Take it to the Next Level with Cognitive Search and Predictive ML
By layering-in cognitive search, structured and unstructured data can be pulled from various enterprise data sources, helping the chatbots provide faster and smarter responses and elevating the entire customer-service experience. Think of it as an advanced version of enterprise search powered by AI that brings together numerous data sources while providing automated indexing and personalization.
The cognitive search solutions currently available use AI capabilities such as natural language understanding (NLU) and ML to ingest, understand, and query digital content from multiple sources. They also use ML to understand and organize data, predict users’ search queries, continuously learn, and improve answers based on user feedback.
Conversational AI powered by cognitive search makes it possible to derive insights from a consistently growing collection of data that can be used across the company. This gives it the potential to greatly improve how an organization's employees discover and access relevant information. For example, agents can enter a query in natural language, and Conversational AI will understand the context and invoke cognitive search to find more insights. To improve the AI engine, these insights can be presented to the agent, who can visualize the selective options, and give feedback on the retrieved information, which in turn improves adaptive learning.
One such Conversational AI-based cognitive-search solution was implemented for a healthcare client’s contact center. The solution provided sales agents with access to widespread digital content including enrollment options, medical supplement details, etc., allowing quicker, more-efficient responses and resolutions. As a result, the company saw reduced average call handling, faster information access, improved sales opportunities, and dramatically improved users’ call-center experience.
Yet to drive business outcomes, companies need customer insights to become more personalized and predictive in nature. Using these insights, businesses can proactively pitch the products that align with each customer’s needs. For example, McKinsey reports that as much as 35% of Amazon’s revenue is generated by its recommendation engine. Combining predictive ML models and cognitive search with Conversational AI can deliver precisely the type of hyper-personalized customer experience necessary to capture these opportunities.
Intelligent product recommendations provide natural and logical upselling and cross-selling opportunities that resonate with the customer. The product-recommendation tool automatically identifies the customer’s interest through historical data and provides the right suggestions. Customers with no purchase intention suddenly find themselves interested in doing so – and small purchases can pave the way to larger ones. Data-driven predictions make customer interactions more meaningful, while helping Conversational AI deliver hyper-personalized, intuitive experiences to customers that also improve the quality and efficiency of operations.
The Importance of training the Machine Learning Model
Ensuring a contact center’s ability to leverage this powerful combination requires a seamless, intelligent system built for the enterprise’s specific needs. This, in turn, requires training the models to perform appropriately. The detailed steps to train the model include:
- Identify features from the enterprise data of user transactions, personal user data, etc. and transform to enrich the data.
- Build the machine learning (ML) model to perform auto classification of the transactional data.
- Evaluate the model accuracy, tune the hyper parameters, and deploy the trained model.
- Expose the model as a REST API and monitor the predictions on an ongoing basis. Manage the models and model versions.
Five Key Benefits of Conversational AI
Conversational AI powered by cognitive search and predictive ML delivers more personalized, intuitive customer experiences while improving the quality of business operations and workflows. In practical terms, this combination of technologies brings five key benefits:
- Efficient functioning: Instant responses to customer queries, reduced agent work (pulling up user data and enterprise data), and improved workflow functioning and management with fewer errors.
- Revenue generation: Efficient operations lead to positive outcomes and better value delivery to customers. This, coupled with superior customer experience, reduces call handling times and boosts customer loyalty – and that can increase revenues.
- Cost-effectiveness: The solution reduces cost involved in customer support and increases productivity of the agents.
- Data-driven decisions: AI and ML prediction helps companies learn about customer preferences and behaviors based on the data related to transactions, interaction, and feedback.
- Scalability: Enterprises can meet future business needs by gaining scalability and the ability to adapt to evolving needs with consistent efficiency.
Conversational AI infused with cognitive search and predictive ML will enable a more-personalized virtual assistant that can address every user request. Multiple chatbots will converge to a single, more efficient, and decisive virtual agent, paving the way for a more-interactive user experience. The ability to identify a user’s mood with voice modulation, body language, and emotional signals makes it possible for evolved chatbots to handle complex questions and carry out multifaceted conversations. Additionally, using big data analytics, companies will be able to predict customer churn and provide recommendations from user data available on multiple data sources including social media. In short, by revolutionizing their contact-center automation, companies can drive efficiency and revenue by moving beyond the scope of simple chatbots.
- Chatbots Will Appeal to Modern Workers - Smarter With Gartner
- Wipro HOLMES™ Platform
- The Future Of Conversational AI (forbes.com)
- 10 Examples Of Predictive Customer Experience Outcomes Powered By AI (forbes.com)
- How retailers can keep up with consumers | McKinsey