A roadmap to understand different types of customer analytics

The significance of customer analytics cannot be overstated. It provides invaluable insights into consumer behavior and preferences, allowing businesses to tailor their strategies and offerings. By analyzing customer data, companies can identify trends, predict future behaviors, and make informed decisions that enhance customer satisfaction and increase sales.

A survey was conducted by PWC where 1000+ business professionals shared their experience of using customer analytics in their business for over five years. The results were clear: customer analytics proved fruitful, and SaaS companies expected to increase revenue by $1 billion.

Here’s more on the importance of customer analytics.

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What is customer analytics?

Customer analytics is the process of using data to understand customer behavior and preferences. It enables businesses to know their customers better so they can provide more relevant and timely offers.

For instance, if a software vendor observes that businesses tend to upgrade their cybersecurity systems in the first quarter, they can prepare by offering promotions or updates during that period. If companies prefer a specific software feature, the vendor can emphasize it in marketing or develop enhancements.

By understanding other businesses' specific needs and patterns, vendors can tailor their offerings more precisely, ensuring satisfaction for their clients and promoting a successful business-to-business relationship.

Benefits of customer analytics

According to the survey by McKinsey & Company, companies that invest heavily in customer analytics tend to outperform their competitors in terms of sales and revenue. Today, in this digital age, customers have unprecedented access to information, enabling them to make informed decisions about where and when to shop, what to buy, and how to make payments. When the company gains a deep understanding of customer’s buying habits and preferences, it can make precise predictions about their behavior.

Understanding customers deeply lets companies help them just when they need it. By meeting their needs at the right time, businesses can make customers happier and more loyal. Happy customers often stick around, helping the business grow and succeed.

Increased customer acquisition

Customer analytics helps businesses identify and target ideal customers. Companies can understand customer’s interests and demographics, along with analyzing customer interactions. One can create effective marketing campaigns and generate more leads by fulfilling customer needs

Reduced customer churn

Customer churn refers to the pace at which customers discontinue their business relationship with your company. Customer analytics is what helps to reduce the overall customer churn rate by predicting customer behavior and taking appropriate steps to retain them.

Improved customer experience

Customer experience refers to the overall perception that customers have of a company based on their interactions with its products and services. Customer analytics plays a crucial role in helping businesses gain insights into customer preferences, identify areas of dissatisfaction, and enhance their overall experience. By pinpointing key touchpoints in the customer journey, companies can make data-driven improvements to encourage more positive customer behavior.

Increase customer upsells

Upselling is a process of selling customers an expensive and upgraded version of a company's service or product. Customer analytics helps businesses identify customers who are more likely to upsell. Having a clear understanding of customers' needs allows you to deliver a more satisfying experience.

Improved product development

Customer analytics allows customers to gather feedback on existing services and products. Companies analyze this feedback and identify the areas to improve customer sales and boost customer retention rates. This data is invaluable for developing new products and can be used to enhance existing ones to meet customer needs.

Fraud detection and prevention

While analyzing customer behavior data, businesses can identify unusual patterns or transactions that might indicate fraudulent activities. Therefore, advanced algorithms can help to detect anomalies and notify businesses in real-time to take action immediately.

Understanding the main categories of customer analytics

Businesses gain valuable insights through customer analytics by redefining strategies and cultivating stronger relationships with customers. For this, companies are making decisions about how to market, sell, and serve customers in a better way.

Descriptive analytics

Descriptive analytics provides insights into past customers’ data. This data can be used to identify trends, patterns, and customer behavior. Let’s say you can track customer purchase history, website engagement, and customer support interactions, which ultimately helps businesses create customer profiles and segments based on various characteristics.

Diagnostic analytics

In this category, it goes beyond descriptive analytics as it uses statistical analysis and machine learning to identify the root cause of customer behavior. This type can be used to identify the factors that are most likely to lead the customer to churn or understand the reason why customers are abandoning their shopping carts.

Predictive analytics

The predictive analytics type uses historical data to predict future customer behavior. It predicts customer’s churning risk, forecasts their demand, and offers personalized sales and marketing campaigns. For example, a retail store can use predictive analytics to predict specific customer demand and allow it to optimize its inventory and marketing efforts.

