Power of Predictive Customer Analytics In Enhancing Customer Support
Historically, customer support methodologies have seen companies take a reactive approach to customer issues. Improving customer experience was more about finding solutions to self-diagnosed customer issues than companies proactively taking steps to improve the value and quality of their products and services.
At best, reactive customer support is time-consuming and “behind the curve”; at worst, it sees support agents unable to own and resolve tickets end-to-end, causing a spike in engineering escalations and further delays and disruption to internal teams and customers alike. The result? Frustration and dissatisfaction on all sides.
Whether you’re in B2B SaaS, e-commerce, or another industry, customer happiness and employee satisfaction are likely to be foundational for success, making alternative approaches increasingly attractive. Yesterday, reactive customer support was the status quo. Today, with the advent of predictive analytics and proactive capabilities, it doesn’t need to be.
“Designing great customer experiences is getting easier with the rise of predictive analytics” - McKinsey & Company.
What is Predictive Customer Analytics?
Predictive customer analytics in support means that companies can help customers resolve issues before they occur and sometimes even before they are even aware of them! Predictive customer analytics leverages AI to observe customer activity and find patterns in the data. The patterns and data uncovered in predictive analytics can then be used to understand how a customer uses the products and detect potential issues that a customer may face.
Salesforce found that 66% of customers expect companies to understand their unique needs and expectations. How do you measure up?
Modern customer support teams use historical and real-time customer data to understand customers’ behaviors, needs, and pain points. Technological advances in the field of Artificial Intelligence (AI) enable this data to be leveraged to solve problems as and when they occur (and sometimes before!), reducing delays and boosting support agent productivity by providing front-line support staff with real-time suggestions on how best to solve potential issues, as well as routes to resolution. The data insights can help prevent customer escalations and churn.
To get predictive customer support right, companies need a technology stack that enables support agents to take effective action to resolve customer issues. What’s needed is the ability to collect customer data and share insights with the support agents in real time. With this, support agents are empowered with accurate and timely customer knowledge.
Types of Data in Predictive Customer Analytics
Customer analytics relies on various types of data collected from multiple sources to provide a comprehensive understanding of customer behavior and interactions. These data sources typically include:
Customer Demographics: Basic information about customers, such as age, gender, location, and income, provides a foundational understanding of the customer base.
Transactional Data: Information on past purchases, including item details, transaction frequency, and purchase history, helps identify buying patterns and customer preferences.
Behavioral Data: Data related to how customers interact with a company's website, mobile app, or other digital platforms, including page views, click-through rates, and session durations.
Customer Service Interactions: Records of customer service inquiries, feedback, and support interactions, offering insights into customer satisfaction and pain points.
Social Media Data: Information gathered from social media platforms, including customer comments, reviews, and social engagement metrics, provides a valuable source of customer sentiment and feedback.
Survey Responses: Data collected through customer surveys or feedback forms, offering explicit insights into customer preferences, opinions, and satisfaction levels.
Email and Communication Data: Information about email interactions, open rates, click-through rates, and the content of email correspondence with customers.
Model Requirements for Customer Analytics
To effectively leverage customer analytics, certain model requirements must be met:
Data Integration: Successful customer analytics relies on integrating data from various sources. This integration involves collecting, storing, and organizing data in a way that ensures it is accessible for analysis.
Data Quality: High-quality data is essential for meaningful insights. Data should be accurate, complete, and free from errors or inconsistencies to prevent the introduction of bias into the analysis.
Data Preprocessing: Prior to analysis, data often needs to be preprocessed, including cleaning, transforming, and handling missing values. This step ensures that the data is ready for modeling.
Segmentation: Customer analytics often involves segmenting customers into distinct groups based on shared characteristics or behavior. Effective segmentation is crucial for targeting the right customers with personalized marketing efforts.
Machine Learning Models: Machine learning models are commonly used in customer analytics to identify patterns and make predictions. These models require feature engineering, training, and validation to perform effectively.
Predictive Analytics: Predictive analytics models are essential for identifying potential customer behaviors, such as churn prediction, upselling opportunities, and product recommendations.
