Transform your data into real-time business insights with New Relic Pathpoint

Understanding the interdependencies between technical performance and business outcomes has always been a challenge across all functions of the digital enterprise—from marketing and product development to customer support and IT operations—making it increasingly difficult for teams to focus on the most impactful areas of their business, rather than just on the health of individual services.

New Relic Pathpoint solves these challenges by providing a strategic, real-time view of critical system health in relation to actual business stages, including relevant internal processes and external dependencies. This unique approach to business observability empowers executives, analysts, managers, and IT teams with real-time insights that enable proactive issue resolution, process optimization, and alignment of IT investments with business goals to drive growth and improve their competitive edge.

Intelligent business observability

Business observability extends the concept of technical observability to include business metrics and key performance indicators (KPIs), providing a comprehensive view of how digital application performance impacts real-world business outcomes. To address this critical need, New Relic introduced Pathpoint in January 2024, bridging the gap between IT metrics and business outcomes within a unified observability platform.

Today, we're launching a new version of Pathpoint built on a completely new code base with substantial improvements. Available in the New Relic Intelligent Observability Platform, Pathpoint features an improved no-code interface to simplify the integration of business and technical metrics into a single source of truth. Additionally, it now includes AI-driven insights that provide IT, engineering, and business teams with actionable data instantly—and it doesn’t require extensive technical expertise.

New Relic Pathpoint empowers IT, engineering, and business teams to better understand the business impact of the software they build and operate with:
 

• Intuitive UX: Improved no-code, entity-centric view that allows you to instantly add any alerts and service levels to your business flows to simplify business analysis.
• Multiple business journeys: Gain deeper insights in complex business environments by modeling multiple business journeys within the same instance for a better understanding of user-impacting technical issues.
• Playback mode: Instantly visualize the status of business stages over time for proactive issue resolution and fast root cause analysis through historical data snapshots.
• Import/export with audit trails: Easily import and export journeys from one instance to another to avoid rework and audit changes to your business journey models for better version control.
• Automatically identify monitoring gaps: Quickly identify instrumentation gaps in business processes and close blind spots for a more complete view of your digital enterprise.
• Real-time business health visibility: Gain a unified view of business health across your enterprise with an unique journey flow layout to visualize stages, steps, and signals tailored for both frontline teams (customer support) and higher-level stakeholders (product managers, business analysts, C-level executives) for a shared understanding of business health across the enterprise.
• AI-driven insights: Pathpoint is thoroughly integrated with the New Relic AI alerting and anomaly engine, providing an at-a-glance perspective on active and past incidents and drill-down capability for rapid recovery.

Modeling your business journey

Digital enterprises across all industries can easily leverage New Relic Pathpoint flexibility to model their unique business journeys, gain comprehensive insights, and optimize business performance. Here are some examples of how different industries can benefit from modeling Pathpoint flows:

• Hotel and/or hospitality: Model the guest journey from booking to checkout, allowing for the optimization of room availability, personalized guest experiences, and timely service delivery.
• Cruise line or airline: Visualize the customer experience from reservation to boarding and in-flight services, improving operational efficiency and enhancing customer satisfaction.
• Rideshare: Mapping the entire ride experience—from ride request and driver assignment to ride completion and payment—ensuring seamless operations and identifying areas for improvement.
• Consumer packaged goods: Monitor the supply chain and product lifecycle stages, from manufacturing to distribution, to optimize inventory levels and ensure timely delivery to retailers.
• Online marketplace: Understand user interactions from product search and selection to purchase and delivery, enabling better inventory management and improving the shopping experience.
• General retail: Track customer behavior from in-store browsing to checkout, helping optimize store layouts, improve customer service, and increase sales.
• Fast-food restaurant: Optimize the process from order placement to food preparation and delivery, ensuring efficiency and high quality in the customer dining experience.
• Mining, construction, oil, and gas: Monitoring key operational stages, from project planning and resource extraction to logistics and safety management, optimizing resource allocation and operational efficiency.
• Digital streaming: Track user engagement from content discovery to playback, allowing personalized content recommendations and improved service quality.
• News media: Understanding reader behavior from article engagement to subscription management, enhancing content delivery and boosting subscription rates.
• Banking and fintech: Map the customer journey from account creation and transaction processing to loan applications, ensuring a seamless banking experience and identifying opportunities for service improvement.
• Insurance: Model customer interactions from policy inquiries and quotes to claims processing, enhancing customer satisfaction and operational efficiency.
• Talent management: Tracking the recruitment and hiring process, from job posting and candidate selection to on-boarding, optimizing human resources operations and improving candidate experience.

The next section provides details on initiating the modeling of your business journeys, and some best practices and considerations to get the right insights for your business.

Focus on your critical business stages first

When developing Pathpoint flows, it's best to focus on the business stages and steps first—a top-down approach. This helps identify what's important to the business before searching for low-level signals. Doing this will also help you prioritize which signals to capture.

