What is Advanced Analytics?
Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.
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What are the Different Advanced Analytics Techniques?
In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:
The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”
The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.
Simulation and optimization
Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.
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Why Advanced Analytics Is Becoming So Important for Businesses?
In this era of digitalization new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:
- New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
- Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
- Advanced analytics software facilitates better analysis of collected data, relationships, and future events.
Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.
Key Benefits of Advanced Analytics
Detailed analysis helps in personalized marketing
Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.
Better performing advanced analytics techniques improve accuracy
Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.
Identify the level of risk in the implementation of the decision
By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.
One of our recent case studies highlighted how advanced analytics helped a consumer products manufacturer to develop an aggressive growth-centric data strategy. Read the full story here.
Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.
We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.
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