Customer Lifetime Value (CLV) Analysis: Calculating CLV helps businesses understand the long-term value of a customer, which can guide decisions on customer acquisition and retention strategies.
A/B Testing: To validate the effectiveness of marketing strategies, A/B testing is often used, where different marketing approaches are compared, and their impact on customer behavior is analyzed.
Privacy and Compliance: Data privacy and regulatory compliance (e.g., GDPR or CCPA) are critical considerations in customer analytics. Businesses must ensure that they handle customer data responsibly and in accordance with relevant laws.
Data Visualization and Reporting: Effective communication of insights is key. Data visualization and reporting tools help present findings in a clear and actionable manner for decision-makers.
Continuous Learning: Customer behavior and preferences evolve over time. Successful customer analytics models should be capable of adapting to changing customer dynamics and trends.
In summary, customer analytics and predictive analytics is a multifaceted approach that relies on various data sources and analytical techniques to gain insights into customer behavior. By meeting the necessary model requirements and leveraging data effectively, businesses can make informed decisions, enhance customer experiences, and drive marketing strategies that resonate with their target audience.
Why are companies moving towards predictive customer analytics?
Some of the ways predictive customer analytics in support can help companies include:
A win-win situation for everyone: Predictive customer support leads to increased customer satisfaction by reducing the time taken to resolve issues -- and sometimes means no delay! Meanwhile, proactive support is likely to see support agents experience greater work satisfaction because they deal with fewer unhappy customers and can focus on impactful work. The engineering team will also likely see fewer tickets being escalated to them. This would ultimately reduce business costs for companies.
Improving customer loyalty: One of the most powerful ways to drive customer loyalty is by providing delightful customer support. Customer support is about providing positive customer experiences by quickly resolving any issues the customer may or is already facing. Doing this effectively will improve customer satisfaction and keep the customer happy, reducing the risk of future churn. According to research by McKinsey, due to the COVID-19 pandemic, 36 percent of US consumers reported trying a new product brand in the previous three months. Thus, more than ever, companies need to adopt predictive customer support to improve customer loyalty.
Reduce customer churn: By gathering customer data with predictive customer analytics, customer support management can identify customers with a high churn risk and quickly take action to improve customer experience. With the help of AI, companies can detect where these customers are having difficulties and offer targeted solutions.
Proactive Support: More companies than ever are finding ways to increase productivity through proactive support. One way to achieve this is by using predictive customer analytics to be ahead of their customers. By detecting potential customer issues, companies can reach out to the customer with a satisfying solution. This can even be a “wow” experience for the customer.
An energy company can use historical and real-time data to determine why a customer’s energy units run out faster than usual. The customer support agent or success team can contact the customer and suggest alternative options or solutions to avoid quickly running out of energy units.
Predictive customer support enables companies to be proactive, providing value to customers, support agents, and the business. IrisAgent uses predictive customer analytics to identify potential customer issues and provide suggestions on how to solve the issues. Schedule a demo today to see for yourself.
Frequently Asked Questions
What is predictive customer analytics?
Predictive customer analytics is a field of predictive analytics that uses historical customer data, machine learning algorithms, and statistical models to predict future customer behaviors, preferences, and needs.
How does predictive analytics benefit customer support?
Predictive analytics helps customer support teams anticipate customer needs, identify potential issues, and customer journeys, and proactively address them, leading to improved customer satisfaction and loyalty.
What types of data are used in predictive analytics for customer support?
Data sources for predictive analytics include customer interactions, purchase history, customer feedback, social media, and more. These data points are used to build models that predict customer behavior and support requirements.
How does predictive analytics enhance the efficiency of customer support teams?
By predicting customer issues in advance, support teams can allocate resources more effectively, streamline workflows, and prioritize high-impact tasks, resulting in increased efficiency and faster issue resolution.
Can predictive customer analytics help in personalizing customer interactions?
Yes, predictive analytics enables personalized customer interactions by anticipating individual preferences, allowing support teams to tailor their responses, recommendations, and solutions to each customer.
