How to Achieve Business Success with Customer Intelligence

Customer Intelligence (CI) is a crucial aspect of generating sales. Without it, it’s difficult to understand the expectations of customers from your brand and which message will force them to make decisions.

Fortunately, it doesn’t have to be challenging to acquire customer intelligence. In this blog, let’s explore how businesses are leveraging customer intelligence for personalized experiences.

What do you mean by customer intelligence?

Customer intelligence is the process of collecting and processing data associated with your clients to understand their requirements and behavior and provide better modes of communication.

The origins of CI data are social media interactions, purchase history, client reviews, and so on. Businesses can monitor these client interactions through data management platforms (DMP) and customer relationship management (CRM).

STP marketing for new customer acquisition

What is the Objective of Customer Intelligence?

The objective of customer intelligence is to obtain useful information that can be utilized properly to enhance customer engagement and experience.

It integrates all data associated with an audience and implements data science and analytics to answer the challenging questions that assist you in understanding the needs of prospects. 

McKinsey Global Institute reports that companies that follow a data-driven approach have a 23 times higher possibility of getting new customers than those who don’t use CI. 

Also, it allows you to personalize such customer analytics for the different aspects of service, marketing, sales, and other departments to visualize relationships, networks, and hierarchies in ways that match their needs.

A customer intelligence platform (CIP) is an advanced level of customer data management.

It uses both ML and AI to analyze, resolve, and inspect unstructured and structured data across the business, such as:

  1. Matching fresh record types and data entities
  2. Adding derived or inferred metrics including journey, engagement, and sentiment value 
  3. Enabling a variety of audiences to carry out complicated analyses through a simple interface
  4. Producing several real-time distinct audience views

What is the Difference between Business Intelligence (BI) and Customer Intelligence (CI)?

Businesses can learn more about their audiences via customer intelligence, whereas they can learn more about themselves via business intelligence.

Customer intelligence involves data that reveals client behaviors, preferences, identities, and needs.

Later, businesses can use this data to improve support strategies, marketing, and sales. 

Business intelligence is a set of information concerning a business’s activities such as sales, client service, finance, and marketing.

Business professionals usually produce actionable insights with the help of this data to track performance, optimize workflows, and make smarter decisions.

Types of Customer Intelligence

Following are the frequently seen types of customer intelligence in marketing:

  1. Transactional data
  2. Behavioral data
  3. Psychographic data
  4. Demographic data
  5. Attitudinal data
Transactional data

This is related to customer transactions or sales records like what type of products they have bought, when they bought them, promotional offers, and how much they paid for them.

Businesses can use such data to monitor purchase behavior gradually and spot trends and preferences.

Behavioral data

It can be extracted from every point of contact a consumer has with you, including their actions on your website, social media interactions, email engagement, and customer support.

For products, this involves in-app behavior like issues and troubleshooting, usage statistics, feedback, and onboarding.

Psychographic data

Your client’s personality can be highlighted by this data. This can be collected from surveys, daily activities, customer settings, and so on.

Demographic data

Let’s know who your audiences are for effortless segmentation. It involves location, sex, education, marital status, income level, profession, and age.

Attitudinal data

It offers useful insights into the opinions of visitors regarding particular services, products, or experiences.

Win new customers with customer journey mapping

How is Customer Intelligence Analytics Used?

Let’s dig deeper into 3 different ways organizations use customer intelligence analytics to produce better results:

Cross-selling: Maintain a database of past orders placed by consumers to spot opportunities to sell products. 

Price optimization: To improve revenue or sales, employ CI analytics to determine which features are crucial to each client and how much they are willing to spend. 

Knowledge-oriented materials: By tracking the most-selling products and materials from the knowledge base that consumers frequently use, you can discover current resource gaps or decide which articles to improve.

Sources of Customer Intelligence Data

According to the Allied Market research, audience information collection and management is the key application of the CIP regional market in North America.

