Descriptive analytics and the customer experience revolution

The modern business environment is inundated with data, but figures are useless without the proper analytical strategy. Descriptive analytics is the key to data-driven decision-making, and it enables businesses to derive useful insights from past trends.

Gartner’s 2023 report indicates that 75% of companies using descriptive analytics as a tool to decrypt summary statistics saw notable improvements in their decision-making process. Not just that, companies have also experienced that using real-time descriptive analytics improved customer satisfaction by up to 33%, as per McKinsey’s 2024 report. 

For B2B companies, strategic moves influence revenue streams, operating performances, and industry positions. Each key decision, whether it is to enter a new market, introduce a product, make a partnership, or reorient internal processes has quantifiable business impact.

These activities dictate how well a firm captures value, rationalizes operations, and maintains its market viability.

Descriptive analytics is more than reporting, it is a differentiator in business. Employed well, it enables organizations to maximize performance, enhance the precision of predictions, and optimize customer interactions.

The blog includes:

• Why descriptive analytics is so vital to decision-making in B2B
• How industry leaders are making large-scale, real-time descriptive analytics a reality
• Trendy developments leading the future of descriptive analytics

By the end of this piece, you’ll understand how to implement descriptive analytics as a strategic enabler for your business.

The role of descriptive analytics in customer success

It is more than just numbers.

B2B companies collect vast amounts of data, but making sense of it requires a structured approach. Descriptive analytics organizes and interprets historical data to reveal business patterns, customer behavior, and operational inefficiencies.

Key metrics companies track with descriptive analytics

• Customer churn & retention trends—Understanding the causes for why customers leave and recognizing customer loyalty trends.
• Sales & revenue pipeline performance—Looking at historical sales trends to better forecast and plan.
• Inventory & supply chain management—Maintaining ideal amounts of stock and not overstocking or understocking.
• Worker productivity & recruitment trends—Maximizing worker performance from prior HR metrics.

Why it matters to industry leaders

To business leaders, the value of descriptive analytics is that it can:

• Offer data-driven decision-making to bring teams in alignment with business goals.
• Optimize operational effectiveness by tracking KPIs in real-time.
• Enhance predictive accuracy as a precursor to advanced analytics.

Beyond legacy descriptive analytics: Scaling for impact

The problem with static reports

A CEO is reviewing their company’s monthly performance report over their morning coffee. The numbers look impressive. Last month’s sales were strong, customer engagement was up, and supply chain efficiency seemed stable. But there’s one problem: The report is already outdated. Have you ever found yourself in a similar situation?

By the time this data reaches your desk, the market has already shifted. A competitor may have launched a new product, a supply chain failure may have occurred, or customer behavior may have changed overnight. But your team is still working on last month’s reality.

This is the inherent weakness of conventional reporting, it is a rearview mirror view and not a real-time navigation system in which businesses are able to change course dynamically.

Automated dashboards & real-time monitoring

To surpass this, enterprises are departing from stagnant spreadsheets and fortnightly reports and adopting real-time dashboards driven by descriptive analytics. These dynamic, real-time ones enable companies to react to trends in the moment, not two weeks later, so data-driven business decisions occur in real time, not after the fact.

There are different ways real-time monitoring can help your business, for example, via

Dynamic sales forecasting—A worldwide software company could adjust sales targets mid-quarter according to real-time demand signals instead of waiting for after-quarter reviews.

Live supply chain tracking—A huge manufacturing company could track supplies in warehouses in real-time using IoT sensors, immediately discovering faults and rerouting logistics operations.

Customer behavior heatmaps—An online business could map the path of visitors in real-time, identifying bottlenecks on its site and rearranging the customer path on the fly.

By combining descriptive analytics with business intelligence software such as Tableau, Power BI, and ThoughtSpot, B2B companies obtain real-time visibility into their operations, enabling them to make data-driven changes prior to inefficiencies taking root.

The future of descriptive analytics lies with AI-driven automation, a revolution taking companies from reporting to proactive, real-time insight. 

Natural Language Processing (NLP) for executive questions. Imagine a CFO querying their analytics platform, “What were the top-selling items last quarter?” and receiving a real-time, AI-generated summary response to an ocean of reports.

