‍Introduction

To start with, let’s talk about data. Across companies, a massive collection of structured and unstructured data is being collected that grows exponentially with time. This raw data can be used to extract information, patterns, etc., for different purposes through data management and analysis. When used well, this can be invaluable to organizations since it provides a host of information to derive meaningful, actionable insights and make informed decisions. 

As a rule, a company that can harness the power of data tends to be more competitive and stay ahead of the curve. When so much data is involved, it can be very time-consuming, and you stand the risk of human errors. This is where Artificial Intelligence (AI) comes into the picture to make a big difference in providing the correct data and information fast and accurately.

Using AI for Gaining Business Insights

AI insights rely on breaking down large amounts of data to effectively come together in creating a pattern that can be used to improve customer experiences. Following are several ways in which AI can be used to get insights:

1. Data-driven Insights: With the exponential growth of big data, information that might be overwhelming for humans has become a child’s play for AI. It can comb through terabytes (TB) of data within seconds to find information, connect relevant ones, and provide insights that might not be possible to get with traditional analytics methods.
2. Predictive Analysis: AI also helps minimize risks and increase growth opportunities by forecasting future trends and events. It uses historical data to predict future behavior of consumers, processes, markets, etc., for which relevant actions can be taken to ensure effective strategies and decision-making.
3. Bias Reduction: Humans are inherently biased based on personal preferences and experiences. AI, on the other hand, is driven purely by the data and information fed to it. Hence, it becomes easier to remove bias through AI decision-making as it will only analyze the information being fed.
4. Faster Information Processing: Apart from sifting through millions of pages of data in a matter of seconds, AI also minimizes the risk of human errors. Hence, it enables organizations to process massive data sets quickly and accurately.
5. Finding Patterns and Trends: With accurate analysis and assessment, correlations and dependencies between data elements start to emerge, and AI can find patterns and anomalies within given data sets that can help in effective decision-making. 
6. Customer Personalization: By analyzing browsing history and consumer behavior patterns, AI generates recommendations or place targeted advertisements to fit consumer needs and expectation for seamless user experiences. Tailored recommendations from Spotify, Netflix, Amazon, etc., are all examples of AI personalization.
7. Resource Optimization: AI also allows you to streamline supply chains, simplifying processes and optimizing resources. The capacity to process large amounts of data has reduced overall workload, allowing humans to focus on more relevant projects.
8. Continuous Growth: Today's market demands constant learning and adapting to evolving consumer needs. By continuously collecting and analyzing data and providing relevant feedback, AI accelerates the growth and success of a brand.

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Examples of AI Used in Creating Unified Insights?

There are various means to utilize AI for deriving unified insights in research. Let us look at some of the relevant technologies that can be used in research today:

1. Facial Coding: If you are in sales or marketing, you must have heard the saying: People buy emotions, not products and services. This technology enables brands to get deep, unbiased insights through facial expressions by measuring and quantifying human emotions and expressions through smartphones, webcams, or even videos. Hence, brands can determine underlying emotions attached to a product or service through this technology and align it according to user preferences.
2. Eye Tracking: Did you know that a logo's noticeability is maximum when placed on the left side of a webpage? That is because the eyes naturally go from left to right, as per most written language. An example of behavior AI eye tracking technology captures the eye gaze movements of consumers through webcams and smartphones. It allows users to measure attention and engagement levels when looking at certain websites, products, media, etc. This helps researchers find if users interact with elements as expected or if improvements are required to make them more noticeable.
3. Voice AI: Powered by Natural Language Processing (NLP), Machine Learning (ML), and AI, this technology identifies, analyses, and measures emotions in human voice through tone, tonality, confidence levels, etc. It can also provide transcription/translation capabilities, leading to rich insights.
4. Click Tracking: A more accessible alternative to Eye Tracking technology is using mouse click data. While Eye Tracking might be more accurate, click tracking records heat-maps and scroll maps according to mouse click behavior of users and finds focus areas for consumers as well.
5. Generative AI: If you are tech-savvy, you have heard of next-gen tools like Chat GPT and Bard. These are all examples of Generative AI technology algorithms that can be used to create original content based on user inputs. Integrating a Gen AI tool along with a research platform can enhance your process since it can uncover deep insights and more relevant recommendations. Co-pilot is one such feature in Entropik’s Decode, which can provide information and actionable insights based on data present within the platform.

Each technology above has its unique advantage, and having them together in one single platform can provide a holistic understanding of qualitative and quantitative research. While many big brands have already started investing in Insights AI based technologies, brands who want to stay ahead of the curve must follow suit.

The Impact of Insights AI across Major Industries

Retail and CPG

Retail and FMCG companies usually have a set traditional research processes, where the time to insights is longer, and studies are divided into smaller parts. The target audience can also be diverse in the case of these industries. AI-driven insights can ensure the intended message resonates with the relevant audience. Hence, there is a vast amount of data which is sitting in silos from different campaigns. This can become overwhelming, and a lot of the data can get lost in the process.

With new-age Insights AI platforms, it becomes a breeze to process all the data since it can also provide a repository where all the past and present data can be collated and presented in one single place. The end-to-end research process for ads, packs, shelf placements, etc., can all be conducted with AI technologies to understand underlying pain points and provide a seamless consumer experience. Entropik’s Decode is one platform that can provide deep insights and give superior decision-making for CPG and Retail.

OTT

With the revolution of over-the-top (OTT) platforms and how we consume content, the role of AI has seen a massive shift in this space. The demand for engaging content is unique to each person, and with that, hyper-personalization has become the new norm. This is possible only because of AI in the OTT industry.

Personalized ads and content recommendations by leveraging AI has changed how we discover and consume new content. AI tools like Facial Coding and Eye Tracking understand if the onscreen content catches enough user attention. It can also go a step further in ensuring consumers find the content engaging enough and provide suggestions to make it better.

This is how the right content is effectively delivered to the right person, making it highly personalized and increasing the chances of conversions.

Final Thoughts

Vast amounts of data are required for AI to be successful. With that comes ethical considerations on which AI has people divided. One where consumers are skeptical about consent, data sharing, and privacy concerns and one where people are optimistic about the prospect of what AI can achieve. Whatever school of thought you belong to, it has the potential to make or break business, and it is here to stay. Related brands must effectively navigate these technologies, building a successful brand.

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