Various methods are available for gathering client data across different channels. They involve:

Audience feedback

Direct opinions from audiences can be obtained in various ways:

  1. Surveys that can be conducted in person, online, or via email
  2. Customer satisfaction ratings are requested by companies on feedback forms
  3. Tracking online feedback and ratings on G2, Google Reviews, and so on
  4. Social media provides more comprehensive insights into audience sentiments and perceptions of the brand
  5. Interviews in case you are more interested in “one-to-one” interactions
Customer communications and behavior

Inspecting audience behavior including social media activity, transaction history, and website traffic can offer much-needed insights related to their preferences and buying patterns.

Organizations get CI data from their interactions with clients. These dealings are not the same and involve:

a) Customer support calls
b) Chat logs
c) Social media activities
d) Call transcripts and recordings
e) Sales conversations

CRM Systems

CRM systems integrate client data such as communication history, past purchase details, and contact information.

Each audience’s interests and preferences are easily available for you to access and inspect to develop long-lasting relationships. 

Customer Intelligence Systems 

Brands use consumer intelligence platforms along with AI to make the customer data analysis process simpler and develop detailed profiles of customers by understanding their preferences. 

Financial data

Identifying the financial background of your potential clients is one of the key data resources.

The Benefits of Customer Intelligence in Marketing

VP of enterprise products for Market Tools, Mr. Justin Schuster, claims that marketing professionals who measure the value of their customer intelligence report that it enhances campaign-oriented KPIs along with customer acquisition and retention. 

The benefits of customer intelligence in marketing are as follows:

  1. Increases the reliability of your content and message
  2. Introducing services and goods that your clients genuinely want
  3. Powerful customer connections via customized communications
  4. Reduces client churn by addressing their pain points
  5. Helps in staying ahead of competitors who don’t use customer intelligence analytics
  6. Reduces marketing budget by telling when and how to allocate expenses and time

Frequently Seen Challenges of Customer Intelligence

Let’s see why it’s important to know the major challenges that come with the implementation of customer intelligence solutions:

Siloed Data

Today, the majority of B2B organizations are relying on various communication channels to communicate with their audiences.

This can involve Zoom video calls, telephonic conversations, ticketing systems, email communications, and in-app chatbots. Can this fragmented data be digested by your customer intelligence solution?

Multiple Xs and Ys

In business-client interactions, there are frequently multiple blind spots. You can have numerous essential points in a single account, along with 12 internal personas involved with them at specific times.

Monitoring such Xs and Ys regularly is challenging.

Too many at one

One central point interacting with an account containing numerous stakeholders has complete visibility.

But this central point can’t monitor everything and can’t regularly share crucial information with various people.

In simple words, all the data belongs to one individual, and others don’t know what’s happening.   

Data privacy

Maintaining a balance between collecting useful insights and respecting audience data privacy in customer intelligence is a challenge that needs careful attention.  

Regulations such as CCPA and GDPR support the protection of data; companies must ensure that they don’t misuse data and maintain transparency in how audience information is used.

Failing to do so can result in a loss of reputation and legal consequences.

Apply customer segmentation to acquire new customers

Typical Mistakes to Avoid When Collecting Customer Intelligence

Focusing primarily on quantitative data

Most of the time, companies focus mainly on quantitative data i.e., numbers about customer analytics and data management.

It simply shows what your audience is experiencing when they communicate with you. 

For example, if you have conducted a survey and noticed that a customer has rated a 4 out of 5, still, you missed a perfect score, why? Numbers are not enough to describe this.

Hence, it’s an important factor to consider for powerful decision-making. 

Simply relying on survey replies

When you say customer intelligence data, it doesn’t mean this involves only information associated with survey replies.

The truth is that such data is not easy to collect, and many businesses don’t conduct surveys due to a shortage of time. 

Not acting upon insights  

Soon after collecting customer data, you must take action. Many businesses put more effort into collecting survey replies from audiences and then storing them in a folder somewhere.

