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What is customer intelligence, and why do you need it?

Last updated

19 September 2023


Dovetail Editorial Team

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In the consumer economy where sales cycles are longer and decision-making is paramount to success, staying connected with customers will enable you to get ahead of your competition.

Customers want an exceptional experience. When a brand doesn’t deliver it, they opt for another that does. Customer intelligence (CI) is invaluable for this reason. It enables a company to stay ahead of the competition and have a 360-degree view of its customers.

Learn about customer intelligence and start uncovering actionable insights that will help grow your business.

What is customer intelligence?

As part of business intelligence (BI), CI refers to collecting large amounts of detailed customer data and processing it to get actionable insights. It’s one of the most powerful sources of insights and is used by many companies.

Businesses can use the insights to develop personalized selling strategies to reach their target audience and interact with customers better. They gain a more in-depth view of customers’ demographic information, interests, needs, likes, and dislikes.

Other uses of CI include driving future business developments and creating marketing strategies that resonate with customers.

Types of customer intelligence

Data that makes up customer intelligence is collected from various internal and external sources, including website activity, purchase reviews, surveys, and one-on-one interviews.

This data is further classified into the four types below:

Behavioral data

Behavioral data is the information gathered on how customers interact with a brand. It shows their browsing or purchasing behavior.

Behavioral data can be collected using heat maps, cookies, surveys, social media engagement, website visits, or live chats.

Analyzing behavioral data reveals your customers’ preferred touchpoints. This is particularly useful for customer support and sales teams. It helps them track the time customers spend at touchpoints, suggesting ways to eliminate obstacles and pain points.

Psychographic data

Psychographics refers to a customer’s motivations for doing something. Psychographic data is information about customers’ lifestyles, attitudes, values, and personality types.

Businesses can segment customers into groups based on psychographic data. Marketing teams can then tailor campaigns to different customer segments depending on their lifestyles and preferences.

Demographic data

Customer demographics include things like gender, marital status, profession, education status, age, income bracket, and geographic location. Demographic data helps companies understand their customer base and tailor targeted campaigns.

Transactional data

This is one of the easiest types of customer data to track. You simply need to dig deeper into the data you have already collected. Think about your customers’ past purchases, wishlist items, items added to the cart but not purchased, devices used, and payment methods.

Transactional data is useful for enhancing customer experience and reducing transactional failures.

The benefits of customer intelligence done right

Customer intelligence is at the core of exceptional customer experience. It’s crucial for driving business growth and keeping your brand health in check.

Businesses also need CI to understand customers on a deeper level—for example, their motivations behind certain behaviors.

When you understand your customers, you can segment them into groups and tailor personalized customer experiences. You are better positioned to craft a selling strategy that strongly resonates with all your different customers. In turn, customers feel valued and appreciated. As a result, they are more likely to become repeat customers or brand promoters, aiding your customer acquisition and retention efforts.

Businesses can reap numerous benefits from customer intelligence, including the following:

Lower customer churn rates

Customer intelligence allows a business to understand what makes customers leave. With these insights, businesses can take steps to avoid problems, improve products, services, and interactions, and provide a satisfying experience.

Increased customer loyalty

CI shows businesses what their customers need and prefer, enabling them to provide a high-quality customer experience. Happy customers are less likely to look for an alternative product or service. Instead, they may stay with the company for many years, accepting upsell and cross-sell offers and possibly becoming brand promoters.

More data-driven decision making

CI drives decision-making, empowering business leaders and stakeholders to make strategic choices based on facts rather than intuition.

The building blocks of successful customer intelligence

The four major building blocks needed for an effective customer intelligence strategy are:

Tools and technology

To effectively collect customer data, you need the tools and technological infrastructure. Tools enable you to glean insights from customer interactions. For instance, sentiment analysis tools will help you uncover how customers feel about your business.

Companies embracing AI technologies are already setting themselves apart from their competitors and delivering exceptional customer experience.

Various regulations surround data privacy and confidentiality, so it’s important to consider legal and compliance issues. Ensure regulations are followed to avoid violations, penalties, and mistrust.

Different types of data

CI involves collecting and analyzing different types of data, including behavioral, demographic, psychographic, and transactional data. This enables the business to classify data for easier analysis, which simplifies maintaining data confidentiality and integrity.

KPIs and metrics

Key performance indicators (KPIs) are vital for measuring how well a business meets its customers’ needs and expectations. Metrics such as customer satisfaction rate, business churn rate, and customer lifetime value provide the specific data points that underpin KPIs. KPIs are derived from these metrics and are crucial for evaluating and enhancing the customer experience and overall business performance.

Customer intelligence processes

The steps below can inspire your CI collection process:

1. Collect data

The first step is to gather customer data through the following channels:

Customer satisfaction surveys

A customer satisfaction survey can determine how happy your customers are with your business’s products or services. The insights obtained can reveal what your customers like and dislike.

You can use a customer satisfaction survey to ask targeted questions when you want to collect specific information and gain a deeper understanding of whether or not you are meeting your customers’ expectations.

Call recordings and transcripts

Call centers collect and retain historical data about past interactions with customers. The recordings and call transcripts can help uncover what your customers are frequently reaching out about. Through this, you can understand what customers are struggling with and what their needs and preferences are.

