Customer research is that magic element that elevates businesses. Staying ahead of the competition requires a deep understanding of your customers.

As we step into 2024, artificial intelligence (AI) continues to revolutionize the way businesses conduct customer research. Harnessing the power of advanced algorithms, machine learning, and natural language processing, AI tools have become indispensable for gaining actionable insights.

We will explore the 5 best AI tools for customer research that are shaping the way businesses connect with their audience in 2024. But first, let’s begin with what artificial intelligence is especially useful for, and what it can do for your business in customer research.

5 Best AI Tools For Customer Research In 2024

Insight7.io

Understanding and responding to customer needs is paramount for success. One powerful tool that can significantly enhance your customer research efforts is Insight7, an AI-powered Customer Insights platform designed for product teams. Let’s briefly explore how this tool can benefit your business in customer research:

Recording and Analyzing Customer Interviews

Insight7 provides a comprehensive solution for recording, transcribing, and analyzing customer interviews. The platform allows you to effortlessly extract themes from multiple interviews, providing a deeper understanding of customer sentiments, pain points, and preferences. By automating the analysis process, your team can save valuable time and gain actionable insights faster.

Streamlining Project Analysis

One of Insight7’s key features is its ability to extract meaningful themes from various customer interviews with just one click.

This streamlined project analysis helps your team identify patterns and trends, enabling you to make data-driven decisions that align with customer expectations. This efficient process accelerates your product discovery phase, a crucial aspect for startups and product teams.

Transforming Feedback into Actionable Insights

Insight7 doesn’t stop at analysis; it empowers your team to turn customer feedback into tangible product artifacts.

From user personas to opportunity solution trees, the platform facilitates the conversion of raw feedback into actionable insights, aiding in the development of features that truly resonate with your target audience.

Amazon Polly Analytics

In a world increasingly driven by voice interactions, Amazon Polly Analytics stands tall. This tool goes beyond mere voice recognition; it dives into the emotional undercurrents embedded in customer voices.

Whether dissecting customer service calls or unraveling sentiments from voice surveys, Polly Analytics extracts a symphony of insights.

It’s like having a virtual emotion analyst, helping businesses not only hear but truly understand the emotions behind every spoken word.

Amazon Polly Analytics offers several benefits and drawbacks, depending on the specific needs and context of users. Here’s a breakdown of its pros and cons:

Pros

1. Speech Synthesis: Amazon Polly Analytics provides high-quality text-to-speech (TTS) synthesis in multiple languages and voices, enabling users to convert text content into natural-sounding speech audio.

2. Customization: The platform offers various customization options, including the choice of voice, speech rate, pitch, and volume, allowing users to tailor the speech output to suit their specific preferences and requirements.

3. Scalability: Built on Amazon Web Services (AWS) infrastructure, Amazon Polly Analytics can scale dynamically to accommodate fluctuating workloads and processing demands, ensuring performance and reliability even with large-scale deployments.

4. Integration with AWS Services: Amazon Polly Analytics seamlessly integrates with other AWS services, such as Amazon S3 for data storage, Amazon Transcribe for speech-to-text conversion, and Amazon Lex for conversational interfaces, enabling comprehensive speech-enabled applications and workflows.

5. Cost-Effectiveness: The pay-as-you-go pricing model of Amazon Polly Analytics allows users to pay only for the TTS requests they make, without any upfront costs or long-term commitments, making it cost-effective for businesses of all sizes.

6. Developer-Friendly APIs: Amazon Polly Analytics provides developer-friendly APIs and SDKs for easy integration into applications and services, enabling developers to incorporate speech synthesis capabilities seamlessly.

Cons

1. Limitations in Naturalness: While Amazon Polly Analytics offers high-quality speech synthesis, the generated speech may still lack the naturalness and expressiveness of human speech in certain contexts, which can affect user experience and engagement.

2. Language and Accent Support: The availability of languages and accents supported by Amazon Polly Analytics may vary, and certain languages or accents may have limitations in terms of pronunciation accuracy and naturalness.

