Customer Intelligence

What is customer intelligence?

Definition: Customer intelligence is the process of collecting and analyzing customer data to understand their preferences, needs, and motivations and use these insights to form business decisions, increase customer satisfaction and create personalized marketing campaigns.

Companies get hold of this data through external and internal sources, and after analyzing it, they distribute and develop strategies around them. 

Tailoring offerings, and enhancing customer satisfaction and loyalty, ultimately drives business growth and conversions for businesses and brands.

Types and sources of customer intelligence

Types

Personal demographics: Relates to individual customer characteristics, such as demographics, psychographics, and behaviors:

• Age 
• Gender
• Education
• Marital status
• Income level
• Debt level
• Values, interest, personality traits
• Lifestyle

Geographic demographics: 

• Population density
• Urban/rural classification
• Climate
• Cultural factors

This type of customer intelligence helps us to identify and target specific geographic markets, customize their marketing and sales strategies to local preferences and trends, and optimize their distribution and logistics operations. 

Attitudinal demographics: 

• Purchase history
• Browsing patterns
• product usage
• Loyalty
• Engagement with marketing campaigns

This helps us to understand customers' motivations, preferences, and pain points, enabling us to develop targeted messaging, improve customer experience, and enhance customer loyalty. 

Sources

• Focus groups  — Interviews, discussions, observations
• Customer service interactions  — FAQs, patterns, and behavior
• Surveys  — Customer satisfaction surveys, Feedback survey
• Reviews  — Online reviews, competitor reviews
• CRM (Customer relationship management)  — Purchase history, customer profiles
• Social media data — Interactions, comments, impressions
• Web analytics  — Website traffic, conversion
• Loyalty programs  — Reactions, engagement levels

Customer intelligence process

1. Define goals and objectives

The first step of the customer intelligence process is clearly defining the objectives and goals of the analysis. Common objectives are:

• Understanding customer preferences
• Finding opportunities for upselling or product development
• Improving customer retention 
• Enhancing customer satisfaction

2. Identify data sources

After setting a clear objective and goal, companies list the potential sources where they need to look for customer data. Potential sources are:

1. Customer service/interaction history
2. CRM
3. Surveys
4. Social listening/social media
5. Campaign results

3. Collect and categorize data

The third step is collecting quantitative and qualitative data from the available sources and organizing it for better understanding. One efficient way is dividing data into:

• Transactional data — Related to the monetary aspect of interactions, like payment methods and transaction amount.
• Behavioral data —  Captures customer interactions through digital touchpoints such as websites or social media
• Psychographic data  — Details about customer lifestyle in terms of interests, attitudes, and behaviors

3. Analyze data

The analysis aims to identify patterns, trends, and correlations in data that help establish customer personas.  Data analysis is done with various techniques such as:

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• Data mining — Using rule mining, clustering, and classification to identify patterns of behavior
• Statistical analysis — Using hypothesis testing and regression analysis to summarize the significance of the data
• Machine learning algorithms — Building a predictive model that makes predictions and classifications based on patterns from historical data

4. Share insights

Insights from data are translated into actionable advice distributed around the company, and all teams use it. The marketing, development, and sales teams use these insights the most because they affect customers the most.

5. Develop strategies

Once the insights have been generated and spread, teams create and implement strategies based on those insights. 

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This involves creating marketing campaigns, product ideation, or optimizing customer touchpoints. The strategies are implemented right away, and the success of these strategies is measured through surveys, feedback, and future data analysis.

6. Monitor and improve

Customer intelligence is not a one-time process but an ongoing one. Companies monitor data and regularly adjust because customer behavior changes and evolves over time.

Customer intelligence KPIs and metrics

1. Average lead response time – Measures the average time it takes for a business to respond to a lead or prospect after initial contact.
2. Customer churn rate – Quantifies the proportion of customers who stop using a product or service, indicating customer retention and loyalty.
3. Average purchase value – Shows the average value of purchases made by customers, which helps businesses understand spending behavior.
4. Net promoter score – Gauges the likelihood of customers recommending a business to others, indicating their loyalty and advocacy.
5. Win/loss ratio – Compares the number of won deals to the number of lost deals, providing insights into the success rate of sales efforts. 
6. Customer lifetime value – Measures the total value a business can expect from a customer throughout their relationship, providing insights into the profitability of long-term customers. 
7. Customer satisfaction (CSAT) – Measures the satisfaction level of customers regarding a particular interaction or experience, giving more insight into the quality of products, services, or customer support.
8. Customer acquisition cost (CAC) – Calculates the cost of acquiring new customers, offering valuable insights into the efficiency and effectiveness of customer acquisition strategies.
9. Close rate – Measures the percentage of leads or opportunities that are successfully converted into customers. 

Customer intelligence examples

1. Personalized marketing — B2B software company analyses customer data to identify patterns in feature adoption. Based on the analysis, the marketing team creates personalized emails targeting certain customers with relevant feature segments, training resources, or how-to materials.
2. New product development — B2B software company conducts interviews with customers to gather insights on the challenges and requirements of their audience. The product development team uses this data to develop new features or integrations with other tools.
3. Customer retention — A B2B consulting company analyses customer data to track customer engagement levels and target customers with low levels. The sales team contacts these customers for exclusive offers or loyalty programs.