Tag: analytics

Is Customer Analytics the New Kingpin for Banking Services Companies?

For modern banking services providers, understanding the customers is the foundation for a sustainable competitive advantage. The internal and external data sources available to banking services companies can be great sources for guiding product development, customer communication, innovation, and growth. This data can be further meticulously analyzed using advanced capabilities such as customer analytics through which banking services companies can get to know their customer at a more granular level. Furthermore, it can help in effective customer segmentation that reveals specific intelligence that could otherwise be obscured by the sheer volume of data. These insights aid banking services companies identify messaging strategies for marketing and customer service strategies and also gain a better understanding of the customer lifecycle and predict customer behavior.

Why is customer analytics important in banking services?

It is high time for banking services companies to up their game in customer analytics. Here’s why:

Analytics is the new normal

 As a result of low-interest rates, moderate fee revenue, onerous regulation, and a less than robust economy, the banking services providers are expected to remain revenue challenged for the foreseeable future. So, it will be more important than ever for banking services providers and credit unions to focus on all possible strategies that would help them reduce costs and increase revenues. Some of these strategies that can be achieved by customer analytics include:

  • Better management (and measurement) of sales leads across channels
  • Inclusion of custom customer incentives/rewards to influence behavior
  • Improved targeting of customer segments
  • Moving from a product focus to a customer focus 

Achieve customer centricity

Customer delivery and communication channels are expanding. This means that more customers interacting with their financial providers and banking services companies are using online and mobile channels, making it easier to gain insights into real-time sales and services. Analytics can respond to this rapid migration to digital channels by:

  • Integrating sales and service tools within a new digital environment
  • Improving branch efficiency and effectiveness
  • Helping to drive high value, high touch traffic back to branches

Technological changes

Customer analytics is no longer a domain that can be used and accessed solely by highly skilled specialists. Today, these solutions can be easily accessed and used by marketers and other business users to answer complex inquiries. Improvements include:

  • Increased number of specialized vendor solutions and expanded talent
  • Collapsing of product silos and ability to process increased data sources
  • Cloud-based solutions

Establishing analytics as a true business discipline can help banking services prContact USoviders to grasp the enormous potential. Get in touch with us to know how we can help banking sector clients in establishing a customer analytics program to suit their organizational requirements.

How can customer analytics help banking services?

We at Quantzig have identified six levers that make customer analytics a core component for banking services companies to consider:

Customer Insights

Most financial marketers are highly interested in the ability to gain a better insight on current customers. Demographics and current product ownership form the foundation of customer insight. However, behavioral and attitudinal insights are gaining in importance as channel selection and product use have become more differentiated. Take the instance of sentiment analysis and social media analysis that are helping companies analyze in-depth about their customer emotions on social platforms. Furthermore, scoring models such as FICO is especially useful for banking services companies to analyze consumers’ credit history, loan or credit applications, and other data to assess whether the consumer are likely to meet their payment obligations on time in the future.

Business strategy

Customer analytics proves to be highly useful in banking services companies for product and channel development as well as economic forecasting, business improvements, risk analysis, and financial modeling.

Managing customer experience

Using customer analytics for customer experience management (CEM) helps banking services providers in delivering personalized, contextual interactions that will assist customers with their daily financial needs. Moreover, if done correctly, customer analytics enables the real-time delivery of product or service offerings at the right time, thereby ensuring a better customer experience.

Risk management

One of the more common uses of ‘big data’ today especially for banking services companies is in the area of risk and fraud management. The applications of data mining have expanded well beyond providing internal purchase and balanced insights. It now even includes transaction patterns and social media interactions that can provide a leading indicator of potential losses or fraud. The integration of structured and unstructured data in banking services can also be leveraged for traditional risk management including pricing decisions.


Another traditional use of customer analytics in financial services is the ability to increase the effectiveness and efficiency of sales and marketing. The ability to derive insights on the likelihood of purchase based on the available information on individual customers has ushered in a seismic shift in marketing from product centricity to customer centricity. Banking services companies and credit unions are now able to make unique, timely, and relevant offers based on available customer insight rather than offering products and services based on what the financial institution would like to sell. This allows banking services providers and financial marketers to significantly improve the efficiency of marketing spending and the close rate of sales leads.

