Customer Intelligence (CI) is a crucial aspect of generating sales. Without it, it isn't easy to understand customers' expectations of your brand and which message will prompt them to make a decision.
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 analyzing data about your clients to understand their requirements and behavior, and to provide better communication channels.
The sources of CI data include social media interactions, purchase history, client reviews, and more. Businesses can monitor these client interactions using data management platforms (DMPs) and customer relationship management (CRM) systems.
What is the Objective of Customer Intelligence?
The objective of customer intelligence is to obtain useful information that can be effectively used to enhance customer engagement and experience.
It integrates all data associated with an audience and applies data science and analytics to answer challenging questions that help you understand prospects' needs.
McKinsey Global Institute reports that companies that follow a data-driven approach are 23 times more likely to acquire new customers than those that don't use CI.
Also, it allows you to personalize customer analytics for the different service, marketing, sales, and other departments, visualizing 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:
- Matching fresh record types and data entities
- Adding derived or inferred metrics, including journey, engagement, and sentiment value
- Enabling a variety of audiences to carry out complicated analyses through a simple interface
- 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, marketing, and sales strategies.
Business intelligence is a set of information concerning a business's activities, such as sales, client service, finance, and marketing.
Business professionals usually derive actionable insights from this data to track performance, optimize workflows, and make smarter decisions.
Win new customers with customer journey mapping >>>>> Read more
Types of Customer Intelligence
The following are the frequently seen types of customer intelligence in marketing:
- Transactional data
- Behavioral data
- Psychographic data
- Demographic data
- Attitudinal data
Transactional data
This relates to customer transactions or sales records, such as the types of products they bought, when they purchased them, promotional offers, and how much they paid.
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
This data can highlight your client's personality. This can be collected from surveys, daily activities, customer settings, and so on.
Demographic data
Let's identify your audience for effortless segmentation. It involves location, sex, education, marital status, income level, profession, and age.
Attitudinal data
It offers valuable insights into visitors' opinions on specific services, products, or experiences.
How is Customer Intelligence Analytics Used?
Let's dig deeper into three different ways organizations use customer intelligence analytics to produce better results:
Cross-selling: Maintain a database of past consumer orders to identify 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:
- Surveys that can be conducted in person, online, or via email
- Customer satisfaction ratings are requested by companies on feedback forms
- Tracking online feedback and ratings on G2, Google Reviews, and so on
- Social media provides more comprehensive insights into audience sentiments and perceptions of the brand
- 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 into 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, including communication history, past purchase details, and contact information.
Each audience's interests and preferences are easily accessible for you to review and build long-lasting relationships.
Customer Intelligence Systems
Brands use consumer intelligence platforms and AI to simplify customer data analysis and develop detailed customer profiles by understanding their preferences.
Financial data
Identifying the financial background of your potential clients is a key data resource.
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, including customer acquisition and retention.
The benefits of customer intelligence in marketing are as follows:
- Increases the reliability of your content and message
- Introducing services and goods that your clients genuinely want
- Powerful customer connections via customized communications
- Reduces client churn by addressing their pain points
- Helps in staying ahead of competitors who don't use customer intelligence analytics
- Reduces marketing budget by telling when and how to allocate expenses and time
Apply customer segmentation to acquire new customers >>>> How it works
Frequently Seen Challenges of Customer Intelligence
Let's see why it's essential to know the significant challenges that come with the implementation of customer intelligence solutions:
Siloed Data
Today, most B2B organizations rely on multiple communication channels to reach their audiences.
This can involve Zoom video calls, telephonic conversations, ticketing systems, email communications, and in-app chatbots. Can your customer intelligence solution digest this fragmented data?
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.
Regularly monitoring such Xs and Ys is challenging.
Too many at one
One central point is that 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 valuable insights and respecting audience data privacy in customer intelligence is a challenge that needs careful attention.
Regulations such as the CCPA and GDPR support data protection; companies must ensure they don't misuse data and maintain transparency about how audience information is used.
Failing to do so can result in reputational damage and legal consequences.
Typical Mistakes to Avoid When Collecting Customer Intelligence
Focusing primarily on quantitative data
Most of the time, companies focus mainly on quantitative data — numbers on 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, but you still missed a perfect score, why? Numbers are not enough to describe this.
Hence, it's an essential factor to consider for robust decision-making.
Simply relying on survey replies
When you say customer intelligence data, it doesn't mean it's limited to information from survey responses.
The truth is that such data is not easy to collect, and many businesses don't conduct surveys because they lack the 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 poor customer experience because audiences are now waiting to hear any improvement or follow-up, since they spent time filling out the survey.
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 unique characteristics and requirements of each segment to tailor 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 gain extensive insight into what encourages customers to make purchases.
Afterward, you can use that data to develop more targeted marketing strategies that 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 analyzing their browsing habits and monitoring engagement levels, companies can identify consumers who are likely to leave and take appropriate steps to retain them.
User flow modeling
The path a user takes through a mobile application or website to complete a task is called the user flow.
Organizations can track all user movements throughout their journey using customer intelligence analysis, enabling them to model on-site user flows and identify opportunities to improve them.
This will enable organizations to make changes to the application interface so that customers don't face any difficulties while browsing or availing services.
5 Ways to Build Customer Intelligence Strategies for Business
Building or implementing customer intelligence strategies for business includes 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 sources to gather customer information.
Data integration
Combine the gathered data from various sources into a central repository. 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 data collection, analysis, and thoughtful decision-making.