There are different ways AI-powered descriptive analytics can help your business, for example, via

Automated anomaly detection—A bank applies AI-powered descriptive analytics to identify deviating patterns in transactions, which point toward likely fraud ahead.

AI-powered marketing analytics—A retail consumer goods company leverages AI for auto-detecting the shift in customer sentiment in real time using social media monitoring and then modifying campaigns in real time.

By placing AI into descriptive analytics, companies eliminate the drudgery of manually interpreting data while saving actionable insights to be automatically pulled to the top. This changes the game with quicker decision-making, enhanced risk management, and greater operational dexterity.

Pioneers who adopt AI-driven descriptive analytics aren’t stuck in the past, they’re looking at what is happening today and getting ready for tomorrow.

Industry-specific applications of descriptive analytics

Descriptive analytics is a game-changer in every industry, delivering detailed insights to inform business decisions. Although the premise is the same, historical data comparison to spot patterns, the application is driven by industry requirements and objectives. Below is how various industries are leveraging descriptive analytics:

Finance & banking: Optimization of risk management

Fraud detection and credit risk analysis are essential to banks and financial institutions. Banks can identify unusual patterns of spending indicative of fraud based on historical transactional data. 

JPMorgan Chase, for instance, uses AI-based descriptive analytics to identify suspicious transactions and avoid millions of fraudulent losses annually. Credit risk assessment software further examines repayment behaviors to enable lenders to make more informed loan approval decisions. FICO scores based on past credit history are a great example of descriptive analytics in practice.

Healthcare & life sciences: Improving patient outcomes

Descriptive analytics is optimizing patient flow in hospitals by examining admission patterns to enhance capacity planning. For example, Johns Hopkins Hospital implemented a predictive bed management system, leading to a significant reduction in emergency room wait times. 

This system utilizes real-time analytics to monitor patient flow, bed availability, and staffing levels, enabling administrators to make informed decisions swiftly. The initiative resulted in a more than 50% decrease in the time patients waited in the Emergency Department after being admitted to the hospital.

Similarly, in the pharmaceutical industry, companies like Pfizer leverage real-world data to monitor drug efficacy and patient outcomes. By analyzing information from electronic health records, pharmacy claims, and patient apps, Pfizer gains valuable insights into how patients use and respond to medications post-approval. This approach helps answer questions not typically addressed in clinical trials, thereby enhancing the understanding of a drug’s performance across diverse populations.

Retail & e-commerce: Improving customer experience

Descriptive analytics is applied by retailers to classify customers according to buying behavior in such a way that marketing may be conducted in a personalized manner. Amazon’s recommendation engine takes into account surfing and buying history to recommend products accordingly, which constitutes a significant chunk of its revenue. 

Descriptive analytics also facilitates the optimization of inventory demand forecasting. Walmart, for instance, monitors the pattern of sales to change stock levels so that neither shortage nor overstocking may occur.

Manufacturing & supply chain: Enabling operational efficiency

Manufacturers are leveraging IoT-driven analytics to track the performance of equipment and minimize downtime. For instance, General Electric (GE) embeds descriptive analytics into its factory equipment, identifying patterns of wear and tear ahead of time before failure, saving tens of millions of dollars in maintenance expenses. On the supply chain side, firms like Procter & Gamble utilize procurement analytics to compare vendor performance, optimizing vendor choice and cost reduction.

Each of these applications demonstrate how descriptive analytics is revolutionizing decision-making in industries. Also, they showcase how analytics is helping maintain business competitiveness, streamline operations, and drive growth.

At Netscribes, we realize the need for contextual agility across businesses of all sizes. Our AI-powered analytics stack, ML, and NLP services harness cutting-edge technology to automate processes, enhance customer experience, and extract actionable insights from vast amounts of data.

The future of descriptive analytics: Trends for businesses

In the fast-changing technology era, some key trends are redefining the landscape of descriptive analytics. The following is a closer look at these developments:

1. AI & ML automated insights

Artificial Intelligence (AI) and Machine Learning (ML) transformed descriptive analytics by streamlining data processing activity, resulting in timely and accurate insights. Contemporary AI-powered analytics tools can sort through big databases, detecting concealed patterns and abnormalities that even the human eye might not notice. To give an example, AI algorithms would be in a position to process millions of pieces of information in a matter of seconds, whereas insights would take weeks or even months to create. 