3. Dependency on Internet Connectivity: Amazon Polly Analytics requires internet connectivity to function, which may pose challenges in offline or low-bandwidth environments where reliable internet access is not available.

4. Privacy and Data Security Concerns: Users should be mindful of privacy and data security considerations when using Amazon Polly Analytics, particularly when processing sensitive or confidential text content, to mitigate risks of unauthorized access or data breaches.

5. Limited Voice Customization: While Amazon Polly Analytics offers some customization options for voice parameters, users may have limited control over fine-grained aspects of voice characteristics and may not be able to achieve highly specialized or personalized voice profiles.

6. Learning Curve: Users may face a learning curve when adopting Amazon Polly Analytics, particularly if they are new to speech synthesis technologies or AWS services, requiring training and support for effective utilization of the platform.

Overall, Amazon Polly Analytics provides powerful text-to-speech capabilities with scalability and integration advantages, but users should carefully consider its pros and cons and evaluate their specific needs and requirements before adopting the platform.

IBM Watson Customer Experience Analytics

Customer journeys are intricate, weaving through websites, mobile apps, and social media. IBM Watson Customer Experience Analytics acts as a navigational guide, unraveling the intricacies of these journeys.

It’s more than just data; it’s about understanding the holistic customer experience. From identifying pain points to predicting needs, Watson’s analytical finesse shapes a comprehensive view, empowering businesses to sculpt seamless and personalized customer interactions

Certainly, here’s an overview of the pros and cons of IBM Watson Customer Experience Analytics:

Pros

1. Advanced Analytics Capabilities: IBM Watson Customer Experience Analytics leverages AI and machine learning to provide advanced analytics capabilities, including predictive analytics, sentiment analysis, and customer journey mapping, enabling businesses to gain deeper insights into customer behavior and preferences.

2. Integration with IBM Ecosystem: The platform seamlessly integrates with other IBM solutions, such as Watson Marketing, Watson Campaign Automation, and Watson Commerce, allowing businesses to leverage a comprehensive suite of tools for marketing, sales, and customer engagement.

3. Multi-channel Data Integration: IBM Watson Customer Experience Analytics can ingest and analyze data from multiple channels and touchpoints, including websites, mobile apps, social media, and call center interactions, providing a holistic view of the customer journey across various channels.

4. Real-time Insights: Users can access real-time data and insights, enabling timely decision-making and proactive response to customer needs and preferences, driving personalized customer experiences and improving overall satisfaction and loyalty.

5. Visualization and Reporting: The platform offers interactive dashboards, customizable reports, and data visualization tools, enabling users to explore data, identify trends, and communicate insights effectively to stakeholders across the organization.

6. AI-driven Recommendations: IBM Watson Customer Experience Analytics provides AI-driven recommendations and insights, helping businesses identify opportunities for optimization, anticipate customer needs, and improve marketing and sales strategies.

Cons:

1. Complexity: IBM Watson Customer Experience Analytics can be complex to implement and configure, requiring technical expertise and resources for setup, integration, and customization, which may pose challenges for some businesses, particularly small and medium-sized enterprises (SMEs).

2. Cost: The platform may involve significant costs, including licensing fees, implementation costs, and ongoing maintenance and support expenses, which may be prohibitive for businesses with limited budgets or ROI expectations.

3. Data Privacy and Security Concerns: Businesses must ensure proper data governance and compliance with regulations such as GDPR and CCPA when using IBM Watson Customer Experience Analytics, particularly when handling sensitive or personally identifiable information (PII) of customers.

4. Learning Curve: Users may face a learning curve when adopting IBM Watson Customer Experience Analytics, particularly if they are new to AI and machine learning technologies or unfamiliar with the IBM ecosystem, requiring training and support for effective utilization of the platform.

5. Dependency on IBM Ecosystem: Businesses heavily reliant on the IBM ecosystem may find IBM Watson Customer Experience Analytics beneficial, but those using other platforms or solutions may face challenges in integration and interoperability, limiting flexibility and scalability.