If your organization does so, it’s a waste of time and effort, and it results in a low customer experience because audiences are now waiting to hear any improvement from your organization or some type of follow-up since they spent time filling out the survey form.     

Use Cases of Customer Intelligence Analytics

Customer intelligence analytics relates to different use cases, such as:

Customized marketing

The secret to customized marketing is segmentation. Disclose the special characteristics and requirements of all segments so you can customize marketing plans and offerings.  

Customer journey mapping

CI analytics reviews different data points throughout the journey lifecycle including social media campaigns, browsing habits, and purchasing history. 

Organizations can obtain extensive knowledge of what encourages customers to make purchases.

Afterward, you can utilize that data to produce more focused marketing strategies, which will satisfy clients and increase conversion rates. 

Churn detection

Sometimes, consumers aren’t happy with your services or products and might turn to competitors to make purchases.

By looking at their browsing habits and monitoring engagement levels, companies can find consumers who are willing to leave and take appropriate actions to retain them. 

User flow modeling

The path a user takes on a mobile application or a website to finish a task is called the user flow.

Organizations can track all movements of users via their journey using customer intelligence analysis, which allows organizations to model user flows on-site and find possibilities to improve user flows.  

This will enable organizations to make changes to the application interface so that customers don’t face any difficulty in browsing and availing of services.

Use behavioral modeling to acquire new customers

5 Ways to Build Customer Intelligence Strategies for Business

Building or implementing customer intelligence strategies for business include the following steps:

Define goals

Businesses need to define clear goals before they start data collection. They need to be clear about what results they want to achieve. 

Determine data sources

Identify related data origins to gather information associated with customers.  

Data integration

Combine the gathered data from a variety of sources into a central depository. This allows a unified view of client information and enables analysis. 

Implementation of technology

Implement relevant technology solutions including data analytics tools, automation platforms, and CRM systems to support the collection of data, analysis, and smart decision-making. 

Constant monitoring and revision

Continuously observe and filter CI solutions according to the needs of business and client behavior.

Review and modify methods related to data collection, analysis concepts, and technology tools consistently.

How Customer Intelligence is Transforming Retail

Let’s see how retailers can offer excellent service to their clients using customer intelligence:

Customized discounts

Companies use retail customer intelligence to reward frequently visiting consumers for their loyalty.

They use sensors installed throughout the store for this. The job of these sensors is to send customized discounts to the smartphones of such consumers through app notifications or SMS when they come close to a specific product. 

The only condition to avail of this reward is to ensure that consumers have selected themselves in the loyalty program. Additionally, retail companies use their website for online-based offline sales.

They use different tools like Hotjar to monitor what types of products are browsed by their customers on their website, and later, once they visit the store, retailers send them customized discounts for similar or the same products. 

Identify clients more perfectly

Retailers use electronic devices to capture data anonymously and analyze it to identify the browsing habits of clients. They use such information to produce client profiles and study client behaviors.

This data plays a crucial role in enhancing the area of in-store item displays and enhancing the accuracy of marketing strategies and communications in areas that have higher traffic to boost point-of-purchase influence. 

Attracting consumers to the store

According to the research conducted by Statista, in 2022, five million people used the mobile internet. Over 60% of worldwide users use the internet via mobile devices to go online. 

This study reveals that they always have a location, Wi-Fi, and data turned on. This provides a chance to send customized promotional offers when they are close to the store.

By generating impressive in-store experiences, retail stores can attract consumers.  

A similar customer intelligence strategy is used by other small-scale retailers to reduce financial problems.  

Supply-chain management

As consumers are increasingly expecting faster delivery along with higher-quality products, CI helps retailers boost speed and accuracy via the supply chain.

They use predictive analytics in customer intelligence to forecast which brands and products will be popular during a specific period. 

Each step of the supply chain, such as planning, production, and return can be enhanced for efficiency. 

