Mastering Organizational Chatbot Deployment

Modern organizations are embracing chatbots to streamline operations, reduce operational costs and improve productivity. Likewise, and largely thanks to chatbot testing assurance, customers and prospects are now much more comfortable engaging with these virtual assistants.

Chatbot adoption

Many companies around the world are already rapidly integrating them into customer service interactions, making them a critical pillar of employee communications. Infact, Gartner predicts that by 2027, approximately 1 in 4 organizations will have chatbots as the primary customer service channel due to the clear business and customer benefits.

Advancements in artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and natural language understanding (NLU) have further propelled this uptake. Enhancements and new learnings in each of these areas have made chatbots more human-like and helped to bridge the gap between technology and human interactions. This has led to greater adoption by organizations and consumers alike, as well as improved customer satisfaction ratings.

To successfully roll out chatbots across their organization without causing unforeseen negative impacts on operational processes, customer experiences, or agent experiences, organizations should follow a number of steps and carefully monitor the process and resultant outcomes at all stages through AI chatbot testing.

Best practices for rolling out a chatbot

1. Understand existing processes and customer interactions: Prior to any planning, organizations must understand exactly how customers currently interact with the business and how the processes before, during and after these interactions occur. It’s essential to take note of any available key performance indicators (KPIs) as they can serve as benchmarks once the chatbot is established.
2. Define clear objectives: Next, to begin the initial planning phase, the team should define specific goals and objectives for the chatbot. To do this, they will need a comprehensive understanding of existing pain points, the chatbot’s specific use cases and tasks, and the desired benefits for the organization.
3. Select the appropriate use cases: The implementation team should choose use cases and training materials that directly align with the chatbot’s specific requirements.
4. Develop an initial training set: Once the use cases are understood, the team must create a specific training set from which the model can learn. This should begin with simple tasks and requests and develop further as the team progresses through the following steps. Much of this process can be automated to reduce resource requirements, as well as time to launch.
5. Understand the audience: Tailor the chatbot’s interactions and responses to match the expected user base, bearing in mind their preferences and expectations. For instance, if the chatbot is designed to answer technical questions, it should respond in a concise and technical manner. Alternatively, if it is intended for handling basic customer FAQs, it should engage in informal, conversational interactions.
6. Develop a conversational design: Based on the understanding of the audience, the team should design a natural, user-friendly conversational flow. These flows should easily and effectively guide users along the interaction. It should ask relevant questions and provide appropriate answers. This process should be refined using automated testing and user feedback.
7. Build a knowledge base: Build a robust knowledge base that the chatbot can learn from and subsequently provide the most accurate and relevant information. Continuous monitoring and regular updates are essential throughout the entirety of the chatbot’s life cycle.
8. Select the right technology: Next, the team must choose the appropriate technology stack and chatbot framework based on their desired objectives and requirements. At this stage, it is critical to understand whether a rule-based or AI-driven chatbot is the most suitable solution.
9. Ensure integration capability: It is vital that the chatbot integrates seamlessly with the organization’s preexisting systems and databases. The chatbot should have the ability to instantly access and retrieve data from these systems and additionally contribute its own experiences and learnings.
10. Maintain data privacy and security: At all times, it is crucial that data privacy and security is kept at the forefront of chatbot development and training. This is especially critical in cases where the chatbot is required to handle highly personal user information such as banking details or social services information.
11. Test comprehensively: Conduct extensive testing, including user experience testing, load testing, and quality assurance testing on an ongoing basis. This will help to more promptly identify and resolve any issues in the chatbot’s functionality and thus limit any adverse effects. While a small amount of this testing could be conducted on a manual basis, it is likely to be an impossible task to complete them all. Ongoing and automated testing is more efficient and comprehensive, saving the organization time and resources.
12. Provide user support: As soon as the chatbot is launched and in use, it is crucial that there is a foolproof way for users to switch to live human assistance when needed, for example if the chatbot is unable to address the query it is presented with. The transition from chatbot to human agent should be smooth and not require users to repeat previously provided information.
13. Monitor and analyze performance: Likewise, following the chatbot’s launch, the implementation team should be continuously monitoring the chatbot’s performance through both statistics and feedback from users. Comprehensive analysis of this data should identify areas for improvement and enhancements. It should also be compared against established benchmarks to ensure a positive impact on the organization. This helps to measure return on investment, cost savings, user satisfaction rates and much more.
14. Scale gradually: The initial rollout should be limited to a specific user base or use case. This allows the team to gather real feedback, and make improvements. Once any adjustments have been made, the enhanced chatbot can be rolled out to other areas of the business. This cyclical process should continue until all desired use cases are accommodated.
15. Promote it: Educate employees about the chatbot’s capabilities and the benefits they will experience to alleviate any concerns. Similarly, encourage customers to interact with the chatbot through website pop-ups or push notifications on mobile applications to drive usage.
16. Evolve and adapt: Be prepared to adapt and evolve the chatbot based on changing business needs, emerging customer concerns or trends, and relevant user feedback.The chatbot will need to undergo continuous testing, monitoring and development throughout its lifecycle.

Through following these best practices and steps, organizations will increase the likelihood of a successful chatbot implementation, reduce agent apprehension and boost user uptake and adoption. Additionally, they will likely see significant operational benefits, including cost reductions, improved efficiency and enhanced customer service ratings.

Automation of chatbot testing

However, as highlighted above the learning and development of chatbots is an ongoing process. This means that continuous monitoring and testing are essential. It would be impossible for this scale of testing to be carried out manually and as such AI chatbot testing solutions and AI chatbot testing tools are a necessity for any organization that wants to ensure a successful rollout of their chatbots.

Cyara Botium is the only automated chatbot quality assurance solution that offers value at every stage of the chatbot’s life cycle. Through regression testing, NLP testing, end-to-end testing, security testing and performance testing, to name but a few, Botium provides organizations with confidence that their conversational AI technology is meeting and exceeding the necessary quality, security, and performance standards, benefiting both businesses and customers alike.

Cyara revolutionizes the way businesses transform and optimize their customer experiences. Cyara’s AI-based CX Transformation Platform empowers enterprises to deliver flawless interactions across voice, video, digital, and chatbot experiences. With Cyara, businesses improve customer journeys through continuous innovation while reducing cost and minimizing risk. With a 96% customer retention rate and world-class Net Promoter Score (NPS), today’s leading global brands trust Cyara every day to deliver customer smiles at scale.

To learn more, visit cyara.com or call 1-888-GO-CYARA.