How to Forecast your Chatbot ROI

chatbot metrics ROI

Forecasting your Chatbot ROI can be tricky. Is your chatbot answering your visitors’ questions? What do they think of it? Whether you already have a conversational solution in place or you are considering implementing it, you should know the answer to these questions. If not,  how can you be sure your chatbot strategy will be successful? 

Performance measures are critical to organizations to track the ROI of conversational technologies. Failure to track performance can give inaccurate results, sidetracking your entire strategy. In this post, we will explore how to identify and forecast the chatbot ROI and benefits. Let’s begin. 

Why Should You Monitor Chatbot Metrics?

Companies implement chatbots across several industries. As each audience is different, each industry requires different parameters to measure success. Some industries require more conversation, others,  such as banking, use intelligent automation to optimize repetitive requests. 

The difference in industries and business goals means you should be careful in selecting the right metrics for tracking the chatbot performance. But what’s a metric, anyway? 

A metric is a quantifiable measure that is used to track and assess the status of a specific business process.”

When designing your chatbot, align the chatbot design to strategic goals. Ask yourself what outcome you want from the chatbot. Then define the metrics that’ll get you the data you need. Once your chatbot is live, you need to monitor the metrics closely, to make sure it is meeting customer expectations. 

“Information is the oil of the 21st century, and analytics is the combustion engine”.(Gartner Research)

Monitoring your chatbot performance enables you to know what do users think about your chatbot and if the chatbot is effectively answering visitors‘ questions. Defining the best KPIs for your company’s chatbot will depend on your market, business goals, and the function you want your chatbot to perform. 

So, why should you track conversational AI metrics? 

Benefits of tracking chatbot metrics 

  • Make data-driven decisions – By using chatbot analytics, you can base your business decisions in data, thus improving the chatbot’s performance. Sometimes chatbot projects fail because the company didn’t optimize the right metrics. 
  • Get first-hand customer insights – As customers willingly interact with chatbots, companies can get first-hand insights of user journeys, tasks and exit points. Chatbot analytics dashboards enable the business to identify patterns and trends in customer interaction. This helps you understand customer satisfaction and optimize your business to meet customer needs. 
  • Constant optimization of the chatbot performance – With a better understanding of the customer journey, you can optimize the chatbot problematic spots. Maybe there is a point in the conversation that turns customers off or the conversation length. You can constantly improve the chatbot performance by analyzing the metrics, ultimately increasing ROI.

Consequences of failing to set and measure key metrics to demonstrate your Chatbot’s benefits 

Not taking care of your measures can actually affect the success of your chatbot product and ultimately your digital transformation program.

  1. Unable to demonstrate a true return on investment (ROI). Many new chatbot projects fall into this category due to starting out as an experiment or innovation project with no clear alignment to strategic benefits. 
  2. Higher build and running costs. Without a focus on benefits, your build and operating costs are likely higher than they need to be. You don’t have to look far to find organisations spending millions on their enterprise chatbot program when only a few hundred thousand dollars targeted will get the job done.
  3. Unable to get more funding for future enhancements. A weak ROI means your executives are less likely to further invest and possibly put the entire product at risk of being cut.
  4. Poor reputation within your organisation and with external customers. This one’s fairly self-explanatory. Who wants to be known for leading a failing project? How do your organization’s customers perceive the value of money invested by the business and customer experience delivered?

Types of Chatbot Metrics

Once you have your chatbot live, you surely want to check if the chatbot is generating ROI. Meaning, is the chatbot profitable? One way to look at it is by comparing it to human services. Meaning, is the chatbot profitable? Is it saving the company money compared to maintaining a customer service team 24/7? Here are some metrics you can use to find out:  

Total number of users

This rate captures the number of people that use your chatbot. This is important in new chatbots since the number of users can fluctuate. This metric can help you know about the exposure of your chatbot and refine market size calculations. 

Self Service rate

This metric determines the rate at which the chatbot resolves the cases by itself without elevating the query to second-level agents. Since the goal is to make the chatbot as self-sufficient as possible, this metric lets you improve the effectiveness of the bot. 

Satisfaction Rate

You can use your chatbot conversation to evaluate the rate on what the customers are satisfied with it. For instance, you can add the question button: “On a rate of 1 to 10, how likely are you going to recommend this chatbot to a friend?”

Machine Learning Rate

You can check if your chatbot can learn independently by analysing what percentage of user questions the bot understands correctly. Conversational AI relies on machine learning and natural language processing to understand user queries. Moreover, AI chatbots can measure progress by measuring self-service rates and satisfaction rates. 

Conversion rate

One of the goals for chatbots is converting visitors into customers, so measuring the conversion rates of the bot can give you an idea of performance. This metric can help you understand how effective the chatbot is. 

Fallback rate

No chatbot is perfect. Even chatbots with natural language processing sometimes have trouble understanding what a user says. Identifying the triggers the errors help you improve in your chatbot. Usually, there are three types of triggers. 

  • Confusion –  when the either user and bot are out of sync on which stage of the conversation they are at, or the user’s enquiry is more complex in language than the bot’s NLU can handle.
  • Out of scope – when the user sends messages that are outside the range of the current understanding of the chatbot.
  • Escalation to human or another channel – when the bot requires escalation to a human-intelligent agent for assistance. 