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Implementing customer analytics for banking services

The future is already here. The only drawback is that it is not evenly distributed. Banks services and companies in every other industry are already deploying advanced analytics to move their businesses forward. Quantzig has identified that almost every top bank lists advanced analytics among its top five priorities. Most plan to invest further in these techniques. A few banking services companies are already seeing the rewards. By establishing data lakes and centers of excellence and using machine-learning techniques these leaders have already built substantial foundations for their business.

 How do you know what analytics capabilities to invest in for your business? All you need is an analytics expert that can guide you on leveraging the available data by avoiding pitfalls in banking services and accessing the broad set of opportunities. At Quantzig, we understand these needs of our clients and are committed to helping them bolster their analytics capabilities. Request a demo to know more about our analytics solutions.


Why Is Sentiment Analysis Fundamental for Modern-Day Companies?

What is sentiment analysis?

Sentiment analysis is a way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral and the degree to which they are so. In the modern world, businesses run on the internet, which has made the role of sentiment analysis critical for business. It is vital for companies as it helps to see what customers like and dislike about them and their brand. Customer feedback that is collected from social media, the company website, the company’s call center agents, or any other source—contains a treasure trove of useful business information. But, this often proves to be insufficient to know what customers are talking about. Businesses must also know how customers feel. Sentiment analysis is one way to uncover the feelings of customers. Here are some of the key benefits that sentiment analysis can offer to companies:

Upselling opportunities

Effective sentiment analysis helps companies analyze their customer’s emotions towards their business. Happy customers are more likely to be receptive to upselling efforts made by companies. With sentiment analysis, you can easily identify customers that are delighted with your business. This helps companies recognize the target audience that might be receptive to spending more, as well as avoiding upsetting disgruntled customers with any unwelcome sales pitches.



The benefits of sentiment analysis can extend beyond helping the human agents of the company. Chatbots can also prove to be highly beneficial too. That’s because chatbots can be trained to recognize, and respond to, customer mood. For instance, sentiment analysis can easily decipher when a chat needs escalating to a human agent or route an engaged prospect through to a sales team.

Identify critical emotional triggers

Emotional triggers are what drives discussions between customers and the company. Using sentiment analysis, companies can easily identify the messages and conversations that act as emotional triggers that can change customer mood. Perhaps the use of certain phrases like “Please wait”, often triggers customer annoyance. On the other hand, using emojis can have a positive effect on the conversation’s overall tone. Understanding the messages that have the power to trigger certain emotions customers can help provide better service and is also useful for creating effective marketing materials.Request Proposal

Live insights

The mood of customers can change at any point during a customer service interaction, and this is often ambiguous. With sentiment analysis, not only can businesses see the mood of each customer in a session, but visual indicators also have the ability to display how this mood changes in real-time. They can get live insight into how well a chat is going, and the ongoing mood of even the flightiest customers.

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Zero Waste Solution: Effective Food Waste Management with Data Analytics

Food waste management is a matter of global concern. According to recent estimates, roughly one-third of the food produced in the world for human consumption every year — approximately 1.3 billion tons — gets lost or wasted. This contributes to the emission of greenhouse gases from landfills. Food waste occurs across the entire food system ranging from producers, retailers and restaurants, to consumers. Forward-thinking businesses are using advanced technology such as data analytics to tackle the issue.

Using big data and data analytics to collect real-time operational data throughout the food waste disposal process allows visibility into the organic waste stream. This further facilitates businesses to identify inefficiencies in food management processing and helps initiate process improvements to create immediate impacts. Measuring and optimizing food waste management not only supports environmental directives but also forms the key to finding operational efficiencies, enabling a business to make informed decisions about purchasing, production or other logistical needs.

How can data analytics help in food waste management? 

Retailers and other businesses dealing with food products are increasingly turning to data analytics solutions in order to manage the food wastes. The information collated and analyzed using data analytics reveals the waste generated by the business and seasonal change in demands, helping business to better plan their food waste management strategy.


