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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


How Advanced Analytics is Changing the Business Landscape in 2019?

What is Advanced Analytics?

Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.

Are you finding it difficult to make informed business decisions, while ensuring information privacy and security? Get in touch with our analysts who can complement you with a shared need to discover and build those capabilities and drive continuous market excellence.


What are the Different Advanced Analytics Techniques?

In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:

Descriptive analysis

The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”

Predictive analysis

The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.

Simulation and optimization

Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.

Are you struggling to manage the high volumes of data generated by high-tech sensors? Leveraging advanced analytics solutions may help. It has the potential to generate predictive insights that facilitate proactive business decisions and pre-emptive planning. Request a free proposal here to check out our portfolio of advanced analytics solutions.


Why Advanced Analytics Is Becoming So Important for Businesses? 

In this era of digitalization  new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:

  • New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
  • Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
  • Advanced analytics software facilitates better analysis of collected data, relationships, and future events.

Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.

Key Benefits of Advanced Analytics

Detailed analysis helps in personalized marketing

Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.

Better performing advanced analytics techniques improve accuracy

Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.

Identify the level of risk in the implementation of the decision

By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.

One of our recent case studies highlighted how advanced analytics helped a consumer products manufacturer to develop an aggressive growth-centric data strategy. Read the full story here.

Final Thoughts

Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.

We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.




Data Analytics Services are Crucial to Your Business. Learn Why and How!

Without data analytics services, business leaders are blind, wandering onto the web like a deer on a freeway.”

To have a good data management strategy into place, underpinning all business processes, is an apparent requirement for all business professionals. But it is tougher than you would think to get the data management process right. Almost every business has a need for data-driven insights today, but many are not yet realizing that ambition. Consequently, data management tends to get disintegrated and driven by multiple stakeholders. This results in high degree of disparate and inaccurate data for organizations and they face a number of challenges in maintaining it. Additionally, with the sheer volume of data, it has become very difficult for organizations to aggregate, manage, and create value from data. Therefore, businesses need to identify a more sophisticated approach to data visualization and the importance of good quality data. Right from building better relationships with customers to overcoming external and internal data visualization challenges, there is a need to revamp and evolve enterprise data management practices.

This is where data analytics services can help. If you are looking for ways to transform raw numbers into targeted solutions by gathering and analyzing day-to-day data within your company, it’s high time that you leverage data analytics services. Also, digital analytics can help you unveil hidden correlations by segmenting your market and customizing your products or services with the help of proper data frameworks. With data analytics services, a huge amount of unstructured data become concise and comprehensive data that are helpful in modeling and predicting your market opportunities. Whether you are in the healthcare, media and entertainment, insurance or chemical industry, data analytics services have become an essential requisite for survival. With that being said, let’s take a look at what is data analytics below and how it can help your business reach the pinnacle of growth.


Top 3 Benefits of Data Analytics That Can Take Wearable Technology to the Next Level

From fitness and activity trackers to “smart clothing” and smartwatches, the possibilities are practically limitless within the industry for wearable technology. And this growth is further enhanced by the promise of high monetary rewards. But design and data collection are only a small component of the potential that wearables offer to both companies and consumers. Taking wearable technology to the next level of usefulness requires companies to build in additional analysis features that increase engagement levels, improves the usefulness of the wearable technology, and provides an unparalleled experience to the consumer. In other words, the benefits of data analytics can help companies to effectively determine what the wearable technology is actually doing for their consumers. But the question that arises here is, what are the benefits of data analytics and how can it help wearable technology to become even more popular. This article has answers to this question. Here, we have discussed in detail some of the significant benefits of data analytics that have potential to take wearable technology to the next level in 2019.

Benefits of Data Analytics

Detailed insights from collected data

One of the most crucial benefits of data analytics is that it can turn collected data into the foundation needed for actionable insights, and in doing so provides additional company and consumer benefits. For example, a sleep tracking device might collect data on how and when a consumer is sleeping. But without the analysis of the collected data, the device only helps in identifying the hours that a consumer sleeps or fails to reach a REM cycle. Does this knowledge help consumers in any way? The answer is simple, no! It is in this context that the real benefits of data analytics are realized.


