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Data Analytics in Banking

Data Analytics in Banking

Banking is getting branch-less, modern, and digital rapidly. As banks compete to get an advantage over their competitors, the need to adopt data analytics becomes even more relevant. If there ever has been a sector where analytics could play a great role, it is banking. Banks have been dealing with data for decades and its scientific analysis can help them bring a huge boost in their performance, leading to an increase in the bottom line. According to a study, over 90% of the top 50 banks in the world are currently using advanced data analytics to achieve their goals. While the applications of data analytics in banking are endless, here are five of the most common applications and how they help in gaining a competitive advantage.

Customer Segmentation

Customer segmentation allows banks to differentiate their customers according to their income and other important factors to address them properly. It is crucial to differentiate customers that make the money and customers that don’t. To grow their wallet share and create more loyal customers, banking firms need to concentrate on selling the right product to the right customer. According to a study, proper segmentation can help banks increase revenue by 30% and decrease costs by 25%. Moreover, segmentation is the top area of investment in marketing and product departments in banks with over 2.5 billion in assets.

While most banks base their segmentation on basic parameters like age, gender, region, education, and so on, data-driven segmentation also takes into consideration their milestones, personal preferences, attitudes, and technological savviness. Based on this, you can market the right product, to the right customer, at the right time, and over the right medium.


Fraud Prevention

Understanding the usual spending behaviour of an individual helps in raising a red flag if any scandalous event occurs. If a sudden increase in the expenditure of a cautious customer is noted, it might mean that the customer’s card was stolen and is being used by fraudsters. Data analytics has helped Americans banks prevent fraudulent transactions worth $22 Billion in 2018. Fraud prevention is so important to banks and other financial institutions that they will spend 9.3 billion for it annually.

Fraud and fraud patterns are evolving and changing more rapidly than banks can keep pace with. That is where artificial intelligence and machine learning-driven data analytics are bridging the gap by helping banking organizations detect risk, breach and fraud quickly and at the source. This early detection can stop over 60% of fraud-related losses and help the bank reissue less compromised cards to its customers.

Risk Assessment

Risk Assessment is one of the top applications of predictive analytics in banking. It is of a high priority to banks as it helps in regulating the financial activities and pricing financial investments. The financial health of a client organization can be assessed for better financing, facilitating acquisitions and mergers, and for investment purposes. Moreover, top banks like JP Morgan Chase and the U.S. Bank already use it consistently to make accurate investment-related decisions.

The traditional credit risk process is slow and labour-intensive, while data-driven predictive models provide instant results. These models use a broader range of data sources and result in a lower rate of default losses, reducing the risk of losing customers to the competitors due to a slow process. Moreover, banks can also find patterns with their customers that can lead to anticipating and mitigating an upcoming risk.

Feedback Management

Feedback management is a great example of the role of data analytics in the banking sector. Predictive analytics help banks to keep up with their relationship with the customers by offering the right products and services based on their needs. To gain a competitive advantage, banks must understand the importance of data analytics and develop strategies based on the insights from their clients’ data. The U.S. Bank has been using data analytics-driven feedback management to improve its services since 2015. It monitors over 24,000 customer reviews across over 23 websites to understand their pain points and adapt their offerings based on them.

Customers expect banks to take care of all their finance-related needs. By collecting feedback from customers, banks can effectively fine-tune their products and services to sync with the customer’s expectations. Banks that use feedback analysis and management can be faster to market compared to their competitors but also arrive there with better products.

Lifetime Value Prediction

Customer lifetime value (CLV) is a forecast of all the value a customer will bring to the bank during the course of their relationship. The importance of calculating CLV is growing rapidly, as it helps the banking organization to create and sustain a highly-beneficial relationship with a select group of customers with high lifetime value, therefore ensuring higher profits and business growth. Recent research suggests that the CLV of a customer can range from $2,000 to $4,000. Columbia Bank and National Bank of Canada regard CLV as an important factor in making business decisions.

Using CLV as a measure requires the whole organization to shift its aim from quarterly profits to establishing a long-term relationship with the customers. It doesn’t just help you track customer profitability, but also sets an upper limit to the customer acquisition costs. Therefore, a thorough CLV analysis helps the bank make accurate and calculated decisions regarding the marketing, sales & pricing for the new customers.

Premier banks like Bank of America, JP Morgan Chase, and U. S. Bank have understood the importance of data analytics in banking and have incorporated it in their business operations. SPIN Strategy offers world-class data analytics that has helped numerous banking organizations to boost their profits and gain competitive advantage. If your banking organization is planning to implement advanced predictive analytics, contact SPIN Strategy now at info@spin-strategy.com

Importance & Benefits of Predictive Analytics in the Ecommerce Industry

Importance & Benefits of Predictive Analytics in the Ecommerce Industry

Predictive analytics is a form of data analytics that helps in getting information from historical data sets to identify patterns in the data and forecast future trends and outcomes. Historically, ecommerce companies have been rather slow to adopt it, but giants like Amazon and eBay have understood the importance of predictive analytics in ecommerce and have incorporated it in their operations.

