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Are Bots Worth a Shot?

According to Oracle’s Executive survey, 80% of leading consumer-facing businesses have already used or are planning to use chatbots by 2020. Chatbots are scalable and cost almost nothing in operation as compared to their human counterparts. But, how practical is chatbot adoption for your business? Let’s see.

5 Key Success Metrics for Chatbots

Different industries can utilize chatbots to serve different purposes. Accordingly, the parameters to measure ROI may vary. For instance, marketers may consider lead generation as a criterion while the sales department takes conversions from chatbots into account. But, of course, the decision to opt for chatbots depends on specific quantifiable measures — to solve specific customer support processes.

What makes bots successful

#1 NLP Maturity

It is the average maturity level of Natural Language Processing capability of bots, measured by the way bot interacts. Initiating conversations with customers is a key focus area among organizations these days. To achieve this, bots have to be well trained in industry-specific jargon.

For instance, if a retail customer has a question about a brand’s return policy, the bot should be able to meaningfully understand the user’s query and provide relevant information as it relates to that specific question, as opposed to an information dump or worse yet failing to understand the query itself. If a bot is unable to process the user input, it contributes to ‘miss-messages’. Such instances occur when the user inputs query in a regional or idiomatic language. 

#2 Response Time

It is the average time taken for the chatbot to respond to customer queries, based on the total number of messages sent by a chatbot during an interaction. Typically this can average around 5-6 seconds. However, research indicates that users will leave a site if key elements take more than 3 seconds to load. 

#3 Intent Prediction

It is the ability of the bot to anticipate what a customer wants in real-time. To achieve this, the bot must be paired with multiple sources of data and AI capable — in order to combine user behaviour, transactions, and profile details. Using this, the bot can determine intent based on both aggregated interactions for known and unknown users, and personalized data pulled from back-end systems.

#4 Retention Rate

It defines the number of users who willingly return to using the chatbot to address their issues. The retention rate varies according to industries. However, the clear formula for increasing user retention is to equip chatbots with the ability to understand user queries and empathically respond to them. This metric is directly correlated with the ability to personalize sales and/or customer service greetings, in 1:1 messaging.

#5 Goal Completion and Fall-back Rate

The number of times a chatbot can resolve the query, manage ticket, generates leads, or results in conversion determines its goal completion rate. However, like humans, bots, at times, might not be handle queries on their own. Such instances account for the fall-back rate of the bots. 

Here’s an insightful read on why businesses should consider chatbots.

Successful Chatbot Adoption Across Businesses

Providing 24×7 support is not impossible for any organization. But, the labour cost associated is high, which makes chatbots a viable solution for instant customer support. IBM reports that globally businesses spend over $1.3 trillion/year to handle roughly 265 billion customer calls. 

The following are examples of chatbots adoption for cost savings.

#Messenger Marketing Bot

ManyChat provides bot platform on Facebook Messenger for marketing, e-commerce, and support. DigitalMarketer incorporated ManyChat’s bot for messenger marketing and have reported very high returns on their ad spend (nearly 500% ROI).

#Insurance Chatbot

Religare has incorporated chatbot on its website and WhatsApp to handle customer queries. It has resulted in 10 times more customer interaction and 5 times more sales conversion.

Here are more insurance chatbot use cases.

#B2C Chatbot Offering Personalization

1-800-Flowers is using IBM Watson’s Gwyn smart virtual shopping assistant. It interacts with customers to understand their gift preferences and accordingly help them select a personalized gift for their loved ones. More than 70% of 1-800-Flowers customers are happily ordering through Gwyn bot.

Here’s a sample Chatbot ROI calculation from a financial perspective.

The Future of Chatbots

CNBC reports, currently businesses are saving $20 million per year globally through chatbot adoption. By 2022, chatbots can cut operational costs by more than $8 billion per year. Also, researchers predict that by 2025, bots will accomplish about 90% of the B2C interactions. Looking at the reduction in cost and ease of operation, investing in chatbots is worth it.

We specialize in building NLP and AI-powered chatbots for enterprises. Drop us a line at hello@mantralabsglobal.com to know more.

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Why Netflix Broke Itself: Was It Success Rewritten Through Platform Engineering?

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Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform. 

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Source: getport.io

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization:  Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

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