Visualize the market changes in real-time  

Keeping an eye on market fluctuations regularly is a challenging task for all types of businesses.

In the retail industry, a little ignorance in monitoring the market can affect the process of acquiring fresh consumers, generating revenues, and ensuring consumer satisfaction.

CI offers relevant insights from a source of steady information. 

Consumer information allows you to grasp wider market trends and allows retailers to combine predictive analytics and effective practices into their operations. 

Increase in sales 

Sales is the major factor for business success and should be qualified by their long-term value. Many factors affect the conversion of a frequently visiting consumer.

Retail companies use CI to discover the relevant strategies to minimize waste and offer proper training to employees. Find out which sales strategies work better by inspecting customer information and opinions. 

However, customer intelligence analytics for small businesses are used to boost the performance of the sales team by smoothening the sales process. 

Develop a strong persona

Retail companies should always pay more attention to their customers. They need to keep modifying their strategies and promotions until they resonate with their needs.

Retail businesses can get information from a variety of sources to build powerful personas of customers and identify their types. 

When they have a clear idea of who their customers are and what they like, it becomes much easier to develop a corporate brand image that matches their interests.

Create strong marketing campaigns by merging these personas with whatever goes into their emails and social media accounts. 

Customer intelligence in retail runs the appropriate AI-based analytical and modeling procedures and produces the desired results.

What is the Use of Customer Intelligence in Finance?

CI is transforming the finance and banking sectors in the following ways:

Accurate customer segmentation

Banks and financial institutions can use AutoML (Automated Machine Learning) to know the banking behavior of consumers online and segment them by looking at their choices to forecast and offer related banking services. 

Smart product recommendations

The CI collected by implementing AI & ML on user data can help both finance and banking companies offer smart and correct product recommendations to their consumers.

Banks can capture user data from user invoices and purchases, analyze it, and implement ML-based recommendation engines to offer customized banking products.   

Faster KYC Checks and Documentation in consumer onboarding

In the present digital world, consumers worry about the documentation process, but they also need the onboarding procedure to be easy and fast.

AI and ML technologies are changing the features of Optical Character Recognition (OCR), which is used by banks to turn the hard copy of a document into its digital one. 

By integrating AI & ML with OCR, financial services companies and banks can flawlessly extract user data from the documents and look for errors that may be encountered during the process.

With such automated consumer boarding, financial institutions and banks can manage required KYC checks immediately and maintain consumer satisfaction levels.

In short, customer intelligence for financial services is a consumer-focused approach. 

Faultless aspect-oriented sentiment analysis

Sentiment analysis, or opinion mining, is an important element of CI. Aspect-oriented sentiment analysis is the best AI-based tool that utilizes text analysis concepts to classify user opinions by attributes.

Banks can utilize opinions posted by consumers regarding their baking experiences on various social media channels such as Facebook and Twitter, product review channels, and consumer complaint forums to produce meaningful insights. 

Aspect-oriented sentiment analysis is an outstanding approach to analyzing audience sentiments and developing a strong CI framework. 

By introducing advanced customer intelligence in finance, institutions can identify unusual activities that might detect fraud like bad debt, money laundering, etc. Money laundering examples involve the use of cash businesses, manipulations with real estate deals, etc.

What is the Use of Customer Intelligence in eCommerce?

In the eCommerce industry, a CIP allows businesses to deliver smart and automated customized experiences, conversational AI-oriented customer care, cost optimization, and loyalty engagement.

This platform allows marketers to not depend on the IT team and other technical experts to create customized, and cross-channel customer lifecycles.  

A major component of successful eCommerce is effective inventory management, and CI is a useful tool in this region. By examining past purchase data, companies can boost their inventory levels. 

Error-free and proper demand prediction helps avoid understocking or overstocking problems, minimize expenses, and guarantee the availability of products for customers when they need them.  

Prospect new customers with lowest churn and highest LTV

How to Use Customer Intelligence Data to Grow eCommerce Businesses?

