The gathering of customer data is becoming increasingly important to businesses. During the COVID-19 crisis, corporate leaders raised their investment in customer data management by a remarkable 92 %, according to the Zendesk Customer Experience Trends Report. Customer Service Analytics helps with the same.
However, simply collecting customer information is not enough. The data you collect must be valuable. You must also understand the facts to draw conclusions and act. Customer analytics comes into play in this situation.
Businesses employ customer analytics (Also known as consumer analytics) to determine what makes their customers tick. Data analysis exposes how customers utilize your product or service to solve problems and the stumbling blocks they encounter along the customer journey.
Leverage consumer analytics to try and predict what your user wants, and you and your team will be well on your way to providing proactive support and consistent brand experience.
What is Customer Analytics?
Customer analytics is the practice of obtaining and evaluating consumer data to acquire valuable, actionable insights into purchasing habits and preferences.
Customers’ data can be collected through various channels, including web pages, applications, social media, and polls. The data can then be analyzed, and a report created, either manually or via customer analytics tools.
Due to these insights, businesses have a greater understanding of their target market, allowing them to produce high-quality products and services. They also assist organizations in determining the best pricing structure, marketing campaigns that target the right customers, increasing revenue, and improving the overall customer journey.
Also Read: Customer Journey Analytics: A Detailed Guide!
Types of Customer Analytics Solution
Following are the 4 types of Customer Analytics Solutions:
Descriptive Analytics:
Descriptive analytics entails gathering and analyzing information about previous customer activities. While consumer behavior data like this can help you understand what happened, it cannot tell why.
Many consumers submit poor ratings to their support interactions in customer satisfaction (CSAT) polls. This pattern would be highlighted through descriptive analytics. It would not inform you why you are getting low grades.
Diagnostic Analytics:
Diagnostic analytics looks at data to figure out what is causing patterns and why customers behave the way they do. For example, diagnostic analytics can explain why your consumers gave you a low CSAT score.
You can get this information via an open-ended research survey or from reading reviews and comments online. After reviewing the statistics, you may discover that long resolution times are the issue.
Predictive Analytics:
Based on historical data, predictive analytics estimates what your consumers are likely to do. This can assist your support team in anticipating customer demands and identifying patterns, resulting in a better customer experience.
You may also use predictive analytics to identify at-risk consumers and prevent churn before it happens. For example, you might see that at-risk consumers use fewer products and contact support less frequently. Knowing whether to intervene can be as simple as recognizing these symptoms.
Prescriptive Analytics:
Prescriptive analytics provides a solution to the question “What should we do?” by advising a plan of action based on prior data. It offers suggestions about how to achieve specific goals. For example, you could aim to cut resolution time by 20% to boost client retention by 50%.
Importance of Customer Service Analytics
For some businesses, gathering and analyzing enormous amounts of consumer data might be challenging. You should collect consumer analytics for a variety of purposes. Following are the Importance of Customer Service Analytics:
1. Boost Your Sales:
Customers receive ads that are suited to their needs and desires thanks to targeted advertising based on customer analytics. More consumers and sales for your organization result from this more successful advertising method.
2. Reduced Customer Acquisition and Retention Expenses:
Gaining and keeping new clients can be costly for certain organizations, so it is critical to figure out why they are departing. Client analytics data can provide you with insights into customer unhappiness.
Customer analytics can also reveal what prospective customers are looking for and which characteristics would entice them to try a new product. If you have this information, you can target the right customers and save money on customer acquisition and retention.
3. Customer Churn is Lower:
The number of consumers who quit your organization over time is referred to as the customer churn rate. Your customer analytics dataset would include information on why consumers left.
Thus, allowing you to take necessary precautions and make them stay loyal to your brand. The first step in reducing client churn is to collect data on what is pushing them away.
4. Improved Customer Loyalty:
Keeping clients loyal to your business is a terrific method to boost sales and generate profits. Customer analytics can explain why customers pick your products and what makes them loyal to you.
You may change your products or services to meet their demands better if you understand why some clients do not return to your site.
5. Increased ROI (Return on Investment) on Marketing and Promotions:
Any business can spend loads of money on marketing, advertising, and promotions. Spending money on advertising to consumers who are unlikely to acquire your products is a waste of money. As a result, ensuring your advertising and promotions are targeted to the correct demographic can make them more effective, saving you money and increasing your ROI.
6. Increased Productivity of the Sales Force:
Providing your sales staff with solid customer analytics data is a simple method to assist them. It is a terrific approach to help your sales team be more efficient by providing data on which markets are working well and what specific clients are interested in.
You may provide your sales staff with a better picture of where the market is headed by employing predictive research, allowing them to plan for impending changes.
The Process of Customer Data Analytics
Here are four steps of customer data analytics:
The first step is collecting data. It is critical to prioritize consumer openness regardless of data collection. Customers will have more trust and peace of mind if you tell them what data you are collecting and why.
The next step is to organize and tidy up the data you have gathered. This will assist you in making appropriate assessments. Your data platform’s analytics tools will eliminate any inaccuracies by deleting unnecessary and duplicate data. The Customer Data Platform (CDP) will organize data sets into categories in the central database once you have applied your tracking strategy.
Then, using your platform, evaluate the data and look for patterns. CDPs (Customer Data Platform) use machine learning to sort through data and uncover trends. Let us say you are trying to figure out what aspects affect user engagement with your streaming service. To track behaviors that encourage users to use the service, the CDP will examine data from your application, website analytics tool, email marketing portal, and customer support tool.
What is next now that you have better understood your customers?
Make use of what you have learned to go beyond their expectations. Do your customers expect quicker responses? For on-demand support, incorporate self-service alternatives into your support channels. Is there a new feature or product that your consumers would like? Inform the product team of their suggestions and concerns. Do they prefer to communicate via social media over email? Engage customers on their preferred channels at the appropriate moment.
Businesses that improve the customer experience proactively will deepen their relationships with customers, resulting in increased loyalty, profitability, and growth.
Final Thoughts:
Customer analytics should be collected and analyzed by all businesses. When this data is used correctly, it can lead to increased customer perception and cost reductions for your marketing strategy. You can reach your audience and retain more potential consumers with the right information.
For more information on how to improve your user data analytics, contact SG Analytics.