Predictive Analytics can give ecommerce companies an unparalleled insight into their customers. Before this, building and marketing ecommerce stores used to involve loads of guesswork. Now, they can easily see what is working and what is not, thereby removing any area that can pose risk. While the benefits of data analytics in ecommerce services are endless, here are a few of them:

 Better Recommendations

Recommendations are very important for any ecommerce business, but it is more important to get them right. Data analytics helps online stores make better recommendations to customers by correlating data from various sources to make a personalized recommendation. 35% of Amazon’s annual sales come from successful product recommendations made using predictive analytics.


Dynamic Pricing

The traditional approach of ‘one-size-fits-all’ is no longer applicable. With the help of advanced analytics, online stores can sell their products at the best price and make huge profits when the demand for the item is high. Amazon has experienced over 143% increase in net profit between 2016 and 2019 after adopting a data analytics-based pricing strategy.

Optimized Supply Chain

No customer wants to see the dreaded ‘Out of Stock’ label in the place of the ‘Buy Now’ button. According to research, two-thirds of customers who experience an ‘Out of Stock’ situation will choose to shop elsewhere. Top brands like Walmart.com and Amazon have invested heavily in predictive analytics to sell products that have not yet arrived in their warehouses.

Better Customer Service

Good customer service directly leads to higher customer retention and conversion, thus bringing more profit. Predictive analytics helps ecommerce companies provide better customer service by identifying issues in the delivery process, the delivery time of the goods, average response time and other factors which customer service staff can act on to reduce the chance of bad service.

The implementation of predictive analytics in ecommerce can become a huge advantage for any e-tailer. However, implementing it properly can be a huge challenge. Powered by Artificial Intelligence, SPIN Strategy provides predictive analytics solutions that help such ecommerce businesses in making strategic decisions that boost business growth. To know more, visit https://www.spinanalyticsandstrategy.com/

Going by market analysis, Data Analytics has played a vital role in the e-Commerce ecosystem and will continue to be the major game-changer.

Thanks to the rising need in a short period of time, the frequent use of Data Analytics in all major departments like Marketing, IT, Operations, etc. became crucial to process potential clients and market data to adopt strategic strategies that will eventually lead to providing services successfully.

That’s why experts say:

“Without Data, you are just another person with an opinion”

Numbers speak it all

  • More than 75% of companies using Predictive Analytics data experienced higher sales
  • 66% of customers feel annoyed when irrelevant offers are sent to them due to lack of relevant data
  • 65% increase in business margins with the use of relevant data
  • 48% of shoppers are more likely to shop on personalized recommendations offers

Data Analytics a Boon for E commerce 4.0

It is no Rocket Science to analyze the fact that customer demand will evolve rapidly in the days to come, and this change will be responded by sellers using AI in a bid to comprehend the demand or what customers would want.

Here are the top 4 ways how Data Analytics will respond to the changing dynamics of eCommerce 4.0 for good:

1. Improved Shopping Pattern observation- Using Big Data Analytics can help sellers to available at the moment, demand spike, etc.

2. Anticipate future operation strategies– Using Big Data Analytics helps sellers manage business operations better, be it inventory, demand forecasting, supply chain, pricing or sales strategies. 

Also, using Big Data Case studies will give a seller a thorough understanding of different business operations and the inevitable problems they involve.

3. More spotlight on Micro-Moments– Successful sellers can leverage this hottest trend for quick actions like- I want to buy, I want to know, I want to go, etc. to predict customer tendencies and action patterns.

4. Improved Online Payment functions– More than 51% of e-Commerce sales are done via mobile platforms as per statistics. This means online payment options need to be more secure and safe than ever before. 

Using Big Data Analytics can help the detection of fraudulent activities and securing of payment gateway by centralizing it into one platform and making it easy for clients.

Analyzing massive Data Sets will not only do wonders for e-Commerce businesses but customers too. 

E-Commerce companies will use data-driven insights to enhance the way customer demands are met and will revolutionize the buying and selling ecosystem.

If you too are a fan of Data Analytics and understand its relevance in your business, get in touch with the professionals at SPIN Strategy and welcome strategic planning and increased ROI for your business, today.Visit: SPIN 


How can Inferential Analytics help business with target customers?


Say, you wish to know the average salary of a data scientist professional of a particular region. What are the possible options you can think of?

