centric culture in the organization to enhance customer service significantly.

Benefit #2: Optimizes business operations

Big data analytics has the potential to enhance the forecasting power of risk models. This can effectively improve the response time and effectiveness of the system. Also, it provides extensive coverage of the risk and automates the process to increase cost savings. Furthermore, this helps in optimizing business operations effectively.

Benefit #3: Enhances employee engagement

Employee engagement is one of the important aspects of any business operation. Application of big data in banking also helps to analyze, enhance, track and share performance metrics of the bank employees. Furthermore, big data in banking helps in identifying and acknowledging the employees who are performing as well as the employees who are struggling and unhappy.

Our portfolio of solutions has helped clients in risk assessment and understanding financial trends across key growth segments while offering a real-time view of the organization’s performance and factors that drive their revenue. Request a free proposal now to know more about our services.

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Benefit #4: Helps in identifying transaction channels

Big data in banking helps banks to understand their customers and their behaviors if they withdraw all the sum available in cash or by debit or credit card. This way the customers who use debit or credit card can be approached with lucrative offers of investments.

Benefit #5: Facilitates cross-selling of products

It is always better to offer a better return on interest to customers to encourage them to spend actively. Big data in banking helps in precise analysis of the financial backgrounds of the customers. This ensures the cross-selling of auxiliary products and engages the customers with more personalized offers.

Benefit #6: Manages and prevents fraud

Leveraging advanced analytics and big data in banking can help banks to understand an individual’s spending pattern which further helps in risk assessment if something suspicious happens. If a customer withdraws all his money from his account via a debit card, this might raise an alarm. Big data in banking can help significantly in assessing risk and preventing fraudulent activities.

Benefit #7: Aids in understanding customer spending patterns

Banking and financial services companies have easy access to the historical data of their customers and their spending patterns. Leveraging big data in banking can help bank employees to apply a variety of filters like macroeconomic conditions to understand the patterns of growth in the salary of the customers. This can be considered as one of the crucial factors for evaluation of mortgage, loan screening, cross-selling of products, and risk assessment.

Want to gain more insights into how our analytics solutions can help your banking business? Request a free demo today!

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How Can Big Data in Banking Help Improve ROI?

#1: By embedding big data analytics into decision making

Senior managers at bank acknowledge the power of big data in banking but they often do not know how to leverage it for better decision making. Though such an approach fails to support data-driven decision making this is where leveraging big data analytics can help.

#2: By aligning analytics priorities to strategic activities

Majority of the banks struggle to connect analytics with their business strategy and this is a big drawback. But using big data in banking can help in risk mitigation, innovation of new and better services and serve the customers in a much better way. Furthermore, this can help in improving annual revenues.

#3: By investing in important analytics roles

Today banks also need to expand their analytical vision and banks can do this by expanding their analytics team. Investing in data scientists and machine-learning engineers can make the task much easy. Furthermore, this can also help in solving business problems easily.

Quantzig’s Advantage

Banking and financial services companies globally have been the first ones to adopt new technologies for enhancing their security and operations. We offer big data analytics services to banking companies to channelize their huge volumes of data and generate real-time visibility. Also, we have helped companies to make data-driven decisions faster and smarter through our customized analytics solutions. Our analytics solutions have helped leading banking and financial services companies in ensuring complete safety across all transactions and have reduced their exposure to risks significantly.

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Demand analysis

How to Improve Your Profitability and Supply Chain Network with Demand Analysis?

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Today’s business environment is highly competitive due to which leading business players find it challenging to sustain profitability and constant growth. With the advancements in technology, life cycles of products and services have shortened, customer demands have aggravated, new competitors have emerged, and new business models have brought in major transformations. This has created constant instability in the market due to which it has become crucial for businesses to analyze the market demand. Wonder how demand analysis can help you tackle these challenges?

For a proper demand analysis, it is important for companies to understand their customers, the buying behaviors and most importantly the capabilities of the company to serve the demand. This understanding can further help in identifying customer needs and market opportunities. Additionally, demand analysis has the potential to help companies in uncovering brand value, supply chain capacity and deal with existing business regulations.

