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Customer Intent Analytics

Customer Intent Analytics

Predictive Intent Analytics

Throughout the lifecycle, customers turn to contact centers to seek answers, or to voice their issues. Every customer call to the contact center leaves trails that are rich in explicit, as well as inferred, intents in the form of data sets which exist in multiple databases, typically in an unstructured format. So it’s important to understand customer wants at scale, and understanding of customer intent has significant business implications.

VOZIQ’s Intent Analytics Transforms Your Contact Center

Post-call text notes are a treasure trove of deep insights into the wants, needs, and pains of your customers. Effectively utilizing them can work wonders for your business by increasing customer retention, boosting customer profitability, and reducing revenue risk.

Most importantly, this piece of data uncovers the real emotions of the customer, along with many other insights.

Our predictive Intent
Analytics Process

VOZIQ’s algorithmic caller intent analytics leverages the recent advances in text analytics technology, and applies them to customer interactions data captured in contact centers, establishing the true caller intent accurately for each and every caller.

1
Data collection
Call centers have unexplored valuable insights (agent notes) that can help understand the exact caller intent. VOZIQ’s engine implements text analytics on the agent notes to extract the call reasons. Then, the speech analytics systems use statistical modeling techniques to provide a statistical gist of the interaction in the call, which can be used to identify call reasons using spoken words.
2
Intent list creation
Machine learning techniques, such as clustering and text NLP analytics, are applied to identify the reasons for customer contacts.
3
Develop rules to assign intent to calls
Based on the intent-activities map, a special program extracts the map and creates the data-driven rules.
4
Build predictive models
Based on customer profile, actions, and behaviors, data scientists build predictive models, with a focus on finding the right set of variables that will optimize the model, and provide the most accurate predictions—even for unseen customer data.

Benefits of Using Caller Intent Analytics for Contact Centers

Predictive caller intent analytics unifies CX data sources, uncovers critical CX gaps, and helps contact centers proactively engage customers across the customer lifecycle.
Unlike sample-based customer research, predictive intent analytics takes into account every customer data source for every call to a contact center. This leads to very accurate and actionable insights.
Caller intent analytics identifies the reason for each call, thereby uncovering opportunities to personalize customer engagement and predict customer actions.
By uncovering deeply granular insights about your customers, agents, and operations, intent analytics helps you focus on actionable insights about customers and operations and build a customer-centric culture.

A good caller intent framework can support various business use cases, supporting cost reduction, customer experience improvement, and the revenue growth goals of any business.

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