Unlocking the power of AI for customer insights

Businesses must deliver the best products and services to be successful. 

Providing customer-centric experiences means deeply understanding your customers’ needs, behaviors, pain points, and preferences. 

Customer insights can help teams do just that, and artificial intelligence (AI) is rapidly evolving the insights experience. 

AI-powered customer insights can simplify the process of spotting trends, analyzing sentiment, gaining real-time feedback, and forecasting future behavior. 

The customer landscape is set to change significantly with the rise of AI. Still, there are significant limitations to keep in mind. 

This guide will help you unlock the power of AI for better, faster, and more reliable insights. Plus, we'll be looking at pitfalls to look out for and best practices to remember. 

AI’s rise is impacting almost every industry on the planet. When it comes to customer insights, AI may be able to perform many customer research tasks––albeit with limitations.  

These insights may relate to customer behavior, pain points, needs, preferences, and more. Businesses can create more targeted, relevant information from those findings to satisfy customers and increase conversions. 

Customer insights AI can help with: 

Sentiment analysis

AI tools can analyze large data sets of customer feedback for overall sentiment. 

Companies could gather this data through: 

• Surveys

• A/B testing

• Social media comments

• Call center notes

• Chatbot conversations 

AI algorithms can analyze sentiment with varying levels of accuracy to detect overall satisfaction and highlight common issues to address. 

Demographic analysis

AI algorithms can analyze large sets of data to tease out customers’ key attributes. 

This demographic information can help teams understand their customers and deliver more relevant products and experiences. 

Customer segmentation

In addition to analyzing customer demographics, AI tools can segment customers by their attributes. 

This helps teams group customer experiences, ensuring companies can better serve customers throughout their journey. 

Personalization

AI tools have the power to leverage certain insights by analyzing behaviors and data. This means it’s increasingly possible to create tailored experiences for customers.

Companies may adapt experiences with: 

• Unique product recommendations

• A personalized email sequence

• A specific customer journey to increase customer satisfaction and boost conversions

The ever-growing need to provide better customer experiences is driving interest in AI for customer insights. 

In our increasingly on-demand world, customer expectations are rapidly increasing. Delivering excellent customer experience is more important than ever before. 

Customers are no longer surprised by personalization––they expect it. 

Research found that 74% of customers are frustrated if website content isn’t personalized. Unsurprisingly, personalized recommendations in shopping carts influence 92% of shoppers. 

Customers also want products and services delivered faster than ever before––whether online or in the real world. 

A January 2020 survey found that fast, free shipping was the main driver for Amazon shoppers. 

In the digital world, customers are increasingly impatient: 41% of customers believe slow and unresponsive service is bad customer service.

Customer insights AI may help organizations deliver on their promise of a positive customer experience. 

With deep data analysis, AI may be able to power offerings like personalization, segmenting, and boosted UX to provide customers with a more relevant experience. 

More companies are using AI to gain customer insights to boost decision-making and offerings. 

While you should apply caution, AI is proving promising in several ways, including: 

Data-driven decision-making

Through deep analysis of large data sets, AI algorithms can analyze customer feedback, sentiment, behaviors, and drivers to improve decision-making across the business. 

Real-time insights

Some AI tools can process and analyze real-time data. This is a huge advantage for stakeholders, allowing them to gain insights and act on them quickly. 

For example, an AI tool could notify the team of large drop-off rates at the shopping cart. This would quicken the response time to fix the bug, with a prompt resolution avoiding loss of sales. 

Predictive analysis

Making accurate predictions is one of the most challenging aspects for businesses. Knowing what customers will want and need in the future and acting on those insights is invaluable. 

AI tools can quickly analyze large data sets, so they may be able to make better predictions as they develop. This could ensure that companies jump on trends and improve their decisions for the future. 

AI is rapidly developing. While there are advantages for the customer landscape, potential downfalls also exist.

Potential benefits of customer insights AI 

Maintaining competitivity

Leaning into AI tools can hasten the data collection and analysis processes, helping teams: 

• Uncover insights much faster than before

• Make better forecasts

• Drive data-led decision-making across the organization

This can be invaluable in helping a business uncover untapped opportunities to stay competitive in its marketplace.

Scalability

Using customer data is essential for providing customer-centric products and services. But scaling that process without AI is near impossible. 

Analyzing large sets of data, making accurate predictions, and providing real-time insights are only possible through the power of AI. 

Teams may be able to use AI to make the most of insights and scale them across the business for fast action. 

Potential downsides of customer insights AI

Data quality

AI tools rely on high-quality data inputs. Data with error or bias will skew results, rendering insights unreliable.

Training

AI tools rely on machine learning algorithms. If companies don't sufficiently establish or train the algorithm, it may provide problematic or even dangerous information. 

