AI customer insights: The AI advantage in consumer research

From successful product launches to viral ads, consumer insights are a necessity when it comes to understanding who your customers are and what they want to see from you. 

And more consumer data is available than ever before. This means there’s more pressure on marketers and innovation teams to know how to generate, collect and process data in order to get deeper insights into consumers’ ever-shifting wants, needs, preferences and behaviors

In this post, I’ll explore how AI is used for consumer insights by brands across the sectors, walk you through the pros and cons of using AI for consumer research and give you tips and tools to help you use AI more effectively to understand your audiences. 

AI is an essential tool for overcoming “data overload,” helping brands pull together and analyze large amounts of data, cut through the “data noise” and derive insights from consumers faster and more efficiently than before. 

AI for customer insights involves using artificial intelligence to help collect, analyze and provide insights into customer preferences, opinions, needs and behaviors. Brands can use these insights to make consumer-centric decisions — from creating hyper-personalized ads to knowing when to take proactive actions to reduce customer churn. 

“With the explosion of digital technologies, companies are sweeping up vast quantities of data about consumers’ activities, both online and off.”

- Harvard Business Review

With the launch of consumer AI-facing tools like ChatGPT in 2023, AI has made it possible to bring together, analyze, process and put huge amounts of customer data to use. With AI, we can get a more in-depth picture of who customers are and what they need from your brand and product lines in less time than ever before. 

Gary Drenik talks about the AI advantage throughout the research field:

“With its ability to process vast amounts of data at unprecedented speeds, AI can significantly accelerate research timelines. Whether it's analyzing genetic sequences in drug discovery or simulating complex scenarios in engineering, AI-driven algorithms can sift through mountains of data, identify patterns, and generate insights much faster than human researchers alone.”

While AI is a great tool for consumer research and deriving a ton of insights into your customers, it also comes with its limitations.

AI models are trained on “human” data. And this data can be full of biases. As a result, AI can replicate and scale societal biases, from race to gender. 

According to McKinsey:

“AI can help reduce bias, but it can also bake in and scale bias.”

The issue of data bias is particularly problematic in the consumer insights space. Bias can undermine your research results and push your company towards making decisions that fail to resonate with your audience and inadvertently support harmful, unnuanced stereotypes. This is especially true if the people working with AI-generated consumer data are dealing with unchecked unconscious biases. 

On the plus side, McKinsey says that by running on algorithms alone, AI can reduce people’s subjective interpretations of research.

Beyond bias, AI tools may fail to pick up on the context and nuance that is essential to understanding your customer. Many of the juiciest, most actionable insights are often less apparent in the data and need to be teased out by thoughtful market researchers and marketers. 

As with all things AI, the tech is a great supporting act in the consumer insights sphere — rather than a replacement for human insight and creativity. 

"Data is the underpinning of everything we do, and it’s that old adage of garbage in, garbage out…Frankly, we can start using AI on top of bad data but we’ll get bad outcomes. So you have to get the data right."

- Steve Phillips, founder and Chief Innovation Officer, Zappi

Let’s take a look at some of the main ways you can use AI to reveal customer sentiments and insights.  

1. Centralize and analyze large amounts of customer data 

Customer data has often been trapped in silos across multiple platforms. Here’s Deloitte: 

“Historically, customer data was collected across outdated and disconnected systems, and limited to call centers and point-of-sale. However, increased digitization coupled with advanced data technologies allows organizations to use their proprietary and third-party data to create detailed pictures of customers and gain a deeper understanding of their preferences and behaviors." 

They note: 

“AI-powered marketing technology can then be used to sift through vast amounts of information in real time, and make insight-driven decisions on the types of interactions to have with each customer.”

Notably, AI has the ability to bring together and analyze vast amounts of unstructured data, which Edward Calvesbert, Vice President of Product Management at IBM watsonx, says reflects most of the data that’s created every day and offers us the biggest opportunity. 

Organizations currently create four times more unstructured data than structured data. Unstructured data is data which is unorganized and uncategorized. Images, emails, audio files, knowledge base articles and instant messages fall into this category.  

Not only can AI pool, analyze and categorize this data, it can make searching through and understanding your data easier. AI excels at summarizing large datasets and can deliver the most essential insights in the patterns and trends in your research. 

