Category: Predictive Analytics

Home / Category: Predictive Analytics

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.

Also Read: IS DATA ANALYTICS AND AI THE FUTURE OF HASSLE-FREE BANKING SERVICES? 

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.

Also Read: 13 PREDICTIVE MODELS THAT ARE TAGGED AS GAME-CHANGER FOR BUSINESS

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/

To be honest, designing business marketing campaigns for existing customers is as difficult as nailing jelly to a tree. That’s given.
To accomplish this feat of tough analysis, access to a list of customers, email addresses, and purchase data is imperative, but the tricky part remains unattended- sending meaningful insights that will boost a customer’s lifetime value and trigger a repeated purchase.

This trick of accurately targeting customers has been cracked by big enterprises with the help of in-house Data Science teams using a particular approach. The name of their approach is- Market Basket Analysis (MBA).
It is one of the key approaches adopted by renowned retailers to unravel the link between items. One of the basic principles of this approach is to track the combination of items that occur together repeatedly in the transactions.
In simple words, it enables retailers to determine the relationship between the items that consumers purchase. Let’s dig deep for a better understanding.

At the core

The very base of MBA, popularly known as Affinity Analysis, is depended on Data Mining, which uses Association Rule Learning to determine the bond between customers and the attributes associated with them.

The more common and stronger a relationship is, the quicker you can put your customers into segments for future analysis. All that is needed for this initiative is customer and order data.

A scenario– In a grocery store, there are numerous products, out of which consumers can lay their hands on any particular group of things. Say Peanut butter and jelly, cream cheese and turkey, etc. Using Market Basket Analysis on the grocery store data, it will be a piece of cake to determine which products are brought together by customers. Adding a feather in its cap is- discovering new bonds between customers and newer product combinations.

Read More for Related Blogs:  

How to make your Season Sales more efficient? Only AI has the answer

To comprehend the relationship between Customer Buying Behavior and MBA in detail, read on.

Comprehending Customer Buying Behavior using MBA

With MBA comes the good news of unearthing the connections between the purchasing pattern of customers, by determining the products or menu items that appear frequently in transactions.

Smart retailers can evaluate this relationship between the products, which consumers purchase and can use this data to come up with new products or pricing models for maximum revenue.

Here are some smart ways to make use of such insights from MBA:

Cross Selling - Up sell - Market Basket Analysis

  • Cross-Sell: Group products which customers buy often from the store
  • Marketing promotions: Focus marketing campaigns to customers and lure them to buy products for an item recently bought
  • Web stores- Propose associated items that are frequently purchased together (“Customers who purchased this product, also viewed this product”)

To read the maximum benefits of this analysis, hiring the services of an expert in the field is the only option.

How SPIN’s MBA Analysis expertise helps

It comes as no surprise that MBA is applied to different segments of the retail sector to pump up sales and open new streams of revenue sources by determining the requirements of the customer and making purchase offers to them.

Banking on this theory, SPIN uses MBA to help its clients with:

Cross-Selling: A sales technique that enables the seller to suggest a related product to a customer after the first purchase is made. With SPIN’s MBA, retailers can comprehend consumer behavior and pitch the right product for cross-selling.Up Sell Cross Sell Market Strategy

Product Placement: It is a technique to place complementary and substitute goods together for the customer to buy them together. Using SPIN’s MBA, retailers can determine the goods, which a customer is more likely to buy together.

Detection of fraud: MBA contains credit card usage details and it can be used to determine the purchasing behavior to detect fraud possibility. SPIN’s MBA will prevent your retailer business from such adversities too.

Customer Behavior: SPIN’s MBA helps to comprehend customer behavior under a host of different conditions, enabling the retailer to determine the connection between two products, which people purchase, and get the knowledge of the customer’s buying behavior.

SPIN’s MBA is the combo of AI and ML

Businesses want to evaluate the different angles of customer behavior inside a store. With the right data sets to determine customer behavior of retail stores, businesses can categorize data to define the :

  1. Right product association
  2. Trip types
  3. Point of sale and marketing

Artificial Intelligence and Machine Learning

After analysis of the consumer behavior inside a retailer store, AI and ML techniques powered by SPIN Strategy algorithms are applied to reap the following benefits for the retail business:

  1. Develop lucrative combo offers
  2. Place associate products together in the store
  3. Customize the layout of the eCommerce site catalog
  4. Manage inventory based on the products with better demand
  5. Categorize different shopping trips to generate the best shopping experience
  6. Create customer profiling and apply segmentation using buying pattern
  7. Determining the best product association

To wrap it up

Market Basket Analysis is used by some of the biggest companies in the world to make informed and strategic business decisions. 

