Operational Intelligence – Building Intuitive Customer-Oriented Business With AI

Operational Intelligence – Building Intuitive Customer-Oriented Business With AI

The enormous amount of data generated across various channels in the organization is the hidden treasure which can help build customer focused strategies. One can never estimate the value it can deliver unless this data is churned to produce results.

This is where operational intelligence comes into the picture. Operational intelligence is derived from real-time dynamic data analysis to deliver insight into the data to produce intuitive results, to manage internal interactions and develop customer oriented business strategies.

Why does your business need Operational Intelligence?

Operational Intelligence refers to the use of dynamic business analytics that provide insights about the business processes that help the management in real time decision making. Every business needs to make tactical and strategic decisions from time to time and having operational intelligence in place can be game changing.

The key driver for organizations in service-based industry is customer interactions. The data patterns generated by ‘How, When and Why a customer interacts and tone of the interaction’ provide real insight into the future products & services to be offered and improve current customer satisfaction level.

This requires a multi-facet product to collect, correlate and predict the future conversations/issues driven by artificial intelligence algorithms. In our research at Aarav Solutions, to build & implement strong products for operational intelligence driven by AI we are working with some advance platforms like Froot.

The platform offers Reactive and Pro-active intelligence engine to reduce revenue leaks, detect problem centers and improve overall operational efficiency. The proactive & reactive methods of intelligence are driven based on the principles of Anomaly detection.

Anomaly detection techniques work on multi-dimensional, multi-layered data patterns to bring right benchmarking, detection and forecasting for possible issues. This makes it possible for a business to take the appropriate action in time to avoid losses and failures or to anticipate risks and opportunities using the data.

Let’s understand the approach to anomaly detection for operational intelligence, which is broadly divided into two ways –


The proactive method utilizes artificial intelligence to the fullest and is the new way of anomaly detection. Here’s how it works

  • It is important to fully understand the technological framework of the dataset being used.
  • Based on the understanding of the data patterns, artificial intelligence can draw certain benchmarks to define “normal” patterns of the data.
  • Predictive analytics and machine learning in real time are used in this method
  • Based on metrics that are most significant to the business anomalies are auto identified
  • Artificial Intelligence works on dynamically updating the algorithms that define “normal” data patterns vis-à-vis anomalies

Advantages of the proactive intelligence method are –

  • Minimizes the occurrence of false positives or negatives due to the dynamic nature of anomaly detection.
  • Performance anomalies are identified before they affect the end-user. This is a very significant feature for the service industry.
  • Analytics in the proactive method can predict future traffic levels using artificial intelligence which is not possible in the reactive method
  • Benchmarks that define “normal” data patterns maybe revised or fine-tuned for parameter based anomaly detection.


This is the traditional approach, here’s how it works –

  • Identifying the issue nucleus as defined by the business management.
  • Defining artificial intelligence based on data models that represent the acceptable data pattern.
  • Using AI algorithms that analyze the data for acceptable and unacceptable patterns in the dataset.
  • Continuous learning by accumulating acceptable patterns. Working to continuously learn and improve the algorithms used. This leads to better accuracy and efficiency.
  • Discover problem clusters among the dataset.
  • Focus on hierarchical model and reporting of anomalies for immediate corrective action.

This is one of the tried and tested methods for anomaly detection and used by businesses across various industries.

Some of the advantages of the reactive intelligence include –

  • This is a time tested method of anomaly detection and has been known to give the best results
  • Continuous updates to the acceptable data patterns help improve the accuracy
  • Algorithms are created based on acceptable patterns of data are defined by the stakeholders. This ensures clarity of objective.
  • Immediate corrective action may be taken as soon as the anomaly is detected.

Aarav Solutions strives to deliver the latest in artificial intelligence and machine learning for their clients. Scalable and efficient anomaly detection systems are a necessity today. A system that uses artificial intelligence to connect metrics with business functions for enhanced efficiency. At Aarav Solutions we understand the importance of operational intelligence at every step for your business.

Aarav Solutions strives to provide businesses across the globe innovative technological solutions. We seek to transform and promote businesses by helping them choose from a wide range of high end technological solutions. Further, we aim to help businesses adapt to newer and more efficient ways of using technology by offering our technological expertise in consultation. We offer a wide range of Telecom Consultancy Services, Cyber Security Applications, Enterprise Solutions and integrated Software Products.

Froot Research is an award-winning,  premier research and development startup focused on creating next generation intelligence models. At the foundation of our research are ideas taken from psychology, neuroscience, statistics and various other fields. Amalgamation of these ideas and our creative problem-solving approach has given birth to ground breaking, patent-pending intelligence models that are target-less and intuitive.