Overall, IBM Watson Customer Experience Analytics offers powerful capabilities for analyzing and optimizing customer experiences, but businesses should carefully consider its pros and cons and assess their specific needs and requirements before adopting the platform.

Google Cloud AutoML Tables

Democratizing machine learning, Google Cloud AutoML Tables empowers businesses to craft predictive models without the need for a Ph.D. in data science. It’s the epitome of customization, allowing businesses to sculpt AI models tailored to their unique needs.

From predicting customer behaviors to making data-driven decisions, AutoML Tables doesn’t just open the door to AI; it hands businesses the keys to their own AI kingdom.

Google Cloud AutoML Tables offers a range of advantages and disadvantages, depending on the specific needs and context of users. Here’s a breakdown of its pros and cons:

Pros

1. Ease of Use: AutoML Tables simplifies the process of building and deploying machine learning models, making it accessible to users with varying levels of technical expertise. Its user-friendly interface and automated features streamline the model development process.

2. Automated Feature Engineering: The platform automates the feature engineering process, saving time and effort for users. It can handle preprocessing tasks such as data cleaning, feature selection, and transformation, allowing users to focus on model training and evaluation.

3. Scalability: AutoML Tables is built on Google Cloud’s infrastructure, which offers scalability and reliability. It can handle large datasets and complex machine learning tasks, ensuring performance and efficiency even as data volumes increase.

4. Integration with Google Cloud Platform: AutoML Tables seamlessly integrates with other Google Cloud services, such as BigQuery for data storage and processing, as well as AI Platform for model deployment and monitoring. This integration facilitates end-to-end machine learning workflows within the Google Cloud ecosystem.

5. Automatic Model Selection: The platform automatically selects the best-performing model architecture and hyperparameters based on the input data and user-defined objectives. This feature helps users optimize model performance without the need for manual experimentation.

6. Customization Options: While AutoML Tables automates many aspects of model development, it also provides users with options for customization. Users can define their own objectives, constraints, and evaluation metrics to tailor the model to their specific needs.

Cons

1. Limited Transparency: AutoML Tables abstracts much of the underlying model-building process, which may result in limited transparency and control for users. Users may have less visibility into how the model makes predictions or handles specific features.

2. Cost: Using AutoML Tables may incur costs, particularly for large-scale or resource-intensive tasks. Users should consider the pricing structure, including fees for training and prediction usage, when evaluating the platform’s cost-effectiveness.

3. Dependency on Google Cloud Platform: Users who are heavily invested in the Google Cloud ecosystem may find AutoML Tables convenient, but those using other cloud providers or on-premises infrastructure may face challenges in integration and adoption.

4. Model Performance Limitations: While AutoML Tables automates model selection and optimization, the performance of the generated models may vary depending on the complexity of the data and the specific use case. Users should carefully evaluate model performance and consider alternative approaches if necessary.

5. Data Privacy and Security Concerns: Users should be mindful of data privacy and security considerations when using AutoML Tables, particularly when working with sensitive or confidential data. Proper data governance and compliance measures should be implemented to mitigate risks.

Overall, Google Cloud AutoML Tables offers a convenient and powerful solution for building and deploying machine learning models, but users should weigh its pros and cons and consider their specific requirements before adopting the platform.

Salesforce Einstein Analytics

Salesforce Einstein Analytics is the virtual sherpa that businesses need in their customer relationship management (CRM) ascent.

Through predictive analytics and machine learning, it dissects customer data, revealing patterns that extend beyond the horizon of conventional CRM.

It’s not merely about managing relationships; it’s about anticipating needs, understanding preferences, and orchestrating a personalized symphony of services that resonates with each customer individually.

Salesforce Einstein Analytics offers several benefits, but it also comes with its own set of drawbacks. Here are the pros and cons of using Salesforce Einstein Analytics:

Pros

1. Advanced Analytics Capabilities: Salesforce Einstein Analytics leverages artificial intelligence and machine learning to provide advanced analytics capabilities, including predictive analytics, forecasting, and recommendations.

