8 ways to gain consumer insights using AI

Regardless of how evil the present entertainment industry portrays the artificial intelligence technology, there is no doubt that AI is the future and a promising one. Fast changing trends instigate fast-changing customer needs. Thankfully, brands have realized how difficult yet important it is to keep up with these trends and bring out useful consumer insights.

The amount of data collected to research upon these trends automatically gives manual data analysis a big no-no. Instead, a smart, fast, and better data handling technology is used to bring out accurate and useful information. AI gives consumer insights which are basically an interpretation of human behavior trends that aims to increase the effectiveness of a product or service for the consumer.

In a previous post, we discussed several ways in which AI can boost consumer insight. Now, let’s take a look at eight applications of AI for getting consumer insights.

1. Text Classification Analysis

TEXT-BASED KEYWORD EXTRACTION

Text-based surveys are the most common way of collecting and analyzing consumer insight. ParallelDots text analysis AI, SmartReader, is a powerful tool that can be used to generate an extensive list of keywords and phrases in near real time to help businesses focus their research towards the most talked about themes or features of their product.

TEXT-BASED EMOTION ANALYSIS

AI that detects the accurate emotion behind any textual consumer insight can prove to be of utmost importance for businesses like Advertising agencies where their selling strategy is to connect to their audience on an emotional level. ParallelDots Emotion Analysis API is trained to classify text in six different emotions — happy, sad, angry, fearful, excited, and bored.

TEXT-BASED SENTIMENT ANALYSIS

Whether the customer responses are positive, neutral, or negative is what any business foremost focuses on after launching any new product or service. These sentiments ultimately decide the next step or strategy for the business. Using AI to analyze text-based sentiments, companies can manage vast amounts of data, understand the insights shared by consumers, and merge all types of social data with other data streams to discover customer needs, intents and preferences, all with unprecedented precision.

ParallelDots Sentiment Analysis API is a state-of-the-art tool that can accurately identify sentiments behind any text-based data.

2. Observational or Field Research

Observing consumer interaction manually is a time consuming and cumbersome process and easily prone to error of judgments. But observational research done using AI can give fast implicit insights that are backed with proper data analysis. They are accurate and cost-efficient, and can easily break open even the smallest of the details and prove to be a game-changing technology for businesses.

Karna AI Perceptron is just the right tool for conducting observational research. With its state-of-the-art technology, Perceptron can yield useful insights regarding how consumers interact with a product in order to understand their needs and motivation.

3. Image Classification

AI used for image classification can give access to a number of insights from consumer sentiments to content preferences. For example, restaurant feedbacks containing images of food can be used to determine the most and least liked cuisine category along with the most ordered food item in that category. With this data analysis, the restaurant can use that particular category to boost their promotion.

Social media is another space where image classification using Artificial intelligence can bring out viable consumer insights. A large chunk of consumer base all over the globe constantly communicates by posting images on social media platforms. According to a report, people share almost 3.25 billion pictures every day across various social media platforms. With image classification AI, the intent behind these pictures can be detected. These insights can be used to deliver a better, more seamless consumer experience across all touch points.

ParallelDots Image Recognition API is a powerful tool capable of highly accurate classification of unstructured visual data.

4. Video Analysis

One of the best implementations of AI for video analysis can be seen in retail businesses where product package designs and product placement strategy is decided by tracking the eyesight of the customer. This data analysis reveals which shelf was the first to catch the most consumers’ attention and how fast.

This can be done best using Eye Tracking technology. The eye tracking tools can scan the consumer’s eye movements and give fascinating insights about their behavior by accumulating data of the most and least focused points.

Karna AI has created an excellent eye tracking tool called SmartGaze, which efficiently maps the consumer’s gaze and provides useful insights regarding what caught their attention and for how long.

5. Speech Recognition/Analysis

This tech comes-in handy where consumers provide vocal feedback. Calls made to Customer Care centers are often recorded for later analysis to determine the overall context and sentiment of the customer.

Similarly, companies conduct surveys and record their vocal feedback to determine the efficiency of their sales pitch. The Speech Analysis AI is used to find out how effective was the interaction based on what the customers remembered or liked.

These tools can not only understand spoken language, but can also detect emotions, accent, behavior patterns, etc embedded within the speech. The data collected allows organizations to derive crucial business strategies in order to predict future outcomes efficiently.

6. Conversational Bots (Chatbots)

The AI-driven chatbots have a huge potential of getting tons of consumer insights and feedback without actually engaging manpower in the process. One can ask the AI Chatbot to bring out consumer insights that are interesting for a specific query. The AI can search through the enormous database, creating connections between different insights and give relevant answers to the questions asked.

While simple chatbots can deliver pre-packaged answers by scanning keywords from a consumer’s query, Artificial Intelligence and Machine Learning powered chatbots are capable of creating more complex responses using Natural Language Processing.

AI chatbots are quite flexible and are in an active learning process while interacting with consumers to help them reach their goals.

You can read this blog to know more about Natural Language Processing and its application.

7. Trend Analysis

In order to get an in-depth understanding of the consumer, retailers can use AI to access and analyze all the available information regarding the preference, choice, purchase history, etc to better understand consumer demands. AI-powered predictive analysis can be used to mine statistics and data to make viable predictions about consumer’s behavior in the future to maximize spending and encourage repeat purchases.

8. Augmented Reality

For augmented reality to work, an accurate computer vision is achieved using AI, machine learning, and huge data set that trains the machine to easily identify objects.

The machine then integrates with consumer’s surroundings in a non-intrusive, relevant manner to derive useful consumer insights. The technology is capable of opening up new possibilities in areas like interactive shopping, product insight, etc.

Several beauty brands are actively using AR to engage maximum consumers and drive return purchases. Consumers can virtually try on makeup, get personalized product recommendations, and get educated regarding what products to use and their proper application.

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