Raveendrnathan Loganathan, EVP, Software Engineering at Salesforce, talks about how the team uses AI for processing and providing insights into unstructured data for their CRM customers: 

“With our Search Index functionality, customers can ingest data, derive knowledge, vectorize, index, and serve unstructured content from a variety of sources such as Salesforce clouds, hyperscaler storage systems, third-party applications, or zero copy partners like Amazon, Snowflake, Google, Databricks, IBM, and more.”

He adds:  

“Customers also benefit from our vector database’s unique multimodality, which means they can interact with an AI chatbot in Spanish and receive an accurate response, even if the knowledge base articles are in English, and help videos are in Japanese. In all of this, we’re giving customers something more than just information — we’re giving them context to unlock another layer of knowledge.”

2. Predict future preferences and needs with predictive analytics 

AI is a great tool for analyzing past customer behavior and preferences and making predictions about what they’ll need in the future. 

Predictive analytics brings together a range of techniques. It uses machine learning, statistical models and data mining to analyze historical data and predict future trends. This data may include on-site user behavior, buying behaviors, preferences and demographic data. 

You can use predictive analytics to make sure your messages, branding and product lines are a fit for your customers’ preferences and needs. Use the insights you get from predictive analytics to give customers what they need, when they need it:

1. Provide product suggestions based on past purchases and wishlist items. 

2. Send discounts to customers at risk of churning. 

3. Personalize campaigns based on customer needs and preferences. 

3. Use AI for sentiment analysis

What are consumers saying about your brand and products online? AI-based sentiment analysis tools track and analyze online consumer conversations and comments about your brand and product lines. They can uncover whether the current sentiment is positive, neutral or negative. 

Sources of consumer opinion include: online review websites like TrustPilot, social media platforms such as TikTok and Facebook, social media forums like Reddit, and ecommerce platforms such as Amazon. 

Without AI, it can take several hours a month to track and record consumer sentiment across a huge variety of platforms. Sentiment analysis tools can perform this analysis in minutes and provide ongoing insights into how consumers think about your brand, products and marketing. 

4. Run better surveys

Surveys are one of the best ways to understand customer perceptions and experiences. With the application of large language models, AI can analyze your results and quickly pick out the key insights within your data. 

For example, AI can quickly spot patterns in your responses and provide concise summaries that highlight trends in customer opinions, behaviors and perceptions.

Salesforce notes that AI can save professionals up to 3.6 hours per week through automation — that’s a 23-day vacation every year. AI-survey tools can automate the bulk of survey creation, data clearing, and analysis. 

5. Track on-site behavior

On-site user behavior brings another layer to your understanding of your customers. 

Customers’ on-site behavior can give you insights into how they respond to your copy, products and imagery. Heatmaps are a great example of this. Heatmaps use different color gradients to represent on-site user behavior, with warm colors like red representing high user engagement and colder colors like blue representing low user engagement.

For example, red may highlight where visitors’ click or scroll, while blue may highlight areas of the page users don’t look at. Here's Dragonfly AI:

“​​AI heatmaps act as highly intelligent trackers for your website, providing detailed insights into user behavior and interactions. They show you exactly how people use your site. These tools provide more insight into user behavior insights to reveal where visitors look first, what grabs their attention, and how they move around your pages. It's akin to gaining deep, real-time insights into your users' thought processes and behaviors.”

AI can improve on non-AI heatmaps by spotting trends and patterns in how customers use your site and making predictions on how changes to your pages will impact customer behavior. 

6. Create richer customer segments  

You can also use AI to analyze different customers behavior across your marketing channels. AI can take large amounts of data from a host of channels, sift through the data, and cluster customers into segments based on their preferences and behaviors. 

By bringing together and analyzing vast amounts of data, more data than we’d be able to collect and analyze ourselves, AI can deliver fast, accurate insights into your core customer segments. You can use these insights to create detailed customer personas that guide your marketing campaigns and strategies. 

Mailchimp says: 

“One of the main advantages of AI services is the ability to process vast amounts of data faster and more accurately than humans. AI can analyze data at scale and discover hidden insights that may not be apparent to human marketers. This allows businesses to identify valuable opportunities for personalization and customization that can drive engagement and increase conversions.”

