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A Detailed Guide To Data Science For Any Business

Post by|Data ScienceQatar30 November,2020
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Data science refers to the art of using data with statistical methods for the benefit of your business. Wherever there is data involved, you can apply data science and analytics techniques. Making decisions for a lucrative business needs data to predict the correct choice. Given that the “thin-line” of separation is thinning out, there is essentially only one correct choice amongst the pool of options. So, to make the right decision, you need data science solutions to help you stay ahead of any contingency.

The management team of any business has to ensure that all their resources – whether human or machine – are well utilized. They can strategize all these business decisions with the help of data. Let’s look at some of the data science systems which come to rescue and deliver a sigh of relief to business management.

Customer Segmentation

It refers to the division of customer data into segments based on their purchasing behavior. There are many different customer segments possible. Segmentation is done based on the purpose of decision making.

The most common segmentation is the RFM model. It refers to the Recency-Frequency-Monetary aspects of a customer to decide the segment to which customers will be assigned.

Recency is calculated based on the number of days since the customer’s last purchase and the next-to-last day of the purchase. Frequency refers to the number of orders of the customer. Monetary value refers to the sum of the number of orders placed by the customer. Recency and Frequency can also be calculated based on the website visits made by customers.

For Example

Customer Order Date Amount
Customer A 20-03-2020 100$
Customer B 25-04-2020 50$
Customer A 16-06-2020 70$
Customer B 10-07-2020 140$
Customer A 18-09-2020 120$

 

Customer Recency Frequency Money
Customer A 92 Days 3 290$
Customer B 75 Days 2 190$

Other types of segmentation include dividing the customer base based on their device used while making the purchase. We can also segment the customer based on where the user made the purchase and many more parameters for segmenting customers.

Customer Review Sentiment Analysis

Nowadays, websites have a facility for customers to leave their comments about the brand/product.

[ Source: elfsight ]

Customer Review Sentiment Analysis refers to the systematic analysis of customer reviews. We segregate reviews into positive and negative. The Analysis uncovers topics about which customers are talking about the most. We can also assess the location of customers and find their preferences.

This analysis helps senior management to track the glitches in the departments for which grievances have been reported. It is beneficial to senior management to know what customers think about their business and formulate strategies to address their concerns. It also helps them to design products in the future, keeping in mind what customers want.

Customer Lifetime Value Prediction

It refers to the calculation of customer lifetime value and predicting the customer’s future purchase based on their past purchase.

Customer lifetime value is calculated as

[ Source: clevertap ]

This prediction analysis informs businesses about how much revenue customers are going to generate. Higher the revenue generated per customer highlights higher customer satisfaction. If there is a decline in average revenue/order, companies need to take steps to raise it.

Customer Churn Prediction

Subscription-based businesses segment their customers into two categories: active and churned. Active customers are the ones who continue their subscription renewal. Churned customers are those who canceled their subscriptions.

Customer Churn Prediction refers to the system that predicts which customers will churn and what traits they possess. This can be age, gender, location, etc., of customers.

Subscription-based businesses need to know what customers are going to churn soon. This helps them prepare for the future and devise plans to retain these customers.

For Example, Video content service loses more of its subscribers in Tamil Nadu because subscribers love watching more local language content. So, to attract more audience, any Company should start showing regional content as well to ensure maintaining subscribers.

Customer Retention Rate

Retention rate refers to calculating the number of customers who have been making multiple purchases on your website. It refers to how many customers a business has been able to retain.

Retention rate is calculated as percentage of difference between existing customers and new customers divided by the number of total customers.

Customer Retention = ((EC – NC)/TC)*100

Where
EC – Existing customers
NC- New Customers
TC – Total Customers

It is believed that 80% of revenue comes from 20% of customers. It is imperative that businesses hold their existing customers as they will contribute more to revenue. It also helps save the cost of acquiring new customers.

Customers are the driving force of any business but there are other factors responsible for successful businesses. To satisfy customers, businesses need to ensure smooth operations, manage to price their products effectively and keep their costs of developing and delivering the products in line with profitability. Following few projects are designed to help management with internal business operations.

Resource Optimization

It refers to the set of processes to effectively and efficiently utilize resources (employees, financial resources, raw materials, machinery, etc.).

We consider the conditions involving how these resources are utilized then come up with a plan to use these resources in the best possible way.

For example, When we are considering assigning employees to certain projects, we may consider the experience of employees and their utilization in other projects to decide whether they should be assigned to a project.

Cost Optimization

It refers to the set of processes to optimize the cost of production. We determine the factors affecting the determination of product price and try to minimize the cost maintaining the quality of the product.

For example, while procuring raw materials from location A, cost of raw material is 100$ and transportation charges are $40, whereas the cost of raw materials from location B is 90$ and transportation costs are $50. Hence, it will be profitable for business to procure from location A.

Price Optimization

It refers to a set of processes to optimize the price of a product. We determine the price of the product based on the supplying materials cost, machinery cost, labour cost and transportation cost of the product delivering to the customer. It is beneficial to the customer as well as the business to keep the prices low.

For example, you price your product at 100$/piece whereas your competitor has priced at 95$/piece. You have ascertained the price based on your material + manufacturing cost + transportation cost. You analysed your cost in comparison to your competitors and concluded that your transportation cost is higher than your competitors then you work on optimizing it to increase your sales.

Inventory Optimization

It refers to the set of processes to optimize the stocks to be stored for future. Stocks can be piled for raw materials as well as finished goods. Businesses need to decide the right amount of stocks to be stored. There are various factors affecting inventory management decisions. It can be from warehouse rent, logistics to time required for manufacturing quantities required at any time to ensure timely delivery of products.

