Artificial Intelligence (AI) has been transforming various industries for years, and its impact on consumer intelligence is growing stronger.

AI is a technology that enables computer machines to learn and create decisions based on data without being explicitly programmed. With the help of AI and the latest Machine Learning (ML) techniques, businesses can collect and analyze vast amounts of consumer data to gain valuable insights that can help them improve their products and services, anticipate market trends, and enhance customer experiences.

Melissa Drew, Associate Partner, IBM, stated the importance of using AI technology in the customer experience programs in the  102 CX study, where CX experts shared CX challenges and solutions to overcome them.  See what she says -

“As more consumers become data literate, there is a realistic expectation, consumers will begin to demand more transparency from the Brand. In the overall consumer journey, I envision the following phrases: 'See Me, Know Me, Understand Me, and Show Me.’ We are just starting to fully realize the 'Understand Me' phase. But, it is the 'Show Me' phase Brands need to be most aware of. Remember, AI solutions do not pop up overnight. Some companies have been developing their AI solution for 2-3 years before finally bringing it to the market. Don't be shy in requesting a demo, specifically of the area(s) utilizing the AI technologies.”

However, it's not just about AI technology alone. Human experts are also essential in harnessing the full potential of AI for consumer intelligence. AI can provide automated data analysis but still requires human intervention to interpret results, generate hypotheses, and make strategic decisions based on the findings.

Therefore, combining AI and human intelligence can help businesses better understand consumer behavior, preferences, and needs and provide personalized and engaging experiences.

In this context, the next wave of tech-enabled consumer intelligence will be driven by the collaboration between AI and human helpers. It will enable businesses to leverage the power of technology and human expertise to gain a competitive advantage in the marketplace.

Consumer Intelligence Comes From Combining Machine Intelligence with Human Expertise

In today's business world, understanding customer needs and trends is essential for any company to succeed. However, with the vast amount of data available, it can be challenging for businesses to process and analyze this bulk amount of information accurately. That is where machine intelligence comes in.

Machine intelligence, coupled with human expertise, can help businesses understand their market segments and customers' needs and behaviors more effectively.


Machine intelligence is the ability of machines to learn from data and make decisions without direct human intervention. Machine intelligence can analyze data from various relevant data sources, such as social media, sales data, and customer feedback, to identify key trends and customer needs. With the help of machine intelligence, businesses can process bulk amounts of data and identify patterns and trends that would be impossible or difficult for humans to identify manually.

However, machine intelligence alone is not enough!

Simply placing a machine intelligence system in your customer experience system can't help to understand customer needs and trends fully. Humans' participation in data analytics and data science expertise is equally important.

Humans bring a unique perspective to deep learning and contextual understanding that machines cannot match. They can help to interpret and contextualize the data analyzed by machine intelligence, providing insights that machines cannot.

Human expertise can also help to identify and prioritize the most critical customer needs and capture emerging market opportunities, allowing businesses to focus on areas that will significantly impact their customers.

Additionally, human expertise can help to create a more personalized customer experience. While machines can analyze customer data, they cannot provide a level of personalization as human experts. By combining machine intelligence with human expertise, businesses can create a more personalized customer experience that meets individual customers' specific needs and preferences.

Moreover, humans are the ones to act upon these insights gathered by machine intelligence. Taking the right step in any initiative is impossible without human brains working together. Furthermore, there should be humans with data science talent and customer insights groups to manage and maintain the system inside the organization.

So, the point here is to avoid creating data overload for humans by leaving the bulk amount of big data to process and analyze to machine learning systems, which is a burden and tedious task for humans. And then, give the small data that have already gone through the analysis, noise reduction, and classification to humans who can digest the insights precisely to make effective business decisions.

An efficient AI-driven consumer intelligence platform can help organizations with this.

3 Factors of a Consumer Intelligence System

Altogether, there are 3 factors that you require in your consumer intelligence program.

They are:

3 Factors of a Consumer Intelligence System

1. AI & NLP technology

NLP is a subfield of AI that focuses on the interaction between computer systems and human language. Its goal is to enable machines to understand and interpret natural language data, such as written text or spoken words, in the same way, humans do.

Hence, natural language processing allows organizations to extract shopper behavior data, language, emotions, and preferences from consumer interactions, reviews, conversations, etc.