We, at Quantzig, understand the difference that demand analysis and demand management can create for any business. And to help businesses to stay ahead of the competitive curve, our team of experts has come up with a detailed guide on-demand analysis. Also, they have examined the objectives of demand analysis and how it can play a key role in improving the efficiency across the supply chain, business decisions, product pricing, product development, and planning.

To gain a competitive edge in today’s competitive marketplace, it is highly essential to adopt a data-driven approach and analyze market trends through demand forecasting and customer analysis. Wondering where to start? Get in touch with us right now.

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What is Demand Analysis?

For any organization, demand is a crucial aspect that defines business success. Demand analysis is the process that helps companies to make better decisions regarding cost allocation, product development, product planning, and production capacity. Thus, the process of demand analysis is important for companies to understand the demand for their products or services in the market and maintain the inventory accordingly. Also, there is a possibility that sometimes production may exceed the demand and demand analysis helps to uncover such instances. Consequently, companies can adopt other ways to create new demand in the market. 

What are the Objectives of Demand Analysis?

#1. Forecast market necessity

If you want to know about the kind of product or services needed in the market you are targeting, demand analysis is the approach to follow. With demand and supply analysis, companies can easily identify the products or services performing well in the market and driving maximum value beforehand. However, companies also need to analyze customer behavior and preferences before launching any new product or service.

#2. Sales forecast

Sales estimation forms an integral part of product development and planning. But to forecast sales, demand analysis and demand planning is the approach that helps. Companies can ensure their success if demand analysis of a product or service is done prior to its launch. Also, this can make it easier for companies to decide on the type and quantity of services or products to be launched.

A data-driven approach to demand management can help organizations in inventory optimizations and consolidate supplier base to reduce the cost of operations. Request a FREE proposal to know more!

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#3. Determining price

Price is one of the main factors that affect demand. So, demand analysis is crucial for determining the price of a product or service. Different customers have different requirements. Some prefer luxurious products while others prefer only the products which are necessary. Therefore, it is important for any company to leverage demand and supply analysis prior to determining the price of a product or a service.

#4. Maximize profit

Companies before defining the profit margins for their products or services, need to analyze demand. This helps in ranking them in the order of priority and prevents the interference of government regulations. The categorization of goods and services can be efficiently by following the approach of demand analysis.

#5. Firm size determination

Demand forecasting and analysis help companies to select the right size of a firm for their business. Large size firms have different requirements than small size firms. The former is suitable if there are chances of the demand for a particular product to increase in the future while the small size firms are suitable if you are targeting a niche market. The size of the firm is directly proportional to the demands of the consumers. Therefore, demand analysis is extremely crucial for businesses to make decisions in such cases. 

#6. Deciding the success and failure of businesses

Demand analysis is the direct method to analyze the success and failure of any business. Companies to succeed in the competitive marketplace, need to analyze their customers and forecast the customer demand in advance. If any business delivers exactly what is needed by the customers, it is highly likely that it will succeed while the companies that fail to do a proper demand analysis tend to taste the failure.

#7. Financial arrangements

Financial arrangement is important to run any business. So, demand analysis helps in deciding the financial arrangements according to the market demands and customer requirements.  Also, this helps in ensuring the liquidity within the organization and facilitates a proper demand management process.

#8. Strategic planning of supply chain

To minimize costs associated with supply chain processes, it is essential that the business strategy synchronizes with a tactical supply chain plan. This can be done by following a proper demand analysis process. Consequently, the company’s profit can be increased, and the value of the shareholder will be enhanced.

Want to know more how demand analysis can make a difference to your business? Request a free DEMO now!

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Benefits of Demand Analysis

  • Sales forecasting
  • Demand manipulation
  • Demand planning
  • Product improvement
  • Improve pricing policy
  • Enhance market share

How can our analytics solutions help?

Global growth of organizations has resulted in complex business operations and reduced supply chain visibility. Consequently, organizations, in order to, overcome stressful global business environment and stay ahead of the curve, need to improve the efficiency of their operations across the supply chain. Our analytics solutions help organizations to deal with these challenges and offer them actionable insights into market trends through proper demand analysis, demand forecasting and demand planning model on real-time dashboards. Also, we offer our clients with the right frameworks to identify key performing metrics in the market which facilitates quicker decision making and better customer service.