Here at SPIN Strategy, our professionals can help you perform such an analysis on your customer base to drive your market growth and design your product.

Interested in exploring MBA? Visit: https://www.spinanalyticsandstrategy.com/

Artificial Intelligence – Leading the way towards global development!

With the world rapidly evolving, customers have become almost equivalent to business owners dictating the shopping terms, sales funnel, marketing efforts, etc. As per their needs, customers decide:

  • How to shop
  • Where to shop, and
  • How to proceed with the transactions-online or offline

Along with adapting these changes in customer aspirations, organizations also need to redefine business operations for every industry to emerge more customer-centric and tailor-made as per customer needs.

Reality CheckCustomer-centricity is not an out-of-the-box topic for organizations, principally for those trying to set a benchmark for client satisfaction.

Before we get further, let’s get an in-depth understanding of what a Customer-Centric company is.

What is a Customer-centric company?

In business terms, being a customer-centric company means offering unprecedented customer experiences right from awareness to purchasing, and finally post-purchase stage.

Customer Centric Business Structure 2020

For a customer-centric company, it is a mandate to execute business operations that stimulates positive customer experiences, prior and post-sales conclusion. This assures to uphold the culture of repeating customers, and boosts consumer loyalty for increased profits.

Does your business is in need of it?

Business needs Customer-centricity: Why

Market hypothesize confirms that businesses that apply customer-centric strategies encounter a 55% increase in profits, year by year. For such businesses, customer-centricity is more than mere words on a paper and goes beyond employee meetings or customer surveys.

If the target is to improve the business value and be in sync with the changing trends, incorporating customer-centric strategies is the key.

As per the core principle of customer-centricity, every organization in any industry requires a robust foundation in 1) leadership and 2) strategy, and the poised use of 3) people, 4) platforms and 5) processes.

How to be a customer-centric business?

Customer-centricity commences with a company’s culture and commitment to customer’s success.

Here is how your organization can turn into a fully-fledged customer-centric business.

Customer Centric Business Strategy

1. Predict customer requirements

Anticipating the market’s future needs is a game-changing business move. To be customer-centric, it is imperative for companies to anticipate customer needs and provide helpful suggestions accordingly.

2. Compile customer feedback

Frequent and regular communication with customers is the key to successful customer-centricity. Thanks to the digital transformation of the world, there are countless encounters to accumulate customer feedback like emails, chat, In-app messages, SMS, FB messenger, etc.

3. Convenient customer support

Contacting the support team of a digitally built business is the most difficult task. Easy access to customer support builds rapport and trust, laying the foundation for long-term business relationships.

4. Deliver proactive customer service

Providing customers with added value beyond the point of purchase, like proactive customer service- resources to customers to solve minor problems independently, is the key differentiator.

5. Move beyond purchase

Only with added benefits that extend beyond the point of purchase can old customers be motivated to  buy again, and create a memorable experience.

 

Measuring customer-centric success is crucial: 

Customer Centric Business Strategy
Customer Centric Business Strategy

To measure the success of a customer-centric business, taking into account the following top three significant customer metrics is fundamental:

Net Promoter Score

Are you happy with the products/services?

NPS that focuses on unraveling customer loyalty uses the answer to define the success of customer-centricity.

Whenever a client answers to this question, the response is segregated into certain predefined criteria.

 Net Score Business

Promoters (9-10) – Such customers love the product/service and are quite likely to refer it. Customers who rate your product/service a 9 or 10 have high customer lifetime value and are indefinitely repeat customers.

Passives (7-8) – Such customers who rate your product/service a 7 or 8 are simply happy being a client, however, they are more prone to switching to a competitor in case of a better and inexpensive product.

Detractors (0-6) – Customers providing such ranking are unhappy with the product/service, and are more prone to damaging brand reputation.

Bottom line- The more Promoters a business has, the better the growth it experiences.

Churn Rate

Procuring new clients on a frequent basis puts most organizations in a quandary. Thus, more business is investing in retaining existing clients instead of chasing more fresh leads. Here’ why:

  • Procuring new clients, compared to existing clients, increases the cost up to 5 times.
  • Minimum 2% hike in customer retention is equivalent to cost-cutting by 10%.
  • On an average, companies lose around 10% of their customer base annually.

