The ultimate guide to customer intelligence
In today’s digital world, companies have access to extensive troves of customer information. Every time a user clicks on a website link, makes an in-app purchase or engages in a customer service chat or social media interaction, they’re providing organizations with invaluable information about their interests, needs, demographic details, preferences and more.
For companies, this data is critical for improving sales and creating an experience that is more responsive to consumers, but privacy and security best practices must remain top of mind in order to gain the full benefits of customer intelligence (CI).
What is customer intelligence?
Customer intelligence is the collection and analysis of customer data by an organization, usually with the help of technology. The goal of customer intelligence is to better understand a customers’ wants and needs, their preferred methods of interaction and ways to improve their customer experience (CX).
Overall, it can set your business up for long-term success by influencing how consumers perceive your brand, along with how much they’re willing to engage with it. The better you understand your customers, the more equipped you’ll be to provide a CX that’s personalized to their needs.
How does customer intelligence work?
Customer intelligence often relies on technology, such as customer relationship management (CRM) systems, data analytics platforms and customer data platforms (CDPs) to collect, store and analyze data efficiently. It is a dynamic process that includes:
- Data collection: This can include gathering information from various sources, such as customer interactions, transaction histories, surveys, social media, website analytics and third-party data sources. Data can be structured (e.g. customer profiles, purchase history) or unstructured (e.g. social media comments, customer reviews).
- Data integration: Data from various sources needs to be integrated and organized into a central repository or data warehouse. This consolidation allows for a holistic view of the customer.
- Data cleaning and validation: Raw data may contain errors or inconsistencies. Data cleaning involves identifying and correcting inaccuracies, duplicates and missing information to ensure data quality.
- Data analysis: Analytical tools and techniques are applied to the integrated and cleaned data to extract insights. This can involve descriptive analysis to understand historical customer behavior and predictive analysis to forecast future behavior.
- Segmentation: Customers are often grouped into segments based on shared characteristics, such as demographics, behavior or preferences. This segmentation helps in targeting specific customer groups with tailored marketing strategies.
- Customer profiling: Detailed customer profiles are created, including information like purchase history, browsing behavior, product preferences and communication preferences.
- Predictive modeling: Predictive models are built — often using machine learning algorithms — to forecast customer behavior, such as churn prediction, lifetime value estimation or product recommendations.
- Business insights: The insights generated from data analysis are translated into actionable business recommendations. These insights can inform marketing strategies, product development, customer service improvements and more.
It is important to note that CI is an ongoing process. Feedback loops need to be established to continually refine and update customer profiles, predictive models and business strategies as customer behavior and market trends change.
Types and sources of customer intelligence
Customer intelligence encompasses a wide range of data and information types, including, but not limited to:
- Demographic data: Information about customers' age, gender, income, education, occupation and other demographic characteristics.
- Geographical data: Location-based data that helps businesses understand where their customers are located, allowing for targeted marketing and expansion strategies.
- Psychographic data: Insights into customers' values, lifestyles, interests and attitudes, which help in understanding their motivations and preferences.
- Behavioral data: Data on customer behavior, including purchase history, product usage, online activity and interactions with the company's website and mobile apps.
- Transactional data: Information related to customer transactions, such as sales data, order history and payment methods.
- Communication and engagement data: Details about customer interactions with the company, including emails, customer support inquiries and social media interactions.
- Customer feedback: Direct feedback from customers, including surveys, reviews and complaints, which provides insights into customer satisfaction and areas for improvement.
- Sentiment analysis: Text and sentiment analysis of customer reviews, social media mentions and other unstructured data to gauge customer sentiment and opinions.
- Competitive intelligence: Information about customers' interactions with competitors, including product preferences, pricing sensitivity and brand loyalty.
Numerous sources offer valuable data for customer intelligence. Publicly available data sources also play a crucial role in enhancing insights, adding layers of information from government records, industry reports and news articles. The combination of these diverse sources weaves a rich tapestry of customer intelligence, providing businesses with the knowledge needed to make informed decisions and tailor strategies to better serve their customers.