2. Integration with Salesforce Platform: It seamlessly integrates with the Salesforce platform, allowing users to analyze data from various Salesforce modules, such as Sales Cloud, Service Cloud, and Marketing Cloud, as well as external data sources.

3. Self-Service Analytics: Einstein Analytics empowers users to explore data, create custom dashboards and reports, and derive insights without extensive technical expertise, thanks to its intuitive interface and guided analytics features.

4. Real-Time Insights: Users can access real-time data and insights, enabling timely decision-making and action in response to changing business conditions.

5. Mobile Accessibility: Einstein Analytics offers mobile applications for iOS and Android devices, allowing users to access analytics dashboards and reports on the go, facilitating remote work and collaboration.

6. Scalability: It can handle large volumes of data and scale according to the needs of growing businesses, ensuring performance and reliability even as data volumes increase.

7. Community and Support: Salesforce provides extensive community support, documentation, and training resources for Einstein Analytics users, facilitating learning and problem-solving.

Cons

1. Cost: Salesforce Einstein Analytics can be costly, especially for small businesses or organizations with limited budgets, as pricing is based on user licenses and data storage usage.

2. Complexity: While Einstein Analytics aims to make analytics accessible to non-technical users, setting up and configuring the platform, as well as creating complex analytics solutions, may require expertise and resources.

3. Dependency on Salesforce Ecosystem: Users heavily invested in the Salesforce ecosystem may find Einstein Analytics beneficial, but those using other CRM or data platforms may face challenges in integration and adoption.

4. Customization Limitations: Despite its flexibility, Einstein Analytics may have limitations in terms of customization and extending functionality beyond its out-of-the-box features, which could restrict certain use cases or requirements.

5. Learning Curve: While Einstein Analytics offers intuitive features, there is still a learning curve for users, particularly those new to analytics or accustomed to traditional reporting tools, which may impact adoption and productivity initially.

Overall, Salesforce Einstein Analytics provides a powerful platform for analytics and insights within the Salesforce ecosystem, but organizations should carefully weigh the pros and cons to determine if it aligns with their specific business needs and requirements.

What Is AI Used For In Customer Research?

In the expansive realm of customer research, artificial intelligence (AI) functions as a powerhouse, catalyzing a paradigm shift in how businesses understand and engage with their audience. Let’s delve into the multifaceted ways AI is harnessed for customer research, shedding light on its transformative impact.

Predictive Analytics for Customer Behavior:

AI is a game-changer when it comes to predictive analytics. By analyzing vast datasets, AI algorithms can discern patterns and trends in customer behavior.

This predictive capability allows businesses to anticipate customer needs, tailor their offerings, and strategize marketing initiatives more effectively. Essentially, AI equips businesses with a crystal ball into future customer preferences.

Sentiment Analysis in Customer Interactions:

One of AI’s remarkable feats is its proficiency in sentiment analysis. Through natural language processing (NLP), AI tools dissect customer interactions across various channels, such as social media, emails, and customer support chats.

This analysis unveils valuable insights into customer sentiments, enabling businesses to gauge satisfaction levels, identify pain points, and promptly address concerns. It’s like having an attentive ear to the vast digital conversations surrounding your brand.

Personalization of Customer Experiences:

AI plays a pivotal role in elevating customer experiences through personalization. By learning from individual customer interactions and preferences, AI systems can tailor recommendations, content, and services. 

This level of personalization not only enhances customer satisfaction but also fosters brand loyalty. Imagine receiving product recommendations that align perfectly with your preferences – that’s the magic of AI-driven personalization.

Optimizing Customer Journeys:

Understanding the customer journey is crucial for businesses aiming to provide a seamless experience.

AI tools, through sophisticated analytics, map out these journeys by analyzing touchpoints across websites, mobile apps, and other channels. This helps businesses identify bottlenecks, streamline processes, and ensure a cohesive and enjoyable customer journey from start to finish.

