How to use Cognitive Analytics to Achieve Better Business Outcomes?

Data and analytics can be described as the oil required to run the business machinery. The ocean of data available in both structured and unstructured formats can be used to arrive at solutions that help in making strategic and business decisions. In other words, data analytics is the process that can drive the economy of the organization. This process includes inspecting, cleaning, transforming, and modeling data to retrieve useful information that helps in arriving at conclusions and decision-making.

The approach of data analytics has evolved over the years from Descriptive to Diagnostic to Predictive to Prescriptive analytics. The next exemplar shift is toward cognitive analytics advancing to high-performance computing by combining artificial intelligence and natural language processing, machine learning, and deep learning techniques with data analytics.

As stated in a report, the Cognitive computing market size was valued at $8.87 billion in 2018 and is projected to reach $87.39 billion by 2026, at a CAGR of 31.6% from 2019 to 2026. The business that is shifting their focus toward cognitive analytics are Healthcare, BFSI, Retail, Government and Defense, IT & Telecom, and more.

To understand how can businesses tap into cognitive computing systems, let us first understand what is cognitive analytics

What is Cognitive Analytics?

Cognitive analytics imitates human brains to do certain jobs that enable them to draw inferences and insights from the existing data patterns. This helps businesses to arrive at critical business decisions and conclusions based on existing data.

Combination of technologies like Semantics, algorithms of artificial intelligence, machine learning, deep learning, and natural language processing results in cognitive analytics. Cognitive analytics thus becomes more effective from the interactions with data and humans. By searching through the entire data present in the knowledge base, cognitive analytics arrive at real-time solutions.

It all boils down to getting the right information, in the right hands at the right time. Organizations are using cognitive analytics to tap unstructured data sources such as images, emails, text documents, and social posts for finding out real-time answers to arrive at conclusions.

Now that we have understood what is cognitive computing, it’s time to look into the ways cognitive analytics is benefiting businesses. 

Business Outcomes from Cognitive Analytics

Cognitive computing is emerging as the technology that provides organizations an edge to gain business advantage. Organizations that adopted the technology at an early stage have benefited the most from the positive results of their investments.

Studies and surveys have shown that cognitive computing is proving to be the key differentiator with improved productivity and efficiency to rapid business growth. 65% of the early adopters of this technology find it essential for an organization’s strategy and success. 58% of the early adopters find it crucial and a must-have for an organization’s digital transformation and to remain competitive.

Listed below are some of the cases where the early adopters of the cognitive technology are seizing the opportunity:

Customer Acquisition

Organizations are taking a strategic algorithmic approach to sales and marketing by using cognitive data. The primary attribute of the cognitive approach is to sift through huge amounts of data that will help in arriving at meaningful insights. The process does not stop here. With human-like intelligence, cognitive analytics can predict and recommend solutions from trends and patterns. 

With cognitive analytics, companies can refine product pricing based on purchase records, and market trends, thus increasing the possibility of customer acquisition and leading to revenue growth.

To further optimize sales and marketing, the inferences can be drawn from the growing volume that is critical to targeting the audiences. The cognitive analytics approach helps in speeding up the process of data analytics to get relevant insights. This then helps in augmenting the decision-making to reach, engage and maintain valuable customer relationships.

Customer Engagement

Cognitive analytics can greatly help companies to improve business-to-customer data analytics that helps in identifying the consumer’s needs and wants and serving them better. Intelligent analytics benefits the consumer and company alike.

The early adopters in the eCommerce industry are able to personalize the customer experience with the help of cognitive inferences. The technology has also helped the companies in increasing customer engagement and is rapidly responding to market and customer needs.

By enhancing customer engagement and experience with the cognitive approach, more satisfied customers show increased engagement and loyalty which are critical for sustainable growth.

Enhanced Customer Service

By automating routine customer care operations, the agents can be better utilized for high-value interactions. Organizations offering customer service through various channels can highly benefit from cognitive computing as it improves operational efficiency.

Cognitive analytics and artificial intelligence combined resolve most of the pain areas of the poor customer service experience like putting the call on hold for a long period of time, repeating the same information to different agents yet the issue not getting resolved, long IVR options to reach to a live agent, and many more.

Cognitive assistants are emotionally intelligent and simulate live agents. By recognizing patterns, mining data, and learning from experience, they offer personalized interaction through natural language processing.

Augment Productivity and Efficiency

Organizations can harness the power of cognitive analytics to overcome resource bottlenecks while gaining valuable predictive insights, thus augmenting productivity and efficiency.

Irrespective of the industry your organization belongs to, the mantra to take the lead and stay competitive in this digital era is to find out the shortest path to the best results. By combining the technologies of machine learning, parallel processing, and sophisticated analytics, one can find answers to a lot of questions and provide recommendations to derive predictive insights.

Data present in the humongous amounts in diverse formats can be churned that not only improves productivity and efficiency but helps in decision making and planning.

Risk Management

As we have been discussing that cognitive computing can ingest large amounts of data to deliver meaningful insights and patterns providing relevant recommendations much faster than humans do. The financial services industry completely relies on data that has to be integrated with regulatory compliances.

Cognitive computing can churn the data in any form, whether structured or unstructured, from diverse sources, thus providing the capability to mitigate risk while adding value, better customer experience, and improved security and compliance.

Specifically, in the data-driven financial services sector, cognitive analytics is a boon that can aggregate insights from various reports, documents, and financial and medical histories improving compliance while reducing risks.