Note: Companies with a higher retention rate grow faster.

Customer Lifetime Value (CLV)

For any growing business, the customer base is one of the most valuable assets.

With CLV, the revenue collected from such clients during their customer lifetime, starting from the first purchase until the time transactions stop is measured.

With the help of CLV final calculations, it is easy to comprehend why a business should keep investing in customers.

Concluding thoughts

It is no news that businesses are reaching out to different ways to incorporate more customer-centric strategies in their approach.

Any organization that has a lack of customer data and priorities revenue over customer needs still has some catching up to do to sustain in the 21st– century business world.

Introducing the customer-centric brand culture is undoubtedly the best route for businesses to align leaders’ choices with the mindset of the team and cultivate the values. To successfully apply the customer-centric strategy, this culture must be in sync with other business strategies.

At SPIN, we maintain robust individual contact profiles and useful CLV insights and purchase likelihood tools that can help your esteemed organization to be customer-centric successfully, and market smarter.

Visit today: https://www.spinanalyticsandstrategy.com/

 

PREDICTIVE MODELS 2020

Be more accurate towards forecasting your data. Know the hidden patterns within your data to explain statistical abnormalities and explore the relation behind the unexpected Customer Patterns and sudden fluctuations in the audience segmentation metrics. 

Here are a few Predictive Models that make it happen:

1. CUSTOMER SEGMENTATION: 

Dividing customers into groups based on common characteristics is Customer Segmentation.  

Knowing your customers and targeting them in the most relevant way to boost Customer Retention and Customer Satisfaction is the primary motive for any business.

Get Answers for every doubt you have related to your customers using this Model:

  •  Know your customer requirement before launching a new product
  • Target specific Customer Branding
  • Rebrand new customers

Customer Demographics, Customer Buying Behavior, Customer Interests, and Customer Profiling are some of the additional benefits of the Customer Segmentation model that businesses look forward too.

2. CROSS SELL AND UP SELL MODELS:

Looking for quick wins and easy growth?

Upselling and cross-selling are considered to be the two most effective ways to boost revenues for retailers. Upsell is used when business owners want to convince their customers to purchase a more expensive  product/ service, while salespeople use cross-sell to encourage consumers to buy more products based on instinct.

Here is the strategy used by Cross-Sell and Up-Sell model:

  • Loyal Clients Feedback First
  • Business Quarterly Reviews
  • Gathering testimonials and Case studies
  • Blog sharing
  • Trial and error
  • Time to time discounts and Offers
  • Know your Needs
  • Employee Retention

3. CHURN MODELS:

Customer Churn Prediction

Churn Rate defines the percentage of Customer Retention for your products/services. Customer Churn prediction tasks will often use customer data to determine:

  •  Time spent on a company website
  •  Products/ services  purchased
  •  Demographic information of users
  •  Links clicked
  •  Text analysis of product reviews

Types of churn Models:

  1. Define Metrics with Consumer data
  2. Shifting insights based on Outputs
  3. Adding machine Learning to Churn

4. SENTIMENT ANALYSIS:

Discover Customer emotional connect towards a product/service and boost Brand Reputation Management with Sentiment Analysis.

Sentiment analysis and opinion mining find numerous applications in e-commerce, marketing, advertising, politics, and research, with:

  • Text Polarity
  • Sentiment ranking
  • Feature Sentiment Analysis

5. PRICE PLANNING AND ANALYSIS:

It is important to analyze the pricing situation to develop a Price planning and Analysis strategy to:

  • Evaluate new product ideas
  • Test marketing
  • Introduce strategy
  • Add Positioning               

Analyzing the pricing strategy benefits the business by:

  1. Establishing the responsiveness of the market to price
  2. Determining cost
  3. Analyzing competition
  4. Assessing legal constraints

6. CUSTOMER SPENDING PATTERN ANALYSIS: 

Customer spending patterns can be divided into regular spend pattern and lifestyle spend patterns.

  • Regular spending  means basic necessities of life, 
  • Lifestyle spending means spending on a computer, internet, car, cell phone, etc.
  1. Cultural
  2. Social
  3. Psychological
  4. Personal

7. PRODUCT RECOMMENDATION:

Product recommendations will work for ECommerce Businesses where there are unfriendly sales assistants to help customers with each step of their shopping journey.