The use of AI in analytics platforms has made it possible to detect anomalies in real-time. This way companies can respond quickly to problems as and when they occur. This proactive measure improves operational efficiency and decision-making.​

Read more: The future of AI solutions for business: Trends to watch in 2025.

2. Cloud-based analytics for scalability

The shift from on-premises business intelligence tools to cloud analytics has helped organizations to avail scalable and elastic data processing capability. Cloud computing provides instant access to data everywhere, facilitating collaboration and quicker decision-making. This shift has been fueled by requirements of agility and support for larger volumes of data without substantial infrastructure investment.

Cloud computing adoption has facilitated easier storage and processing of large data points. Besides, it is allowing AI algorithms to process data at a faster rate and greater accuracy than ever.

3. Descriptive meets prescriptive

The intersection of prescriptive and descriptive analytics has enabled organizations to not only learn from history but also make decisions based on evidence for future activity. The marriage of these analytical methods can assist companies in automating decision-making, which could result in better efficiency and performance. For instance, supply chain optimization software driven by AI will be able to detect inefficiency and propose possible solutions, eliminating waste and cost.

With the advent of hybrid AI models that blend structured data analysis with unstructured data from sources such as text, images, and videos, new avenues for improved data analytics and decision-making have been provided with more informed and enriched insights.

4. NLP- enabled data exploration

Natural Language Processing (NLP) has made it easy to explore data through natural interaction with data in the form of a common language. Data democratization has become possible due to this innovation, and datasets can be queried by non-technical stakeholders without technical knowledge. For instance, NLP-based analytics software can understand and respond to questions posed in natural language, making data analysis easier across departments.

The emergence of LLMs has greatly elevated the capacity of NLP systems to comprehend and create human-sounding text. It is accelerating the precision and usability of data discovery tools driven by NLP.

5. Embedded analytics for decision-making

Integrating analytics within enterprise resource planning (ERP), customer relationship management (CRM), and marketing platforms has ensured that insights are a part of business operations on a day-to-day basis. Integration helps make decisions timely and based on data by giving context-based analytics. For instance, customer analytics can be offered to the sales force via their CRM platforms. It can help them target more precisely and economically.

Business intelligence solutions incorporating large language models (LLMs) enable non-technical users to receive accurate responses about their data. Additionally, they allow users to address even complex business queries, and thus improve decision-making abilities.

These reflect the dynamic behavior of descriptive analytics to keep up with shifts in technology for increased data processing, availability, and decision-supporting capabilities. Being current on these is essential to those organizations wishing to stay in the forefront in the present era of big data.

Best practices for industry leaders

Without a well-defined strategy, companies usually don’t derive actionable insights from their data. By implementing best practices, B2B leaders can close the gap between raw data and actionable information. This can ensure that analytics programs meet business objectives, enable real-time decision-making, and drive a data-driven culture throughout the teams.

Here is an infographic that summarizes the best practices to leverage the power of descriptive analytics in B2B decision-making:

By embracing these practices, organizations unlock faster decision-making, and greater efficiency. They enable a more strategic exploitation of data in order to spearhead the marketplace.

Descriptive analytics: Fueling the industry leaders of tomorrow

Those who effectively apply descriptive analytics:

• Shave 20-30% of operational inefficiencies from optimized processes driven by data.
• Boost customer retention as they leverage an understanding of past buying behavior.
• Increase accuracy in forecasting revenue, enhancing strategic planning effectiveness.

Descriptive analytics is not just an analytics methodology foundation, it’s a business enabler. Leverage maximum value from descriptive analytics; it can be the difference between reaction-based decisions and the proactive implementation of strategy.

Real-time understanding, AI-facilitated automation, and broad business integration are the way forward for descriptive analytics. Organizations that will spend on such technology won’t just make better decisions but also gain a long-term competitive advantage.

Our end-to-end, AI-based data analytics solutions enable companies to unlock real value from their data. We help them overcome obstacles to support intelligent, data-driven decision-making for sustainable growth. We are experts at accessing, storing, visualizing, and enriching structured and unstructured data so that all insights are actionable. Through strategic analysis and automation, we don’t merely analyze data, instead, we reveal opportunities that drive your business forward.

Want to unlock new opportunities for your business? Explore our data analytics solutions and achieve data-driven success.