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Understanding customer analytics: Its uses, process, and best practices

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Understanding customer analytics: Its uses, process, and best practices

Success is a ripple effect – and it starts with your customer’s success.

Then, these successful customers drive successful companies – which is why companies must understand their customers to be able to serve them well.

So the first question in order is, how well do you know your customer? Followed by, what makes your customers tick?

All this and more can be answered through customer analytics, a combination of technology and techniques that provide actionable insights for businesses to understand complex customer behavior.

Gartner survey finds customer data and analytics as the top priority for achieving customer service and support goals in 2023. (source)

In this blog, we will explore a detailed understanding of customer analytics, its significance, benefits, practices, and tools to get you started on your business growth journey.

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What is customer analytics? And what is the process?

Customer analytics, also known as customer data analytics, is a process of collecting, analyzing, and interpreting data from customer interactions and behavior about their requirements, expectations, and most importantly, experiences.

The process is powered by technologies such as Artificial Intelligence (AI), cloud, and data science and is driven by techniques such as data visualization, predictive analysis, and information segmentation.

Since customer analytics is a process, let’s understand it step by step and as it takes place during a customer journey.

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We understand that every tech company is different, but it is also true that people (customers) are the same. So, businesses must understand their customers, and following a roadmap to understand different types of customer analytics is the first step towards improving the overall customer experience.

Why do businesses use customer analytics?

Businesses that understand their customers always stay ahead of the curve – they always have, and they always will be. Take ‘Apple,’ for example, the company’s understanding of customer needs and preferences is reflected in its product innovation. They use customer analytics to anticipate technological trends – to tailor their features, user experience, and marketing strategies to resonate with their target audience, fostering customer loyalty and strong market positioning.

Let’s understand the importance of customer analytics with data that is backed by theories and examples.

The three key experiences today’s customers expect and are found common in multiple survey results.

Personalization – 73% anticipate a more personalized experience with technology advancements, shared in findings by Statista.

Speed – 53% of consumers hired the first business that responded to them, shared in an article on Forbes.

Convenience – Buyers who experience a self-driven learning path are 147% more likely to buy more than initially planned, as found by Gartner.

Examples: From then and now

An old-fashioned example: Take a classic neighborhood shopkeeper who, through genuine conversations, got to know his customers – learned their purchasing patterns, deciding factors, preferences, family dynamics, and even special occasions.

He locates the perfect product, tailors recommendations, and, most importantly, suggests additional items the customer had not even thought of. He creates a unique, personalized, and convenient customer experience, earning customer loyalty.

Now, fast-forward to today, when companies are operating online, it is a peculiar scenario.

A modern-day example: Take a successful product company that, by implementing customer analytics tools and techniques – gathers customer insights, including customer pain points, behavior, preferences, patterns, and more.

With the data-driven approach, the company provides a personalized offer that is most suitable to the customer’s business requirements, enabling them to achieve their business goals.

Now, take a step further and understand what your customers want so that you can deliver the experience they need/expect from your business.

What are the customer analytics best practices?

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Customer data collecting, processing, and managing can be a lengthy and overwhelming process, but with the right strategy and a robust customer service platform such as DevRev, it can be faster, more efficient, and more insightful.

Let’s go through the steps of how customer analytics work.

Customer analytics best practices

  1. Define your objective and KPIs
  2. Choose the right type and tool
  3. Collect the right customer data
  4. Ensure safety and security
  5. Categorize and process your customer data
  6. Identify patterns and extract actionable insights

Define your objective and KPIs

Specific goals: Clearly outline what you aim to achieve with customer analytics, whether it's improving customer retention rates, understanding purchasing behavior, and achieving higher customer satisfaction.

Set measurable objectives: Establish Key Performance Indicators (KPIs) that align with your goals, enabling you to quantify success and track progress effectively. You can analyze the customer reviews and find out how many are negative and positive, along with the reasons, to enhance their experience and reduce customer churn rate.

Choose the right type and platform

Here are the top five factors to consider before choosing the right customer analytics tool for your business.

Step one: Data type relevance

Select data types aligned with specific business objectives. For instance, customer support interactions help in reducing customer churn, demographic data (age, location) aids in targeted marketing campaigns, and transactional data (purchase history) is valuable for understanding customers’ buying habits.

Step two: Platform features and functionality

Evaluate analytics tools based on features essential for your data analysis, such as data visualization, predictive modeling, and scalability, ensuring they align with your objectives and analytical requirements.

You can consider AI-native tools such as DevRev to enable smart and automated data gathering, processing, and management, taking your customer experience to the next level.

Learn about the role of AI in data interpretation and decision-making and how you can make the best of it with DevRev.