Personalized services based on habits 

By leveraging the benefits of data analytics, wearable devices can provide personalized offers to consumers. By analyzing the data captured by wearable technologies, companies can create marketing offers that are customized to each consumer. For example, a step-tracker can reveal that a consumer takes the most steps around lunch time. By leveraging this information viaRequest Proposal personalized offers, companies can encourage the consumer to take more steps throughout the day. 

Improved employee health and productivity

One of the key benefits of data analytics is their ability to improve the health and productivity of employees. Wearable technology can collect the data needed for a company to analyze the productive hours of the day of their employees. Additionally, the devices can also monitor employees’ health, so that the company can mitigate risks associated with lack of sleep, high levels of stress, and other health symptoms that contribute to an ineffective and unhappy workforce.

Want to gain better insights into the benefits of data analytics and its ability to enhance the capabilities of today’s wearable devices?

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Role of Advanced Analytics in Reducing Health Care Costs

There are several questions asked by healthcare professionals like – Are there enough measures available that must be taken in case of flu? Or are the patients operated yesterday likely to catch an infection? How can recruiting staffs and providing facilities be done in a cost-effectiveContact US manner? These questions can be answered accurately using advanced analytics, which, in turn, can help in reducing health care costs. As the number of patients keeps increasing along with the associated costs, there is a dire need for adopting advanced analytics in healthcare. Advanced analytics has become a tool in reducing health care costs for many healthcare organizations. It can help in reducing health care costs through various segments, ranging from stock management to patient care to staff deployment. But before delving into the correlation between advanced analytics and health care, let’s first understand what is advanced analytics.

What is advanced analytics?

There is a simple difference between analytics and advanced analytics. Traditional analytics is used to get insights on the current happenings; whereas, advanced analytics helps understand the future to forecast upcoming behaviors and trends. This tool can be categorized into data mining and big data. Advanced analytics uses various mathematical techniques and statistical modeling techniques to analyze current and past data and predict future scenarios.

How can advanced analytics reduce healthRequest Proposal care costs?

There are several ways in which advanced analytics can aid in reducing health care costs. Predicting demands of operating rooms, reducing the rate of readmissions, adding intelligence to pharmaceuticals, and optimizing staffs are some ways in which advanced analytics can help reduce costs.

#1 Predicting demands of operating rooms

Operating rooms are quite expensive to maintain. So, every hospital tries to optimize the operating room without compromising on patients’ health. This goal can be accomplished by recognizing the role of advanced analytics in better understanding the relationship between the operating rooms that can lead to mismanagement of effective scheduling. Thus, advanced analytics can help in streamlining the operating room schedule and reduce health care costs. 

#2 Reducing rate of readmissions

Unnecessary readmissions are very frequent in the U.S and it leads to confusion of discharged patients who fail to understand how to take care of their health or take precautions after they get back to their home. Due to this, an unnecessary burden of cost is also created. This is where advanced analytics comes into the picture.

Advanced analytics helps in reducing health care costs effectively. New advanced analytics algorithms take into account various clinical factors, which helps identify patients who need to spend less than two nights in the hospital. This tool also helps doctors to know when a patient requires observation and, thus, helps in reducing health care costs to a large extent. 

CTA QZ#3 Adding intelligence to pharmaceuticals

This is one of the most powerful features of advanced analytics since it can helps investigate every corner in detail and unveils available opportunities and forthcoming challenges. The historical data available in the clinics and hospitals can help in creating predictive models that can subsequently help the pharma companies to respond to the expected and unexpected changes. Advanced analytics can also be used to uncover the opportunities for internal savings caused by inventory standardization and, thus, help in reducing health care costs.

#4 Optimizing staffs

Advanced analytics can help in trimming costs of labor and predict demand in advance to match resources and staff; thereby, minimizing the last-minute unnecessary expenses. Optimizing the staff skill using advanced analytics can be of great help, especially in reducing health care costs.

