Why Do You Need Advanced Analytics?
Part 2

In the world of business intelligence, “advanced analytics” refers to a set of techniques that tackle important questions with statistical and technical methods. In this blog post, I explain why you should use advanced analytics to make important business decisions.

This is the second part in a 2 part series on advanced analytics. If you’d like to know what exactly advanced analytics is, be sure to view the first part!

Read ‘What is Advanced Analytics?’

In my last blog post, I talked about advanced analytics and what makes it different from traditional business intelligence (BI). If you found yourself asking, “So what?”, here is your answer!

While traditional BI techniques provide business users an easy way to organize and flexibly slice and dice large amounts of data to gain insights from different angles, there are challenges many business functions may experience with only traditional techniques. An advanced analytics (or cloud-based analytics) architecture offers a set of techniques that help deal with these challenges through statistical and technical methods, ultimately supporting strategic and fact-based decisions.

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The sections below describe some of the questions various business functions strive to answer – questions where a traditional BI architecture and techniques may fall short and advanced analytics shine.

Marketing and Customer Analytics

Customers are becoming more sophisticated and finicky, so understanding them in order to improve their experiences is the key to success. Organizations that can accurately predict a customer’s needs and match it to the right product at the right price will gain an upper hand over their competition.

While BI systems can integrate data from multiple sources and present historical performance to users in a clean and predefined format (e.g., reports and dashboards), they are limited in their ability to uncover hidden patterns in a timely manner.

Also, simple operators and formulae may not be able to adequately show the significance of differences observed in the numbers. . In other words, cloud-based analytics tools can make sense of data required to predict customer behavior. Advanced analytics, fortunately, provides a quantitative approach to answer these questions and address these challenges.



How much should we spend on obtaining new customers?Design marketing campaigns with the awareness of the budget benchmark:

  • Reduce expenditure by eliminating inaccurate or inefficient marketing activities
  • CLTV increases over time
  • Overall cost per acquisition decreases, thus improving return on marketing investment
What is the effect of competitive pricing on my brands?Recognize the real competitors and consider price elasticity when planning for a discount promotion:

  • Support more accurate sales prediction
  • Maintain sustainable competitiveness
  • Maximize the total profit from product selling
What synergistic items should be bundled and promoted together to drive incremental sales?Bundle products intelligently and scientifically design cross-sell promotions:

  • Increase margin/sales volume gained from cross-sales / up-sales
  • More accurately drive product procurement and manufacturing forecasts
What impact can personalized advertisements for existing customers have on sales?Tailor promotions for existing customers using data from different sources:

  • Provide more incentive for previous customers to return
  • Meet customer needs more effectively and improve brand perception

Operational Analytics

Operations involve the design and execution of repeatable processes, as well as the consumption and management of assets and labor.

Whether at a restaurant, a retail store, a manufacturing floor, construction site or drilling rig, improving the efficiency of processes, maximizing the value of assets and labors, reducing unnecessary costs and ultimately maximizing profitability have been ever-present topics.

Because of rapidly changing market demands, more efficiently aligning an organization’s processes and resources to the market creates a big and dynamic challenge for most organizations. Traditional BI techniques, while successful in measuring and informing users of historical and current performance, lack the capacity to optimize decisions quantitatively and support prescriptive decisions. Advanced analytics and an agile cloud-based architecture not only do this, but they also help users piece together disparate data sources to enable more impactful insights. Cloud repositories such as Azure Data Lake and Snowflake allow teams to bring together disparate systems and enable them to have the capacity to handle these problems.



Which performance metrics are the most important indicators of revenue, sales volume or profit?Prioritize metrics based on correlation to sales performance, adjust the presentation of metrics on performance-related dashboards, in order to manage the most important indicators:

  • Deliver better customer service and increase customer satisfaction
  • Produce less waste, drive higher productivity, and increase profit margin
  • Manage Operations smarter and bring better top-line growth
What is the best resource allocation strategy?Simulate and optimize resource allocations:

  • Maximize profit per unit of resource invested
  • Ensure efficient satisfaction of market demand
Why do various locations perform so differently?Extract key performance factors and manage the operation of low-sales locations based on characteristics of high-sales locations:

  • Resolve operational issues in underperforming locations leading to increase revenue
  • Provide an approach to evaluate whether the sales in high performing branches are sustainable
How might unscheduled events change forecasting for production?Allow for individual store locations to adjust predictions for the day based on the local events occurring around them:

  • Recalibrate projections for the day based on changes in available labor and materials
  • Limit waste when nearby events will reduce or increase customer traffic

Supply Chain Analytics

Globalization and large-scale manufacturing create many more manufacturing units, plants, and suppliers. At the same time, the complication of market demand and customer preference increases product proliferation. Traditional BI allows users to slice and dice to review information about on-time delivery, capacity commitments and inventory turnover control, but uncertainties still exist regarding supply risks, demand volatility, and cost fluctuations.