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sales force effectiveness

Sales Force Effectiveness: Key to Maximize Revenue and Profit for Every Business

thumbnailWhat is Sales Force Effectiveness?

Sales force effectiveness primarily deals with different systems, processes, decisions, and programs for which sales leaders are held accountable. Sales force effectiveness is driven by different technologies that focus on helping organizations to improve their sales results. Sales force management enables companies to build effective sales teams and meet customer demands effectively. This can result in increased profit and revenue for the company.  With the change in the market environment and aggravating competition, there has been a significant increase in the demands of customers and consequently the pressure on prices has increased. This has slowed down the income growth and reduced the profit margins. This is where sales force effectiveness comes into force that helps companies to deal with longer sales cycle, fulfill customer demands and make sales competent.

Are you facing difficulty in improving customer service processes due to inefficient sales force? Get in touch with us to know how our sales analytics solutions can help you improve your sales force effectiveness.

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How to Improve Sales Force Effectiveness?

Tip #1: Try to avoid having too many sales teams

To improve sales force effectiveness, many managers depend mostly on deadlines and metrics. But this should not mean that you increase the number of the sales team in order to meet the target rather find different ways to reward and motivate your existing sales team. This can help in bringing out the best in them and can ultimately contribute to the revenue of the company.

Tip #2: Understand the key performing indicators well

For the sales force effectiveness to improve, it is crucial to deploy a good channel of communication between the sales team. Also, to enhance sales operations and sales strategy, it is important that you know what exactly is expected and when it has to be delivered. Furthermore, this can help in defining the timeline of delivery and can boost customer experience.

Tip #3: Leverage salesforce analytics

Salesforce analytics is an important pillar for effective salesforce management. It helps organizations to understand the potential customer segments and serve them according to their needs. Additionally, Salesforce analytics helps organizations to identify new opportunities for growth and revenue generation.

We understand the challenges companies face in improving sales management, tracking business performance and identifying key metrics that drive sales. We offer salesforce analytics solutions that provide best-in-class frameworks to help organizations improve their sales force effectiveness and operational efficiency by data integration, sales force automation, and better sales management. Request a free demo below to gain actionable insights.

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Tip #4: Opportunity management

Managing a sales opportunity is an essential component to improve sales force effectiveness. Therefore, for a sales manager, it is very important to understand that the process of sales is well-analyzed by the whole sales team. The alignment of the process with the way customer make a purchase is very crucial for enhancing the sales force effectiveness. 

Tip #5: Work on sales skills

Do your sales team possess the required prospecting skills? If yes, then this is an indication of effective salesforce. To improve sales force effectiveness, the sales manager needs to monitor the call planning of the sales team and their proficiency at identifying priorities. The sales team needs to effectively overcome objections and serve the customer’s priorities effectively.  

Are you wondering how to develop effective sales strategies to drive revenue growth for your business? Salesforce analytics and effective sales force management may hold the key to success. Request a FREE proposal now to gain more insights into our portfolio of services.

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#3: Customer attrition rate

Tracking customer attrition rates is an equally important metric to measure sales force effectiveness. It is especially very helpful in the new and dynamic markets where companies try or launch some new product or service. Also, this can be beneficial in identifying trends that can result in loss of business and catastrophic revenue loss. 

How can our sales analytics solutions help in improving sales force effectiveness?

Today several social media platforms have emerged due to which numerous challenges have cropped up for companies such as- managing marketing, sales, and customer relationships. Our sales analytics solutions that comprise data integration, data modeling, and analysis techniques help companies to identify new opportunities for growth and revenue generation. Also, our sales analytics solutions improve sales force effectiveness by sales force automation and making the process more efficient and data-driven to drive accurate results and reduce operational costs. At Quantzig, our team of analysts and consultants complement clients with best-in-class services to drive market excellence. We help companies in examining the sales force effectiveness and productivity at the granular level and develop effective sales strategies.

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