Prescriptive analytics

The prescriptive analytics type goes beyond predictive analytics as it recommends actions that businesses need to take to improve customer outcomes. Also, it uses historical data and machine learning to interact with customers in the best possible way, helping them create personalized experiences. This offers an excellent way to resolve customer support issues.

What are the different types of customer analytics

Customer analytics understands customers’ requirements and preferences and helps businesses tailor their services accordingly. Here, we have grouped different types of customer analytics and how each type tackles the different market needs effectively.

Customer journey analytics

A customer journey is a process of tracking and analyzing customer data by understanding the business interaction across various channels. Here, the data can be used to identify the customer’s touch points, optimize the customer experience, and improve customer loyalty. It’s a tool that collects data from different sources like mobile apps, websites, customer support, and other social media channels.

If you’re looking to add innovative business solutions, foster different types of customer service, and bridge your business gap by adding aligned strategies.

Customer experience analytics

Enhancing customer experience involves the systematic collection and analysis of data derived from customer interaction. It’s a practice to enable businesses to make informed data-driven decisions along with fostering customer loyalty and satisfaction. Several companies employ customer experience analytics (CXA) to pinpoint customer’s pain points, monitor their satisfaction levels, use effective marketing strategies, and achieve business goals with ease.

One of the biggest benefits of using CX analytics is getting better customer insights, lower customer churn, and increased loyalty and retention rates. Presently, a multitude of SaaS companies are leveraging customer satisfaction scores to enhance the customer satisfaction rate. Likewise, to improve the seamless multi-channel experience, adding the DevRev OneCRM platform can offer a smooth customer experience, ensuring consistency, and personalization. Its advanced features streamline data management, enabling data-driven decisions, boosting efficiency, and increasing customer satisfaction and loyalty.

Customer engagement analytics

Customer engagement analytics is about gathering and understanding data on how customers interact with a business at various points. The main goal of customer engagement analytics is to improve overall customer satisfaction and increase business performance. For a product company, a robust customer engagement strategy is indispensable. Understanding customer interactions profoundly can impact customer behavior, improve customer retention, heighten loyalty rates, and reduce churn rates. Utilizing the CES formula helps to measure overall engagement, provide actionable insights, and build insights for strategic decision-making.

Transform every interaction into an opportunity to strengthen your brand’s relationship with users and customers. With solutions like DevRev, you can achieve a seamless experience, enhancing your overall customer service quality and paving the way for extraordinary interactions that leave a lasting impression.

Customer interaction analytics

Customer Interaction Analytics (CIA) is about gathering and studying data from chats, emails, phone calls, and surveys to understand better and enhance the customer experience.

This kind of customer analytics uses top-notch tech like natural language processing, artificial intelligence, and machine learning to provide solutions.

Did you know?

84% of customer service and support teams believe that customer data and analytics are required to achieve organizational objectives. And, with expanding business, customers are required to take a deep dive into the data.

Customer behavior analytics

Customer behavior analytics is a study of how people make buying decisions based on their product, service, and organization. The results are in the form of insights that help to create effective strategies for business growth. It’s a process to gather customers' data and understand customer preferences, habits, and requirements to personalize the behavior of the company.

Customer behavior analytics provides valuable insights for businesses, enabling them to make data-driven decisions and improve overall performance. Here are some common business objectives that can be achieved through customer behavior analytics:

• Improve customer satisfaction by providing better service
• Retain customers by increasing customer loyalty
• Offer accurate personalization
• Identifying ways to look for new customers
• Increase marketing ROI by optimizing marketing campaigns

Customer loyalty analytics

Customer loyalty analytics uses data to understand customer actions and likes. Loyal customers often buy more. Businesses can enhance customer service by gathering and studying data from different places. This tool is important for businesses of all sizes.

