Whether you’re requesting that the customer stay on the line or sending a follow-up email or text message, your contact center is probably asking customers to share feedback about their experience. But why should they have to complete a survey? They’re already telling you all about their experience during the interaction.
While traditional post-call surveys have long been the standard for tracking how customers feel about their interactions with companies, many contact centers are now implementing predictive survey technology. With predictive survey models, businesses don’t need to ask customers to rehash their experiences—they can learn from what customers say verbatim in their contact center interactions.
What are predictive surveys and how are they used in the contact center?
Predictive surveys are a new category of advanced contact center analytics. They use AI models to assess customer sentiment for every interaction, giving contact center leaders a way to measure and track the customer experience without relying on traditional post-call surveys. For example, Creovai’s proprietary CSATai model analyzes the language and context of customer interactions to predict whether customers would likely rate themselves as satisfied, dissatisfied, or neutral.
Predictive survey models help contact centers overcome survey fatigue and make decisions based on the experiences of every customer their agents have spoken to, not just those who were motivated to submit a survey. They also give contact centers immediate feedback to act on. Rather than waiting days or weeks for survey results to trickle in, contact center leaders can get CX insights after every completed interaction, allowing them to course-correct on negative experience trends or even follow up directly with churn-risk customers.
How does predictive survey technology work?
Predictive survey technology uses machine learning, a type of AI. Machine learning analyzes complex mathematical relationships to predict outcomes. For instance, Creovai’s CSATai model was trained on millions of CSAT surveys and their preceding interactions, allowing the model to capture the relationship between survey scores and words or phrases used in the interactions. By training and fine-tuning the model, Creovai can now generate predicted CSAT scores for new interactions with a high degree of accuracy.
Getting the “why” behind the predicted survey score
Predictive survey scores give you a quantitative metric for tracking the customer experience in your contact center. You can easily identify individual interactions with a specific score (e.g., satisfied or dissatisfied) and monitor changes in scores for your customer base over time.
However, it’s not enough just to attach predicted survey scores to your interactions. You must be able to pinpoint the factors impacting customer sentiment and satisfaction so you can make meaningful improvements to the contact center experience.
The best way to get to the “why” behind the score is to combine predictive surveys with conversation intelligence (one of Creovai’s core functionalities).
Conversation intelligence software uses machine learning to analyze words and phrases in context. It then categorizes and labels specific events within contact center interactions, including call reasons, product mentions, effort drivers, and agent behaviors. Combining these insight categories with a predictive survey model like CSATai gives you a deep understanding of what your contact center is doing well and what you need to improve upon.
Contact center leaders are using predictive surveys and conversation intelligence to take a more proactive approach to service delivery. Arnel Deguito, Customer Experience Analyst at Thrasio, shares that CSATai “helps the call center anticipate problems, offer personalized suggestions, and even anticipate returns and exchanges. This not only boosts the customer experience but also prevents negative reviews and returns, protecting the business’s reputation and profits.”
Predictive surveys and conversation intelligence can also give contact center leaders the data they need to stack-rank their priorities—and to get buy-in from other department leaders to make cross-functional improvements. Creovai’s reporting lets you view top dissatisfaction drivers alongside volume (i.e., how many times those drivers came up in calls) so contact center leaders can quickly identify the improvements that will have the biggest positive impact on the contact center and the broader organization.
With Creovai, you can also filter and view predicted dissatisfied interactions that contain specific keywords or categories, or that were handled by a specific team or agent. You can see a preview of each interaction with all its insights categories, predicted scores, and an AI-generated summary. This lets you quickly identify calls to review more closely or to use as examples when coaching agents. It allows you to learn from specific interactions and uncover feedback that a customer might not submit in a traditional survey.
Getting strategic with predictive surveys
Predictive surveys can be a valuable tool for uncovering and organizing insights from unstructured call data. However, you won’t get the full value of predictive survey scores unless you’re using them alongside conversation intelligence.
From viewing big-picture trends to zooming in on specific interactions, predictive surveys and conversation intelligence help your contact center make data-driven improvements to your customer experience. These complementary technologies let you extract value from a voice-of-the-customer data source that often goes overlooked: the contact center conversation. With predictive surveys and conversation intelligence, your contact center can move beyond being a reactive service provider to being a center of customer insights.
Top takeaways
- Predictive surveys can supplement and reduce reliance on traditional post-call surveys by using AI to assess customer sentiment directly from interactions, reducing survey fatigue and increasing data accuracy.
- Machine learning models, like Creovai’s CSATai, accurately predict how customers would rate their experiences by analyzing the language and context of contact center interactions, providing a reliable alternative to manual surveys.
- Understanding the “why” behind customer sentiment is key, with predictive surveys and conversation intelligence helping leaders pinpoint dissatisfaction drivers and prioritize improvements.
- Insights from predictive surveys enable proactive improvements, allowing contact center leaders to quickly address negative trends and follow up with at-risk customers.
- Predictive surveys transform contact centers into strategic customer insight hubs, enabling data-driven decision-making that enhances both customer experience and business outcomes.
