Conversational AI Terminology

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A/B Split Testing
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Glossary  /  A/B Split Testing

A/B Split Testing - Definition & use cases

In general, A/B Test explains the effectiveness of one process over another. It is one of the popular ways to understand what works best for a specific set of conditions. Traditional marketers have employed A/B test to evaluate the efficacy of marketing campaigns. However, when it comes to A.I chatbots, the significance of A/B Test further heightens. The scope of business chatbots can be best defined by 'how they start, advance, and end a conversation' with users. As conversations are the primary means to interact with your customers, it's essential to build a flawless conversational flow for your chatbot. You can do so by composing and A/B testing two different conversational flows focused on the same campaign. You can also define the percentage of users that should be interacting with a particular conversational flow. With A/B test, you can uncover the effectiveness of a conversational flow over another. And, by making use of Goal Tracker in the conversational flow, you can further understand at which conversational point users are leaving.

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