The importance of customer intelligence
Customer intelligence plays a pivotal role in the success of businesses in today's fast-paced market environment.
It enables businesses to connect with their audience on a deeper level by providing brands with a profound understanding of their customer landscape — this encompasses demographics, behaviors and preferences. Armed with this knowledge, companies can tailor their products and services to better align with customer expectations.
By leveraging CI, businesses can stay ahead of market trends and anticipate customer desires, positioning themselves as industry leaders. And when it comes to strategic planning, CI can assist businesses in setting realistic goals and KPIs based on market insights.
Benefits of customer intelligence
The benefits of customer intelligence range from positive word-of-mouth marketing to improved sales efficiencies, and everything in between. When you use CI to create a better CX, and you build your strategy with a strong foundation in privacy and security, you can realize the following benefits:
- Enhanced customer engagement: By understanding customer preferences and behaviors, businesses can engage with customers in a more personalized and meaningful way.
- Increased customer satisfaction: Personalization and customer satisfaction go hand in hand. A survey conducted by The Harris Poll on behalf of TELUS International found that 42% of Americans are likely to switch brands if a company doesn’t provide them with a personalized experience. The survey also found that when a brand provides a more personalized customer experience, 76% of Americans are more likely to complete a purchase and 53% are more likely to pay a bit more for products and services. Delivering on these data points can lead to higher customer satisfaction ratings and has the potential to boost your Net Promoter Score (NPS).
- Loyalty and retention: Naturally, increased satisfaction also leads to increased loyalty and retention. The same survey results show that when a company provides a CI-informed personalized experience, 70% of Americans are more likely to choose that brand over others. And Ad Age — a global media brand that publishes news, analysis and data on marketing and media — recently reported that 60% of consumers are likely to become repeat buyers after a personalized experience.
- Higher conversion rates: Targeted marketing efforts result in higher conversion rates as customers are more likely to respond positively to relevant messages.
- Increased revenue: Targeted marketing, cross-selling and upselling opportunities uncovered through CI contribute to revenue growth.
- Resource efficiency: By targeting the right customers with the right messages, businesses can allocate their resources more efficiently, reducing wasteful spending.
- Innovation: CI Insights can inform product and service development, driving innovation that aligns with customer needs and desires.
Customer intelligence examples
What does customer intelligence look like in action?
In the ecommerce industry, leading brands are using customer data to make more accurate suggestions for future purchases. Aside from Amazon and eBay, which are known for relying heavily on customer insights when making product recommendations, brands like Walmart are also going all-in with customer data. Operating more than 10,500 stores in 19 countries, and with a substantial ecommerce presence, Walmart has the ability to tap into an enormous pool of data generated by various customer interactions. Whether it’s transactions conducted at brick and mortar stores, or the multitude of actions like product searches, views and additions to online shopping carts via the website or mobile app, each interaction serves as an opportunity to gather valuable insights into consumer behavior. Not only is this data used to personalize the shopping experience for customers, but the retail giant is also using it to make its pharmacies more efficient, improve store checkout, manage its supply chain and optimize product assortment.
In the travel and hospitality sector, Canada’s two largest airlines, WestJet and Air Canada, are using customer data to provide personalized offers with the goal of making the flight booking process more efficient. A streamlined list of flights are presented based on a user’s interests and price preference, reducing the need for customers to spend time sifting through an arduous list of flight options.
Meanwhile, Delta Airlines Inc. utilizes software to help monitor basic customer information during their travels. Flight attendants input real-time data into the tool to help the airline deliver a customized experience. For example, an apology email could be sent to each customer impacted by a flight delay, or a thank you email to a loyal customer for collecting a milestone number of frequent flier miles.