Optimum inventory level

Analyzing sales information, weather forecasts, and seasonal trends, help manufacturers to identify an optimum inventory level which they can then use to reduce the effects of food wastage. Predictions of consumer demand during a particular time can then be made and promotional plans and sales approaches can be structured around sell-by and expiry dates. This is primarily intended to cut down the food wastage and the knock-on environmental and cost issues that arise.

Predict changes in demand

Data analytics can be used to identify seasonal changes in consumer demand for food products. This helps retailers or restaurant chains to plan what quantity of a particular food item must be produced or procured, consequently leading to reduced wastes and better food waste management. Data analytics also helps rRequest Proposaletailers determine the products that are closer to expiry and aggressively reduce the prices on such items so that they are consumed before their expiry date and not wasted.    

An example of analytics in food waste management

A notable success story for using analytics in food waste management is that of the British multinational groceries and general merchandise retailer – Tesco. The company uses a data-driven approach to reduce food waste and ensure effective food waste management. Tesco’s systems order approximately 110 million pounds of food products every day. So, the retailer turned to data analytics to improve the supply chain and minimize the instances of food wastage. Their systems utilize large amounts of data from its many store locations to develop, train, and test their algorithms. They utilize weather forecasts to increase their accuracy in predicting how the demand for food will change. Common sense tells you the seasonal change in people’s demand patterns. Data tells you exactly how much the change is and plan the inventory accordingly. This method helps minimize food waste by ensuring the right quantity of food products are available at each location. In addition to reducing waste and ensuring better food waste management, these initiatives have a positive economic impact for the retailer as well.


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Four Digital Analytics Mistakes You Need to Stop Making Right Now

Today, most of the forward-thinking companies have their strong presence on the internet. In fact, websites have become the new address for businesses. One of the key benefits of operating on digital platforms is that companies have access to a large amount of data that they can leverage to track and improve performance. However, a notable challenge of digital analytics lies in knowing what metrics to track, and what value to gain of them. Moreover, the knowledge and expertise required to set up and use digital analytics programs are complicated. The investment for digital analytics tools and required expertise can also be high. Often, despite putting in the best efforts many clients see unclear returns from their analytics programs. This might be due to some of the following mistakes in the company’s digital analytics strategies:Request Proposal

Mistake #1: Unclear goals

The first challenge of digital analytics is not having a specific goal for using digital analytics tools.  Marketers are often unclear on what metrics to track, and what value want to derive out of them. This results in businesses finding it difficult to link their business results with the factors that drive those results. So, it must be ensured that every analytics program answers specific business questions and concerns.

Mistake #2: Choosing overkill analytics tools

Companies tend to believe that choosing over-the-top or expensive digital analytics tools give deeper insights and solve their problems better. And this assumption could prove to be a Demograve mistake for them in the future. Advanced digital analytics tools may offer more sophisticated analytic capabilities when compared to the features offered by fundamental tracking tools. But the big question is whether the business needs all those capabilities or not. The idea is to select an analytics tool that is based on the company’s goals and business needs, not by how advanced the tools are.

Mistake #3: Failing to detect tracking errors

Tracking errors can produce unreliable data and misleading analysis and can hence prove to be devastating as far as a company is concerned. Yet many companies tend to overlook tracking issues when they occur. Tracking errors can occur in the form of the developer mistakenly removing the tracking pixels, transferring incorrect values, and the tracking code firing unstably or multiple times. In order to tackle this problem effectively, companies must frequently check their data accuracy and look for unusual signs in reports.

Mistake #4: Following too many metrics

When companies have access to advanced digital analytics tools and strong computational power, it could become tempting to make the most of the tool by capturing every data point possible. However, following too many metrics can dilute the company’s focus on the core metrics that reveal the pressing needs of the business.

