A/B testing helps you to optimize your conversational artificial intelligence (AI) strategy. It is sometimes also referred to as bucket testing, split-run testing, or split testing. A/B testing is used in behavioral analysis and is especially helpful in the business world. It gets its name from the experiment that underlies its test. A/B testing is an experiment that runs with two variables—one control and one that is identical except for one change in a user’s behavior.
Dasha AI encourages A/B testing with its products to help you create seamless conversations between your customers and software. This article will outline the correct metrics to use, the areas of impact test, and roadmap prioritization.
What Are the Right Metrics?
There is no one-size-fits-all answer to the correct metrics to use when engaging in A/B testing for your organization. Your company’s unique character, your products and services, and your goals for growth moving forward will all play a role in the metrics that you choose.
To pick measuring metrics, begin first with something in your company that you would like to improve. For example, here, the improvement being discussed is the customer conversational experience. Perhaps you would like your artificial intelligence to communicate more naturally with your audience.
Once you have a goal, you can choose metrics that will best determine how to achieve that goal. You will then choose two options—one control and one more experimental metric—to see which option helps you to achieve your goal better. Note that your metrics can measure both qualitative and quantitative data. However, quantitative tests will provide you with data that is easier to measure and less subjective.
Some basic ideas of metrics to use to create a conversational AI strategy include but are not limited to:
Greetings to use when introducing your AI;
What to say when attempting to clarify what a customer is asking; and
How to respond to a customer that wants to speak to a human representative.
Impact Area Test
While running your A/B experiment, you will want to closely pay attention to the areas that are impacted by your chosen metrics. Sometimes, though a chosen metric may work for your selected goal, it could have negative consequences in other areas. Alternatively, perhaps you will identify additional benefits of your chosen metrics that have never crossed your mind and will want to expand on these benefits in additional testing.
Like a human conversation, AI conversational strategy is interdisciplinary and intertwined. Saying the wrong thing in a conversation could impact its direction and the customer’s perception of your brand. So, it is important to perform holistic testing and reduce your blind spots in the outcomes of testing.
Roadmap Prioritization
A roadmap is essentially your experiment design. Just like you had to develop a hypothesis and experiment for your high school biology class, you must do the same in A/B testing. After creating a hypothesis and choosing your metrics, you will have to design a test that actually tests your metrics correctly. After all, experiment results are only as good as the experiment itself!
Roadmap prioritization to build a conversational AI strategy is typically done using a flow-chart sequence. You can draft a rough sketch on paper and then transfer it to the digital realm using Dasha AI’s programming language. Once you run your experiment, the real work begins—analyzing your results to see what you can do with your findings!
Conclusions
Conversational AI strategy is complex and requires patience and technical competency. A/B testing will likely take you a few tries to get the hang of and actually get results that will help your software. Like human conversation skills though, the more you and your AI practice together, the better you both will get.
Check out the different ways you can train and test your AI today with Dasha’s software. Fortunately, you do not need to have previous programming experience and can learn as you go. So, what are you waiting for? Get to testing today!
