5 top self-service analytics use cases

Marketing

User behavior analysis

User behavior analysis tracks how users navigate through websites, mobile apps and other digital channels. By integrating data from numerous web browsing logs, marketers can find trends in how users navigate a website and identify any bottlenecks or areas of improvement. With self-service analytics, marketers can work with these data sets directly to test new hypotheses and rapidly try potential UX enhancements without waiting on centralized reports.

Customer segmentation

Effective marketing at scale involves identifying customer groupings by demographics, geography, spending habits and even what devices they use. This segmentation is a key component of effective campaigns: If marketers can find meaningful segments, they can reach out with careful targeting. This can be an involved task that requires advanced modeling and integration of multiple data points. Self-service analytics allows marketing teams to experiment with numerous segment definitions so they can run exploratory analyses and see what might be effective without overwhelming IT or data specialists. Good marketers have hunches -- and insights -- that they can work with, such as niche audience preferences. Self-service analytics enables them to iterate quickly over many ideas before launching a campaign into the market.

Campaign performance analytics

After launching a new marketing effort, campaign performance analytics focuses on tracking its reach across various channels, such as social media, email and search engines. Marketers look to measure and understand reach, engagement and ROI. This requires gathering data from several platforms -- often one for each channel -- and creating dashboards to visualize performance metrics so teams can share and refer to them throughout the campaign. Generic, centrally deployed dashboards can be useful, but they lack the customization that might be needed for new marketing concepts. Self-service analytics enables marketers to build interactive dashboards and conduct on-demand analysis, enabling campaign managers to spot and respond to emerging trends as they arise.

Conversion rate optimization

Conversion rate optimization monitors the efficiency of what marketers call customer journeys. In e-commerce, this includes how potential buyers navigate your website from initial awareness through browsing and viewing products, adding items to a cart and hopefully to a final purchase or subscription. A self-service approach enables marketers to run their own data analyses and roll out small-scale tests rapidly for specific products or campaigns. Instead of being locked into generic reports, every campaign can be optimized with its own insights.

Healthcare

Patient optimization

Patient flow optimization focuses on analyzing admission data, wait times, and patient experiences as they move through different departments of a hospital. Hospital managers must spot bottlenecks, such as long emergency room wait times, and quickly make changes to keep the flow going. Self-service analytics tools enable department heads and frontline staff to explore data for changing conditions, such as shifts to patient flow in flu season, or the effect of new equipment, such as faster imaging.

Quality improvement monitoring

Quality improvement monitoring involves measuring critical healthcare metrics -- such as wait times, readmission rates, treatment outcomes and patient satisfaction -- to identify improvement areas. Typically, analysts would identify relevant metrics for their specific hypothesis and then submit requests for customized reports. With self-service analytics, healthcare teams can directly access curated data themselves and rapidly test solutions like new protocols or training initiatives. This hands-on approach provides specific insights for different departments and different procedures, with IT support to ensure data is well-governed for privacy and confidentiality.

Resource allocation and staffing

Staff schedules, equipment usage, and facility space are a constant challenge for hospital administrators and medical staff because conditions can vary so much in response to patient demand. Traditional BI processes work well for reporting on efficiency quarterly and annually but are less helpful with responsive reallocation. With self-service tools, department managers directly view current operational metrics, forecast busy periods, and adjust staffing levels or resource distribution on the spot in response to sudden changes, such as seasonal spikes or unexpected patient influxes.

Predictive analytics for patient outcomes

Predictive analytics has traditionally been an advanced field of analysis, especially in mission-critical applications like healthcare. Today, AI and machine learning built into service analytics can help project patient outcomes by analyzing historical and real-time health data, such as vital signs, medical history and lifestyle factors. These predictions are not medical prognoses, but they can help care teams forecast potential risks and prescribe preventive measures. A self-service approach can accelerate the deployment of targeted preventive care programs, ultimately improving patient well-being and reducing healthcare costs.

Finance

Fraud detection and prevention

If your credit card has been put on hold because of a suspicious transaction, then you have experienced an automated and highly centralized fraud analysis system. These systems apply generic rules that look for patterns of behavior and spending associated with known patterns of fraud. But how are these patterns discovered? Increasingly, fraud analysis teams turn to self-service analytics to investigate anomalies and detect emerging patterns that might become new rules. Given how rapidly fraud patterns can evolve, this added agility helps financial institutions proactively minimize losses and protect customer trust.

