How do you calculate ROI for an AI chatbot and email bot?

Chatbot ROI

How to know if your company would benefit from adopting AI 

Considering creating an AI chatbot or email bot for customer service? Calculating chatbot ROI is easier than you think.  

Companies are launching chatbots to work alongside human agents. This can improve customer experience even while controlling headcount growth as volume increases. AI-powered chatbots solve very specific, tangible problems like decreasing resolution time, improving CSAT and other customer service KPIsAfter implementing a chatbot or virtual customer assistant, organizations report a reduction of up to 70% in call, chat and/or email inquiries that need to be handled by live human agents, saving up to 30% of their customer service costs. This is because AI-powered chatbots automatically resolve up to 80% of a company’s everyday tickets like order status and refund requests for retailers, early-check in and flight updates for travel companies, and troubleshooting and account updates for streaming companies. 

How do you know if the cost of building an AI chatbot and email bot is worth it? In this post, we’re diving deep into how to calculate chatbot ROI. 

What contributes to high customer service costs 

Before we dive into how to calculate your chatbot ROI, it’s important to consider why customer service is so expensive. It’s estimated that 265 billion customer support requests are made every year, costing $1.3 trillion. According to Help Desk Institute, the average cost/minute for a live chat is $1.05, and the average cost per chat session is $16.80.  The primary drivers of customer service costs fall into a few categories: 

  1. Agent Salaries:  The ability to rationalize the company’s headcount was the least-often cited impact of bots (about one in 10). However, adopting bots does help companies avoid additional headcount growth as ticket volume increases. The average hourly rate for customer service agents is $21, and when you also factor in employee benefits, capping your out-of-pocket employee salary costs can save hundreds of thousands of dollars depending on your agent team.  
  2. Day-to-day costs: Licensing fees to human agent desk platforms, overhead costs, hardware, paid time off, sick days and more. 
  3. Hiring and Training: Customer service remains one of the top jobs for employee attrition with an annual agent turnover rate of 45%. Recruiting and onboarding / training new employees is approximately $4,000.00.

Calculate your chatbot ROI with our new calculator

Calculating your chatbot ROI is easy with our new tool. You’ll need to know just a few things: 

  • Number of agents 
  • Country of support team (to understand agent salaries)
  • Industry (resolution rate differs among industries)
  • Email and Chat ticket volume (Web, Messaging, and SMS customer service)
  • Average resolution time 

Let’s look at an example. An airline with 50 US-based agents has 5,000 chat tickets and 5,000 email tickets per month, and the average resolution time is 5 minutes. Our proprietary formula takes into account standard AI resolution rates per industry, agent salaries per country, and ticket count / resolution time to determine the expected benefits a company will enjoy with Netomi’s AI. In this case, the airline would save $139,109 in the first year, which would grow to $157,320 in year two and $170,840 in year three as resolution rate increases over time.  With an AI chatbot on the team, this airline’s support team could manage a 7.2% increase in tickets without hiring any additional agents. 

Chatbot ROI

How to get the biggest chatbot ROI 

How can companies increase their ROI of a chatbot? There are five key ways: 

  • Solve the right issue: Use AI to automate the right kinds of customer queries. These are high-volume, costly tickets that are easily and fully resolved by AI. Don’t guess which use cases are best suited for automation – identify these top use cases based on an analysis of your historic tickets and data. 
  • Optimize training: If you’re using a modern AI platform that leverages deep reinforcement learning, it will improve over time. How it performs on day 1, 30, 60 and 120 will be very different. Monitor conversations to  reinforce when the chatbot did something right and conduct additional training if it didn’t classify a user’s intent correctly. 
  • Deploy chatbots on the right channels. One of the biggest mistakes that companies make is launching a bot on the wrong channel. Chatbots don’t necessarily have to be on a live chat widget on your website, but can be deployed across email, social media, messaging sites and even voice platforms. Launch your chatbot on a channel that has high volume and resolution time. 
  • ….And then scale  to other channels. Once your AI has launched on a high-volume channel and had time to improve from real interactions, extend it to other channels. Our client WestJet, for instance, launched initially on Messenger, and then extended the same “AI Brain” to Google Assistant and WhatsApp
  • Integrate with back-end systems. Empower your AI chatbot to fully resolve tickets and deflect tickets from other channels by integrating with CRM, order management and e-commerce, and other back-end systems. Your chatbot should have the ability to access personal data to solve issues on a personal level. 

It’s not just dollars: What about the Return on Experience (ROX)? 

ROXmeasures across the company to find correlations that have decisive influence on the customer and employee experience.”  Increasingly, customer service experiences have a direct correlation on loyalty and future spend. Investing in ways to provide the effortless support that people expect will impact the company’s bottom line: 90% of Americans use customer service as a deciding factor when choosing to do business with a company. So, even making a moderate improvement in CX would impact the revenue of a typical $1 billion company an average of $775 million over three years

Find out what your ROI will be if you build an AI chatbot. Try our free chatbot ROI calculator today.

 

Mani Makkar

Mani is a product marketer at Netomi where he works at the intersection of product, marketing and customer success. He has worked in media marketing at Sony Pictures as well as an A/B testing startup, VWO. He loves to read and write about all things marketing.
 

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