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Supply Chain Analytics: A Game-changer in Boosting Your Supply Chain Efficiency

A strong supply chain is one of the biggest assets that a company can possess. Today, companies have come a long way in terms of improving supply chain efficiency and enhancing the speed of operations. However, global operating systems, pricing pressures, and the ever-increasing customer expectations are putting extreme pressure on supply chain operations. This also demands businesses to move out of their traditional supply chain execution systems and change over to techniques that can better accommodate the dynamic market conditions and customer demands.

But how can businesses identify the gaps in their supply chain? The answer is to leverage supply chain analytics. Supply chain analytics assesses supply chain performance and identifies the shortcomings and inefficiencies in the supply chain configurations. The ultimate aim of using this technique is to improve the end-to-end performance of the supply chain in terms of operational, financial, as well as managerial aspects. Supply chain anCTA QZalytics enable companies to dig deeper into the supply chain data and examine savings and efficiency gaps. The importance of supply chain analytics doesn’t end there, some of the key benefits it offers to companies include:

Accelerate sales 

The main aim of any business is to produce products/offer services that will eventually attract customers and result in a sale. All the materials in the supply chain ranging from inventories, raw materials, and work-in-progress are there to serve this purpose. What companies need to do is to identify whether they possess the right inventories, raw materials, and work-in-progress, and supply chain analytics is the key to help them do this. Supply chain analytics highlights a redundant or slow-moving stock that has occurred due to wrong forecasts and inaccurate estimates. It can also help identify instances where there was the potential to sell more products, to meet the demand that was there but left unmet.

Speed-up material movement 

When the supply chain moves quickly, it facilitates the reduction of inventory levels and frees-up cash. But the catch her is to know how to make it move quickly. This is where supply chain analytics comes in handy. Using this technique, it is easier to identify factors such as the impact of order size rules and policies, or stock-keeping rules and policies, and demand variability. For instance, the impact of production batch sizes on finished goods, raw material levels, and work-in-progress.Get More Info

Improve standards 

Not just the internal decisions and factors, but several external factors also have an impact on the supply chain.  Take the case of poor performing suppliers for instance. This can have a major impact on other supply chain factors such as customer service levels, on-time delivery performance, and inventory holding. Supply chain analytics can help by taking an ERP system’s raw data and transform it into detailed performance metrics, supplier by supplier. With this companies can easily identify gaps in supplier performance and quickly act to rectify it.

Efficient utilization of data 

Modern supply chains produce unprecedented amounts of digital information. Such data from transactions and events were once recorded on paper. Now the data is being digitally captured and stored. By using advanced capabilities like supply chain analytics, companies can easily identify their shortcomings and optimally improve performance.

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Rise of Sports Analytics: New Developments by Sports Analytics Companies and How Analytics in Sports is Changing the Way the Game is Played

Analytics in sports is a relatively new concept that gained rapid popularity after Michael Lewis released his book on “Moneyballin 2003. Sports analytics, today, has become a part and parcel of every athlete’s life and most professional sports team have a team of analytics experts grinding the numbers and uncovering insights to improve performance. Teams of today scan scouting reports from clipboards and send them across to data scientists, who crunch the numbers that scouts and managers use to shortlist players for their teams. This helps in creating a profile of players who are the best fit for the team. Sports analytics, or analytics in sports, is the future of professional sports as it helps teams to gain a competitive advantage.

The popularity of sports analytics h1as nothing to do with the technology but has more to do with the audience. Fans of today consume more analytical content than ever before and, as a result, there are numerous websites that are solely dedicated to analyzing and researching sports-related data or developments. To make the most of this trend, many sports analytics companies are now setting up domains that provide more value than just sports coverage. Such websites contain numbers and predictions that estimate the outcome of the upcoming matches. From the coaching staff to athletes, sports analytics has a big role to play when it comes to reshaping touchdowns, preventing injuries, and signing contracts. That being said, here’s Quantzig’s list of the top benefits of using analytics in sports.