7. Step up your personalization  

92% of businesses say they’re using AI-driven personalization to grow their business. 

“With AI, we can get a world of hyperpersonalization — where every experience is specifically curated for you and only you.”

- Mohannad Ali, CEO, Hotjar

By bringing together and analyzing a wealth of individual customer data, AI is one of the best tools for personalizing individual customers' brand and marketing experiences. 

Here are some of the best ways to use AI for personalization:

1. Use personalized pricing based on browsing and purchasing history to increase purchases. 

2. Roll out personalized ads that feature messaging and imagery tailored to your audience. 

3. Deliver personalized blog, email and video content recommendations based on user interests and preferences.

Let’s take a look at how brands are using AI for consumer research. 

Unilever: Hyper personalization 

Unilever is using AI to deliver hyper-personalized beautify recommendations to their customers in Thailand and the Philippines. 

On BeautyHub PRO platform, consumers complete a beauty quiz and share a selfie. Unilever uses their Computer Vision AI to assess up to thirty visual data points to provide personalized product recommendations across their beauty brands. 

Since launching, BeautyHub PRO has seen a 39% higher total shopping basket value for their customers in comparison to consumers who shop elsewhere. While BeautyHub PRO shoppers are 43% more likely to complete a purchase. 

Netflix: Predictive analytics 

Netflix is a great example of predictive analytics in action. The video streaming giant uses data analytics to personalize the user experience by looking at a variety of consumer data points such as: 

1. Browsing habits

2. Members with similar tastes and preferences

3. Searches from the search bar

4. Watch habits — including shows and movies that were paused, rewatched and fast-forwarded

5. Content abandonment times and rates

With this data, Netflix improves the customer experience by:

1. Delivering personalized show and movie recommendations

2. Showing personalized thumbnail images based on user watch habits

As a result, 80% of the content people watch on Netflix is found through personalized recommendations. 

Netflix says:

“​​Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. Here we might personalize this decision based on how much a member prefers different genres and themes. 

Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian.”

Creating immersive listening experiences with Spotify 

Spotify is a great example of how brands can use AI-derived customer insights to create more immersive, personalized brand experiences. 

“In a world with 100 million tunes and 600 million users, Spotify is banking big on AI to not just curate, but also predict your musical preferences. From AI DJs to mood-matching algorithms, the streaming giant is on an attempt to transform its massive collection into your personalized soundtrack.”

— Anshika Mathews, AIM Research

As one of the most popular music streaming services, Spotify has made great use of AI to understand their customers and turn these insights into immersive, personalized listening experiences. 

In 2023, the company released AI DJ, which combines AI-collected user data, generative AI, and the voice of an AI-generated DJ to curate unique playlists and commentaries for listeners. 

Spotify also shows brands how they can use AI-derived customer data to directly engage consumers. Take Spotify Wrapped, released in 2016, a feature that analyzes user’s listening habits throughout the year and delivers these insights in a neat line up of personalized stats on their favorite artists, songs, and albums at the end of the year. 

Personalized product recommendations with Amazon 

Amazon regularly uses AI to turn customer data into personalized product recommendations. This data includes information on their shopping habits, browsing habits, preferences and reviews to make unique recommendations. 

They place these suggestions across each customer’s homepages and product pages. 

While you probably already know Amazon offers personalized product recs, you may not know how deep this personalization goes. 

Beyond product recommendations, Amazon also bakes their customer insights into their product descriptions. For example, if you regularly search for cookbooks, Amazon will make language related to home cooking more prominent in their descriptions for homeware, furniture, and appliances. 

Amazon also offers personalized recommendations and answers to FAQs with their conversational chatbot Project Amelia. 

Enriching customer segments with Coca-Cola 

“One of the things we keep repeating in Coca-Cola is it’s about AI and HI. It’s artificial intelligence and human intelligence and ingenuity. I really believe our opportunity as humans is to continue to work on the creative side, on the values side, and use AI to scale ideas.”

- Javier Meza, CMO - Europe, Coca-Cola

Coke’s European CMO Javier Meza talks about how in a world of AI, Coke’s new marketing strategy is centered around developing a deeper understanding of the brand’s customer segments by collecting first-party data. 

As a result, the brand has shifted from a “static, traditional” segmentation model to a “real time dynamic” behavioral segmentation model.

