BPM Analytics

Exploring various types of customer analytics

Understanding your customers — their requirements and frustrations — is integral to the success of any business and can be enhanced through diagnostic analytics. Analysing and applying insights gleaned from customer data collected at every interaction and transaction provides organisations with a clearer picture of prevalent buying habits and an understanding of what influences purchase decisions, helping businesses optimize marketing strategies and improve customer retention.

Customer analytics is vital for businesses looking to analyse customer data effectively and help businesses predict customers’ needs and market trends and adapt to meet the constantly evolving demands of their target audience. There are several methods and approaches to collecting customer data, and we’re going to discuss the various categories and types of marketing customer analytics to figure out which suits your organisation’s requirements the best.* Let us start by identifying the key benefits of having suitable marketing customer analytics processes in place, including improved customer retention and reduced customer churn:

• Enhanced satisfaction and retention among customers
• Reduction in lead generation costs
• Better customer engagement
• Boost in sales and profits
• Improvement in brand awareness

Drawing practical inferences from various types of consumer data can help organisations craft personalised experiences for customers, create targeted marketing campaigns, and improve offerings based on reviews. Gathering the necessary data to accurately map the customer journey through various touchpoints and interactions is essential to arrive at actionable insights. That’s why it’s important to understand and recognise what type of marketing analytics would work best for your organisation.

Different types of marketing customer analytics

• Customer journey analytics:

  Tracking a customer’s interactions with your product or service - even before first contact to well after completing the purchase - is instrumental in evaluating every step of a customer’s journey. This type of customer analysis can identify stages in the journey where customers may be facing inconvenience or other issues leading them to abandoning a sale or a transaction. Understanding and acting on the data gathered will streamline the customer journey and keep your clients engaged and interested through every interaction.

• Customer experience analytics:

  Customer experience data provides insights into how customers feel when interacting with your brand. Customer experience data can be gathered by evaluating customer satisfaction and the time taken to respond to and resolve queries or concerns. What the customers perceive is shaped by their interactions with customer support, which is why it is vital to have access to customer support metrics and the need to conduct regular customer satisfaction surveys.

• Customer engagement analytics:

  Keeping customers engaged with personalised services and relevant marketing activities involves analysing structured and unstructured data from multiple channels. When done right, this data can accurately predict what customers need, enabling organisations to respond to these needs instantly. This metric involves analysing feedback data from various touchpoints and keeping track of email marketing metrics such as click rates and click-through rates.

• Customer loyalty and retention analytics:

  Maintaining a low customer attrition rate is key to long-term business expansion. Retaining existing customers by ensuring that businesses meet the customers’ needs is easier and less expensive than acquiring new ones. This type of marketing customer analytics helps create personalised services and marketing strategies by analysing customer behaviour through qualitative surveys, online reviews, and social media interactions.

• Customer lifetime analytics:

  This metric provides a broader view that combines customer journey and customer experience analytics, with the added metric of customer lifetime value (CLTV) and prescriptive analytics to guide decisions. The objective is to predict the expected profitability from one customer through the life of the business relationship. Declining CLTV is an indicator of customer retention issues and may imply that your customer acquisition and marketing campaigns aren’t up to scratch.

• Customer thought analytics:

  Tracking positive and negative comments about your business on social media and through surveys provide insights into customers’ opinions, issues, and expectations. Knowing the customers’ sentiments and what they are thinking can help businesses adjust their processes and marketing activities accordingly.

KPIs and Metrics for Effective Customer Analytics

Customer analytics is only as effective as the metrics you use to measure success. Here are key performance indicators (KPIs) for each type of customer analytics:

How can Infosys BPM help with marketing analytics?

Applying the right type of customer analytics at the right time and in the right situation to achieve the desired results and insights is a complicated task. You may need to collaborate with an experienced data analytics partner to get it right by utilizing the right analytics tool for effective collaboration. Infosys BPM offers Marketing and Customer Analytics Services that include innovative automated solutions for tracking and analysing a huge variety of consumer data. Reach out to know more about how your organisation can benefit from leveraging customer data.

Risk and Governance in Marketing Analytics

Using customer data for analytics comes with significant governance and compliance risks. Key considerations include:

• Data Privacy Compliance: Ensure all customer data is collected and stored in line with data protection laws such as GDPR or CCPA.
• Auditability: Implement clear audit trails for data access and analytics processes to ensure transparency.
• Ethical Use of Data: Regularly evaluate the ethical implications of using customer data and avoid practices that may breach customer trust.

*For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms, will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

Frequently Asked Questions

What is the difference between Customer Experience Analytics and Customer Engagement Analytics?

Customer Experience Analytics focuses on understanding how customers feel about their interactions with your brand. Customer Engagement Analytics tracks how actively customers engage with your products and marketing campaigns. Both are critical for driving improved customer loyalty and personalized marketing strategies.

How does governance impact the effectiveness of customer analytics?

Effective governance ensures that data used for customer analytics is accurate, secure, and compliant with privacy regulations. This leads to more reliable insights, minimizing the risk of non-compliance. Strong governance frameworks improve decision-making and protect the organization from potential legal issues.

How quickly can a company see ROI from investing in customer analytics?

ROI from customer analytics is typically visible within 6–12 months, depending on the business's maturity in using data-driven strategies. The key to quick returns lies in actionable insights that improve customer retention and reduce churn, ultimately leading to increased profitability.