July 27, 2022
The most frequent complaint from a chatbot client is, “My employees initially experimented with our chatbot to explore some fascinating features, but they quickly lost interest, and everything became dormant. So, the chatbot doesn’t seem to be performing in our organization.”
This merits the question – is this a user engagement or a chatbot capability issue?
Simply deploying a chatbot and expecting employees to realize the value and immediately start using chatbot for all their requirements will not help the organization achieve the desired ROI.
Before we pin the blame on chatbots or technology in general, we must acknowledge that we, as an organization, are accountable for our chatbot engagement, which is the fundamental KPI for its success. And thus, we need to encourage our employees to engage more with the chatbot.
The first step to achieve this is to ensure that the chatbot is seeking feedback after every conversation from the employees, inculcating that feedback in its conversation flow and asking for more feedback. If this loop fails to show any success, then you might have to reevaluate the chatbot’s capabilities.
What does chatbot success look like to you?
The most frequent error is when organizations evaluate the effectiveness of their chatbots by counting the number of interactions between the chatbot and employees, much like how they count the number of views on their website through analytical tools.
After the successful implementation of the chatbot, your operations will continue to run as is, so it’s important to establish norms for your chatbot KPI based on things you already know and that are relevant to your use cases. Avoid trying to boil the ocean by determining novel success by measuring chatbot KPIs.
Don’t force any new channels on your employees; your chatbot should operate more efficiently in the channel where your organization currently operates. Again, a chatbot is not responsible for the lack of interaction. Therefore, avoid comparing the engagement results of your chatbot with other chatbots on the market. Moreover, it is impossible to identify how other chatbots are performing the same functions (use cases) and collecting the same data as you have configured in your environment.
Improve your chatbot’s performance across all channels, especially on your employee-preferred channels. It’s ideal to compare your chatbot’s performance against its other channels, rather than that of other chatbots.
Performance indicators to evaluate the effectiveness of your chatbot
The following KPIs can be measured and analyzed regularly to understand how your chatbot is performing.
- Self-service: Measuring the success rate of self-service capabilities can help you determine the number of users who were able to get the assistance without any human intervention.
- Natural language: Checking the chatbot’s accuracy rate can help you understand how your chatbot interprets the user’s statements.
- Frequently asked questions: Analyzing repeated inquiries will assist you in understanding the user’s requirements. This will allow you to concentrate on the use cases that are most important to your users.
- No response rate: Tracking this indicator will allow you to determine how frequently your chatbot fails to respond to user questions or how frequently users receive irrelevant responses from your chatbot.
- User feedback: Asking for simple feedback will help you know the user’s general satisfaction rate with the chatbot’s suggested solutions.
- Actionable use cases: Measuring the success rate of chatbot activities will allow you to determine the automation capabilities and the rate of service ticket reduction.
- Interaction rate: Monitoring this KPI indicator will allow you to determine the genuine user engagement rate. That is the average number of chats exchanged between the user and the chatbot per conversation.
- Understanding skills: Analyzing how many questions your chatbot must be asked before it can offer the essential information to its users can assist you to determine the chatbot’s efficiency.
- Usage distribution: Tracking this indicator will help you determine the most suitable time of the day when the users wish to use your chatbot.
- Conversation length: Measuring this parameter will allow you to assess the average length of your chatbot’s interactions with users.
- Response rate: Analyzing this key indicator will allow you to determine how many queries your chatbot has answered successfully or not, based on the user’s expectations.
- Point of failure: Understanding this indicator can help you determine which user statements in the conversation have caused your chatbot to struggle to respond.
- Returning users from unique users: Tracking this indicator will allow you to determine how many users utilize your chatbot regularly.
- Open sessions: Tracking this indicator will let you determine the efficiency of your chatbot by tracking the number of sessions that are concurrently active with your chatbot.
Knowing these KPIs is critical for determining the overall performance of your chatbot. There is no single perfect indicator that every organization or chatbot should track all the time. It is the organization’s responsibility to select the most effective ones based on its business strategy, goals, and user expectations.
The focal point for the chatbot clients
There are many chatbots on the market that can fulfill human demands, but only a few chatbot providers can stand behind their chatbot competence. Most chatbots have low user engagement and success rates. Therefore, identify a chatbot vendor who has a feature-rich chatbot and experience in implementing and customizing chatbots for any organizational needs.
Our readers can explore DRYiCE Lucy, an AI-powered intuitive and conversational platform that enables organizations to fulfill requests by offering superlative user experience. It provides consistent, two-way, human-like communication with seamless handoff across many domains such as IT, Operations, HR, Admin, and Security. Lucy has enterprise-level scalability and continuously learns and improves over time. It allows organizations to feature timely responses round the clock, devoid of human error, across multiple channels like chat, text, email, and voice.
Existing integrations with enterprise systems and 500+ pre-built use cases can be easily augmented, supplemented, and maintained, which allows for rapid time-to-value.
For more information on DRYiCE™ Lucy click here.
He is passionate and keen to remain knowledgeable about IT infrastructure, Artificial Intelligence (AI), Automation, Neural nets, Machine Learning (ML), Natural Language Processing (NLP), Image Processing, Client Computing, Blockchain, and Public/Private Clouds. His research interest circles around stack solutions to enterprise problems.
Kumar has a MS in Software Engineering (SE) along with several technical certifications like MIT’s Artificial Intelligence (AI), Machine Learning (ML- Information Classification), Service Oriented Architecture (SOA), Unified Modeling Language (UML), Enterprise Java Beans (EJB), Cloud and Blockchain, etc.