Chatbot A/B Testing: How to Boost Bot Performance

It is 2021, and the role of chatbots is getting vital and better with every passing day. Today, the Conversational AI market is growing at an exponential rate! Over 55% of B2B websites already leverage chatbots for boosting customer engagement.

While talking about the numbers, as per Cognizant, the global chatbot market will reach a whopping USD 1.3 billion by the fiscal year 2025.

So, as you read this article, chatbots are trawling through enormous swaths of data and empowering business use cases such as lead generation, content delivery, product ordering, flight booking, companionship, healthcare diagnoses, and more.

Why Are Chatbots Important?

Thanks to clever conversational design chatbots are taking over workflows, servicing customers better, and becoming an indispensable part of businesses. Brands are providing:

Instant Responses: Chatbots bring aboard automation and state-of-the-art features such as real-time interactions, smart responses and thus, enable a seamless user experience. Hence, chatbots dramatically reduce potential churn and customer frustration.

Higher Accuracy: Chatbots eliminate the scope of human error. Often performing the same task time and again can be strenuous for agents. With chatbots, brands can ensure error-free task execution and enjoy the same performance throughout time.

Self-Service: Today, customers like to be in charge. When paired with a conversational interface and instant personalization, self-service takes a whole new level and brings incredible value to the brand. Chatbots today can easily direct customers towards answers. Thus, reducing ticket deflection rates and boosting customer satisfaction manifolds.

What Makes a Chatbot Effective?

Chatbots are emerging as an effective tool to cast a good first impression on the audience and encourage them to become loyal customers.

Now, the way a chatbot interacts with the audience draws the line between a closed deal and a missed opportunity. A/B testing is one such method that helps you optimize the digital experiences of the audience.

A/B Testing & Chatbots

A/B testing, also known as bucket testing, is a methodology of running multiple variants of a service or product to check which outperforms the other. In other words, A/B testing helps find the variant of the service or product that encompasses the best engagement & accomplishes the desired goal most efficiently.

Now, in the purview of chatbot applications, A/B testing allows makers to deploy multiple chatbot variants in parallel, test every aspect of the interface & determine the effectiveness of the target feature. Through A/B testing, devs and makers can determine which part of the flow yields more conversion and discover the flow parts that warrant improvements.

Through effective A/B testing, you can determine pre-sales sequences that have the potential to generate better leads, provide a better onboarding experience that assists customer conversion or discover customer success sequences that lead to higher consumer satisfaction.

In other words, you can determine the efficacy of different onboarding and buy flow scenarios across different variants in your A/B testing pipeline. Thus, calculate drop-off rates and other KPIs (Key Performance Indicators) and make critical changes to boost user experience.

How To Use A/B Testing to Create Better Chatbots?

1. Pick a Platform For A/B Testing

The primary step is to select the right platform on which you can build, test and deploy your chatbots.

Online, you can find an array of free/premium no-code chatbot-builder & testing platforms such as Landbot.

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How to choose the platform depends solely on your needs and custom requirements. Different platforms offer different functionalities to experiment with the characteristics of your chatbot.

Thus, once you decide on a platform, A/B testing can help you conduct randomized trials and determine which variant is better, has better quality assurance & should go to production.

2. Understand The Customer Journey

While creating the service strategy for chatbot users, you must get to know your customers in detail. Back in the day, getting the attention of the customers wasn't all that difficult. However, today the market is overcrowded and customers are more dynamic, exhibiting an array of shopping behaviors.

Thus, just like targeted marketing and sales plans, you can also consider including different chatbot A/B testing flow setups. This will help you discover which flow entices the customers the most.

One may consider asking questions like:

What are the common difficulties people face with my product?
Which particular part of my product confuses most people?
What are common questions or FAQs that customers ask for support?

Once you've all the answers, you can start fine-tuning your chatbot and create a potential, standardized chatbot flow.

Through A/B testing, you can devise an array of interactive chatbot flows and record which variant churns out more engagement and accomplishes the goal more readily.

3. Pay Attention to Chatbot Funnel

According to a study by Forrester, most chatbots fail to make it big for a variety of reasons.

Often creators don't clearly define the chatbot's purpose or set goals that are too ambitious for the existing technology. Now, to boost the effectiveness and viability of a chatbot, you may consider analyzing the chatbot funnel and start monitoring certain factors.

What differentiation factors to choose to monitor performance depends on your case.

For example, if you want to improve the Sales & Marketing of your brand through chatbots, you may consider monitoring the 'Number of Leads Generated’. You may also monitor visual factors such as the chatbot's design, color, fonts, content placement, and conversational factors such as tone of chat for maximum user engagement.

4. Must-Have Monitoring Metrics

How closely you monitor critical chatbot metrics paves the way to user engagement and chatbot success. While AB testing, you may consider measuring key metrics such as:

Activation Rate: How long does a customer take to respond to the chatbot
Fall Back Rate: FBR or fall back rate is the metric that tells us how often a chatbot fails a task
Retention Rate: Number of visitors who return to the chatbot in a specific timeframe
Self-Service Rate: How well a chatbot resolves a query without any manual intervention
Confusion Triggers: The frequency of your message misinterpretation by a chatbot

5. Capture A/B Testing Data

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Often chatbot testing involves an overwhelming number of A/B test scenarios & metrics.

To create a near-perfect chatbot flow, developers consider running an array of testing simulations and checking every flow for results. It's necessary to record and analyze the A/B testing results from each scenario.

To effectively capture the results for all A/B tests, you may use ready-to-use templates and record metrics such as Fall Back Rate (FBR), Retention Rate, Activation Rate, User Satisfaction, etc. The practice helps you make sense of the data and aid your AB testing decision-making. You can further visualize this data in the form of graphs, charts and generate valuable visual insights.

Are you wondering how to make timelines for an effective AB Testing scenario analysis?

Intuitive-looking, ready-to-use templates help users visualize testing data, compare & contrast between different variants and choose the best performer with added ease.

Final Thoughts

With A/B testing, the end goal is to boost user engagement and hence, the conversion rate. You can come up with a state-of-the-art customer service strategy for chatbots via gathering AB-test data and simultaneously implementing changes in your existing chatbot strategy.

You can also run multiple AB tests on top of each other. The second AB test should run on top of insights gained from the first experience. Thus, by reiterating the strategy, you can achieve the desired Sales & Marketing results in no time. As you read, there are brands out there leveraging AB testing to increase leads, sales and churn out happier customers.