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Top Tips to Get Your AB Testing Right

One of the most famous quotes in advertising is one which says that it is almost impossible to track down the effectiveness of an advertising dollar is proving to be untrue because of new digital technologies. AB testing makes it possible to instantly analyze which two versions of a website or ad copy perform better. Using statistical analysis, a marketer can analyze which variation performs better for a given conversion goal. AB testing takes out the guesswork out of website optimization and marketing promotions and focuses on informed decision making backed by data. However, it depends on how a marketer uses the AB testing tool. Testing anything and everything without any setRequest Solution Demo protocols may not yield ideal results. Here are some tips to help you with your AB testing:

Run AB tests only as long as necessary

AB test is fruitful when it achieves statistical significance, which implies that the results didn’t occur by mere coincidence. The amount of time taken to reach statistical significance depends on a number of samples obtained. If you gather enough data to support statistical significance, you should stop the test and implement the winning variable. Running it for too long results in a lost opportunity.

Avoid testing if it conflicts with SEO

It may be tempting to test everything on a website from CTA buttons, text, to banner images. Doing so alters the H1s, body text, or other ranking factors, which can have a negative impact on your site’s SEO. One of the best practices to perform such AB testing is to test it out on underperforming pages and subsequently scale those changes to higher value pages. Testing it out directly on high-value pages may lead to lost conversions and rankings.

Get rid of old test pages

Once an effective version of the page is identified with AB testing, it becomes essential to update the original page and erase all digital signs of the failed versions. It will confuse the Google bots when it tries to find one version at a later point in time. It is essential to use 301 redirects to bring back the winning page to ensure Google doesn’t index the failed version.

Don’t AB test SEO campaigns

AB testing allows you to test virtually everything. Marketers might be tempted to test on-page ranking factors to increase conversions. However, it is not possible to test on-page SEO factors, as Google bot cannot split its visitors into two versions. Google bot will see the pages as duplicates and throw one out of the index and in the worst case penalize one page. As a result, it is advisable not to test ranking factor for an AB test.

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Why use Hadoop MapReduce Programming?

Its an age dominated by big data. Big data jobs are flourishing, and companies are paying over the odds for data scientists. All news revolves around data analytics, machine learning, and artificial intelligence. All such advanced technologies have to start somewhere. It all begins with the generation and processing of big data sets. Appropriate tools are required to handle such massive data sets which can then be used further for machine learning, AI systems, or generating business insights. Of multiple tools available in the market right now, Hadoop MapReduce is one of the most preferred data processing application, which is based on the Apache Hadoop framework. In short, Hadoop is an open-source software framework which stores data in a distributed file system, and its processing part is called MapReduce. The MapReduce framework is used by several players in the e-commerce industry including Amazon, Yahoo, and Zventus for high volume data processing. So why is MapReduce application so popular and what are theFree demo advantages of using it?

Simple coding model

Programmers using MapReduce framework needs to specify two program functions namely map function and reduce function. MapReduce uses a simple coding model as the programmer doesn’t have to implement parallelism, distributed data passing, or any other complexity. It not only simplifies the coding process but also reduces the amount of time taken to create analytical routines.


The Hadoop architecture is highly scalable, as companies only need to add new nodes when they need to increase data storage and computational power. The structure allows it to distribute large datasets across plenty of inexpensive servers which can operate parallelly. Hadoop MapReduce programming enables companies to run applications from large sets of nodes which can use thousands of terabytes of data.


Any sort of data mining process requires substantial computational power, which translates to higher power needs. Also, instead of buying dedicated servers and workstations, companies can just keep on adding new systems to the existing server to increase their computational power. Traditional relational database management systems incur high cost when scaled to levels of Hadoop MapReduce. As a result, businesses have to classify their data storage needs and downsize data to get rid of data which they think weren’t necessary. In such process, companies might end up deleting raw data to serve short-term priorities. Hadoop MapReduce allows the storage and processing of data in a very affordable manner. The storage cost per terabyte of data has reduced from thousands of dollars to few hundred dollars with this application.


Hadoop MapReduce programming allows businesses to have access to new sources of data and operate on various types of data. Since the programming enables enterprises to access both structured and unstructured data, significant value can be derived by gaining insights from multiple sources of data. Additionally, it also offers support for multiple languages and from sources ranging from social media, email to clickstream. Since MapReduce processes simple key-value pairs, it supports data type including images, meta-data, and large files. Consequently, programmers feel that MapReduce is easier to deal with than DBMS for irregular data set.


