Astronaut loading animation Circular loading bar

Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(5)

Customer Journey(12)

Design(36)

Solar Industry(6)

User Experience(56)

Edtech(10)

Events(34)

HR Tech(2)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(17)

Testing(9)

Android(47)

Backend(30)

Dev Ops(7)

Enterprise Solution(27)

Technology Modernization(2)

Frontend(28)

iOS(43)

Javascript(15)

AI in Insurance(35)

Insurtech(63)

Product Innovation(49)

Solutions(19)

E-health(10)

HealthTech(22)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(132)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(17)

FinTech(50)

Banking(7)

Intelligent Automation(26)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

Here’s How You Measure the ROI from Chatbots

5 minutes, 6 seconds read

IBM reports that globally businesses spend over $1.3 trillion/year to handle roughly 265 billion customer calls. Chatbots spring up to minimize the expenditure on handling customer queries, especially the most redundant ones.

It’s quite common for businesses to assess the return on investment before adopting new technology.

However, ROI from chatbots may vary according to the purpose it serves. For example, an insurance chatbot ROI differs from that of an HR chatbot. Here are certain parameters to consider for calculating the return on investment from chatbots.

#1 Average Human Live-chat Cost

The total number of tickets raised per month and the number of agents involved gives an idea of the average price per contact.

According to Help Desk Institute, the average cost/minute for a live chat is $1.05, while the average cost per chat session is $16.80. Assuming an organization handles 10,000 chats in a month, the cost incurred sums up to $168,000/month.

Depending on the number of people involved and their compensation, you can calculate the amount you’re spending on your organization’s customer support. Here’s a salary reference, which can be used in further calculations.

sample customer support operational cost

The salaries mentioned are referred from Job Futuromat 2019 wrt 12 months, 18 working days, 8 hours.

The actual operational cost also depends on material resources invested like office space, conveyance, communications, gadgets, etc. You can consider these aspects on your chatbot ROI calculator.

#2 Bot Installation Cost

The phases of bot installation cost involves brainstorming sessions, integration, and training both bots and agents.

During kick-off sessions, stakeholders discuss the scope of the bot, define goals and responsibilities, and make a project plan. After this, programmers and managers integrate the bot on the organization’s website and other platforms. Customizing the bot according to the client’s support cases covers the bot training phase. Testing the bot and training agents to use it are also factored into the ‘bot’ installation costs.

According to Ometrics, the average development charge for a chatbot may range from $1,000 to $5,000. But, this is a one-time charge, and after that the bot-developer may bill for maintenance charges.

chatbot roi calculator: installation cost

If the chatbot requires a higher level of customization, then the bot-developer may also claim additional charges. Also, the number of days spent for bot installation varies according to industries and organizations.

#3 Gains through Bots

Here we’re assuming all the customer queries are routed through the bot and it is accurate 50% of the time. Out of the 50% queries handled by a bot, if half of them are self-served and the remaining required human intervention, then monthly gains from the bot can be-

chatbot roi calculator: gains from chatbot

You can find the exact cases and accuracy from your bot’s analytics dashboard.

#4 Monthly Maintenance Cost

Like humans, bots also require human assistance for its successful operation. Its monthly maintenance cost is a summation of the organization’s human resources it needs and developer’s charges. Here, let’s assume a chatbot maintenance fee, which ranges from $100 to $1,000 a month. Similar to the bot development charges, maintenance fees vary according to bot capabilities.

chatbot roi calculator: montly maintenance cost

#5 Chatbots Return on Investment Calculation

The return on investment is a ratio of benefit from the investment to the cost of investment. It evaluates the efficiency of an investment. Mathematically, ROI = (Current Value of Investment – Cost of Investment) / Cost of Investment.

Since chatbots incur a one-time development cost and recurring monthly maintenance cost, here’s the chatbot ROI calculation from both perspectives.

Chatbot ROI during the first month: This includes the bot installation charges. 

For the above case,

ROI = (Gains through bot – Installation charge – maintenance charge)/(installation charge + maintenance charge)

ROI = ($63,000 – $9,292 – $3,647)/($9,292 – $3,647)

ROI = 3.9 or 390%

Chatbot ROI after the first month: This excludes the bot installation charges. 

For the above case,

ROI = (Gains through bot – maintenance charge)/(maintenance charge)

ROI = ($63,000 – $3,647)/($3,647)

ROI = 16.3 or 1630%

Using this method, you can build your own chatbot ROI calculator considering your own business parameters.

NLP and AI-powered chatbots can yield a better return on investment. For instance, Religare has incorporated a service chatbot on its Web portal and WhatsApp integration to handle customer queries. It has resulted in 10 times more customer interaction and 5 times more sales conversion.

Conclusion

For the above case, where bots are able to handle 50% of customer queries, there’s a direct 50% capital gain to the organization. The human-time saved can be utilized for more productive tasks, which can eventually accelerate the organization’s productivity. 

Powerful bots result in better success rates for customer facing operations. For example, Diageo’s iDia chatbot has led to a 55% drop in help desk tickets. 

Here are more enterprise chatbot use cases.

Researchers predict that by 2025, chatbots will accomplish more than 90% of the B2C interactions. Also, chatbots can cut operational costs by more than $8 billion per year in the next three years.

AI Chatbot in Insurance Report

AI in Insurance will value at $36B by 2026. Chatbots will occupy 40% of overall deployment, predominantly within customer service roles.
DOWNLOAD REPORT

We specialize in developing industry-specific AI-powered chatbots. Drop us a word at hello@mantralabsglobal.com to learn more.

Cancel

Knowledge thats worth delivered in your inbox

10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot