Using modern technology, it's now easy to transform raw customer data into practical insights that can help your business and contact center improve customer experience outcomes. 

You can experience better customer engagement, increased sales and revenue, and long-term customer retention. 

But what specific kind of technology do you need, what's the best tool for you, and how can you apply it to achieve these results?

Let's explore customer intelligence, what it means, and how the right tool can transform your business or contact center operations. 

What is Customer Intelligence?

In a contact center setup, customer intelligence (CI) is the use of data analytics and artificial intelligence to collect, sort, and analyze customer data from agent-customer interactions to improve the customer experience through personalized engagement.

After analyzing customer data, particularly from agent-customer interactions, you can gain actionable insights that help personalize customer interactions and relationships, ultimately enhancing the customer experience and driving business growth.

We’ll discuss the benefits and key elements of customer intelligence shortly.

Customer Intelligence vs. Business Intelligence: Key Differences

Let’s start with the differences between customer intelligence and business intelligence (BI):

Quick Note: Contact center customer intelligence tools turn agent-customer conversations into actionable insights, which are typically referred to as “business insights or business intelligence”. 

For example, you can gain revenue insights on upselling or cross-selling opportunities by analyzing text, email, or voice conversations.

Benefits of Customer Intelligence

As mentioned, customer intelligence ultimately drives business growth. The practice delivers connected advantages that lead to this main benefit. 

These can include:

• Personalized Interactions: Contact center agents use customer intelligence to tailor support, offers, and recommendations to specific customers. As a CX leader, you can base targeted personalization on a customer's buying history, past service issues, needs, preferences, and interactions with their agents. 
• Improved Customer Experience: You can foster better customer engagement and experiences once you understand customers’ pain points through conversation analysis. The insights from these analyses help you fine-tune your processes and close experience gaps, leading to higher customer satisfaction and loyalty. 
• Improved Agent Empowerment: Contact center leaders use insights from conversation analytics to deliver targeted coaching to their agents. You can train them to resolve issues more efficiently and cultivate better relationships with customers. 
• Data-Driven Decision-Making: Customer intelligence analytics explain the “why” behind certain customer behavior and agent performance. You can use these insights to make better decisions about customer service, marketing, sales, and overall business strategy. 

Core Components of Customer Intelligence

For the benefits to be realised, your customer intelligence process must encompass several key components. 

The most critical ones include:

• Data Collection and Integration: You must gather data from every customer interaction touchpoint across channels such as CRMs, emails, chat, social media, web analytics, and phone calls. For a contact center, your main considerations will be email, text, and voice interactions. Next, consolidate the data into a unified, centralized platform for profiling and analysis. 
• Data Analysis and Insights Generation: Use a contact center conversation intelligence platform that supports techniques such as sentiment analysis and predictive customer analytics to analyze the data and uncover insights. 
• Insights Visualization and Reporting: Conversation intelligence platforms typically present the insights in the form of visualizations, dashboards, or reports. These presentations make the insights easy to study and understand for decision-makers. 
• Actionable Applications: Use the insights you gain from the conversation analyses to personalize customer experiences, optimize your operations and agent performance, and make strategic decisions.

Types of Customer Intelligence Data

A clear understanding of various data types for customer intelligence makes it easy to apply them for actionable insights. 

Consider data types such as:

1. Interaction Data

Interaction data includes information collected from customer interactions that happen across various text, voice, and email channels. 

The data can include email exchanges, support tickets, chat transcripts, and call logs.

Interaction data helps improve agent performance and customer interactions once you address common issues like poor agent communication skills or difficult customers. 

2. Sentiment Data

Conversation intelligence software can review your customers' emotions and sentiments during engagements with your agents and brand. 

Sentiment data shows whether your customers are satisfied and how they perceive your business, enabling you to address negative sentiment early and prevent churn. 

3. Behavioral Data

With behavioral data, you extensively cover how customers interact with your business. 

Besides assessing their interactions with your call or contact center, you can also track their activity on your website, app, or social media. 

Behavioral data makes it easy to understand customer behavior, which can inform your agent performance optimization strategies. 

Pro Tip: While these three data types are related to customer engagement, you don't have to limit your contact center data to them only. 

You can leverage other types of data, including:

4. Demographic Data

Demographic data captures the identifying characteristics of your customer base, such as gender, age, geographic location, income, and education. 

You can use this data to segment and group your customers, making it easy to keep track of the segments or groups that typically interact with your customer service agents. 

5. Psychographic Data

Your customers' lifestyle choices, opinions, hobbies, attitudes, and interests fall under psychographic data. 

You can use the data to train your agents to engage customers in personalized ways that resonate with customer interests, preferences, and values. 

6. Feedback Data

Feedback systems like reviews, customer surveys, ratings, Net Promoter Scores, and Customer Satisfaction Scores can provide data that directly relates to and stems from customers' experiences. 

Such feedback data offers practical insights that can help improve your services, products, agent performance, and overall customer experience. 

Note: The key is not to limit yourself to data that's only directly related to customer service. You'll want to integrate it with cross-departmental data from sales, finance, and marketing teams to gain a more holistic view of the customer beyond contact center interactions.

Our Customer Intelligence Software 

All these types of data require analyzing through customer intelligence tools. Since there are plenty of solutions available, you may be spoiled for choice. 

In this section, we'll discuss Hear as the customer intelligence solution for customer experience leads and contact centers. 