Product recommendation engines fall into 2 main categories: 

  1. Unpersonalized
  2. Personalized

8. IMPACT ANALYSIS OF SALES PROMOTION:

What are the promotional offers that has the highest impact at present?  This question is relevant for every product/service-based business.

Promotional analysis is a technique of analyzing success or failure of a promotion using past time series data. 

Types of Promotion Analysis include:

  1. Quantity/Product concession
  2. Price discount
  3. Ads
  4. Shipping promotion

9. CONSUMER CHOICE MODEL:

The consumer choice model is used to determine the buying decisions for several commodities with different results. Such models take into consideration different families, classes, attitudes, etc.

The basic steps for Consumer Choice Model include:

  1. Recognition
  2. Information search
  3. Alternative evaluation
  4. Purchase decision
  5. Post-purchase behavior

10. AD OPTIMIZATION

Some ads do well than others and generate more clicks, revenue, conversions, higher conversion ratio, etc. Based on these features, you can display the better performing ads more and show a red flag to the poor ones to boost the ROI.

The Ad Optimization models operate by:

  • Adding a call-to-action model
  • Adding emotional content
  • Using Ad Extensions
  • Trying dynamic keyword insertion

11. PROPENSITY MODEL

The method of predicting the possibility that visitors, customers, or leads to conduct certain actions are termed as Propensity Analysis.

Based on Propensity modeling, marketing teams forecast the likelihood of whether a lead will convert to a customer, or will churn. Add to it, propensity modeling also helps to predict whether an email recipient will unsubscribe or not.

Here is how Propensity Modeling works:

  • Determining the features
  • Preparing the propensity model
  • Calculating your Propensity scores

12. TIME SERIES AND CASUAL ANALYSIS:

Time Series Analysis is used to clarify, track, and forecast casualty behaviors of customers. Whatever time-based patterns business experiences, Time Series Analysis can be used to determine it.

Time Series models can be applied to the following applications:

  • Business- Web traffic, Supply Chain, etc.
  • Finance- Stock option, econometrics, etc.

13.FRAUD MANAGEMENT AND PREVENTION:

Spotting potentially fraudulent behavior and identifying unusual patterns of behavior consistent is termed as Fraud Management and Prevention.

Fraud Prevention models provide:

  • Expert alert scenarios
  • Real-time integration
  • Quick roll-out
  • Audited workflow and case management
  • High performing testing tools

In the true sense, Farming is by far one of the oldest lines of work in the world.

But, with the passage of millennia, Humanity has come a long way and so did agriculture. From conventional methods to grow crops to the usage of AI in Agriculture, Humanity has indeed taken a big leap.But with land getting in short supply and population growing by leaps and bounds, using creative methods to produce crops and boost productivity in limited space has become the need of the hour. 

Change has stepped in. And this can be testified by the fact that the worldwide agriculture industry which is roughly estimated to be around $5 trillion, is stepping in the shoes of other sectors, shifting to what is known as Precision Farming

For instance, adopting AI technologies to reap healthy crops, monitor soil, control pests, accumulate data for farmers etc. and eventually perk up a number of agriculture-related errands in the food supply chain.

Artificial Intelligence in Farming

Digital Agriculture: Farmers are using AI to increase crop yields

Artificial intelligence holds the promise of driving an agricultural revolution at a time when the world must produce more food using fewer resources.

Artificial Intelligence has various applications in agriculture ranging from rural automatons, facial acknowledgment, computerized water system frameworks, and driver less tractors. These applications are done in relationship with an alternate sort of sensors, GPS frameworks, radars, and other cutting edge contraptions dependent on AI.

Innovative progressions and the modernization of GPS are making ranchers and the agriculture specialist co-ops anticipate that additional upgrades will increase the profitability.Increasing adoption of the mechanical technology and IoT gadgets in agriculture is additionally assessed to drive the AI in agriculture.

Agriculture is slowly becoming digital and AI in agriculture is emerging in three major categories, (i) agricultural robotics, (ii) soil and crop monitoring, and (iii) predictive analytics.

Agricultural Robots – Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.

Crop and Soil Monitoring – Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and/or software-based technology to monitor crop and soil health.

Predictive Analytics – Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.AI With Agriculture Farming

How Analytics and AI steps in actually

Intelligent farming practices that have eventually transformed into knowledge-based agriculture, increases production levels and product quality to significant numbers. 