Sports analytics benefits

Higher accuracy  

Today, many sports analytics companies have started implementing technologies that track the accuracy of pitches. Though this technology has some way to go before it is implemented on a full-scale, sports like cricket and football have already started implementing such technologies to have a better clarity on whether a ball has been pitched perfectly or whether a goal has been scored. Get More Info_QZ

Real-time data

For all the number crunchers out there, there’s a plethora of new websites available that contain the data of their favorite teams and players. The growing acceptance of sports analytics as a medium to gain insights has resulted in the availability of sites that offer a comprehensive break-down of data related to players and their teams. Sports analytics companies now track metrics such as the individual performance of a player or store data on umpire’s calls to help the audience gain more insights into a game’s outcome.Get More Info_QZ

Taking wearables to the next level

With the rising popularity of wearable technologies, many sports analytics companies have started entering the wearable technology market. The use of analytics in sports can help determine the performance of top players and estimate who needs a rest. Real-time statistics such as heart rate, speed, and acceleration also help coaches and fitness doctors to plan their training and rehabilitation accordingly.

The demand for sports analytics will continue to evolve, and with time, it will become an essential tool for coaches, players, and the audience alike. Though the use of analytics in sports can improve greatly, continuous innovations in areas of predicting the mental state of the players and on-field performance will make sports analytics one of the biggest developments of the modern era.  



5 Ways in Which Sports Analytics Will Help Score Big Points

Analytics has made its mark in various industries by simplifying processes and giving actionable insights. But the use of analytics in sports is a relatively new concept. Today, professional sports have become so highly competitive that a minute can change the course of the whole game. Also, fan following for various sports teams have grown more than ever before, putting higher pressure on players and teams to monitor their performance. They are realizing the need for an accurate performance tracking system that can help take corrective measures after studying accurate performance metrics. This is where capabilities such as sports analytics come into the game plan. Sports analytics can make a difference in scoring touchdowns, signing contracts, or preventing injuries. Sports analytics uses sports-related data such as weather conditions, players’ statistics, and information from expert scouts to build predictive models around this data. As sports managements are competing to gain a competitive edge over the other, there has been a subsequent surge in the use of analytics in sports. Here is why sports Request Solution Demoanalytics can solve major problems for coached, management, and the players: 

Sports analytics uses sports-related data such as weather conditions, players’ statistics, and information from expert scouts to build predictive models around this data. Click To Tweet

Live field data 

Currently, a large amount of data is collected manually during a game of sports matches. But since the actions take place at a rapid speed on the field or during a match, it becomes difficult to track live data. Companies such as MotionWorks Sports Solution makes RFID tags that are attached to the ball, equipment, and even the players to track movement, distance, and speed.

Fan preferences 

Teams and ticket vendors are trying their best to provide the most comfortable and enjoyable experiences to fans watching the match. They can use sports analytics to get to know the audience better and cater to their needs. Furthermore, it becomes easier to deliver a better experience to followers using team specific mobile apps that provide fans with special content, in-seat concession ordering, and bathroom wait times.

Coaching decisions 

Coaches can leverage sports analytics to gain important and accurate data sets that would help them adjust their tactics for better results on the field. Using data, coaches and players can make more informed decisions that could decide wins and losses. They can also analyze the data from past matches or tournaments to formulate a better game plan and eliminate the tactics that do not give favorable results.

Data from wearable technology 

Wearable technology is now slowly being experimented for sports analytics. Several vendors such as Adidas have introduced technologies that work by attaching wearable devices to the jerseys of players. Data from the device helps the coach identify who the top performers are and who needs rest. It also provides real-time stats on each player, such as speed, acceleration, and heart rate. This type of real-time data could help trainers and physicians plan for better training and conditioning.

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Web Analytics Vs Customer Analytics- What’s the Difference?

Web analytics and customer analytics are often confused with one another. However, both these terms are distinct from one another. Web analytics refers to the collection and analysis of web data to maximize the usage of the site. Web analytics measures things that are considered by the webmaster. This includes factors such as the number of views, page loading time, time spent by the visitor on the site, and other factors. The performance of the website can be greatly improved using web analytics. It also helps to measure the effectiveness of advertisement campaigns. Customer analytics, on the other hand, is the process of identifying the customer information, which will help companies to deliver and meet their needs. Customer analytics forms the backbone of any marketing activity for a business. It includes techniques such as data visualization, predictive modeling, and segmentation. Here are some of the key parameters that distRequest Solution Demoinguish web analytics from customer analytics:

End users

Data and product teams are the end users of the data gained from web analytics. It does not have anything to deal with customer-facing departments. Customer analytics, on the other hand, includes customer-facing departments, who play a major role in implementing customer knowledge into action in the organization.

