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Are Predictive Journeys moving beyond the hype?

4 minutes, 42 seconds read

Predictive Analytics is disrupting the business-consumer dynamic. To improve engagement with their customers, organizations have begun identifying potential segments (predictive audiences) that are likely to convert with them. Modelling data to learn about the potential ‘new’ customer, their preferences and spending behaviour has already proven demonstrably higher conversion rates and lower churn rates. In fact, the market value for these types of services is expected to touch $12.4B by 2022.

As we transition into a semi-connected world supported by global IoT sensors and devices, the real-time analysis of past and future-probable events is evolving business actions more prescriptive in nature. Every touch or interaction triggered by an individual customer is a data point that is captured, stored and examined for insights. Data is an interminable asset that continues to grow exponentially while storage likewise is getting cheaper each year. With nearly infinite cloud computing and scaling it becomes much easier to process these extremely large amounts of data.

But, are customer journeys actually getting better? Are these journeys still reactive? How much of the world has moved to a predictive-first approach? and, has it really helped CXOs address their business goals? Let’s evaluate the state of real-time predictive trends that are being put to use by global enterprises. 

First, let’s look at some easily identifiable use cases that have some verifiable results.

  • Identity Resolution — understanding the individual persona consistently and accurately across -domain, -device and -channel, while maintaining stringent privacy compliance. This approach typically gives you a singular view of a potential customer. (ex: LiveRamp, Full Contact)
  • Customer Journey Data Integration — data integration transcends the siloed view of traditional web analytics. For these multiple integrations like web, mobile app, email, social media, CRM, call centre, device, etc. are essential to understand customer flow across channels. (ex: FirstHive)
  • Customer Segmentation and User Experience Recommendations — It is done using clustering models to perform highly accurate segmentation creating micro-segments and tracking each customer as they shift from one segment to the other. (ex: Lattice-Engines)
  • Personalization — It marks which marketing campaigns, channels, touches, and behaviours users are responding to, and contributing to a business outcome, using a machine learning-based attribution. (ex: Everage)
  • Lead Scoring, Prioritization & Allocation — It helps identify which leads will convert, churn and which customers will buy one or more products for a cross-sell or upsell. (ex: Mantra Labs LCA, Pardot
  • Automating Prediction & Rule Setting — Use automated machine learning for predictive modelling. Enables rapid iteration cycles. (ex: Nokia, DataRobot)

The total number of journey interactions the world over is an unquantifiable number. It is predicted, though, that there will be nearly 2MB of data created by every individual in 2020, every second. With all this data to go around, why are companies so invested in them? It’s because customer experience has become the number one marketing activity of 2019, and will continue to rank highly over the next five years. 

In fact, Gartner predicts by 2019 more than 50% of organizations will redirect their investments to customer experience innovations. For SaaS enterprises, there is a lot to gain. Research indicates CX initiatives can double an organization’s revenues within 36 months, and this extra share will come from the customer’s wallet. Good CX will create real value for your customers, which means they will spend more.

According to Accenture, 87% of organizations agree on traditional experiences no longer satisfy customers. To counter this, Businesses are now investing in customer journey management. Interestingly, insurance (39%) is showing the highest adoption rates outside of retail (42%). The tech industry comes up third behind them at 7%. 

Customer journeys are orchestrated into three: Acquisition, Conversion and Growth. Majority of journeys are identified as growth journeys (64%), and typically run for nearly 34 months on average.

Has it made a difference in Experience?

Yes, and there’s data to support it.
The predictive journey allows businesses to place real-time marketing bets on the behaviour of the customer. We don’t have to look any further than the example of Netflix and its impressive predictive recommendation system. Almost 80% of the content watched on Netflix is attributed to recommendations. A robust predictive analytical engine working behind the scenes is able to perform two critical aspects of the customer life cycle: Needs forecasting and churn reduction. The system is estimated to save Netflix at least $1 billion each year in customer retention.


What about the Impact to Business Goals?

The short and long answer is yes.
According to a salesforce study, the key to building highly personalised journeys begins with predictive intelligence. The report found on average, predictive intelligence recommendations influenced 34.7% of total buys. The lift in conversion rate within the first 36 months is around 23%, which is significantly high. Imagine what 23% more in conversions can do for any business. The real value from predictive intelligence is that it gets more intuitive with time. After 36 months of implementation, there is 40.3% more influence in revenue from this technology.

Continuous Predictive Learning Model
Continuous Predictive Learning Model

For future engagements, customers want businesses to proactively reach out to them and offer them tailored products and services that will be highly relevant to their needs. On the other hand, businesses prefer to study their consumers by looking at their data under the strict regulations enforced in data privacy laws — because it will certainly avoid long term risk to their business models. The results are clear: A predictive journey is the only way forward. 

Mantra Labs is an Insurtech100 company creating AI-first products and solutions for the evolving digital enterprise. To learn more about how we are using predictive journeys to create the Internet of Intelligent Experiences, reach out to us on hello@mantralabsglobal.com

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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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