Customer intelligence best practices
While utilizing customer intelligence has proven to be beneficial for brands, there are a number of steps between data collection and actionability that are intrinsic to developing a strong CI strategy. Keep these customer intelligence best practices in mind:
- Prioritize privacy and security: If customers have high standards when it comes to personalization, they have even higher expectations when it comes to the privacy of their data. Government regulations — such as Europe’s General Data Protection Regulation (GDPR), Japan’s Act on the Protection of Personal Information and California’s Consumer Privacy Act — give consumers more control over the personal data they provide organizations. The mishandling of data can have lasting negative impacts on your brand, so being transparent with how customer data will be used and ensuring safe-guards are in place to secure the data is key to protecting your reputation and building trust with your customers.
- Choose the right technology to analyze the data: What good is data if you can’t tap into it for actionable insights? Choose an analysis solution that’s reliable and robust.
- Create a strategy: Use data to build customer profiles that can inform both your sales and customer service departments and act on the data you collect. Consider personalizing emails and text messages, making in-app suggestions and recommendations based on your customers’ preferences, as well as equipping your customer care agents with the right tools to create better customer experiences.
- Measure success: CI isn’t meant to be a quick fix, but rather a long-term commitment. Update your strategies on a regular basis based on your results to better meet your customers’ evolving needs.
- Partner with an expert: You’re not alone in this. Partner with a leading digital experience and CX solutions provider that has a 360-degree view of the customer experience.
Customer intelligence: Common mistakes to avoid
Avoiding common mistakes is crucial to ensuring that the customer intelligence insights gathered are accurate and actionable. These include:
- Overlooking data quality: Poor data quality, including inaccuracies, duplicates and outdated information, can lead to incorrect conclusions and ineffective strategies. Regularly clean and validate your data to ensure its accuracy.
- Neglecting data integration: Customer data often comes from multiple sources. Neglecting to integrate these sources can result in fragmented and incomplete insights. Invest in data integration to create a unified view of your customers.
- Lack of focus on relevant data: Collecting vast amounts of data without a clear purpose can be overwhelming and can lead to information overload. Focus on collecting and analyzing data that directly relates to your business goals and customer insights needs.
- Ignoring qualitative data: While quantitative data is essential, qualitative data from customer feedback, surveys and open-ended questions can provide valuable context and nuance.
- Limited data sources: Relying solely on internal data can limit your view of the customer. Explore external data sources, market research and competitive intelligence to gain a broader perspective.
- Ignoring negative feedback: Negative customer feedback can be uncomfortable, but it often contains valuable insights. Addressing and learning from negative feedback is essential for improvement.
Having a robust CI plan in place that integrates best practices and looks to avoid these common mistakes is paramount for brands looking to harness the full potential of their data. But it is also important that brands keep their finger on the pulse of any new technologies that arise which could revolutionize the customer intelligence landscape.
The future of customer intelligence
The future of customer intelligence holds exciting possibilities. Generative AI (GenAI) — a subset of artificial intelligence that focuses on creating content, such as text, images and videos — is poised to take center stage, offering a multitude of opportunities. The amount of data available to brands to mine for insight is ever expanding. GenAI’s ability to analyze extensive datasets and use techniques like neural networks and deep learning to mimic human creativity, will help take personalization to the next level.
AI has played a pivotal role in collecting and understanding CI data in the past, but with the advancements of GenAI, the analysis of data becomes not only faster, but also scalable. This is particularly evident in GenAI’s capability to harness unstructured data from sources like customer surveys, reviews, social media mentions and more. Through its natural language processing capabilities, GenAI excels in reading and summarizing large amounts of comments and feedback, providing actionable outputs with remarkable efficiency. This helps brands streamline their processes, better understand customer sentiment and quickly identify emerging issues.
As consumers’ expectations continue to rise and technology evolves, customer intelligence will become indispensable to businesses regardless of industry. Brands that prioritize data privacy and security, and take an ethical approach to CI, will build strong customer relationships. Reach out to one of our digital customer experience experts to establish a winning CI approach that can prepare your business for ongoing success.