Customer reviews

Reviews are a treasure trove of customer intelligence. Businesses can read what their customers are saying about the brand and identify pain points. With these insights, brands can fine-tune their offerings to align with what their customers need and prefer.

An integrated CRM

Customer relationship management (CRM) comprises customer profiles, sales calls, and emails. Investing in an integrated CRM platform is important, as you can use it to monitor customer interactions and obtain valuable reports.

Sales teams can use a CRM platform to improve their performance and interactions. The platform can determine customer engagement levels and identify frequent customers and top-performing products.

An integrated CRM can also enable businesses to identify the most effective communication channels.

2. Categorize data

Once collected, the second step is to divide behavioral, psychographic, demographic, and transactional data into three categories:

  1. Direct: collected from customers via questionnaires, complaints, and surveys

  2. Indirect: collected from third parties like social media platforms

  3. Inferred: collected from cookies and purchase histories

Categorizing data enables businesses to identify the types they want to analyze and better understand.

3. Analyze data

Next, you need to analyze the data rigorously to pull insights and get a better understanding of your customer base.

Data analysis is used to identify patterns and correlations in data. Statistical analysis, data mining, and machine learning (ML) are some of the techniques you can apply.

A customer intelligence platform can sort and analyze customer data swiftly in real time.

4. Share insights

Once insights have been pulled from the raw data, they need to be shared with other departments in the company.

Shared insights empower teams to make improvements. Teams like marketing, customer service, and sales can use the data to tailor selling strategies and campaigns.

Customer intelligence examples

Here are four examples of customer intelligence in the real world:

Example 1: Enhanced product recommendations

Imagine a scenario where a customer is shopping online for clothing. CI can analyze their browsing and purchasing history and identify preferences such as color, style, and size. This information can be used to provide highly personalized product recommendations.

By leveraging CI, businesses can suggest items that align with the customer’s unique tastes and preferences. This ultimately increases the likelihood of a successful sale and enhances the overall shopping experience.

Example 2: Social media sentiment analysis

In the age of social media, customer opinions and sentiments can greatly impact a brand’s reputation. CI tools can monitor and analyze social media mentions, comments, and reviews to gauge public sentiment. This real-time insight allows companies to promptly respond to customer feedback, address concerns, and leverage positive sentiment for marketing campaigns.

Social media sentiment analysis also provides valuable data for product improvements and competitive analysis.

Example 3: Customer segmentation

Customer segmentation involves dividing customers into groups based on similar characteristics. The technique allows businesses to understand customer preferences and build buying personas that speak to each customer.

Behavioral segmentation, for instance, classifies users who make the same purchases. It also helps engage them more effectively, since content and marketing campaigns resonate with their preferences.

Example 4: Customer relationship management

CRM platforms consolidate customer data that the business can easily access.

When CRM is integrated with customer intelligence, the business can predict a customer’s future buying behavior. For instance, if the customer’s saved details are known, the business can easily predict products that will appeal to them.

In addition, using a CRM system speeds up customer support as businesses are more familiar with what customers require.

Customer intelligence software

Having the right software is key to getting the most out of customer intelligence. CI software is a system that allows businesses to monitor and understand customer needs in real time. It automatically collects customer data from various sources and turns it into actionable insights.

Here are a few examples of CI software that is available on the market:

1. Service Hub (by HubSpot)

HubSpot’s customer service software is called Service Hub. It’s a powerful tool that allows businesses to have personalized interactions with customers and provide them with tailored content. 

Popular features include the live chat function, omnichannel messaging, a customer portal, and a knowledge base.

2. Signal

Signal is a lightweight intelligence platform that allows businesses to collect real-time data from across all touchpoints, including device context, user IDs, and behavioral data. Signal also gathers CI data from every touchpoint with its Tag Management solution.

3. Intercom

Intercom allows businesses to easily track customers and segment them into groups at a more defined level. It also enables businesses to review base customer profiles and track interactions while gaining insights that will improve customer satisfaction rates.

4. Asseco

Asseco customer intelligence is a widely recognized leader in customer analytics. It analyzes customer profiles and uses self-learning algorithms to make recommendations in real time. Its open architecture means it can be integrated with any system.

5. Clarabridge

Clarabridge provides a customer intelligence platform that allows businesses to understand customers’ emotions and intent. Clarabridge analytics go beyond natural language processing to discover the emotions behind customer sentiments.

6. Dovetail

Dovetail is a customer insights platform that provides access to real-time business analytics. It allows businesses to turn their raw customer data into actionable insights.


What’s the role of customer intelligence?

Customer intelligence allows businesses to understand how customers interact with them and use their products or services. With insights gathered from customer intelligence, businesses can tailor their offerings to appeal to different types of customers.

How do you develop customer intelligence?

CI is developed by collecting and categorizing customer data for analysis. The analyzed data can then be used to make informed business decisions that positively impact things like ROI, marketing campaigns, and loyalty programs.

What is a customer intelligence platform?

A CI platform collects detailed information about customers and unifies it for effective analysis.

What’s the difference between customer intelligence and customer analytics?

CI is collecting and analyzing customer data to get actionable insights into their needs and expectations. For instance, your business might collect, analyze, and interpret information about customers.

Customer analytics, on the other hand, is the process of examining customer behavior data accumulated across various departments to help companies make business decisions via market segmentation and predictive analytics.

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