Step three: Scalability and compatibility

Assess the tool's scalability to handle growing data volumes and complexities. You can ensure compatibility with existing systems, such as databases, for seamless integration and data sharing. The integration is pivotal for maximizing effectiveness with your past data and utilizing the collective potential of your technological resources.

In addition, you can ensure the tool's capacity for scalability, particularly its ability to accommodate increasing data volumes and manage escalating complexities effectively.

Step four: Usability and user interface

Consider the tool's ease of use and interface intuitiveness. A user-friendly platform encourages wider adoption and better collaboration across teams for effective utilization of insights.

For example, DevRev’s smart cluster feature helps support teams to group similar tickets as per customer complaints and types, making it easy to focus on customer-centric product improvement.

Step five: Consent and legality

Prioritize tools offering robust security measures (encryption, access controls) to protect customer data. Maintain complete transparency about the data you collect with your customers, all the while ensuring compliance with data protection regulations (GDPR, CCPA) to maintain trust and legality.

Collect the right customer data

Omnichannel data gathering: Implement strategies for automated customer journey data collection from various customer touchpoints, including websites, mobile apps, social media interactions, and customer service channels. They will give you a comprehensive view of behavior and customer preferences.

Relevant to objectives: Focus on collecting data that directly aligns with your analytics objectives. For example, if aiming to improve product recommendations, gather data related to customer support queries, purchase behavior, and feedback on products and services.

Ensure safety and security

Robust data protection: Go beyond standard security protocols; implement encryption and anonymization techniques to safeguard sensitive customer information.

Regulatory adherence: Stay updated with evolving data protection laws and regulations such as GDPR. You can regularly audit and update security measures to maintain compliance.

Categorize and process your customer data

Effective data cleaning: Employ automated tools and processes to cleanse data, removing duplicates, errors, or inconsistencies while preserving data integrity.

Structured data organization: Employ data modeling techniques to categorize and structure data into meaningful segments or categories, simplifying subsequent analysis.

Data processing and maintenance: Implement ongoing data maintenance processes to ensure data quality and relevance. You can regularly update and validate data to keep it accurate, consistent, and up-to-date, optimizing its usability for analytics purposes.

Identify patterns and extract actionable insights

Advanced analytical methods: Utilize predictive analytics, machine learning algorithms, or AI-driven models to understand complex patterns and predict future customer behavior accurately.

Behavioral analysis: Conduct cohort analysis or segmentation to understand distinct customer groups and their behaviors, enabling personalized marketing or customer service strategies.

Implementing these comprehensive steps in customer analytics ensures a structured and effective approach toward deriving actionable insights from customer data, fostering data-driven decision-making and customer-centric product development and business growth strategies.

Customer analytics example

Spotnana uses DevRev to enhance customer engagement and team collaboration

Spotnana operates at the intersection of technology and travel, empowering a network of agencies, suppliers, and technology partners to innovate and elevate the travel experience.

They use DevRev’s unified customer support and product enhancement solution to improve customer satisfaction. As a result, they observed the following business benefits:

Enhanced customer support: With AI leading the front, they resolved customer inquiries more efficiently, providing quick, consistent, and accurate responses.

Unified data view: Collating data from product and customer experiences in one place, Spotnana now drives more data-driven decisions.

Streamlined internal communication and product management: DevRev’s features eliminated the need for multiple platforms, bringing Spotnana’s operations into a unified, cohesive workflow.

Turn your customer data into action

Customer analytics provides the key to retaining customers, keeping them satisfied, and improving products. It guides you in understanding what your customers truly want and need, making sure you are always on point. How?

With the valuable insights –

  1. Your sales and marketing targets the right audience on the right platform at the right time.
  2. Your support team predicts issues before they arise and hence solves them promptly, stopping them from becoming bigger problems.
  3. Your product team enhances products as per your customers’ problems and needs.

Ultimately, customer analytics helps you grow with a strategic, customer-focused approach. It allows you to make informed decisions so that you can build a business strategy that helps you drive business growth consistently.

Frequently Asked Questions

CRM (Customer Relationship Management) in customer analytics refers to software and strategies for managing and analyzing customer interactions and data throughout their lifecycle, aiming to enhance relationships and business performance.

The major types of customer data include demographic data, behavioral data, transactional data, psychographic data, and interaction data.

Challenges include data privacy concerns, integration of disparate data sources, ensuring data accuracy, and the need for expertise to interpret data for actionable strategies.

To ensure a secure analytics environment for customer data integrity and privacy, encrypt data, comply with regulations, minimize collection, train staff, and monitor systems.

Jeel Patel
Jeel PatelMember of Marketing Staff

Jeel Patel, a marketing team member at DevRev, excels in writing about customer support, CRMs, and startups. His expertise shine in every piece!