Beth works in the tourism and hospitality industry and worries about recent dips in revenue, increases in customer complaints and hostile employees. She decides to invest in a business analytics solution to identify the root cause of problems and implement preventive measures. With advanced analytics, she can offer personalized services to her guests while increasing business ROI.

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This Guide Covers:

• What Is Advanced Analytics?
• Benefits
• Techniques
• Use Cases

What Is Advanced Analytics?

Advanced analytics is a methodology that uses predictive analytics, machine learning, deep learning, business process automation and other statistical techniques to analyze data from various sources.

Why Is it Important?

With the advent of business intelligence, business executives can draw insights from historical data. But that isn’t enough. They use advanced analytics alongside BI tools to optimize their processes, predict trends and gain a competitive advantage. Advanced analytics enables you to gain in-depth insights from raw datasets regardless of their location, format or type.

It lets you address complex business problems that traditional BI reporting tools can’t handle. For example, to create lucrative marketing campaigns, marketers should answer the following:

• What time of the day are target audiences likely to open their emails?
• What price are they willing to pay for products or services?
• What is the campaign ROI during a specific period?
• What type of messages generate responses?

Advertisers can use business intelligence with predictive analytics and AI to determine specific answers to the above questions.

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Primary Benefits

Let’s look at the benefits of advanced analytics:

• Predict the Future: Leverage advanced analytics to make data-backed decisions. Gain insights into market trends, customer preferences and competition to devise strategies for selling goods and services while satisfying consumer demand.
• Manage Risks: Make accurate predictions with advanced analytics. Use what-if analysis to test different scenarios with hypothetical data to gauge outcomes. It allows you to identify and manage risks at the onset.
• Retain Customers: Identify and analyze consumer trends and behavioral patterns. Respond quickly to dynamic demands to achieve customer satisfaction and boost retention.
• Achieve Greater ROI: Streamline processes, cut costs, maximize revenue and make better decisions by capitalizing on available data.
• Encourage Initiatives: Consolidate and integrate data across multiple sources to facilitate collaboration. It encourages employees to take initiatives that help business growth.

You can accrue these benefits by employing different analytics tools and techniques.

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Techniques

Here are some advanced analytics techniques:

Data Mining

Sort through large datasets to identify patterns, relationships and anomalies. Scout information to discover possible opportunities and risks. It helps businesses cut costs, generate revenue and enhance customer relationships.

Retail stores use market basket analysis to figure out product associations and decide where to place products that users frequently purchase in adjacent aisles or shelves.

Sentiment Analysis

Leverage natural language processing and text analysis to identify emotions behind the text. Through social media monitoring, you can gain valuable business insights about how customers feel about your brand, products and services.

It allows you to mine information from social media posts to identify feelings and emotions from conversations. It gives you better perceptions of consumers’ reactions to your products and services and their reasons.

Cluster Analysis

Group similar data points into clusters to identify distinct customer groups, behavioral patterns or sales transactions. Streaming services like Netflix use cluster analysis to identify and combine users into groups based on their viewing habits.

They collect user information like minutes/hours watched per day, total viewing sessions each week, the number of unique shows per month, preferable content categories and more. They use these metrics to segregate and target high and low-usage consumers with relevant marketing campaigns.

Complex Event Processing (CEP)

This technique consolidates data from different sources to identify complex patterns and cause-and-effect relationships between variables. In other words, it can predict high-level events based on low-level factors.

For example, self-driving cars use complex event processing to halt at a specific spot by recognizing stop signs, calculating distance and choosing the deceleration rate.

Predictive Analytics

It forecasts the likelihood of events occurring in the future by employing data mining, machine learning and statistical models. For instance, you can create a predictive model that generates a credit score based on an individual’s creditworthiness to predict if they will pay their dues on time.

Big Data Analytics

Examine structured, unstructured and semi-structured data to unearth hidden insights, patterns, correlations, outliers and more. With big data analytics capabilities, Google Maps can tell you the least traffic-prone route to reach the destination. You can plan alternate routes based on fuel consumption, traffic, vehicle type and more. Real-time processing can pinpoint accident-prone areas.

Data Visualization

Translate numbers and text into charts, graphs and maps for straightforward interpretation. Healthcare professionals use choropleth maps, where each region has a specific color based on numerical value, to view mortality rates or heart disease across different territories.

A choropleth map displaying COVID rate across the US states. Source

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Use Cases

Let’s look at some real-world examples of advanced analytics:

Marketing

Marketers employ advanced analytics techniques like marketing mix modeling (MMM) to model interactions between investments, sales drivers, seasonality and more to devise short and long-term strategies while attracting new customers. It also creates targeted campaigns, identifies up-selling opportunities and optimizes the marketing funnel.

Retail

Retailers leverage advanced analytics to create robust predictive models based on market research. It helps develop products to fulfill customer needs and demands. It provides personalized services and experiences throughout the customer journey while resolving queries instantly.

Telecom

Collect insights about customer satisfaction, service quality and more to prevent churn. Optimize network capacity by rerouting bandwidth, managing limitations and decoding infrastructure investments. Use predictive analytics to offer unique products, address customer pain points and resolve usability challenges.

Manufacturing

Predict equipment failure, discover potential manufacturing unit issues and optimize production lines. Assess production processes, gain customer feedback and predict demand to boost revenue and drive business growth.

Psychology

Big data analytics play a critical role in organizational psychology to gauge employee motivation and satisfaction in the workplace. Therapeutic counseling allows practitioners to create behavioral models of a patient and their tendencies while devising personalized therapy programs.

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Conclusion

Advanced analytics is essential to derive valuable insights from the data to propel business growth. Analytics drive competitive advantage in every industry through data mining, machine learning, statistical analysis, and other techniques.

Is there anything you would like to add to this advanced analytics guide and how it has helped your business? Let us know in the comments below.