Constant monitoring and revision
Continuously observe and filter CI solutions based on business needs and client behavior.
Review and consistently modify methods related to data collection, analysis concepts, and technology tools.
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 frequent shoppers 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 that consumers must have selected themselves into the loyalty program. Additionally, retail companies use their websites for online and offline sales.
They use tools like Hotjar to monitor which products their customers browse on their website, and later, when 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 client data anonymously and analyze it to identify clients' browsing habits. They use such information to produce client profiles and study client behaviors.
This data plays a crucial role in improving in-store item displays and enhancing the accuracy of marketing strategies and communications in high-traffic areas to boost point-of-purchase influence.
Attracting consumers to the store
According to Statista research, 5 million people used mobile internet in 2022. Over 60% of global users go online via mobile devices.
This study reveals that they always have a location, Wi-Fi, and data turned on. This provides an opportunity to send customized promotional offers when they are near the store.
By creating compelling in-store experiences, retailers can attract consumers.
Other small-scale retailers use a similar customer intelligence strategy to reduce financial problems.
Supply-chain management
As consumers increasingly expect faster delivery and higher-quality products, CI helps retailers improve speed and accuracy across 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 — planning, production, and returns — can be optimized for efficiency.
Visualize the market changes in real-time
Regularly monitoring market fluctuations is a challenging task for all types of businesses.
In the retail industry, a lack of market monitoring can affect customer acquisition, revenue generation, and consumer satisfaction.
CI offers relevant insights from a reliable source.
Consumer information helps you grasp broader market trends and enables retailers to integrate predictive analytics and effective practices into their operations.
Increase in sales
Sales is the primary factor for business success and should be qualified by its long-term value. Many factors affect the conversion of a frequent visitor.
Retail companies use CI to identify relevant strategies to minimize waste and provide proper employee training. Find out which sales strategies work best by analyzing customer data and feedback.
However, customer intelligence analytics for small businesses are used to boost sales team performance by streamlining 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 gather information from a variety of sources to build robust customer personas and identify customer 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 integrating these personas into their emails and social media accounts.
Customer intelligence in retail involves implementing appropriate AI-based analytical and modeling procedures to produce 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 understand consumers' online banking behavior and segment them based on their choices, enabling forecasts and the offer of related banking services.
Smart product recommendations
The CI generated by implementing AI & ML on user data can help both financial and banking companies offer more accurate product recommendations to their customers.
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 enhancing Optical Character Recognition (OCR) capabilities, which banks use to convert hard-copy documents into digital versions.
By integrating AI & ML with OCR, financial services companies and banks can flawlessly extract user data from documents and identify errors that may occur during the process.
With such automated consumer onboarding, 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 essential 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 use consumer opinions about their banking experiences on social media channels, product review platforms, and consumer complaint forums to generate meaningful insights.
Aspect-oriented sentiment analysis is a practical approach for analyzing audience sentiment and developing a robust CI framework.
By introducing advanced customer intelligence into finance, institutions can identify unusual activities that might indicate fraud, such as bad debt or money laundering. Examples of money laundering include the use of cash businesses and the manipulation of real estate deals.
What is the Use of Customer Intelligence in eCommerce?
In the e-commerce industry, a CIP enables businesses to deliver innovative, automated, and 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, cross-channel customer lifecycles.
A significant component of successful eCommerce is effective inventory management, and CI is a valuable tool for this. By examining past purchase data, companies can boost their inventory levels.
Error-free, accurate demand prediction helps avoid understocking or overstocking problems, minimizes expenses, and ensures product availability for customers when they need it.
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.
The 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 the user's issues
Almost all e-commerce websites can track why a user would buy a product from them. If they fail to address users' pain points, the likelihood of attracting potential users will be lower.
Hence, making changes to landing pages, ad copy, services, or products after thorough research into 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.
Win new customers with customer journey mapping >>>>> Read more
Customer Intelligence Examples
Below are some commonly seen examples of CI:
Analysis of purchase history
As explained, CI provides details on customers' purchase history, enabling businesses to identify patterns and trends.
For example, an analysis of a retailer's purchase history might reveal that most customers buy juice and snacks in June and August.
A retailer might use this data to plan sales, send personalized promotional emails or messages about snacks and juices, and manage inventory for these periods.
Social media sentiment analysis
Social media is the best place to understand how customers feel about a product, brand, 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 identify potential issues that could damage their reputation and respond to audience concerns.
Tracking website behavior
Monitoring audience behavior for a brand can offer much-needed data on what audiences are willing to buy.
It also highlights potential issues with the brand's conversion funnel, such as product pages receiving sufficient traffic but experiencing few or no 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 identify areas for improvement.
For instance, if multiple clients express the same opinion about a restaurant's food quality, the restaurant manager can take corrective action to ensure quality food.
Future of Customer Intelligence
Many businesses have invested valuable resources in analytics solutions, but do not understand the importance of investing in data management and data governance, resulting in poor outcomes.
To offer more customized, automated services that audiences want, businesses need a CIP combined with AI and ML to analyze data from various sources at high volumes and speeds.
AI plays a critical role in automating back-office and client-facing processes to deliver more personalized digital and omnichannel experiences.
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 relatively new concept, thousands of revenue experts are recognizing 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 leverage AI to turn customer voices into a growth driver.
Conclusion
Implementing technologies for offering an advanced user experience is essential to increasing ROI and retaining users. The organization desperately needs to adopt customer intelligence to make strategic, data-driven decisions.