With trained professionals in this art, companies like SPIN Strategy extracts insights from numerous data sources that are integrated into an Advanced Big Data Framework with data analysis decision-making, and automated data recording. 

Result- Customized data for better plant health.

At SPIN, with the combination of smart farming and AI, we assemble, analyze, and digitize massive amounts of data to aid farmers to optimize their production systems. And, that’s why we like to be termed as the Farmer’s Little Hand.

With the use of technology, we:

  • Determine the ripeness of the crop
  • Help farmers preserve water
  • Customize production

Let’s dig deep to understand how ML and AI make a difference in Smart Farming using IoT.

Role of Artificial Intelligence in Agriculture

The agricultural industry is just like any other industry beginning to show interest in implementing the best-in-class technologies to save on resources and create more efficient processes. Agriculture is responsible for the survival of human beings, and the industry has made steady technological improvements in the last few years.

IoT in Smart Farming

How SPIN’s ML and AI programs make the difference in Agriculture: 

Machine Learning in Farming: 

Provides faster and precise results by evaluating the Leaf Vein Morphology that has more data about the leaf properties.

Artificial Intelligence in Farming: 

Uses algorithms and previous field data to determine crop performance in different environments, and builds a Probability Model to forecast the genes beneficial for the plant.

Here is a detailed overview, how SPIN’s AI programs turn the tables for agriculture:

1 . Water Management:

An AI-based application that can be connected with more successful use of irrigation systems and forecasting of daily dew point temperature, which is the base to determine any weather phenomena and analyze evaporation and transpiration.

Water Management with AI

2. Yield Prediction: 

Moving beyond the traditional prediction of historical data, SPIN incorporates computer vision technologies to supply data on the go and conducts a thorough multidimensional analysis of weather, crops, economic conditions, etc. to reap the maximum benefit of the yield for farmers and the population.

Yield Production

3. Crop Quality:

The precise detection and categorization of the crop quality can shoot up the product price and cut down waste. Compared to human counterparts, machines avoid meaningless data to determine the quality of the crops and any possible anomalies.

Soil Management

4. Disease Detection:

At SPIN, we evaluate field images with Conventional Neural Networks to classify pests and diseases, track agro-technical activities, and gather data. To be more efficient, this approach needs more pesticides that lead to huge environmental expenses. ML is used as a general agriculture management to determine diseases and cut those costs.

Disease Detection

5. Monitoring Crop’s health: 

Hyper spectral imaging, together with sensing techniques and 3D laser scanning are vital to establish crop metrics across the land. SPIN crop health monitoring agent has the potential to change farmland monitoring by farmers and can significantly cut down on the effort. 

Monitoring crop’s health

How SPIN Contributes to Smart Farming

Confirmation and extensive testing of emerging AI applications in the Agriculture sector is estimated to be quite vital, since agriculture is affected by environmental factors that cannot be tamed, unlike other sectors where the risk is easy to predict.

At SPIN, we ensure a steady adoption of AI in agriculture with the help of Image Sensor Technology that helps in:

  • Real-time monitoring, analysis, and control of pest & disease
  • Pollination, Phrenology, Fertilization, Irrigation
  • Pollination, Phrenology, Fertilization, and Agri-Technical activities
  • Monitor and forecast yield performance in real-time to optimize results
  • Using Support Vector Machines to predict yield and crop quality
  • Using Artificial Neural Networks for crop management and weed detection

Scenario

Issue-One of our clients, a Colorado-based organization, wanted a preventive measure for defective crops, and optimize the potential for healthy crop production.

Solution– The trained AI professionals at SPIN conducted a comprehensive Soil Analysis and developed a system that will use Machine Learning to deliver clients with an idea of the soil’s strength and weakness. This way defective crop production could be prevented to a significant degree.

To conclude with

Artificial Intelligence and Farming have the potential to pave the way for an agricultural revolution, especially when the world needs more food production with limited resources.

As per the UN Food and Agriculture Organization, the population will hit the roof by 2 billion by 2050. However, experts are of the notion that only 4% of the additional land will fall under cultivation category. In tune with this, the use of the latest technology to do smart farming still takes the front seat.

AI-Powered solution will enable farmers to do more with limited resources and produce the finest quality of crops that amazes even the producer.

Need professional guidance to reap the benefits of using AI in farming? Visit SPIN Strategy today: https://www.spinanalyticsandstrategy.com/