The stages of a flow represent the high-level concepts that compose the journey. When determining which stages to focus on first, creating a high-level business process draft or "journey" will help detail the sequence.

Stage examples

Break your stages into steps

Steps are used to bridge the gap between the abstraction of stages and the detail of signals. While stages may be understood by higher-level stakeholders, steps may sometimes be more technical or operations specific. Steps do not require a link to a single specific signal; however, signals are what connect the underlying system data.
 

Step examples

Adding signals to your journey

In Pathpoint, you can select any entity or alert as a signal to connect the system data to the flow. To better identify what signals to include, we recommend working in the following order:

1. Journey-critical frontend transactions (browser/mobile) that directly impact revenue or customer experience.
2. Journey-critical backend transactions (application performance monitoring or APM) that directly impact revenue or customer experience.
3. Journey-critical third-party services (synthetics).
4. Analyze the overall health of the most important services in each step (general service levels).
5. Support infrastructure and platforms relevant to each step (alerts based on cloud services such as load balancers, container services, managed databases, or messaging systems).

For example, for a login step, you may consider these metrics:

1. A JavaScript error rate alert for the login page action.
2. The latency alert for the login page action.
3. Service level indicators (SLIs) related to backend transactions for authenticating.
4. Backend transaction alert related to user lookup.
5. Database health of databases used for storing user information.
6. Alert or service level for a load balancer in front of the authentication or search service.
7. Redis database health for a cache of user-state information.

Consider selecting signals that have a level of independence to easily identify the root cause of an incident by simply looking at the signal color.

Best practices for a successful business journey model

Work from the top down

When you design a Pathpoint flow, you may not have all the signals needed to properly cover the flow. Working from the top down helps identify coverage gaps that need to be addressed to develop a complete business journey. These gaps can be addressed later in the process, since they might have other dependencies, like new instrumentation or agent updates.

It’s better to have a well-thought-out flow with some signal gaps than a flow that's put together hastily with a random set of signals.

Create multiple flows to avoid complexity

Trying to force all relevant signals into a single flow, or using a signal just because it’s available, can lead to a congested and confusing flow. Consult with internal stakeholders (and the New Relic account team) to see how to organize flows optimally. For example, an insurance company might want to separate the following flows:

• Finding and purchasing new policies
• Filing and paying out insurance claims

Being selective and focusing on the signals likely related to immediate business or user impact is the best route to identify signals.

Don’t be afraid to improve existing signals

Noisy signals will create a noisy Pathpoint flow. Considered an anti-pattern, noisy signals can overwhelm systems with irrelevant alerts, hindering the identification of critical issues. Rather than focusing solely on immediate outcomes, it's crucial to reduce these signals on both the flow process and your overall observability practice. Consider implementing alert quality management (AQM) to streamline alerts and enhance monitoring quality. Consider refining alert thresholds or service level queries to make signals more actionable and meaningful. If a signal is red, there should be real or at least likely risk to the business process.

Use synthetics to capture external dependencies

Your business process may depend on one or more third-party APIs for services like payment (Stripe, PayPal), location services, shipping, etc. Synthetics is the conventional way to capture the general health of those systems. 

You will also find that specific calls to these APIs will show up in APM (external calls), mobile (MobileRequest), and browser (AJAX) telemetry. Take advantage of these when possible, as they’re associated with real users.

Use workloads to group entities

Workloads offer a powerful method to consolidate related entities into a unified view. By aggregating infrastructure components such as load balancers, relational database management systems (RDBMSs), and other cloud services, critical issues affecting individual sub-entities can be efficiently surfaced at the workload level. Additionally, workloads can be used to combine multiple service levels into a single, overarching service level for simplified monitoring and management.

Use metadata tags on your entities

It's good practice to include tags on your entities, such as service levels, workloads, synthetics, etc. It's especially helpful to add flow, stage, and step tags to your entities. This will allow you to use these signals more easily with Pathpoint.

Focus on important counts, monetary values, etc. related to your flow

Utilize the Pathpoint KPIs row to monitor essential counts and monetary values relevant to your workflow. This includes critical events such as user logins, registrations, checkouts, and cart abandonments, as well as financial metrics like total checkout and returned product value. Many of these metrics can be derived from existing telemetry data and do not require association with specific entities. They can be sourced from logs, custom metrics, or other available data points.

Let’s say you have a stage called “search.” You may want to have KPIs like:

• Successful searches
• Slow searches
• Failed searches

If you have stages for order, cart, or checkout, you may want to have KPIs like:

• Order count
• Failed orders
• Order value
• Abandoned cart
• Average cart value
• On this page
• Pathpoint elements
• Create flows

Ready to unlock better business insights? 

Business observability is transforming how enterprises understand and optimize every aspect of the digital journey. By providing the proactive insights that only intelligent observability can offer, Pathpoint delivers the strategic view digital business needs to make better decisions and grow. To know more about how business observability can help your organization, visit our Docs page.