How data-driven chatbots are going to ensure success
The hype regarding chatbots can no longer be ignored. They have been on the rise for years, however, 2018 is thus far the flourishing chatbot era. It will come as no surprise that we can use the technology well for sales, marketing and service and therefore technology contributes to the achievement of our business objectives. Yet too often we see organizations starting chatbots to join the hype, without keeping the clear KPIs in mind.
This article was previously published via Frankwatching (Dutch only).
Data from online conversations and knowledge hubs, for example, is broadly available. However, these data remain often unused in the process of developing a chatbot. This while data can help your organization to get the most out of your chatbot. I have spoken to AI Specialist Arent Stienstra and Data Analyst Koen Hallmann about the added value of data for chatbots. This because the combination of insights and technology is the magic combination to ensure the best results.
To bot or not to bot; data has the answer!
‘Not because it’s necessary, but because it’s possible’ is the starting point for many organizations to start with chatbot technology. After all, it is interesting to keep pace, innovate, to see what technology can do and where opportunities lie. Still, it would help if our thoughts would flow from the goal point of view, rather than from the means point of view.
For example, when you use your chatbots for service, what is the purpose? Faster service and better response times? Accessibility outside opening hours? Or create efficiency for service employees by having chatbots prepare the work? The solution lies in data. Use the numbers available to predict whether a particular task would be cost effective if a chatbot would run it. Assess in advance if the chatbot can start to pay for itself and avoid incurring costs to develop a chatbot that does not meet your business goals.
Data analysis as a basis
For several months now, Arent Stienstra and Koen Hallmann have been developing data analyses to predict the performance of a chatbot. Such an analysis offers you the necessary insights to make a decision for your own organization to develop a chatbot.
“By doing an analysis, you can choose a chatbot based on hard figures, instead of relying on intuition or a fascination with the technology. An analysis of webcare conversations shows what a chatbot delivers in terms of cost savings or reduction in response times for an organization, but also provides insight into where a chatbot should not be used. Think of a complaint from a customer who requires an empathic response and can therefore be better answered by an employee,” says Koen.
Data visualization & ROI
Through an analysis of the data and a subsequent calculation, you are able to calculate the expected result of a chatbot for a specific task. By making this visual in a report, you’ll be able to prove to management why a chatbot would be appropriate.
“So do not look at the ‘what can you do with it’ and then start with chatbots, but look for where the real challenge lies. Does it solve a problem? Does it ensure cost efficiency? Does it improve your response time? Get a realistic estimate of the expected outcome before committing. An analysis allows you to determine at an early stage whether a chatbot will deliver anything. If the analysis shows that this is not the case, you will save costs by stopping the process early or giving it a different direction,” adds Arent.
Creating a realistic view
When you start using data as a predictor of the success of your chatbot, it is important to ensure quality. Do not assume that a chatbot can be used anywhere. Technology is not a solution for every task. If an analysis shows that a chatbot is not suitable for a particular task, do not continue. So far, showing sympathy and empathy is really only for the human agent. Therefore, let them answer complaints or other negative experiences instead of having them answered by a chatbot.
“The combination of people and technology remains crucial to making the chatbot a success,” says Arent.
Data-driven chatbots: 4 tips
- Develop your chatbot based on data-driven research and make sure your chatbot is problem-oriented.
- Make a problem or opportunity demonstrable in advance by insights from data instead of your own intuition.
- Stay realistic: does a chatbot most likely not deliver what you want? Then stop early or send the process to another, better application of technology.
- Always keep in mind that a chatbot must contribute to the customer experience. Avoid irritation and make sure that a chatbot connects seamlessly with your customer, but also that your chatbot does not get in the way of the customer and pass the baton on to the employee if that is good for the conversation.
Are you curious about what data can mean for your chatbot? Contact us!