He says:

“We are doing better audience planning so we can make our dollars work better by targeting who we really want to target and with data, we can track and measure better the return on investment. That’s the agenda.”

Let’s take a look at some of the best tools for collecting, analyzing and deriving insights from customer data. 

 Zappi  

Zappi’s AI Quick Reports offer AI-generated summaries and recommendations, drawing out the main insights from your customer surveys to help you understand how your ads are resonating with your audience and what you can do to optimize. 

"AI Quick Reports reimagines how insights teams can harness AI to put consumers at the center of their creative thinking. By fast-tracking the ability to turn raw data into rich consumer insights, we can empower insights teams to spend less time managing research projects and spend more time influencing strategy; that's how brands become truly consumer-centric." — Steve Phillips, Founder and Chief Innovation Officer, Zappi

By using advanced large language models (LLMs), our Quick Reports can instantly analyze huge amounts of quantitative and qualitative research data, providing a succinct yet detailed overview of the essential insights held within the data. 

But it doesn’t end there — Zappi also introduced AI Concept Creation Agents for innovation, which transforms traditional, time-consuming research into an agile, continuous process. This allows insights teams to iterate faster, make data-driven decisions and optimize products and concepts based on real-time consumer feedback with the help of AI.

By embedding AI into their innovation research system, you can now create product concepts within minutes, based on what your consumers want, need, like or dislike. 

Stravito 

Stravito is a generative AI search tool that was built to provide companies with access to already-available market research that you can use to enrich and validate your consumer insights. 

Stravito provides consumer insights summaries on your pre-determined market segments — pulling data from hundreds of available reports. Alongside each summary, you’ll get a link to the source for the data, making it easy to fact check the information Stratvito delivers.  

You can use the conversational AI feature to ask follow-up questions and dig deeper into the research. Then save your research to your account for easy review.  

Gong 

“Customer conversations are like pieces of a puzzle. When put together, they reveal patterns.” — Priyanshu Anand, Threado

From sales and customer support calls to recorded interviews, customer and prospect conversations are one of the best sources of customer insights.

Gong is an AI platform you can use to record, transcribe and analyze calls with prospects and customers.

“Gong uses modeling to identify important themes and topics discussed in a conversation. It recognizes patterns and creativity notifies you if certain things occur on a call. All of those things are packaged into insights that you can see, which helps you act more strategically.”

- Elvis Lieban, Product Marketing Manager, Gong

Gong provides full viability into customer and prospect conversations across your channels. With Gong, you can pull comments directly from the voice of the customer and use them to shape your future campaigns, messaging, branding or product developments. 

Hotjar 

Hotjar is one of the best tools for understanding on-site behavior, giving you a way to track digital customer journeys on your site. Hotjar goes beyond basic analytics, helping you monitor and analyze user behavior across your landing pages with tools like sessions recordings, heatmaps and on-page surveys. 

“With AI — the more data you consume and analyze data on your customer, the more you develop a deeper understanding. And some of that understanding and analysis can happen in real time — which we are not necessarily always able to do today. Allowing companies to capture and respond to trends in real time is something that would be very powerful.” — Mohannad Ali, CEO, Hotjar

You can use Hotjar AI to run on-site surveys, analyze your data and automatically create reports. Use Hotjar to get unique insights such as:

1. The customer satisfaction score for each page 

2. Insights into page engagement 

3. Trends and patterns on on-site customer behavior

Brand24

Brand24 is a sentiment analysis tool you can use to gather customer insights from over 25 million online sources, tracking consumer sentiments across platforms in real time. It’s a great tool for learning what consumers are saying about your brand online. You can track consumer sentiment across social media, news sites, blogs, videos, forums, apps and music streaming services. 

Brand24 automatically segments consumer sentiment into positive, neutral, and negative mentions. The platform provides insights with easily digestible “word clouds,” charts, metrics and trend analysis. While the Brand Assistant feature offers strategic recommendations on how to use your data. 

Brands like Coca-Cola, Netflix and Spotify are all using AI and consumer insights in innovative ways to get a deeper understanding of their customers and create immersive, personalized brand experiences. 

From predictive analytics to sentiment analysis, AI tools give you access to more consumer data than ever before — giving you richer, actionable insights into your customers.