Here are some strategies to use CI data to grow eCommerce businesses:

Create personalization

Showing customers that the eCommerce stores care about them, and understand what they need can make a huge difference. 

Following are a few tips to personalize marketing efforts:

a) Including the name of the customer while sending an email or message can grab their attention
b) Suggesting an appropriate product to the suitable customer
c) Making changes in the ad copy based on audience segments
d) Make changes to the product prices

Changing based on user’s issues

Almost all eCommerce websites can track why a user would buy a product from them. If they fail to work on the pain points of users, the chances of acquiring potential users will be lower.

Hence, making changes to the landing pages, ad copies, services, or products after conducting thorough research on pain points can yield better results. 

Updating eCommerce marketing approaches

After analyzing customer behavior in eCommerce businesses, it’s also important to refresh marketing strategies. If businesses already have their CI data, it will be easy for them to target their potential audiences.

So, they need to refresh their marketing approaches based on likes, dislikes, devices, browsers, etc. to expect the best outcomes.

Customer Intelligence Examples

Below are some commonly seen examples of CI:

Analysis of purchase history

As explained, CI provides details related to the purchase history of customers, through which businesses can identify patterns and trends. 

For example, an analysis of the purchase history of a retailer might disclose that the majority of audiences shop for juice and snacks every June and August.

This data might be used by a retailer to plan sales, send personalized promotional emails or messages related to snacks and juices, and stock up on inventory for these durations.

Social media sentiment analysis

Social media is the best place to know how customers feel regarding a product, a brand, an experience, etc.

Even though they aren’t sharing their opinions on websites, it’s possible to collect insights from social media conversations that can help businesses take appropriate steps. 

Brands can rely on social listening and social media monitoring tools to track social media conversations and sentiments involved in comments, mentions, or messages.

By inspecting these, brands can discover possible issues that might damage their reputation and respond to audience concerns.

Tracking website behavior

Monitoring audience behavior for a brand can offer much-needed data into what audiences are willing to buy.

It also highlights possible issues associated with the brand’s conversation funnel, like product pages getting enough traffic but no or fewer conversions. 

So, personalization through customer intelligence is the only solution to enhance product recommendations.

Client feedback analysis

Online reviews, client service interactions, and feedback surveys are valuable resources for CI. These are some ways clients freely express their opinions, especially those that could harm a brand’s image. 

Businesses can use customer intelligence tools to highlight areas that need improvement. 

For instance, if multiple clients express the same opinions about food quality in a restaurant, the restaurant manager can take corrective action to provide quality food.

Future of Customer Intelligence 

Many businesses have invested valuable resources in analytics solutions but simply do not understand the importance of investing in data management and data governance, which results in poor outcomes.

To offer more customized, and automated services that audiences want, businesses need a CIP combined with AI and ML to examine data from various origins at huge volumes and speeds. 

AI plays a critical role in automating the processes associated with back-office and client-facing communications to offer digital and omnichannel experiences that are more personalized. 

These client expectations are forcing organizations to work with analysts and consultants to determine the best methods of implementing a client intelligence system to gain insights into the behavior of individual audiences.

How does Express Analytics Function as a Platform for customer intelligence?      

Even though customer intelligence is a fresh concept, thousands of revenue experts are realizing its significance. Express Analytics is leading this rapidly growing technology with its AI-based customer-centric services.

By inspecting millions of audience data points, Express Analytics automatically transforms them into churn alerts and growth possibilities. You can adopt AI to convert the voice of customers into a growth driver.


Implementing technologies for offering an advanced user experience is essential to increasing ROI and retaining users. The organization needs to start adopting customer intelligence desperately to make strategic and data-powered decisions.

Build sentiment analysis models with Oyster

Whatever be your business, you can leverage Express Analytics’ customer data platform Oyster to analyze your customer feedback. To know how to take that first step in the process, press on the tab below.

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