For example, transportation charges for a single set of inventory are 50$ whereas warehouse rent for the same set of inventory is 40$ then it is beneficial for business to store stock rather than procuring inventory again .

Product Recommendation

Recommendations refers to suggesting products to the customers based on their preferences. Product recommendations are determined based on the past purchases, reviewed products, viewed products, products purchased by other users who also purchased products like you, etc by the customer. We can also recommend in generic fashion like popular products, cross-selling products, upselling products to the customers.

These recommendations ease the user experience and raise the chances of sales since customers can be intrigued with the products of their choice.

Fraud Detection Systems

Data science also plays an important role in the prevention and detection of financial frauds. Basically, payment frauds refers to any fraudulent transactions due to which the business owner didn’t get his revenue from the sale. It can be due to phishing, identity theft, page jacking and many other reasons. Businesses suffer great deal of losses in form of frauds. They need preventive measures to ensure avoiding these losses.

Fraud Detection systems are the systems finding the features common in the fraudulent transactions and predicting what future transactions might be fraudulent. Features common in fraud transactions can be location of sale, amount of transactions, number of previous fraud transactions of the customer, etc.

Chatbot

Data Science has its ties strong with Artificial Intelligence too. Let’s refer to the scenario of AI-assisted chatbots. Chatbots refer to automatic responses to customer query. There are different types of chatbots like support bots, assistant bots, transactional bots, etc. These bots satisfy customers with their doubts and ease customer experience. Chatbots also help businesses to reduce operating costs as they don’t have to hire personnel to handle client queries.

Sales Forecasting

It refers to predictive analysis of past transactions to find what sales will be in the foreseeable future. It helps in finding trends and seasonality among the orders.

Forecasting sales helps businesses to prepare the stocks for the future. It also helps them ensure resources are available to handle manufacturing and sales. Business management can also plan their budget for expenditure thus avoiding huge expenses in less sales.

For example, clothing stores can prepare stocks for the holiday season based on data science insights derived.

How To Use Data Science In The Health Industry?

Medicine researchers have been continually using the patient records of illness and medications to improve medicines and prepare for future epidemics and pandemics. It helps them to analyze how drugs interact with various genetically varied patients.

People are very health conscious these days and constantly try to look out whether T they are able to maintain a healthy lifestyle. In order to stay healthy, people continually wear fitness bands and fitness watches. These bands collect real-time data of users.

Companies use this data to suggest what kind of exercises users need to follow, diet plans to be implemented and track sleep patterns to ensure healthy sleep.

Data science has helped Medicine companies to prepare for seasonal diseases like flu by maintaining appropriate stocks and optimizing supply chain to deliver the supplies to medical stores in advance of the seasonal diseases.

How To Use Data Science In The E-commerce Industry?

The E-commerce industry has loads of data about transactions,customers and products.

Business Management teams can plan everything efficiently by just making the right use of data.

For example, shoe store has the following problems:

1. Decide What Stocks To Maintain?

They can decide products to be maintained in stock based on time series analysis of sales and predict product sale in a particular time period. Suppose they have seen that close-toed shoes are common in the winter season so they will maintain high stocks of such shoes.

2. Decide Promotional Strategies For The Store?

We can use customer segmentation and divide customers on their RFM(Recency-Frequency-Monetary) values. Suppose, the latest shoes are in the range of ₹500 -₹1000. We will contact the customers who purchased earlier from us within that price range.

3. Devise Improvement Plans For The Store?

We can use customer review sentiment analysis to know the customer’s purview of the store. We analyze the reviews to know what customers are satisfied about and what dissatisfies them. Suppose, customers are complaining about xyz brand so you can reduce stock of that brand or customers are not satisfied with the support staff then you can address the concern and make improvements with the support staff.

There are many more such problems which can be addressed by data science experts with just a little data analysis and success is on its way for your store.

How To Use Data Science In The Finance Industry?

Finance is the fuel of the business. Managing finances is really important to ensure smooth operations of the business. A company handles finances like bank loans and how to assign them to the right business operation.

To mitigate risks of handling finances, Data Science comes to the rescue. We use Risk Analytics to

  • Decide which sources of finance are reliable to get investment
  • Decide which operations are in dire need of funding and profitable to fund back
  • Assess market scenario to judge and decide how to handle your financial situation

Fraud Prevention is another example of how data science becomes savior of your finances. Whether it is an eCommerce store or bank, these institutions always face the threat of fraudulent transactions. Data Science helps to analyze the past transactions and predict potential fraud threats to mitigate fraud risk.

How To Use Data Science In The Transportation Industry?

The Transport Industry handles logistics and uses data science for deciding routes for delivery. It helps them to find most cost effective paths for delivering goods.

Example, Google Maps shows you the best possible route to reach a destination.

How To Use Data Science In The Manufacturing Industry?

Machinery is the heart of the manufacturing process. Using the right machine to get your goods manufactured is a difficult decision. Management has to consider the output of machinery as well as cost of the machinery. Manufacturing industry uses resource optimization to decide which machinery will be giving us optimum outputs of goods ensuring minimum time involved and cost incurred.

Closing Thoughts

We looked at various systems that can help a business and we read about how these systems are used by different industries to solve their concerns. You can achieve greater efficiency by automating processes or taking data-driven decisions to assist your business. ZealousWeb takes pride in acknowledging that we can and we have helped a lot of businesses to achieve their goals by implementing data-based strategy and raising their profits through skies. We would be happy to land in your data kingdom and introduce you to the magical world of data science solutions.

FAQ

Can we apply data science in all fields?

Yes we can, in fact, data scientists have changed almost every industry with the correct usage of data and statistics.

Like other technologies, will data science also become an obsolete one fine day?

Perhaps not! Data scientists are unlikely to become obsolete until we get a rise of Artificial General Intelligence, which means AI with general capabilities.


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