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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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Platform Engineering: Accelerating Development and Deployment

The software development landscape is evolving rapidly, demanding unprecedented levels of speed, quality, and efficiency. To keep pace, organizations are turning to platform engineering. This innovative approach empowers development teams by providing a self-service platform that automates and streamlines infrastructure provisioning, deployment pipelines, and security. By bridging the gap between development and operations, platform engineering fosters standardization, and collaboration, accelerates time-to-market, and ensures the delivery of secure and high-quality software products. Let’s dive into how platform engineering can revolutionize your software delivery lifecycle.

The Rise of Platform Engineering

The rise of DevOps marked a significant shift in software development, bringing together development and operations teams for faster and more reliable deployments. As the complexity of applications and infrastructure grew, DevOps teams often found themselves overwhelmed with managing both code and infrastructure.

Platform engineering offers a solution by creating a dedicated team focused on building and maintaining a self-service platform for application development. By standardizing tools and processes, it reduces cognitive overload, improves efficiency, and accelerates time-to-market.  

Platform engineers are the architects of the developer experience. They curate a set of tools and best practices, such as Kubernetes, Jenkins, Terraform, and cloud platforms, to create a self-service environment. This empowers developers to innovate while ensuring adherence to security and compliance standards.

Role of DevOps and Cloud Engineers

Platform engineering reshapes the traditional development landscape. While platform teams focus on building and managing self-service infrastructure, application teams handle the development of software. To bridge this gap and optimize workflows, DevOps engineers become essential on both sides.

Platform and cloud engineering are distinct but complementary disciplines. Cloud engineers are the architects of cloud infrastructure, managing services, migrations, and cost optimization. On the other hand, platform engineers build upon this foundation, crafting internal developer platforms that abstract away cloud complexity.

Key Features of Platform Engineering:

Let’s dissect the core features that make platform engineering a game-changer for software development:

Abstraction and User-Friendly Platforms: 

An internal developer platform (IDP) is a one-stop shop for developers. This platform provides a user-friendly interface that abstracts away the complexities of the underlying infrastructure. Developers can focus on their core strength – building great applications – instead of wrestling with arcane tools. 

But it gets better. Platform engineering empowers teams through self-service capabilities.This not only reduces dependency on other teams but also accelerates workflows and boosts overall developer productivity.

Collaboration and Standardization

Close collaboration with application teams helps identify bottlenecks and smooth integration and fosters a trust-based environment where communication flows freely.

Standardization takes center stage here. Equipping teams with a consistent set of tools for automation, deployment, and secret management ensures consistency and security. 

Identifying the Current State

Before building a platform, it’s crucial to understand the existing technology landscape used by product teams. This involves performing a thorough audit of the tools currently in use, analyzing how teams leverage them, and identifying gaps where new solutions are needed. This ensures the platform we build addresses real-world needs effectively.

Security

Platform engineering prioritizes security by implementing mechanisms for managing secrets such as encrypted storage solutions. The platform adheres to industry best practices, including regular security audits, continuous vulnerability monitoring, and enforcing strict access controls. This relentless vigilance ensures all tools and processes are secure and compliant.

The Platform Engineer’s Toolkit For Building Better Software Delivery Pipelines

Platform engineering is all about streamlining and automating critical processes to empower your development teams. But how exactly does it achieve this? Let’s explore the essential tools that platform engineers rely on:

Building Automation Powerhouses:

Infrastructure as Code (IaC):

CI/CD Pipelines:

Tools like Jenkins and GitLab CI/CD are essential for automating testing and deployment processes, ensuring applications are built, tested, and delivered with speed and reliability.

Maintaining Observability:

Monitoring and Alerting:

Prometheus and Grafana is a powerful duo that provides comprehensive monitoring capabilities. Prometheus scrapes applications for valuable metrics, while Grafana transforms this data into easy-to-understand visualizations for troubleshooting and performance analysis.

All-in-one Monitoring Solutions:

Tools like New Relic and Datadog offer a broader feature set, including application performance monitoring (APM), log management, and real-time analytics. These platforms help teams to identify and resolve issues before they impact users proactively.

Site Reliability Tools To Ensure High Availability and Scalability:

Container Orchestration:

Kubernetes orchestrates and manages container deployments, guaranteeing high availability and seamless scaling for your applications.

Log Management and Analysis:

The ELK Stack (Elasticsearch, Logstash, Kibana) is the go-to tool for log aggregation and analysis. It provides valuable insights into system behavior and performance, allowing teams to maintain consistent and reliable operations.

Managing Infrastructure

Secret Management:

HashiCorp Vault protects secretes, centralizes, and manages sensitive data like passwords and API keys, ensuring security and compliance within your infrastructure.

Cloud Resource Management:

Tools like AWS CloudFormation and Azure Resource Manager streamline cloud deployments. They automate the creation and management of cloud resources, keeping your infrastructure scalable, secure, and easy to manage. These tools collectively ensure that platform engineering can handle automation scripts, monitor applications, maintain site reliability, and manage infrastructure smoothly.

The Future is AI-Powered:

The platform engineering landscape is constantly evolving, and AI is rapidly transforming how we build and manage software delivery pipelines. The tools like Terraform, Kubecost, Jenkins X, and New Relic AI facilitate AI capabilities like:

  • Enhance security
  • Predict infrastructure requirements
  • Optimize resource security 
  • Predictive maintenance
  • Optimize monitoring process and cost

Conclusion

Platform engineering is becoming the cornerstone of modern software development. Gartner estimates that by 2026, 80% of development companies will have internal platform services and teams to improve development efficiency. This surge underscores the critical role platform engineering plays in accelerating software delivery and gaining a competitive edge.

With a strong foundation in platform engineering, organizations can achieve greater agility, scalability, and efficiency in the ever-changing software landscape. Are you ready to embark on your platform engineering journey?

Building a robust platform requires careful planning, collaboration, and a deep understanding of your team’s needs. At Mantra Labs, we can help you accelerate your software delivery. Connect with us to know more. 

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