  1. You personally meet with every data scientist of that region and make a note of his or her salary.
  2. You hand pick few professionals of that region and calculate the average salary.

Although the first method is not impossible, it is a Herculean task indeed, which will consume a lot of resource and time. And keeping in mind the swift moves of companies these days, easy and quick solution is not just preferred but a priority too.

So, what method should be used to figure out the average salary of the data scientist of that area? 

The answer is Inferential Statistics.

For starters, and to put it simply for all, Inferential Analytics is typically designed to draw assumptions beyond the present data available. 

How it works-By taking a random sample of data from a particular set of population and making assumptions and inferences about the people.

But how can this demographic analysis help a business meet its sales targets? Read on to know more.

How to grow business with Inferential Statistics?

With time, the marketplace shifts and evolves, and so does the list of your clients. 

So, it’s time to get ahead of the curve and get a step ahead in the competition-it is time to take the help of demographic data, and what better serves the purpose than ‘Inferential Analytics’. 

Here are three tips on how you can boost the revenue of your organization by making the most of Inferential Statistics.

  1. Development Plans– Often business leaders plan to expand their company or open a branch at a new location, but how to derive information about the customer base, delivery system, distribution scheduling etc. ? 

With Inferential Analytics, you can get key insights on such aspects, coupled with business intelligence reports. Such information is extremely crucial for expansion plans, especially at a new location.

  1. Locate your audience– With the help of Inferential Analytics that examines your current customer data, it is possible to find out where people are most likely going to take benefit out of your product or service. Add to it, you can also narrow down the region where the possibility of customer potential and expansion is high.
  2. Create a marketing campaign– With the help of Inferential Analytics, a business leader can narrow down the branding requirements and focus on specific consumer preferences in a bid to stand out of other competitors in the region. Together with such data, it becomes easy to create a successful marketing campaign.

Grow your business with Inferential Analytics

Expansion of business is nothing less than a big challenge. Not only it takes time and dedication, but also careful location planning. Only with proper location segmentation that helps categorize targeted customers can a business reach its heights.

When the importance of location and population is so impeccable, Inferential Analytics importance cannot be ignored.

If you are blown away by the relevance of Inferential Analytics on business and wish to incorporate it into your company, team members of SPIN Strategy can be of great help.


As far as the fragment of human imagination travels, speed that up into hundred times more and still, you would not be in calculative terms of understanding to what extent can modern technology lead up to! Barring all limitations, technology evolution in the 21st century seems to be unfolding in such an escalating manner that even the direst of things are not impossible attainments for Scientists and Data Engineers.

Highlighting under this context, one of the massively growing and pervading all sectors of business and lives, is the emergence of Artificial Intelligence. With the sarong demands of the youth, business professionals, and corporate leaders for more upgraded versions of the technology, Artificial Intelligence does complete justice to the aforementioned productively. Additionally, the complimenting elements of more expediency, more flexibility, and more ease of use with AI make it even more incredible.

Here are few of the most in-trend technologies accelerated by artificial intelligence:

Deep Learning

Eminently categorized as one of the best forms of Artificial Intelligence, Deep Learning performs developmental actions that produce algorithms known as artificial Neural Networks. The base model of this has been stimulated by the human brain function and structure. Amplifying much ahead than the equivalent progress of Machine Learning, Deep Learning has outdone 40 fold times more between the years 2015 and 2018. Sectors like autonomous vehicles, computer vision, and automatic text generation have Artificial Intelligence technology at its peak with deep learning being its core use of operative mode.

Facial Recognition

Being regarded as the future of Artificial Intelligence, facial recognition is a colossal breakthrough in the field of technology. Its mounting popularity and progressive growth in the market leaves no room for doubt about its current functionality. With the advent of the year 2019, facial recognition takes giant leaps and dominates the urban mass with its significant power of technological autocracy. To state an example from the current hour, AI-based fun application known by the name FaceApp is a direct testimony of the factual existence of AI’s exponential growth. The app analyzes your face and shows your aged look due to the app’s functionality based on AI + Analytics and ML.


Enhancing and streamlining business operations and managing data storing systems superfluously, the cloud is a fantastic example of AI-empowered technologies. The global expansions of MNC’s have pushed forward the occurrence of the largest public cloud adoption in the year 2019.

Therefore, transformations of businesses digitally have laddered up to a whole new level that the future predictions are even challenging to fathom. Building a concrete reputation as a leading AI-empowered business solution provider, SPIN Strategy is a name that is going to be your need of the hour. With a highly qualified and certified competent team of engineers and Data Scientists, SPIN Strategy understands the essence of the AI revolution and its impact on business and leaves no stone turned to improvise operational excellence.

Interested to know more? Get in touch! https://www.spinanalyticsandstrategy.com/