From New Product Design to Customer Experience (And Everything in Between)

According to a BCG report, 72% of manufacturing companies are improving their productivity using advanced analytics. Today, the applications of data science have become pervasive.

Industries like finance, healthcare, and customer service are jumping onto the bandwagon, and rightly so. After all, data science gives them the ability to improve real-world use cases. For example, they can augment their industry knowledge, create a human-centered design framework, inform UX processes, and much more when designing products.

Customer-centric companies use data science to understand customer behavior, make real-time business decisions, reduce human error, and negate adverse business outcomes.

That said, here’s a look at 10 business areas most influenced by data science.

1. Customer Service

Happy customers are the cornerstone of every business. Data science enables businesses to understand their customers better and provide exceptional service. For instance, data science tools can be leveraged to improve customer service by:

• Collecting and analyzing customer data for business insights

• Identifying customer trends and patterns

• Predicting customer behavior

• Developing customer self-servicing methods

Additionally, through regular customer feedback, companies can gauge customer satisfaction levels and rectify problems, if any.

2. Finance

Data science is an integral part of the modern banking and finance industry. On its part, the finance industry generates massive volumes of data — most of which goes unutilized.

Data science tools in the financial domain are used for a variety of use cases like fraud detection and designing personalized services for individuals.

To that end, the top data science applications in the finance domain include:

• Data-based risk analytics to understand the scope of financial risks

• Real-time analytics on historical financial data of any company or entity

• Consumer analytics to personalize financial products for individual customers

3. Human Resource

With the growing labor shortage, employers are struggling to attract and retain talent across industries. Data science-based HR analytics tools can help employers make improved workforce decisions and enhance work productivity.

For example, data science can help HR teams carry out employee sentiment analysis and accordingly redesign their HR policies.

Similarly, data science tools can measure the effectiveness of employee training programs and customize workforce development initiatives for a better future.

Predictive analytics in the recruitment process can further improve the talent acquisition process based on historical data.

4. IT Compliance

Companies that collect customer data must adhere to regulations regarding data security and privacy. Without IT compliance, organizations are most likely to face violation-related penalties and fines.

Favorably, organizations can leverage data analytics to check if the collected data is compliant with industry regulations. For example, they can:

• Structure compliance-related data for high-quality and governance

• Understand IT compliance-related risks

• Improve internal compliance-related procedures and processes

With data-driven compliance, companies can now avoid a variety of non-compliance risks, such as internal fraud, ESG risks, and corruption.

5. Management

With data-driven solutions, companies can also improvise their business management and achieve their business goals. Effective management analytics can support and influence business decisions with accurate insights.

At its core, a data-driven organizational culture enables efficient collaboration among multiple stakeholders, including product teams, project managers, and shareholders.

Further, data science skills enable potential business leaders to include data-driven problem-solving abilities in their daily work. It helps management teams arrive at smarter decisions based on the existing business data.

6. Marketing

Data science in marketing is enabling companies to analyze their customer’s needs and provide them with personalized offerings. For example, companies like Spotify and Amazon can recommend content based on each individual’s previous interactions.

Besides providing recommendations, data science has multiple use cases in marketing, including:

• Performing sentiment analysis on customers’ posts and feedback on social media pages

• Developing a customer churn model to analyze the customer’s pain points and ways to retain users

• Segmenting customer profiles into various subgroups and targeting each group with personalized marketing

• Designing or improving products that can meet customer needs and solve their problems

7. Procurement

Effective cost management is the top priority for procurement teams post the COVID-19 pandemic. The 2020 Deloitte survey concludes that procurement leaders must be resourceful about cash management while limiting supply-related disruptions.

As it stands, data science technology can streamline procurement activities and increase efficiency.

Data-based procurement analytics can analyze real-time procurement data for accurate business insights. Here are some of the benefits of data science in the procurement process:

• Improved budgeting and forecasting

• Improved risk mitigation and disruption management

• Effective contract management and discounting with approved vendors

• Improved benchmarking of procurement performance based on item category, quantity, and country

8. Quality

Be it manufacturing or healthcare, high-quality products or services are creating new market opportunities for organizations. And data science has a significant role to play here.

Besides improving decision-making, quality-related data enables organizations to cut costs and improve their products or services.

For example, data-driven quality control can help companies detect issues at an early stage, thus avoiding the high costs of product recall or rework. Additionally, quality control enables companies to identify operational risks and improve their overall inspection process.

9. Sales

Data science technology can improve customer satisfaction, which in turn, leads to higher sales. Besides, by gathering and analyzing customer information, data science provides sales teams with more cross-selling and up-selling opportunities.

McKinsey reports that 72% of the fastest-growing B2B companies plan their sales using data analytics as compared to 50% of the slow-growing companies.

Data science in sales enables organizations to:

• Maximize the customer lifetime value (CLV)

• Predict future sales

• Prevent or reduce customer churn

• Identify cross-sell and up-sell opportunities

• Optimize their product pricing

10. Supply Chain

Every manufacturing or distribution company must optimize its supply chain to prevent delays or disruptions. At the same time, they need to manage their supply chain costs.

Typically, supply chain operations involve a host of operations, including:

• Procurement of raw materials

• Inbound and outbound logistics

• Inventory management

• Order fulfillment

With data science tools, companies have real-time visibility into their supply chains and the ability to simulate possible scenarios. Additionally, they can leverage data-driven analytics to achieve business sustainability and optimize their production planning.

Conclusion

To summarize, data science is playing a transformational role across business areas, influencing everything right from product design to end-user experience.

With its unified data science platform, Rubiscape enables organizations to achieve the desired business outcomes on the back of high-quality data. We provide a host of data science services, including data engineering, data visualization, and IoT.

Let’s kickstart your journey in data science. Get in touch today to learn more.