Since Hadoop used a distributed file system, data is stored in a cluster and is easier to map. Hadoop MapReduce programming can access data much faster wherever they are stored in the server. The speed is so impressive that it can skim through terabytes of unstructured data in a matter of minutes.


Hadoop MapReduce programming used HDFS and HBase security platform which only allows access to approved users to operate on the data. Thereby, it protects unauthorized access to data and enhances the system security.

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Gaming is no More a Child’s Play: How Data Analytics is Transforming the Gaming Industry

About two decades ago, if you asked somebody about their most favorite ‘Video game’ (as it was popularly known), the most cliché answer would be “Super-Mario.” Today, if you were to ask the same question, you would get a plethora of answers, and most importantly from a wider age group of audience. This speaks volumes about how the gaming industry has evolved over the years. According to Technavio’s report on gaming,  “Vendors are incorporating sophisticated techniques in their games to trigger the intelligence and maturity of gamers, making them more interactive.” Many players in the gaming industry are also resorting to advanced techniques such as data analytics to create a more superior user experience.Free demo

How Does the Gaming Industry Benefit from Data Analytics?

Wonder how the gaming industry is going to benefit from data analytics? Here is how:

User Engagement

With the help of various data analytics techniques such as behavioral analytics and engagement analytics, gaming companies can understand the mindset and usage patterns of their users, helping them to build better features and interface to keep the users glued to the gaming screen.


The marketing experts are leaving no stone unturned and no path unexplored to reach out to their customers. With such an enormous chunk of the population spending a significant amount of time in gaming, many brands are spending huge amounts of money understanding their target gaming audience.

Metrics Used for Data Analytics in Gaming

Here is the list of some of the matrices that facilitate data analytics of user data available to the gaming industry:

Time Metrics – This metrics depicts the average daily time spent by users, the count of users per day/week, the churn out ratio per week/month, etc.

Involvement Metrics- This metrics helps to understand the number of users making in-app purchases, qualifying for certain levels, the change in engagement levels after a certain stage in the game, sharing of achievements on social media, etc.

Revenue Metrics – Using the revenue metrics, companies can understand the cost incurred to acquire a new user, daily/weekly/monthly revenues, gaps in revenue between different time periods, etc. This will help companies incorporate necessary changes in their revenue models.

Event Metrics –  Understanding these metrics is very important to assess the reaction of customers concerning certain events in the game, and are highly useful in expanding the customer base. For example, observing the churn rate due to or after a particular event in the game and making necessary changes can keep the users more engaged.


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Customer data management

Big Data Analytics in the Retail Industry

The retail industry has been threatened by the changing customer demographics and preferences over the last decade. With the increase in the number of millennials entering the workforce and their purchasing power, marketers in the retail industry have been compelled to direct their efforts toFree demowards targeting this profitable customer segment. The retail companies have accessibility to large amounts of data that can be leveraged to gain customer insights by using big data analytics and other related advanced analytics services. The millennial population is tech-savvy, highly connected, and has high expectations regarding product quality and services. Several advanced data analytics tools are available that generate valuable insights that can help retail companies to customize their marketing strategies.

Big Data Analytics: What’s The Problem?  

Retail companies have realized the benefits of big data analytics and the impact it has on the organization’s bottom-line. But, why is the retail industry shying away from leveraging these advanced analytics solutions, you ask? Here’s why. Marketers are plagued with privacy and data security issues despite using top-notch data analytics services. The data collected by major businesses in the industry may be not worthwhile for third parties, but they still spend millions of dollars to collect this data and generate insights. Collectively, the retail industry players have the opinion that in spite of spending resources, they might not end with a feasible and fool-proof solution that can drive profitability.

Leveraging Big Data and Advanced Analytics

Today, every organization generates vast amounts of data through various sources and touchpoints, which has made it essential to understand how to effectively leverage big data. Firstly, the retail companies must focus on capturing data from various sources such as mobile applications, social media platforms, website traffic, transaction history, and CRM platforms. Secondly, the companies in the retail industry must invest in an effective data management system that allows organizations to collate and store large amounts of data. Finally, the retail sector companies must ensure data integration – historical and present – to gain a holistic view of the retail industry landscape.


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