2. Smart analytical framework

An analytical framework is a set of methodologies, tools, and processes designed to help businesses make better decisions by giving them more profound insights into their operations, customers, and markets. These frameworks use to analyze large amounts of data and generate valuable insights that enable insights professionals and teams managing data to work seamlessly.

3. Human-machine partnership

Human-machine partnership refers to the collaboration and interaction between humans and machines to perform tasks and solve problems. It is based on the point that humans and machines can complement each other's strengths and weaknesses to achieve better results than either could on their own.

Presenting the insights derived from the AI and machine learning tools precisely in a dashboard and visualizing them helps human-machine collaboration to make better decisions.

Case Study of a Top Grocery Delivery Brand

The covid-19 pandemic was a problematic situation worldwide, reflected in massive changes and transformations in market trends and consumer behavior. During this time, the top grocery delivery service provider brand X has struggled to understand their customers’ changing needs, and all the efficient strategies they made for marketing and products started not to work.

This time, they adopted a robust AI and machine learning based consumer intelligence platform, Clootrack, that provides detailed and comprehensive actionable insights around their customer experience.

They gathered 64,195 customer conversations from multiple online data resources and analyzed major customer experience drivers before and after the pandemic.

Before covid, food quality was the top priority for customers. Post covid, food quality is not at all a priority for customers. During that difficult time, customers required more customer care and services and were concerned about order cancellation and refunds due to the high demand for products.

Delivery charges, delivery timeline, and app-user interface also emerged as significant customer concerns in the post covid.

So, suppose the grocery delivery brand solely prioritizes food quality and customer experience even post covid. In that case, the customers will get frustrated since they don’t get what they need according to their changing life situations and requirements.

Focusing more on customer care and service, cancellation and refund, delivery charges, and time helped grocery delivery brand X regain customer satisfaction and loyalty.

To know other insights explored on the grocery delivery brand by the AI-driven consumer intelligence tool, read the complete case study.

Like this grocery delivery brand, using an efficient AI-driven consumer intelligence tool will be a life-changing initiative for all organizations!

Best Practices While Using an AI-Based Consumer Intelligence Tool

When adopting an AI-driven consumer intelligence tool to explore customer experience insights, and customer analytics, here are some points that brands should keep in mind:

Best Practices While Using an AI-Based Consumer Intelligence Tool

  1. Define clear objectives

    Brands should understand their objectives for adopting an AI-driven consumer intelligence tool. This includes identifying what kind of customer experience insights they want to explore, the specific business outcomes they hope to achieve, and the metrics they will use to measure success.
  2. Quality of Data

    It is essential to ensure that the data used to train the AI-driven consumer intelligence tool is accurate, complete, and unbiased. Brands must also verify that their data privacy and security policies are in place to protect consumer data.
  3. Understand AI Algorithms

    Brands should also understand the AI algorithms used in the consumer intelligence tool. They should verify that the algorithms are transparent and fair and can provide actionable insights to drive business outcomes.
  4. Evaluate the Need

    Organizations must assess whether they have the in-house expertise and resources to use the AI-driven consumer intelligence tool effectively. If not, they should evaluate the options to get support from the vendor or external consultants.
  5. Use Cases

    Brands should clearly understand the use cases for the consumer intelligence tool. They should explore how the tool can help them in customer service, product development, customer retention, and acquisition.
  6. Ongoing Maintenance

    Companies should not view adopting an AI-driven consumer intelligence tool as a one-time activity. Regular maintenance is essential to ensure that the tool continues to provide actionable insights and delivers business outcomes.
  7. Human touch

    Finally, it is essential to remember that AI-driven consumer intelligence tools should not replace human interaction with customers. Brands should ensure the tool is used with human insights to provide the best possible customer experience.

On the whole,

It is clear that AI technology is set to revolutionize the field of consumer intelligence. With the help of advanced NLP and machine learning capabilities, AI-powered tools can provide businesses with unprecedented insights into consumer behavior, preferences, and needs. Moreover, these technologies are constantly evolving, becoming more sophisticated and user-friendly, which means they will become increasingly accessible to businesses of all sizes.

However, it is important to note that humans will continue to have an irreplaceable role in developing and deploying AI-powered consumer intelligence tools. As such, businesses must ensure they have the right talent and skills to effectively leverage these technologies for maximum impact.

Ultimately, the future of consumer intelligence will be shaped by the collaboration between humans and, AI & machine learning as we work together to unlock new opportunities and drive growth in an ever-changing market.

Read more: How To Humanize Experience Using Technology and Customer Data?