Every type of trigger can give you information about the performance of your bot.  

Goal completion rate

This metric measures the percentage of chatbot interactions that result in a successful engagement. According to the goals you set for your chatbot, you can track them using conversational analytic tools, like on the example below by Bot Analytics.

4 Steps to Forecast your Chatbot ROI

The key to having a successful chatbot, one that brings on revenue, is to measure how useful the chatbot is.  As with websites and any online, customer-oriented product, you can speculate what your users want or you can measure it. You need to make sure you are serving customers according to clear metrics. Here are four steps to forecast your chatbot ROI. 

1. Define your metrics strategy 

First, when you design the chatbot, you need to define what metrics will bring you the results you’re looking for. Defining the right metrics can be the difference between knowing what’s going on with your chatbot and flying in the dark.

So check first what is the goal you want your chatbot to achieve? Is it customer retention? Is it onboarding new customers? Then, check for bounce rate, and new customers rate among overall metrics of performance. Once you have the list, it is time to look for the right tools to check them. 

2. Add a chatbot analytics platform

Using a chatbot analytics platform will help you automate and have a clear vision of your metrics. You can categorise analytics solutions on three tiers: 

  1. Built-in analytics – you rely on built-in analytic tools that come with your website or software solution. For example, some companies use Google Analytics to measure the performance of their chatbots. This generalist option typically brings poor results, as it is not geared specifically to chatbots. 
  2. Specialized tool or add-on –  an example of this can be adding Google’s Chatbase, their analytics tool for chatbots. While a bit more complete, still doesn’t cover specific business goals or measuring ROI. 
  3. Custom analytics – For optimal performance, it is better to use an enterprise analytics platform. A customized solution can include the particular metrics to demonstrate clearly how much money your chatbot saved, the ROI, and other KPIs to align with your business goals. 

Some specialised tools that can help you gain more insight and calculate your chatbot performance.

  • Bot metrics – This tool measures automatically every interaction a user has with your bot. This prevents having to tag events manually. You can get answers in real time without needing to wait for the code to deploy. 
  • Dashbot- The advantage of DashBot is that it not only measures unstructured data but processes images, audio and the users’ own words.   
  • BotAnalytics – This tool has the advantage of allowing multi-platform analysis, supporting up to 12 different platforms. This enables companies to see the results of their chatbots in all platforms at once. 

3. Track and forecast your metrics

Once you have your metrics and your tools, it is time to start tracking. Keep an eye on metrics that give you a picture of how the chatbot is saving you money. You can group analytics into two different types according to what you want to measure:

  1. Chatbot performance – This includes KPIs that measure conversation length, fallout rates, machine learning rate, fallback rates, self-service rate. . 
  2. Business outcome KPIs–  For instance, customer retention, goal completion rate,  bounce rate (the number of times the chatbot was opened but not used). By learning more about your customer journey, you can calculate and predict the ROI of your chatbot. 

You need both groups of metrics to track both chatbot performance and business outcomes. This will enable your company to apply continuous improvement and ultimately meet set business goals. 

4. Calculate and predict the ROI of your Chatbot

 Comm100, the chatbot analytics company, brings a four-step system to calculate your chatbot ROI. 

  1. Identify Eligible Queries – what queries do you receive over your chat a chatbot can solve.
  2. Calculate Percentage of Eligible Queries– what percentage of the chats are made of simple queries vs complex queries. 
  3. Calculate Agent Cost – how long your agents spend on these simple queries. Multiply it by year and hourly pay of your agents.
  4. Calculate Chatbot Cost – how much it costs a custom chatbot solution. 

Compare both options to find out how much you can save by installing a custom chatbot solution. Hiring a professional chatbot development company can save money and hassle from the start. Pure Speech Technology designs and develops chatbots focused on increasing ROI. We define the metrics from the ideation and use case selection process, working with them in mind to bring optimal results. Check below an example that gained the government agency multiple award wins. 

How the members of the Pure Speech Technology team focused on  benefits to demonstrate clear ROI and focus on continual improvement on Transport for NSW’s Chatbot

The Transport Bot is a digital virtual assistant which provides travel and service information in real-time for Sydney Metro and NSW, Australia. The challenge was to create an intelligent automation strategy that catered to citizens preferences in channel engagement. Whilst the approach below is simple, you might be surprised on the number of chatbot deployments that have no clear metrics and can’t demonstrate a ROI. Here’s how we approached it:

  1. Identified key contributing metrics to desired high-level benefits (such as typical drivers you might find in a customer-centric organization to “improve customer experience”, “increase self-service enquiries”, “reduce cost to serve”).
  2. Set up the tools and processes to aggregate the data and report on ROI across multiple customer channels.
  3. Applied financial data to derive dollar impacts.
  4. Key metrics were reviewed on a daily basis, then weekly, to identify opportunities for continual improvement against goals.

The result was not only a multi-award winning voice assistant / chatbot, but TfNSW was able to gain visibility into key metrics which influence core benefits. This ultimately helped prioritise CX improvements, demonstrated a reduction in cost-to-serve and prove dollars saved.

Don’t guess if your chatbot strategy will be successful. Partner with those with a proven track record

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