Here's what you can do with Hear:

• Analyze 100% of the customer interactions across email, voice, and chat at scale to obtain extensive data for analysis. 
• Detect customer sentiment that indicates the likelihood of customer churn. 
• Uncover actionable business insights at scale through conversation analysis, including upselling and cross-selling opportunities for increased revenue. 
• Obtain clear reports through interactive customer intelligence dashboards that provide unlimited visibility into customer sentiment and agent performance.
• Flag compliance risks associated with agent mistakes, incomplete data, or inconsistent monitoring. 
• Deliver better customer experience through enhanced agent performance and personalized customer service. 
• Boost operational efficiency by reducing average handling time and increasing first-call resolution rates. 
• Surface product feedback directly from customer conversations. 

With these and other capabilities and benefits, Hear stands out as an advanced customer intelligence platform for CX-focused teams in sectors such as insurance, finance, telecom, and e-commerce. 

Uncover actionable insights from every agent-customer conversation — schedule a personalized demo today.

How to Implement a Customer Intelligence Strategy

Once you have the right customer intelligence software, it should be easy to formulate and implement a reliable strategy. 

Here's what to do:

• Define Goals and Objectives: Determine what you want to achieve with customer intelligence. For example, you may be looking to increase customer loyalty, uncover new revenue streams, reduce churn, or enhance the customer experience. 
• Gather and Integrate Customer Data: Collect relevant data from all your voice, email, and chat interactions and consolidate it into your Customer Data Platform (CDP). 
• Use Customer Intelligence Software: Apply AI customer intelligence tools for speech analytics, trend identification, sentiment analysis, predictive analytics, and surfacing insights. 
• Act on Insights: Use the insights to personalize interactions, optimize customer journeys, enhance agent performance, and identify business opportunities in the form of upsell and cross-sell chances. 
• Cultivate a Customer-Centric Culture: Promote a customer-first mindset amongst your team members by ensuring they prioritize customer satisfaction.

Challenges and Solutions in Customer Intelligence

The customer intelligence journey isn't without challenges. 

You can expect the following issues:

• Data Management and Integration Problems: Obtaining and handling vast amounts of data from multiple channels can be overwhelming. You may find it difficult to extract valuable insights. 
• Data Privacy and Security Issues: Contact centers deal with sensitive customer information, which can be difficult to protect from breaches, leaks, or compliance violations. 
• Agent Training: Upskilling agents to personalize interactions, work with advanced customer intelligence technology, and manage difficult customers can be tricky and costly. 

You can apply different solutions to deal with these and other challenges. Consider the following: 

• Using cloud-based customer intelligence platforms that rely on artificial intelligence for conversation analysis. 
• Implementing robust agent training programs to reskill and upskill agents as well as increase their confidence in new techniques and technologies. 
• Investing in omnichannel solutions that offer unified data and consistent customer experiences across multiple channels to gain better insights. 
• Establishing clear guidelines for the use of AI, including being transparent about AI usage, recording conversations, and analyzing them for insights.

Frequently Asked Questions (FAQs)

We'll wrap things up with quick answers to questions people usually ask about customer intelligence:

Is Customer Intelligence the Same As Market Research?

Customer intelligence is different from market research. 

Market research is a specific process that collects data about specific customer segments or the market as a whole to inform strategic business decisions. 

Customer intelligence is a broader and more continuous process of understanding individual customers to personalize messaging, customer experiences, and business strategies. 

The two processes work together, as market research provides the foundational data that supports customer intelligence. 

Market research and customer intelligence combine to provide a more comprehensive picture, enabling businesses to develop more effective marketing strategies. 

What is the Relationship Between Customer Intelligence and Predictive Analytics?

Customer intelligence and predictive analytics are complementary processes that work together to enhance decision-making and various business operations. 

In a contact center, customer intelligence provides the raw data and understanding of customer behavior. 

Predictive analytics uses this intelligence to forecast and project future customer behavior, needs, and potential challenges. 

Predictive analytics transforms the insights from customer intelligence into actionable forecasts and projections and proactive solutions that support personalized service, improved efficiency, and better customer retention.

How Do Companies Measure the ROI of Customer Intelligence?

You can measure the ROI of customer intelligence at your company using predefined KPIs, quantified costs and benefits, and the formula: ROI = (Benefits - Costs) ÷ Costs x 100.

After establishing your customer intelligence objectives, you can define key performance indicators (KPIs) such as Net Promoter Score, churn rate, and Customer Retention Rate. 

Next, calculate the costs and benefits related to customer intelligence. These can include software, training, and personnel fees. 

Quantify benefits such as increased revenue, reduced customer service costs, improved operational efficiency, and risk reduction. 

Apply the basic ROI formula. For example, if your customer intelligence initiatives generated an annual gain of $30,000 from an investment of $10,000, the ROI is:

ROI = (Total Benefits - Total Costs) ÷ Total Costs x 100

ROI = ($35,000 - $1,0000) ÷ $10,000 x 100 = 250%

Conclusion

Adopting a robust customer intelligence platform at your contact center can be a gateway to reduced churn rate, increased customer retention, and improved customer satisfaction. 

To achieve these benefits, the platform must have capabilities such as predictive customer analytics, sentiment analysis, and intuitive reporting. 

With Hear, you can apply these capabilities to analyze 100% of your agent-customer interactions — via voice, email, and chat — to gain insights for improving customer service alongside other business operations. 

See how customer intelligence can transform your CX outcomes — get started with Hear today.