Automated Customer Support:

AI-powered chatbots and virtual assistants have revolutionized customer support. These intelligent systems can handle routine queries, provide information, and even troubleshoot issues without human intervention.

This not only ensures round-the-clock availability but also frees up human resources to focus on more complex tasks, enhancing overall efficiency.

Data-Driven Decision Making

Perhaps one of the most profound impacts of AI in customer research is its contribution to data-driven decision-making.

By processing and analyzing immense volumes of customer data, AI empowers businesses to make informed choices, guiding strategy development, and resource allocation based on real-time insights.

This shift from intuition-based to data-driven decision-making is pivotal in today’s competitive business landscape.

In essence, AI serves as the linchpin of modern customer research, permeating every facet of the customer-business relationship with its analytical prowess, predictive capabilities, and the ability to glean nuanced insights from vast datasets. 

As businesses continue to embrace and refine AI applications, the depth and accuracy of customer research are set to propel companies into unparalleled realms of customer understanding and satisfaction.

Bonus: AI Tools for Customer Research in 2024

Here are additional examples of AI tools and brands that people may use for customer research in 2024:

SurveyMonkey

SurveyMonkey offers AI-powered survey analysis tools that help businesses collect and analyze customer feedback. The platform uses machine learning algorithms to uncover insights from survey responses, identify trends, and generate actionable recommendations.

Medallia Experience Cloud

Medallia provides an Experience Cloud platform that includes AI-driven analytics and feedback management capabilities. It enables businesses to collect, analyze, and act on customer feedback across various touchpoints, driving improvements in customer satisfaction and loyalty.

Qualtrics CustomerXM

Qualtrics offers a CustomerXM platform that provides AI-powered customer experience management solutions. It includes features such as text analytics, predictive modeling, and customer journey mapping, enabling businesses to understand customer needs and preferences and deliver personalized experiences.

SAS Customer Intelligence

SAS offers Customer Intelligence solutions that leverage AI and machine learning to analyze customer data and generate insights. It includes features such as customer segmentation, propensity modeling, and campaign optimization, helping businesses improve customer engagement and loyalty.

Adobe Analytics

Adobe Analytics provides AI-powered analytics tools for understanding customer behavior and preferences. It offers features such as predictive analytics, customer journey analysis, and audience segmentation, enabling businesses to deliver personalized experiences across digital channels.

Hootsuite Insights

Hootsuite Insights is a social media analytics platform that uses AI to analyze social media conversations and trends. It provides features such as sentiment analysis, competitive benchmarking, and influencer identification, helping businesses understand and engage with their audience on social media.

Reltio Customer 360

Reltio offers a Customer 360 platform that provides AI-driven customer data management and analytics capabilities. It enables businesses to create a unified view of their customers, analyze customer data, and deliver personalized experiences across channels.

Sprinklr Experience Cloud

Sprinklr provides an Experience Cloud platform that includes AI-powered social media management and customer experience solutions. It offers features such as social listening, sentiment analysis, and social media engagement, helping businesses understand and engage with their audience on social media.

These are additional examples of AI tools and brands that people may use for customer research in 2024, each offering unique features and capabilities to help businesses understand and engage with their customers more effectively.

A Future-Forward Lead With Customer Research

As we draw the curtain on this exploration of the AI-infused landscape of customer research, it’s evident that we’re not just witnessing a moment but a transformative epoch.

The amalgamation of artificial intelligence with customer research has not only redefined the rules but has orchestrated a future-forward leap that businesses can’t afford to overlook.

In this brave new world, where customer expectations are dynamic and diverse, the role of AI in customer research is akin to a guiding compass.

It doesn’t just decipher data; it weaves a narrative, unraveling the intricacies of customer behaviors, sentiments, and desires.

The journey through the five AI tools showcased paints a vivid picture of the multifaceted impact AI has on understanding, engaging, and delighting customers.

The future of customer research is not about static surveys or outdated methodologies; it’s about dynamic conversations, predictive analytics, and personalized experiences.