More accurate prediction of future demand, better measurement of supplier performance, and optimizing logistical processes can be enabled with cloud-based advanced analytics. These more accurate predictions serve to control supply chain costs and pursue process excellence. Similar to operational analytics, data ingestion tools such as Azure Data Factory allow for a larger stream of data to capture the requirements of your models.

Download “Accelerating Your Journey to Cloud Analytics” to learn more about how we’re modernizing the analytics architecture!



How can we reduce transportation costs without harming delivery timeliness?Distribute products and supplies under the guidance of new transportation plan:

  • Reduce ongoing distribution costs (labor, fuel, time, etc.)
How can we identify at-risk vendors?Monitor the status of the roots of risks and take the best proactive risk management actions:

  • Avoid production delays because of delays or failures in procurement
  • Bolster trusting collaboration with vendors

Human Resource Analytics

Human resources are arguably the most valuable assets in an organization, and thus deserve at least as much attention as other business assets. Organizations see challenges in identifying key knowledge/skills required to succeed in a specific role, evaluating team member performance, and retaining valuable employees.

However, for most organizations, human resource data is used only for basic reporting purposes. In terms of getting deeper insight through factor selection, prediction and network evaluation, traditional BI techniques are not able to provide HR the ability to look ahead and act strategically. Cloud analytics has the ability to handle unstructured data to better determine sentiment or understand employee needs.



How can we identify which employees are most likely to leave?Predict the probability to leave and take proactive actions to remove potential reasons for attrition:

  • Reduce the probability of unexpected turnover
  • Reduce talent acquisition and training costs
What is the real impact of each type of reward or promotion? Which one(s) can best drive performance improvement?Make fact-based decisions about where/how to invest to maximize performance:

  • Reduce costs from avoiding ineffective rewards
  • Increase employee satisfaction and retention
What trends can be discovered from employee surveys?Capitalize on employee feedback using text-based analysis:

  • Identify prevailing trends in surveys quicker
  • Continue to reduce turnover by acting on employee requests

Risk Analysis

Organizations seek approaches to measure the exposure and tolerance to risks which could affect the profitability and strive to mitigate the risks. This requires the ability to perform analyses with a variety of internal and external resources, such as purchase history, economic indexes, unstructured social media data, etc.

The key to success is identifying how to manage large-scale data of different structures and combine them to drive risk mitigation insight. In some cases, the probability that a risk occurs is very low. Because of the infrequency, it becomes even harder to identify trustable patterns using only traditional BI techniques. Thankfully cloud-based tools address these issues better than traditional BI techniques would.



How can we detect payment fraud?Detect indicators of fraud and decline a payment once it is predicted as a fraud:

  • Avoid revenue loss from fraudulent orders
  • Save labor costs from time-consuming validations and costly chargebacks
How can we monitor the reputation of our organization?Analyze related social comments and news in real-time, react immediately towards events with the potential to negatively affect reputation:

  • Mitigate negative influence on brand equity and business operations
  • Rapidly identify and resolve customer satisfaction issues, potentially turning a negative customer situation into a loyalty-building opportunity.

Key Benefits of Advanced Analytics

One key benefit of advanced analytics is that it helps to improve the accuracy of business operations. In contrast with intuition or experience-based decision-making approach, the data-driven approach of advanced analytics makes predictions based on facts, and thus directs you to the option with the highest expected benefit.

Detailed level analysis – Make personalized sales/marketing possible

Advanced analytics techniques find patterns from less aggregated datasets, such as at an individual customer level or store level. For example, instead of simply concluding the overall market demand of a customer segment and offering the full audience’s favorite product, it is possible to capture the characteristics of each individual customer. Using an agile architecture, various data sources can be integrated to provide a more complete picture of the customer and allow for a more personalized experience. Good models can significantly increase marketing effectiveness as well as customer satisfaction.

Better performing advanced techniques – Increase prediction accuracy

Predictive analytics requires robust statistical models and algorithms. However, this type of data science has become much more accurate, faster, and achievable due to current data mining techniques. For instance, artificial neural networks greatly improved pattern recognition performance by generating a collection of functions transforming the input variables to approximate the targeted outcome. Deep learning algorithms are applied to create more powerful hierarchies. Mathematical computations and algorithms which allow machines to learn the complex analytical models are becoming more accurate and reliable.

Confidence level – Provide risk level information of a suggested decision

Most commercialized data mining techniques provide a confidence level for the prediction (e.g., 95% confidence interval, false-positive rate or false-negative rate) and models are selected based on performance measures. It benefits business people by conveying the risk level of implementing the recommended action. In some circumstances where there is a high cost for acting on false-positive or false-negative predictions, we may adjust the model’s parameters to avoid high-cost false prediction or choose not to act on low confidence predictions.

Look at the prediction confidence to determine what to do.

In conclusion, advanced analytics helps organizations to resolve challenges and confusions by delivering fact-based and future-oriented insights. It strategically extends traditional business intelligence from being descriptive to predictive, and ideally prescriptive. In our next blog, we will briefly introduce the advanced analytics methodology and implementation process.

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