Use one of the best customer analytics tools and improve your customer relationships while achieving business goals. There are several ways to analyze customer loyalty analytics. A few of them are listed below:

• Calculate customer retention rate (CRR)
• Measure net promoter score (NPS)
• Measure customer satisfaction score (CSAT)
• Sum up customer effort score (CES)
• Measure repeat purchase rate (RPR)
• Audit reviews and social media

Customer lifetime analytics

Customer lifetime value (CLV) is the total money a customer might spend with a business over time. The support team plays a significant role in keeping customers around. The longer customers stay and buy, the more they benefit the business, raising its overall value.

Let’s take an example:

Let’s say, a SaaS company provides cloud-based project management tools for businesses. It charges its corporate clients $500 per user annually for its services. Here's how we calculate its CLV.

Step 1: Calculate the average customer lifespan.

Assume that an average corporate client stays subscribed for 5 years.

Step 2: Calculate customer lifetime value (CLV)

CLV = Average revenue per user * average customer lifespan = $500 * 5 = $2500 per customer

Step 3: Calculate CLV based on customer segmentation

Get insights to find customers who subscribe through mobile apps compared to website subscriptions. This will help them in optimizing marketing strategies.

Step 4: Calculating customer acquisition cost

CAC is calculated to know the spending on new customers. Also, if CAC is lower than CLV, the company will make a profit. If higher, then the company needs to reconsider its sales and marketing strategies.

Step 5: Using retention strategies

The company can get more profit from retaining existing customers than acquiring new ones. They might invest in the company’s loyalty programs and can share feedback to improve the company’s behavior.

Step 6: Forecast and business plan

CLV data helps to make a long-term forecast and plan its budget and resources effectively. Also, set future goals based on the expected lifetime values of customers.

Customer retention analytics

Customer retention analytics is a process of analyzing the data and insights with an understanding of customer behavior, preferences, and satisfaction rates. One can employ analytical techniques in their business by identifying the patterns, trends, and potential areas to add improvement. This helps to enhance the customer loyalty and reduce the churn rates.

For instance, a cloud-based project management tool provider monitors customer usage data. When they detect decreased engagement, they offer personalized consultations and tailored solutions to address specific customer challenges, ensuring client retention, and satisfaction.

How can businesses implement customer analytics?

Customer analytics is a powerful tool that helps businesses of all sizes to understand their customers and make better decisions. By following the below steps, one can implement customer analytics successfully into their business.

Define goals

Clearly defined goals are the foundation of any customer analytics strategy. Businesses are required to identify specific objectives such as increasing sales, reducing churn rates, and enhancing customer satisfaction. These goals provide businesses with a clear direction for the data collection and analysis process. Here are some of the types of goals businesses might set in the realm of customer analytics.

• Customer satisfaction enhancement
• Increasing sales and revenue
• Churn retention
• Customer segmentation
• Cross-selling and upselling
• Optimizing marketing ROI
• Improving customer retention

Collect customer data

Gathering data from different sources is crucial for a holistic customer view. Utilize CRM systems, social media platforms, website analytics, and survey forms to collect data. These diverse data sets help businesses to understand customer behavior, preferences, and sentiments across multiple touchpoints.

Clean and organize your data

Raw data often comes in different forms and can contain errors or duplication. Cleaning and organizing data involve a meticulous process of data cleansing. This ultimately ensures that the data is accurate, reliable, and consistent. Removing duplicates, correcting errors, and standardizing formats are important tasks to enhance data quality.

Use advanced analytical data

Analyzing data involves employing advanced analytical tools including machine learning algorithms and statistical strategies. These tools help to uncover hidden trends and correlations within the data. The businesses can create customer segments based on demographics, purchasing behavior, or other relevant criteria. These segments later offer valuable insights into different customer groups.

Derive actionable insights

The real power of customer analytics lies in the ability to translate data into actionable insights. Businesses can tailor their marketing strategies, product offerings, and customer support services by understanding customer segments and their preferences. For instance, if the analysis reveals that a particular segment of customer prefer online interaction, the business can invest in improving its online customer service platforms.

Tech and people together improve customer experience

The fusion of technology and insights from customer analytics redefines how businesses engage with customers. Companies can create personalized experiences, anticipate needs, and cultivate lasting loyalty through customer analytics. This synergy is necessary for companies to stay competitive and customer-centric in today’s rapidly evolving market.