AI is in its infancy, so many tools are currently unreliable for decision-making.

Lack of nuance

Human emotion and behavior are complex elements, and context is key to a deep understanding in data analysis.

AI tools lack human discernment and empathy, limiting their scope. 

Humans are still very much required in the customer insights process to consider context, nuance, and the quality of findings.

Costs 

The cost of implementing AI technologies may be prohibitive for some teams. 

You can perform several AI activities to gain essential customer insights. These can drive change across the business and benefit customers. 

Some insight-gathering methods include: 

Chatbots and virtual assistants

AI-powered chatbots and virtual assistants can interact with customers as they navigate products––whether on a website, app, or software. 

Chatbots can assist customers as needed and gain core feedback when issues arise. This data can be essential for business improvements. 

Natural language processing (NLP)

AI-powered NLP techniques can analyze feedback from various sources and extract the most important aspects. This helps with sentiment analysis and segmenting.  

Social media monitoring

Tools can monitor, collect, and analyze data to provide insights from social media.

A/B testing

AI tools can perform A/B testing to gain feedback about customer preferences and behaviors. This ensures companies can gradually optimize and improve products over time.

Machine learning

Companies can implement machine learning to gather insights in many ways. Machine learning can analyze data sets to identify patterns and trends. From there, it can make predictions and segment customers. 

Some people tout AI tools as ‘fix-all’ products, but AI is very much still developing. 

Many algorithms may produce unreliable results. Therefore, it's often best to gradually implement AI and look out for issues before a wider rollout.

Some essential steps for using AI for customer insights include: 

Start with clear objectives

An AI tool can only solve problems if you start with clear, measurable objectives. 

Choose the right AI tools

The right AI tools should relate to your project goals and provide the specific insights your team requires. 

Before choosing a solution, research it first. Ensure it suits your needs before implementation across the business. 

Also, consider how the tool’s algorithm makes its calculations to ensure it aligns with how you analyze data. 

Keep data quality high

Outputs are only as good as their inputs. If you use problematic data, you’ll get low-quality results. 

Before inputting data into any AI tool: 

• Clean the data

• Remove any outliers

• Consider and rectify any potential bias

Segment customers

Your customers will think and feel differently about your products or services. As they're from different demographics, their behavior and preferences will vary. 

Segmenting your customers by key characteristics can help you deliver a personalized experience, boosting satisfaction. 

Use AI to present findings

AI can turn a series of data points into a helpful story that drives change across the business. 

Use graphs and colors and highlight notable sections for broader stakeholder understanding. This ensures all team members can digest and act upon the findings.

Continuously train AI

Machine learning algorithms require constant training to provide high-quality results. 

It’s important to recognize that AI tools can provide inaccurate, inconsistent, and biased information. 

Continuous training will gradually improve the results, increasing their reliability. 

Test and optimize

For the best results, make small changes and test again. 

This ensures you continuously listen to and act on feedback from customers and stakeholders.

Apply caution

AI tools are largely in their infancy. While they can power business improvements, human discernment must stay at the forefront. 

AI tools can assist in projects but cannot replace skilled team members.

As AI tools radically revolutionize the customer feedback analysis process, great opportunities are on the horizon for organizations. 

• AI may help teams deliver on the promise of great customer experiences.

• AI tools can boost customer insights, leading to widespread business improvements. 

• Continuous machine learning may make AI even more valuable over time.

You should apply caution, though. 

AI is progressively developing—it is not foolproof. Without human discernment, there are potential pitfalls. Skilled team members need to manage these tools, assess outcomes, and apply empathy, nuance, and creativity. 

In time, it’s likely that AI will radically transform customer insights to deliver better, more relevant experiences to those who matter most.

Companies discover customer insights by analyzing customer data. 

They can source this data from:

• Surveys

• Focus groups

• Usability testing

• Social media comments

• Call center notes

• Chatbot conversations

Examples of customer insights include: 

• Customer segmentation

• Churn analytics

• Customer satisfaction statistics

• Customer journey mapping

• Social media sentiment

• Purchase behavior

• Competitor analysis

Uncovering these insights can drive change across a business, boosting decision-making accuracy and helping teams optimize their products for customers. 

As AI develops, companies are gradually using it to boost customer experience. 

Algorithms and machine learning speed up the data collection and analysis process. Additionally, AI may improve data accuracy.

AI's analysis of big data can:

• Increase personalization

• Provide virtual support to customers

• Lead voice assistance

• Detect fraud for customer safety

• Streamline the entire customer journey

As AI improves, it may help teams better understand customers' wants, needs, and behavior. With this improved knowledge, companies can provide better, more relevant customer experiences.