5 Critical Data Science Use Cases in Modern-day Business

Today, data drives business. Gone are the days when stakeholders in organizations had to devote hours just to speculate how will markets look like in the coming years. Data has helped organizations replace speculation with informed and definitive decisions.

The business world is blessed with a massive amount of structured, un-structured, and semi-structured data. However, such data is useless if one cannot derive the underlying meaning out of it.

Data science is the discipline that help analysts derive meaning out of disparate data sets. It is the process of writing algorithms and analytics solutions that process large amount of data. This provides insights into the nature and behavior of the entity from which the data has been derived.

In this article, we will highlight the top 5 data science use cases in business.

Data Science Use Cases #1- Inventory Planning

Inventory surplus and stock-out are the two significant reasons why organizations bleed money. Excess stockpiling results in inventory cost overheads. Lower inventory level results in businesses losing out on sales opportunities.

Businesses need a foresight to maintain a balanced inventory that will neither result in surplus nor stock-out. Data science is key to gaining this foresight to achieve a robust inventory planning.

Analysts apply superior artificial intelligence algorithms-driven analytic models to parse through historical data. Such data is derived from the sales department. For instance, a shoe selling retailer can use analytics tools to segregate the historical sales data into size, types, gender, and country.

This helps gain insights into the nature of demand for specific shoe types and sizes among genders in different countries. Based on these insights, a shoe retailer can plan their inventory for the future.

Data Science Use Cases #2- Credit Risk Management

Data science is imperative for credit risk management. It is more so in an era of economic uncertainty where the status of a qualified borrower changes overnight.

Business analytics tools conduct loss analysis on data pertaining to a borrower’s historical and present financial activities. Sophisticated AI algorithms study trends in the data. The algorithms capture any sudden upheaval or a nosedive in a borrowers’ purchasing trend.

Such trends point at either a stark improvement or a deploring financial health of the borrower. Insights from this analysis help credit risk managers to price commercial loans more effectively.

Business analytics tools also conduct migration analysis to spot any alteration in the borrower’s risk profile.

Data Science Use Cases #3- Demand Planning

An efficient demand planning underpins important aspects of a business which are budgeting, sales and marketing campaign, risk assessment, and others. Any iota of ambiguity in demand planning will have a domino effect on the overall business performance.

Demand sensing is one of the newest analytical methods of forecasting demand by capturing fluctuations in purchase behavior in real-time. Data is extracted from warehouses, POS systems, and other external sources and is run through the demand sensing solution.

This generates insights into sales fluctuations and factors triggering those fluctuations. Such insights are critically important for businesses to adjust their demand planning.

Data Science Use Cases #4- Marketing Campaign

A marketing campaign is destined to fail if it is not targeted to the right kind of audience. In another way, if it does not include the right message that can appeal to the audience targeted by a business.

The importance of data science could not be stressed more in improving the chances of a marketing campaign to succeed.

Data scientists build a spend analysis model to parse through a marketer’s spend and acquisition data. These data insights guide marketers to optimally distribute their fund across channels, locations, and other mediums that will market a product.

Customer sentiment analysis involves using natural language processing. It examines text data that includes product reviews, call center conversation records, survey responses, and others. Such data helps a business to understand a customer’s preferences. This understanding is the podium of creating personalized marketing campaigns.

Data Science Use Cases #5- Cybersecurity

Businesses deal with high-value and highly confidential data on a regular basis. Such data is vulnerable to cyber-attacks. Cyber attackers have harnessed technology advancements to make their intrusions more lethal.

Data science is playing an integral role in helping businesses thwart such advanced cyber-attacks. For instance, the intrusion detection systems feed historical and present data to machine learning algorithms. This enables the system to forecast attacks and system loopholes that make those a cyber-attack hotspot.

Analysts can train data models driven with deep learning algorithms with sample cyber-attack cases. This helps the data model to learn about the nature and time of the attacks. It enhances the ability of such data models to forecast cyber-attacks and reduce false positives.

How We Assist Our Clients in Optimal Utilization of Data Science?

We begin the process by getting a thorough understanding of the business context. Our team of experts gather all the past efforts done to address the context. We derive important levers from the past efforts and our present actions to create an action plan for data models.

Our team of experts conduct controlled experiment with such hypothetical data models to determine the applicability of the action plan. Once determined successful, we run the data models across the business to get faster and assured results.

As a prominent data science solutions provider, we have employed the strategy above with clients across diverse industries to harness the wonders of data science in their business.

For instance, our team of experts had created statistical data models for our client who was an eminent vacation rental company.

The data models parsed through responses generated from customer experience surveys. This allowed an overview to our client about the overall brand impression among communities. It generated actionable insights into what was working and what was not in our client’s accommodation operation. Based on the insights, the client exercised a major operation overhaul which resulted in a 4x boost in customer experience.

Who We Are and Why Are We Considered as Industry Experts?

This article is penned by experts at getSmartcoders, a prominent data science solutions provider. Our scope of services includes big data lakes solution and consulting, lead scoring, cognitive process automation, among others.

Contact us now to discover how cutting-edge data science solutions bring transformational changes to your business.