Examples and Applications of Cognitive Computing

Cognitive computing is used in the tasks that are required to resolve large amounts of data in meaningful assessments. For example, in computer science, cognitive computing and big data analytics identify trends and patterns and understand human language to interact with customers.

Some of the industries that are the early adopters of the technology are healthcare, financial services, manufacturing, and retail. However, exploiting the benefits of cognitive analytics can boost the growth of the organization in any sector.

Mentioned below are some of the use cases of cognitive computing explaining how different industries approach the transformation.

Healthcare

Both doctors and patients can benefit alike. Cognitive computing can manage unstructured data from various sources such as patient reports, medical histories, diagnoses, conditions, and more to make recommendations to medical practitioners. This helps doctors in making better treatment decisions and providing better patient care.

Cognitive image interpretations can detect the minutest of the details that human radiologists might miss or might not comprehend. Machines are anyways much more capable than humans of analyzing different types of images and critical patterns that can provide better insight, thus better treatment and patient care through cognitive analysis.

Patients/individuals can benefit from AI machines and cognitive technology by enhancing their health in the form of devices that will capture the information and will provide recommendations from customized exercises to diet plans, behaving like a health and fitness coach.

The gist is that AI and cognitive technology will empower the healthcare industry to provide better treatment, better decision-making for practitioners, greater cost-effectiveness, patient empowerment, and better health, and fitness.

[Also Read: How data analytics in healthcare is reducing costs]

Retail

By analyzing the basic information of the consumer and the details of the product that the customer is looking for, cognitive analytics can provide personalized suggestions that meet the specified criteria.

AI with cognitive technology gathers information from various sources like social media sentiments, past user preferences, customer reviews, and even the geographical location for the weather of the customer’s location. 

With the unstructured details gathered in different formats, the data is churned and processed to create buyers’ persona based on demographics. AI and cognitive computing make sense of this data based on which the retailer can engage customers exactly on the criteria of their needs and preferences. 

Banking and Finance

Banks have been using chatbots to interact with customers to resolve their queries. Cognitive analysis can analyze customers’ questions, sentiments, etc. to create a database of its own. 

This helps both banks and customers in loan management by analyzing loan needs based on their financial transactions, needs, queries, and much more. The category and type of loan can also be suggested with this technique. Creating new products based on this data helps in increasing the bank’s portfolio.

Financial advisors can use this data collated from cognitive insights to manage customer portfolios, offering them suggestions for the right kind of loans and investments. The right advice and the right returns will only lead to better customer satisfaction and engagement.

The cognitive analysis is also beneficial for analyzing the data for decision making and alarming for the risks of potential frauds. Several insurance companies too have begun to take the advantage of cognitive analytics

Cognitive Analytics Examples

Some examples of cognitive analytics which are in use today include Microsoft’s Cortana, Apple’s Siri, and IBM’s Watson. Other examples of cognitive analytics are:

Royal Bank of Canada uses AI and ML to scan clients’ transaction histories and usage patterns to provide them with more personalized solutions. Cognitive analytics has helped the bank and the clients with better customer service, and personalized financial products by simplifying complex security mechanisms.

The Lark app leverages AI and health IoT devices to cognitively gather data and provides personalized health advice for treatments and fitness.

USAA financial services company has been the early adopter of cognitive analytics that checks the policy application for approvals and disapprovals.

With cognitive computing, researchers at the University of California, Los Angeles were able to identify people with diabetes changes by mining the patient records.

Cognitive Analytics with Appinventiv

Solutions offered by Appinventiv cater to diverse industries that are designed and formulated with a focus on fulfilling ever-changing marketing needs.

Appinventiv takes pride in providing data analytics services that have enabled organizations to identify the gaps and successfully deploy digital solutions across different levels of the organization.

For instance, Appinventiv has provided successful data analytics and solutions to a US-based telecom giant. By offering a customer-centric product solution, we created an ecosystem that could process high volumes of data to classify it according to customer behavior and preferences.

We have worked with some best innovative brands and ideas and are eager to transform your business idea into reality. Speak to our expert.

Wrap Up!

Cognitive analytics is the next big thing that will transform your business with robust and agile solutions. Cognitive analytics being the extension of our intelligence and skills, has the potential to strengthen overall business capabilities. It will not be an exaggeration to say that cognitive analytics is becoming a must-have for businesses.

FAQs

Q. How does cognitive computing improve the business?

Cognitive computing helps in improving the business outcomes by:

• Accurate data analysis
• Leaner and more efficient business processes
• Improved customer service and interaction

Q. What are the differences between cognitive analytics and AI?

A. Following are the major differences between the two technologies:

• AI uses ML, NLP, neural networks, and deep learning whereas cognitive analytics uses all of these and sentiment analysis.
• AI has the capability to find patterns in big data to learn and reveal information and deliver solutions to complex problems whereas cognitive analytics mimics human thoughts in finding solutions to complex problems and decision making.
• The purpose of AI is to automate processes whereas cognitive analytics enhances human capabilities.

Q. What are the must-have attributes of cognitive analytics?

A. With self-learning technologies that use data mining, pattern recognition, and NLP, cognitive analytics mimics human intelligence. To achieve this, it must have the following attributes:

• It should be adaptive to the dynamic data in real-time and should be able to adjust as the environment and data changes.
• It should be interactive with other processors, devices, and cloud platforms.
• It should be iterative and stateful.
• It should be contextual and able to understand, identify and mine contextual data from structured and unstructured information.

THE AUTHOR

Sudeep Srivastava

Co-Founder and Director