Customer insights

Web analytics is not designed to provide information at an individual customer level. It is used to identify trends and patterns in the behavior of a group of customers. Customer data analytics provides in-depth insights on each and every individual customer of the business. This can help businesses track the traits of customer’s individually and target the right customers especially for marketing and ad campaigns.


Web analytics does not have the capability to help companies predict the future based on the traffic driven to the site or by the number of customers visited the site.  It can merely depict how successful the marketing campaigns of the company has been. Customer data analytics involves understanding customer behavior before reaching out to them. This behavioral study will let the company predict future leads, new customers, and references. This tool is really helpful for companies who have a huge number of customers like B2B, insurance, and real estate.

Information provided

Marketing and data teams obtain information about the website traffic with the help of web data analytics tools. Customer analytics tools provide contact and business information to sales and customer support teams who face the customers directly. This helps the customer-facing teams to get in-depth behavioral insights about the customers.

Data organization

Data is organized around the website like page views, landing pages and others in the case of web analytics. Customer data analytics, on the other hand, organizes data about the customers. Instead of focusing on the website data, if we concentrate on the customers’ data then the data becomes easier to control. This is because data related to customers are simpler to understand and use.

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Why HR Analytics is More Important than You Think

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Why HR Analytics is More Important than You Think

It goes without saying that people are vital to the success of any company. The way in which companies recruit, train, and retain their employees has a major impact on their business performance. Today, human resource managers have access to huge amounts of employee data. However, the difficulty lies in collating, integrating, and analyzing data, which is highly essential for turning data into valuable insights. This is where advanced techniques such as HR analytics prove to be useful. HR analytics tools use HR and big data analytics to interpret the useful data and transform it into useful statistics.HR analytics help companies to understand what could improve their workforce management, create a team that consistently meets its targets, identify why certain managers continue to thrive whilst others burn out, and predict exactly which candidates are likely to succeed. That’s not all, here are some essential benefits of HR analytics that you cannot ignore:Request Solution Demo

  • Improved hiring decision

HR analytics makes predictive analysis easier and helps managers to make the better choice based on historical data. HR analytics also prevent companies from making mistakes by understanding the past errors. Moreover, it allows the recruiter to learn more about candidates through online resume database, applications, social media profiles, and records.

  • Training and development

Training is an essential part of workforce management for any company. However, if there are any gaps in the training process, it could cause some serious problems and increase the cost to the company. The training and development programs which are not handled properly can cause havoc to the budget of the company. Use of analytics tools helps the organization identify if employees are making the full use of the opportunities and the knowledge imparted to them during the training and development sessions.

  • Employee retention

Retention of employees is one of the most challenging aspects of workforce management. HR analytics is an important tool for human resource managers to identify the reasons why employees leave or stay with the organization. It is highly effective in finding the skill gaps and areas where employees are struggling. Various tools like employee satisfaction surveys, team assessments, exit and stay interviews can be used to identify the reason for employee attrition and chart out the strategies to retain them.

  • In-depth insights

With the help of advanced analytics tools, the company can gain deeper insights into the employee’s professional life. This can be done by tracking, sharing, and analyzing the performance-related data. Furthermore, the employee’s performance data could be used by the hiring manager to identify the great talents. This data not only provides more insights about the employees but also shapes the strategies to boost the employee morale, retention, and engagement.

  • Re-engagement of candidates

Candidate records often go untouched or forgotten after the job posting is closed. Analytics allows organizations to re-engage a targeted group of candidates to determine their interest level for other vacant positions in the organization. It can also help reflect new positions, work experiences, or skills that might have acquired by the candidates since the last time they were engaged.

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