Risk assessment and management

Analyzing risk requires collating market conditions, credit data and operational metrics to identify potential vulnerabilities in financial portfolios. Self-service analytics allow risk officers to pivot their focus when conditions change quickly. For example, adjusting default risk models in response to new economic forecasts. Traditional BI or data warehouse systems can generate large-scale reports but have lengthy data extraction and transformation processes. These systems are good for analyzing the entire risk position of a bank or brokerage, but individual investment managers need self-service tools for faster, more targeted analysis to make timely decisions.

Customer lifetime value prediction

Financial services prioritize long-term customer relationships. Customer lifetime value prediction involves understanding the long-term profitability of an individual account by analyzing consumer behavior, transactional data and engagement metrics. This often requires multiple rounds of data preparation and modeling, which can slow down insights when done solely through a centralized data team. With a self-service platform, relationship managers and marketers can use different predictive models to optimize their contact and advice for an individual customer, family or commercial client.

Manufacturing

Predictive maintenance

In manufacturing, breakdowns are a significant drag on the productivity and profitability of any process. Predictive maintenance uses the equipment's sensor data, historical repair records and operational logs to forecast potential machinery failures. Rather than waiting for periodic maintenance windows or reacting to unexpected downtime, manufacturers can schedule repairs at optimal times. Self-service analytics platforms can enable plant managers and engineers to directly interpret sensor readings and to set up custom alerts for equipment they specifically want to monitor.

Supply chain efficiency analysis

Manufacturers need to maintain an excellent supply chain of components to sustain production. Metrics such as supplier performance, inventory levels and logistics data are all critical for identifying bottlenecks and streamlining operations. When these work well, manufacturing runs smoothly. However, supply chains can sometimes throw up unpleasant surprises, such as a ship getting stuck in the Suez Canal. No centralized data warehouse model had that probability built into its scenarios. In such cases -- which can include weather disruptions, earthquakes or even political upheaval -- only self-service tools can enable supply managers to do ad hoc analysis to adjust their plans.

Quality control and defect prediction

Quality control is a well-established discipline in manufacturing, so there are well-established centralized processes for monitoring and managing quality. However, unusual circumstances do arise; unexpected quality audits, a sudden change to a new supplier, or a batch of faulty components can all require fast and effective responses. A self-service environment gives production supervisors real-time access to relevant data, enabling them to isolate issues and test preventive measures. They can also prepare quickly for stressful on-site audits. This timely visibility not only enables more responsive quality control but swift interventions, and it reduces waste and rework.

Retail

Demand forecasting

Predicting future customer demands requires analyzing historical sales data, market trends, and external variables such as seasonal factors or economic indicators. Traditionally, generating forecasts involved specialized forecasting tools that delivered pre-configured BI reports. But retail is rapidly changing, and conditions are changing, too. With a self-service approach, store managers and inventory teams can directly tweak forecast models or run what-if scenarios in response to sudden changes in consumer behavior, which could be anything from an impending weather event to a major sports game in town. This immediate feedback loop helps retailers adjust stock levels proactively for individual stores or districts with increased insight and accuracy than centrally delivered reports.

Price optimization

Market conditions, competitor pricing and customer buying habits all interact to determine optimal product price points. Typically, pricing specialists had to wait for static, enterprise-level analyses. These analyses can be effective at spotting trends or automating pricing, but they are less useful for experimenting with new tactics. Self-service tools allow for quick testing of various pricing models, such as discount experiments or premium positioning, while reviewing real-time sales impacts.

Store layout and product placement

In-store shopping is carefully designed and analyzed today, whether in a fashion retailer or a supermarket. Designers, managers and architects explore foot traffic patterns, purchase data and merchandising to improve the in-store experience. For example, linking point-of-sale data with anonymized data from security cameras allows managers to see where customers spend the most time. Self-service analytics enables the stores to easily try different layouts or adjust product displays, giving immediate performance feedback. This autonomy enables continuous improvement of store designs, ensuring maximum visibility for high-margin or high-demand products and tailoring stores for local conditions.

Customer churn prediction

Successful retailers are passionate about keeping their customers. Customer churn prediction identifies those at risk of going elsewhere by analyzing their purchase frequency, returns and browsing behaviors. The problem is that when a potential churn has been detected, you might have to respond very quickly. Traditional data warehouses, with relatively slow data refreshes and complex analytics pipelines, can delay this process. With a self-service approach, retailers can explore churn signals and quickly choose retention strategies like targeted promotions or loyalty programs.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.